Artificial Neural Network

Open Access Article

1 - A new method based on texture analysis for the classification of automatic detection of breast microcalcifications of mammography images
Zahra Maghsoodzadeh Sarvestani Jasem Jamali mhdi taghizadeh Mohammad h Fatehi

Mammography is a diagnostic technology used in screening programs to find breast cancer early. By using two techniques for image enhancement and highlighting breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor fil More

Mammography is a diagnostic technology used in screening programs to find breast cancer early. By using two techniques for image enhancement and highlighting breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor filtering method, the study's objective was to assess the viability of automatic separation of images of breast tissue microcalcifications and to assess its accuracy. The decision tree classification algorithm is used to categorize the clusters of breast tissue microcalcifications after the clusters have been identified. The samples that are thought to have microcalcification are next highlighted and masked for segmentation, and in the last step, tissue properties are extracted. Then, it was possible to distinguish between benign and malignant forms of segmented ROI clusters with the aid of an artificial neural network (ANN). The results of this work show a high accuracy of 93% and an improvement of sensitivity of 95%, which shows that the presented solution can be reliably applied to detect breast cancer.. Manuscript profile

Open Access Article

2 - Predicting stock prices using data mining methods.
Mojtaba Hajigholami

This article discusses data mining methods for predicting financial markets and analyzing sustainable development in financial matters. It also examines the impact of using data mining methods in the stock market and their effectiveness in this area. The research introd More

This article discusses data mining methods for predicting financial markets and analyzing sustainable development in financial matters. It also examines the impact of using data mining methods in the stock market and their effectiveness in this area. The research introduces a machine learning approach that generates information using publicly available data and uses this information for accurate prediction. It also explores various data mining methods relevant to financial market analysis, focusing on predicting stock market movements and trends. The study demonstrates that due to the dynamic and variable nature of financial markets influenced by economic, political, and social factors, the use of machine learning and data mining methods can lead to more accurate predictions of stock price movements. Given the extensive and complex data in financial markets, data mining methods have the potential to discover hidden patterns and determine relationships between various variables. Various machine learning algorithms such as artificial neural networks, support vector machines, and random forests, alongside statistical analyses, help improve the analytical capabilities of analysts and investors in making economic decisions. Furthermore, the use of big data and complex analyses has contributed to the development of intelligent trading strategies that can help optimize returns on investments. For example, analysts can enhance the accuracy of their predictions by incorporating sentiment data from social networks into their models. The study emphasizes that sustainable development in financial markets requires a deeper understanding and more precise analysis of data, ultimately leading to stronger data-driven decision-making and trading processes. Manuscript profile

Open Access Article

3 - Future Studies for Presenting Model of Personal Differentiation Role on Guerilla Advertising with Using Perceptron Artificial Neural Network
tara taefi vahidreza Mirabi Ghasemali Bazaee soheil sarmad saeidi

Guerilla Advertising in Contrast of Definition Pattern, Attempt to Use Simplicity and Flexibility Idea For Penetrate and Attract In Customer Heart to Have Maximum Profit With Minimize their Cost. Personal Differentiation Research is so important for result some side of More

Guerilla Advertising in Contrast of Definition Pattern, Attempt to Use Simplicity and Flexibility Idea For Penetrate and Attract In Customer Heart to Have Maximum Profit With Minimize their Cost. Personal Differentiation Research is so important for result some side of this Attitude Made by Attention on personal personality. The goal of this research is Future Studies Presenting for Presenting Model of Personal Differentiation Role on Guerilla Advertising. The Purpose is Exploratory, the Method is Descriptive Survey. Two Indicator Recognize such as Attitude with psychology Motivation and technical dimension, personality With Myers-Briggs Indicator Test. Population is Go sport Customer, use classify method to choose the sample, number of Sample with Cochran Formula 277 person. For data collecting have2 Specific and closed Questioner including standard MBTI Questioner with 70 Question and another one with 25 Closed And Specified Questioner Made by Researcher , with use Perceptron Artificial Neural Network by (MATLAB Software). the result of Neural Network Get Fit Model With 92%Coefficience Confidence to Define 4 clearly strategic of Interdependencies between Guerilla Advertising and personality, Attitude Indicator for Predict and analysis the new input and select the suitable strategy. Manuscript profile

Open Access Article

4 - Forecasting of the Students’ Performance in Military Higher Education Using Artificial Neural Network Prediction Algorithm (Case study: A military organization)
Mohammad Fallah Hamideh Reshadatjoo

Background: One of the basic issues in a country's higher education system is the foundations of the quality of graduates’ and university students’ performance, which make up two of the seven major issues in the field of quality in higher education and is im More

Background: One of the basic issues in a country's higher education system is the foundations of the quality of graduates’ and university students’ performance, which make up two of the seven major issues in the field of quality in higher education and is important in incorporating multiple components in improving the quality of the higher education system of each country, and any ambiguity in it, especially in military higher education, which has a higher sensitivity, will lead to irreparable consequences. Purpose: The main objective of this paper is to forecast the performance of military higher education students using the artificial neural network prediction algorithm. In addition, the main components of student performance quality have been studied. Method: In this paper, using predictive artificial neural network prediction algorithm, forecasting the quality of students' performance in three phases of learning, validation and neural network test was performed. The statistical society consists of faculty members of Shahid Sattari Air University, students and graduates of this university, as well as the members of the Office of Strategic Studies and Theoretical Research, were then interviewed using a semi-structured interview and a researcher-made questionnaire. Finally, MATLAB software was used to model the neural network. Results: Using artificial neural network algorithm, a model with accuracy of 85.5% was designed and tested. Conclusion: By using artificial neural network algorithm and modeling the quality of students' performance, we can accurately predict the quality of the graduates' performance in the Air Force organization. Manuscript profile

Open Access Article

5 - The prediction of Bankruptcy Risk Investigation Using Artificial Neural Networks Based on Multilayer Perceptron Approach (Empirical Evidence: Tehran Stock Exchange)
Somayeh Saroei Hamid Reza Vkili Fard Ghodratolah Taleb Nia

The aim of this research is Identification of the effective factors on bankruptcy prediction of Iranian companies by findings of artificial neural network (ANN) system based on Multilayer Perceptron Approach (PS) , and providing an appropriate statistical model for esti More

The aim of this research is Identification of the effective factors on bankruptcy prediction of Iranian companies by findings of artificial neural network (ANN) system based on Multilayer Perceptron Approach (PS) , and providing an appropriate statistical model for estimating the bankruptcy of Iranian companies by using the findings of The ANN implementation. we seek to answer the following question: Are we able to design a valid statistical model by using findings of artificial neural network (ANN) system to predict the bankruptcy of Iranian companies? The statistical population in this study is all of listed companies in Tehran Stock Exchange. By considering the criteria and method of systematic deletion, 172 companies from this statistical society have been selected as the sample in this research from 2007 to 2016. In order to make statistical analyzes in this study, we used from methods such as artificial neural network system based on multilevel perceptron approach, binary logistic regression, and tests such as Akaic, Schwarz, Hanan Quinn and Z wang test. The results of the analysis of the research data show that the ANN system can identify of the factors affecting on bankruptcy of Iranian companies in the year before bankruptcy by Precision equal 98%. Manuscript profile

Open Access Article

6 - Expanding the Application Models Box Jenkins, Artificial Neural Network and Adjusted Exponential forecasting Social Phenomena (Case study: forecasting of marriage and divorce in Ilam)
Mohammadreza Omidi Nabi Omidi Ardashir Shiri R. Mohammadipour

One of the most important tools in the hands of managers and experts to make strategic decisions is Methods of forecasting and futures. Despite the development of prediction methods, but less likely to use these methods in predicting social phenomena such as marriage, More

One of the most important tools in the hands of managers and experts to make strategic decisions is Methods of forecasting and futures. Despite the development of prediction methods, but less likely to use these methods in predicting social phenomena such as marriage, divorce and population growth are discussed. In this study, using data from marriage and divorce between the years 1992 to 2013 in Ilam province to forecasts, the number of these phenomena using models Box Jenkins, Artificial Neural Network and Adjusted Exponential has been studied for years to come. The results showed that the prediction accuracy Box Jenkins model to predict the number of marriages and Artificial Neural Network model to predict the number of divorces is more than any other prediction methods. The predicted values showed that the proportion of marriages end in divorce in Ilam province between the years 2014 to 2018 following the gentle slope, to reduce the move. Manuscript profile

Open Access Article

7 - Expanding the Application Models Box Jenkins, Artificial Neural Network and Adjusted Exponential forecasting Social Phenomena (Case study: forecasting of marriage and divorce in Ilam)
Mohammadreza Omidi Nabi Omidi Ardeshir Shiri Rahmatullah Mohammadipour

One of the most important tools in the hands of managers and experts to make strategic decisions is Methods of forecasting and futures. Despite the development of prediction methods, but less likely to use these methods in predicting social phenomena such as marriage, d More

One of the most important tools in the hands of managers and experts to make strategic decisions is Methods of forecasting and futures. Despite the development of prediction methods, but less likely to use these methods in predicting social phenomena such as marriage, divorce and population growth are discussed. In this study, using data from marriage and divorce between the years 1992 to 2013 in Ilam province to forecasts, the number of these phenomena using models Box Jenkins, Artificial Neural Network and Adjusted Exponential has been studied for years to come. The results showed that the prediction accuracy Box Jenkins model to predict the number of marriages and Artificial Neural Network model to predict the number of divorces is more than any other prediction methods. The predicted values showed that the proportion of marriages end in divorce in Ilam province between the years 2014 to 2018 following the gentle slope, to reduce the move.   Manuscript profile

Open Access Article

8 - Developing A Fault Diagnosis Approach Based On Artificial Neural Network And Self Organization Map For Occurred ADSL Faults
Vahid Golmah Mina Tashakori

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Open Access Article

9 - Water Quality Index Estimation Model for Aquaculture System Using Artificial Neural Network
Taliha Folorunso Musa Aibinu Jonathan Kolo Suleiman Sadiku Abdullahi Orire

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Open Access Article

10 - Estimating Energy Consumption of Educational Spaces Using Artificial Neural Networks (ANNs)
مهسا فلاح نیا

Size of classroom’s windows has significant effects on both comfort level of users and electricity consumption for lighting. Moreover, windows are the main source of energy loss in classrooms in both cooling and heating Sectors. Considering the large number of education More

Size of classroom’s windows has significant effects on both comfort level of users and electricity consumption for lighting. Moreover, windows are the main source of energy loss in classrooms in both cooling and heating Sectors. Considering the large number of educational buildings and long life cycle of such buildings, choosing proper window size is crucial for energy saving in sustainable architectural design. Despite the role that windows have in energy consumption, the literatures are surprisingly limited in providing detailed recommendations for architects in determining the appropriate window size in different climates. Therefore, energy based window design has always been complicated for architects due to the number of involved different components and variables. In order to help the architectural designers, in this paper a new methodology is developed using a well-known artificial intelligence technique. In proposed methodology, a predictive model for energy consumption cost in terms of window to wall ratio (WWR) and the window facing was created using Artificial Neural Network (ANN). The methodology consisted of a limited sets of direct numerical energy simulations for any specific climatic zone to generate the data required for training the ANN. The DOE-2 is suggested in the proposed methodology for direct numerical energy simulations of the daylighting scenarios required for training the ANN. The DOE-2 is a popular and powerful computational model developed with financial support of U.S. department of energy. The trained ANN-based model provides a fast and convenient way of comparing the different daylighting scenarios in designing stage. Indeed, further calculations for direct energy simulations are not necessary and an architect can readily utilize the trained ANN-based model as a powerful tool for forecasting the total energy consumption cost. In order to show the applicability and performance of the proposed approach, 288 daylighting scenarios for a standard classroom in a warm and dry climate, Shiraz-Iran, were simulated to determine the corresponding electric and gas consumption. A square classroom of side 7.4 m is the standard classroom defined by Iranian Organization for Renovating, Developing and Equipping Schools. The DOE-2 is utilized for simulating the defined standard classroom in the study area for estimating the annual gas and electric consumption of the generated scenarios over a 50 years period. The included daylighting scenarios were randomly split into train and test sets. In this study, around 80 percent of data were used for training, and the rest were used to evaluate the performance of the trained ANN. The best training and learning functions for different number of layers and neurons was determined in a trial-error process. Correlation Coefficient (CC), Mean square error (MSE) and Root mean square error (RMSE) are the statistical indices used for training procedure. The best results were obtained with 2 hidden layers and 6 neurons per layer. The 'Levenverg-Marquardt back propagation (trainlm)' and 'perceptron weight and bias learning function (learnp)' were the best training functions found for this research. The results show that the trained ANN can accurately predict the total energy consumption cost (RMSE=0.0811, MSE=0.0066, and CC=0.9672). Manuscript profile

Open Access Article

11 - Estimating Energy Consumption of Educational Spaces Using Artificial Neural Networks (ANNs)
Mahsa Fallahnia

Size of classroom’s windows has significant effects on both comfort level of users and electricity consumption forlighting. Moreover, windows are the main source of energy loss in classrooms in both cooling and heating sectors.Considering the large number of educa More

Size of classroom’s windows has significant effects on both comfort level of users and electricity consumption forlighting. Moreover, windows are the main source of energy loss in classrooms in both cooling and heating sectors.Considering the large number of educationalbuildings and long life cycle of such them, choosing proper window size is crucial for energy saving in sustainablearchitectural design. Despite the role that windows have in energy consumption, the literatures are surprisinglylimited in providing detailed recommendations for architects in determining the appropriate window size in differentclimates. Therefore, energy based window design has always been complicated for architects due to the numberof involved different components and variables. In order to help the architectural designers, in this paper a newmethodology is developed using a well-known artificial intelligence technique. In the proposed methodology, apredictive model for energy consumption cost in terms of window to wall ratio (WWR) and the window facing wascreated using Artificial Neural Network (ANN). The methodology consisted of a limited sets of direct numericalenergy simulations for any specific climatic zone to generate the data required for training the ANN. The DOE-2issuggested in the proposed methodology for direct numerical energy simulations of the daylighting scenarios requiredfor training the ANN. The DOE-2 is a popular and powerful computational model developed with financial supportof U.S. department of energy. The trained ANN-based model provides a fast and convenient way of comparing thedifferent daylighting scenarios in designing stage. Indeed, further calculations for direct energy simulations are notnecessary and an architect can readily utilize the trained ANN-based model as a powerful tool for forecasting thetotal energy consumption cost. In order to show the applicability and performance of the proposed approach, 288daylighting scenarios for a standard classroom in a warm and dry climate, Shiraz-Iran, were simulated to determinethe corresponding electric and gas consumption. A square classroom of side 7.4 m is the standard classroom definedby Iranian Organization for Renovating, Developing and Equipping Schools. The DOE-2 is utilized for simulating thedefined standard classroom in the study area for estimating the annual gas and electric consumption of the generatedscenarios over a 50 years period. Included daylighting scenarios were randomly split into train and test sets. In thisstudy, around 80 percent of data were used for training, and the rest were used to evaluate the performance of thetrained ANN. The best training and learning functions for different number of layers and neurons was determined ina trial-error process. Correlation Coefficient (CC), Mean square error (MSE) and Root mean square error (RMSE)are the statistical indices used for training procedure. The best results were obtained with 2 hidden layers and 6neurons per layer. The 'Levenverg-Marquardt back propagation (trainlm)' and 'perceptron weight and bias learningfunction (learnp)' were the best training functions found for this research. The results show that the trained ANN canaccurately predict the total energy consumption cost (RMSE=0.0811, MSE=0.0066, and CC=0.9672). Manuscript profile

Open Access Article

12 - Performance of machine learning system to prediction of almond physical properties
Mohsen Mokhtarian Hamid Tavakolipour Hassan Hamedi Amir Daraei Garmakhany

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Open Access Article

13 - A new method for ranking of Z-numbers
M. Matinfar S. ezadi

In this paper we propose a new method for ranking Z- numbers and generalizations. This method is based on the internal structure of the artificial neural network, which suggests that the structure of this network consists of inputs weights and the transfer function line More

In this paper we propose a new method for ranking Z- numbers and generalizations. This method is based on the internal structure of the artificial neural network, which suggests that the structure of this network consists of inputs weights and the transfer function linear, nonlinear and sometimes linear and nonlinear. It is shown that the proposed method while possessing the ranking properties for Z -numbers whose components of the limiting part are equal and their confidence interval having the same center of gravity has a more logical ranking than those using the center of gravity. While some of the available methods for Z numbers whose boundaries are equal but not equal to their reliability but have the same focal gravity they rank equally which can not be logical in all cases. Therefore, the proposed method overcomes this problem. In some examples the correctness of the subject is shown. the results are compared with some existing methods. Manuscript profile

Open Access Article

14 - Solving of stochastic Voltaire integral equations by fuzzy artificial neural network method
Hadi Abtahi Hamid Reza Rahimi Maryam mosleh

Voltaire integral equations as the output of problems in basic sciences and engineering have a special application in advancing the solution of complex problems. One of the most widely used types, which consists of a random process under external motion, is the equation More

Voltaire integral equations as the output of problems in basic sciences and engineering have a special application in advancing the solution of complex problems. One of the most widely used types, which consists of a random process under external motion, is the equations of random Volta integral. Solving this type of equation has always been a challenge for researchers. On the other hand, with the development of artificial intelligence and the presentation of fuzzy artificial neural network method as a model inspired by the process of thinking and analysis in the human brain, advanced models of algorithms have been designed. Some of these learning algorithms have been used in fuzzy artificial neural networks to solve equations. In this paper, using this method and designing a learning algorithm, the random equations of random Voltaire type is investigated. The method presented in this article, in addition to being more accurate than the previous methods, posseses more speed for solving problem. This topic provides an acceptable level of confidence for researchers when dealing with such issues. Manuscript profile

Open Access Article

15 - Using Neural Network to Determine Input Excesses, Output Shortfalls and Efficiency of Dmus in Russell Mode
D. Modhej M. Sanei N. Shoja

Data Envelopment Analysis (DEA) has two fundamental approaches for assessing theefficiency with different characteristics; radial and non-radial models. This paper isconcerned the non-radial model of Russell which is a non linear model. Conventional DEAfor a large datas More

Data Envelopment Analysis (DEA) has two fundamental approaches for assessing theefficiency with different characteristics; radial and non-radial models. This paper isconcerned the non-radial model of Russell which is a non linear model. Conventional DEAfor a large dataset with many inputs/outputs would require huge computer resources in termsof memory and CPU time. Artificial Neural Network (ANN) is one of the most populartechniques for non linear models and for measuring the relative efficiency of a large datasetwith many inputs/ outputs. Also in the last decade researches focused on efficiencyevaluation via DEA as well as using ANN. In this paper we will estimate the input excessesand the output shortfalls in addition to efficiency of Decision Making Units (DMUs) inRussell model through ANN. The proposed integrated approach is applied to an actualIranian bank set; the result indicates that it yields a satisfactory solution.works. Manuscript profile

Open Access Article

16 - پیش بینی نرخ ارز در بازار سرمایه با استفاده از مدل های میانگین متحرک خود رگرسیون انباشته و شبکه عصبی )مطالعه موردی: دلار استرالیا، دلار کانادا، ین ژاپن و پوند انگلستان(
Mohammad Ehsanifar Reza Ehtesham Rasi

Monetary policy in order to prevent losses arising from changes in exchange rates of disruptive are Always trying to find a suitable method to predict exchange rates. However, multi-dimensional characteristics of the converter makes it is complicated and nonlinear behav More

Monetary policy in order to prevent losses arising from changes in exchange rates of disruptive are Always trying to find a suitable method to predict exchange rates. However, multi-dimensional characteristics of the converter makes it is complicated and nonlinear behavior. One of the traditional methods of forecasting, time series analysis, which is based on two as sumptions static linearity. Some doubts about the performance of these traditional models have been created One of the alternative methods, artificial neural networks that In some cases are shown a good potential for time series prediction. In this Article , After reviewing the research conducted to clarify the predictive ability of mass moving average models and Artificial Neural Networks to compare The two methods for the prediction of the daily exchange rate has been made in the period from 01.01.1990 till 01.01.2012. The results showed that the neural network approach estimates the Autoregressive Integrated Moving Average (ARIMA) method provides better responses. In this study, MATLAB software and computational tools and data STATGRAPHICS economies of Australia, Canada, Japan and the United Kingdom, and the dollar exchange rate in those countries than in America is using. Manuscript profile

Open Access Article

17 - Reviews of Manipulating Prices using QDF & ANN-GA Models in Tehran's Stock Exchange
M. Hossein Poustfroush Alireza Naser Sadrabadi Mahmood Moeinaddin

In this study, Quadratic Discriminant Analysis (QDF) model and the hybrid model of Genetic Algorithm based on Artificial Neural Network)ANN-GA) are used to estimate manipulation of stock prices in Tehran Stock Exchange. In this study, first by using screening data metho More

In this study, Quadratic Discriminant Analysis (QDF) model and the hybrid model of Genetic Algorithm based on Artificial Neural Network)ANN-GA) are used to estimate manipulation of stock prices in Tehran Stock Exchange. In this study, first by using screening data method, a sample of 345 companies listed in Tehran Stock Exchange were selected and then information about the 'TEDPIX' index, closing price, volatility of closing price and trading volume in the timeframe years 1387 to 1391 were collected. Afterwards the selected companies categorized into manipulated and non-manipulated groups by using duration dependence test and run test. Then with scrutiny of the trend of Tedpix's chart and volume chart of the manipulated group, Start of price manipulation is determined. In next step by using Linear Discriminant Function)LDF) ,Quadratic Discriminant Function)QDF) and Genetic Algorithm based Artificial Neural Network and by using closing price, volatility of closing price and trading volume variables and also using information in range one year before starting manipulation group and in range four years for non-manipulation group, designed models for forecasting manipulation. At the end, the prediction ability of the models was examined. According to the results, the prediction ability of QDF model compared to the ANN model is better. Manuscript profile

Open Access Article

18 - Anticipation of Iran Mercantile Exchange (IME) gold coin price using Artificial Neural Network Approach with GMDH Algorithm
عباس معمار نژاد وحید فرمان آرا

The economy of every country is composed of different sectors in which, the relationship amongst them determines the dimensions of the economy of that country. The capital market together with money market make up the financial market as the main arteries of an economy. More

The economy of every country is composed of different sectors in which, the relationship amongst them determines the dimensions of the economy of that country. The capital market together with money market make up the financial market as the main arteries of an economy. Their operation has a significant influence on the growth and development of the economy. In cases where there is no constructive relationship between the financial market and economic sectors, economic performance might be subject to distortions. The stock market as a fundamental pillar of the financial market plays a crucial role in facilitating investments in the capital market. Given the importance of expectations in different economic fields, the main purpose of this study, as its title explains, is to anticipate of Iran Mercantile Exchange (IME) gold coin price Therefore, after a brief review of dominant economic theories, a new method, artificial neural network GMDH, is used to forecast the impact of macroeconomic variables( including the rate u.s. dollar as foreign exchange, the price of gold coin, the price of gold and oil in termes of dollar, the over-all index of stocks, the delivery date of gold coin) on the gold coin price. The GMDH Algorithm is a nonlinear model to anticipate complex systematic relationships between variables of the model. The special feature of this deductive algorithm is recognition and screening of the most effective variables to estimate the model with training samples and omit the non-significant ones from the simulation process with testing samples. So, an attempt is made to solve the model via iterative methods to minimize the typical standard Error like RMSE, MAPE, and so on. Manuscript profile

Open Access Article

19 - Forecasting Stock Market Using Wavelet Transforms and Neural Networks: An integrated system based on Fuzzy Genetic algorithm (Case study of price index of Tehran Stock Exchange)
Ali Anvary Rostamy Nor Mousazadeh Abbasi Mohammad Ali Aghaei Mahdi Moradzadeh Fard

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Open Access Article

20 - Modeling Customer Evaluations of the Quality of Health Care Using Artificial Neural Network (Case Study of Birjand University of Medical Sciences)
Zahra Hashemi Marzieh Faridi Masuleh

Introduction: The service quality is always one of the managerial concerns to supply customer’s satisfaction. Preparing qualified service needs to exact knowledge about the key factors of service quality and their effectiveness in the level of customer’s sat More

Introduction: The service quality is always one of the managerial concerns to supply customer’s satisfaction. Preparing qualified service needs to exact knowledge about the key factors of service quality and their effectiveness in the level of customer’s satisfaction. So implementing the different methods of measuring service quality could make it more explicit the unknown aspects of this factor effectiveness on the satisfaction. So the aim of this study was to evaluating the health care quality methods with artificial neural network approach. Methods: This study was a descriptive-correlation and an applied research. The statistical population of research consists of customers in hospitals of medical sciences Birjand University with an indefinite number. Referring to Cochran sampling formula a number of 385 individuals were selected using in access approach and validated questionnaires of study distributed among them. To measure the service quality it used the 4 approaches of weighted and un-weighted SERVQL and SERVPRF and the effect of service quality dimensions in each 4 approach were evaluated on the satisfaction. In this study to analyze the data is used of Spss software and the results of four methods to measure service quality using artificial neural networks have been studied. Results: The results showed that the method of measuring the quality of services achieved the lowest level of error for SERVQUAL 0.18 Weighted number That measure the quality of service in terms of weight SERVQUAL model using artificial neural networks have been more accurate in predicting customer satisfaction. Conclusions: methods of measuring service quality have different performance in predicting customer’s satisfaction under the scale of measuring service quality. Also the artificial neural networks regarding to implement predicting algorithm, may contain weaker forecast rather than classic statistical methods. Introduction: uality of service has always been one of the main concerns of managers in providing customer satisfaction. So, employing different methods to measure the effectiveness of this agent's unknown aspects of service quality can be more transparent on customer satisfaction. Methodology: This study was conducted cross-correlation functional investigation. The population of Birjand University of Medical Sciences were all customers that their number was not specified. According to Cochran sampling about 385 of them were selected based on availability of validated questionnaires were distributed among them. To measure the service quality it used the 4 approaches of weighted and un-weighted SERVQL and SERVPRF and the effect of service quality dimensions in each 4 approach were evaluated on the satisfaction. The data were analyzed using multi-layered artificial neural networks.Findings:The results showed that the method of measuring the quality of services achieved the lowest level of error for SERVQUAL 0.18 Weighted number That measure the quality of service in terms of weight SERVQUAL model using artificial neural networks have been more accurate in predicting customer satisfacti Conclusion: methods of measuring service quality have different performance in predicting customer’s satisfaction under the scale of measuring service quality. Also the artificial neural networks regarding to implement predicting algorithm, may contain weaker forecast rather than classic statistical methods Manuscript profile

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21 - Data Mining as an Intangible Model of Information Therapy and Seeking Behaviors in Immune Deficiency Disease Specialists
Sedigheh Mohammadesmaeil Shiba Kianmehr

Introduction: This study analyzed the information therapy behavior of immunologists in the country, based on the Cohennon self-organized neural network model. Method: Applied research has been done by descriptive survey method using neural network technique. The tool is More

Introduction: This study analyzed the information therapy behavior of immunologists in the country, based on the Cohennon self-organized neural network model. Method: Applied research has been done by descriptive survey method using neural network technique. The tool is a researcher made-questionnaire that was distributed among 149 people. Using MATLAB software, specialists based on the main components of clustering research, and then by removing each of the main sub-components,, the most effective and least effective option was determined. Results: Analysis showed in information retrieval skills; 63.75% of the population are in the first cluster with an average of 29.88 and in the second cluster 36.24% with an average score of 30.22, and the most important component is the use of keywords and terms related to the required information. About ways to get information; 22.14% of the population with an average score of 54.36 in the first cluster, 18.12% of individuals with an average of 48.11 in the second cluster, 14.09% with an average of 43.28 in the third cluster, 16.1% with an average of 0.04 49 were in the fourth cluster and 29.53% of the people with an average score of 53.72 were in the fifth cluster, and the most important way to find information was to use electronic information sources. Based on the use of various information services, 46% of people with an average score of 54.85 in the first cluster, 20.66% with an average of 49.38 in the second cluster and 32.66% with an average of 43.08 in the third cluster and the most important component of information therapy services has been familiarity with various sources and information services in the specialized field. Conclusion: Neural clustering of information therapy behaviors of the study population and the resulting information transactions, in addition to resulting in awareness of the needs and information resources required by users, as an accessible and low-cost method that improves the quality of information of immunodeficiency specialists leads to the provision of more effective medical services to patients, provides the necessary basis for anticipating information-oriented arrangements and decisions to meet the needs and information carriers requested by users of medical databases and provides managers and staff This field, and as an effective strategy with the highest level of possible standards, leads to the discovery of the intangible pattern of information seeking behaviors of health users, and teaches the audience to use information media intelligently. Manuscript profile

Open Access Article

22 - Artificial Neural Network Model for Predicting Insurance Insolvency
Ade Ibiwoye Olawale Ajibola Ashim Sogunro

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23 - LDF, QDF & ANN-GA based models for stock market surveillance in Tehran's Stock Exchange
M. Hossein Poustfroush Alireza Naser Sadrabadi Mahmood Moeinaddin

In this study, Discriminant Analysis (DA) model and the hybrid model of Genetic Algorithm based on Artificial Neural Network (ANN-GA) are used to estimate manipulation of stock prices in Tehran Stock Exchange. In this study, first by using screening data method, a sampl More

In this study, Discriminant Analysis (DA) model and the hybrid model of Genetic Algorithm based on Artificial Neural Network (ANN-GA) are used to estimate manipulation of stock prices in Tehran Stock Exchange. In this study, first by using screening data method, a sample of 345 companies listed in Tehran Stock Exchange were selected and then information about the 'TEDPIX' index, closing price, volatility of closing price and trading volume in the timeframe years 1387 to 1391 were collected. Afterwards the selected companies categorized into manipulated and non-manipulated groups by using duration dependence test, skewness & kurtosis test and run test. Then with scrutiny of the trend of Tedpix's chart and volume chart of the manipulated group, Start of price manipulation is determined. In next step by using Linear Discriminant Function (LDF), Quadratic Discriminant Function (QDF) and Genetic Algorithm based Artificial Neural Network and by using closing price, volatility of closing price and trading volume variables and also using information in range one year before starting manipulation group and in range four years for non-manipulation group, designed models for forecasting manipulation. At the end, the prediction ability of the models was examined. According to the results, the prediction ability of QDF model compared to the LDF model and ANN model is better. Manuscript profile

Open Access Article

24 - Forecasting the Type of Audit Opinions: A Data Mining Approach
محمدحسین ستایش فهیمه ابراهیمی سیدمجتبی سیف مهدی ساریخانی

Data mining methods can be used to assist auditors with providing audit opinions.The purpose of this research is to forecast the type of audit opinions using data miningmethods and compare the performance of these methods. Artificial neural networks,support vector machi More

Data mining methods can be used to assist auditors with providing audit opinions.The purpose of this research is to forecast the type of audit opinions using data miningmethods and compare the performance of these methods. Artificial neural networks,support vector machines, nearest neighbors and decision tree methods were used toconduct the research. The sample consists of 842 observations between 2001 and 2010.The observations were divided in two groups: one group for training and the other forassessment of the method. A comparison of the performance of methods indicates thatsupport vector machines approach outperforms the other approaches with a predictiveability of 76%. Also measuring type I and type II error rates of each method shows thatthe performance of support vector machines is higher than the other methods. Manuscript profile

Open Access Article

25 - Comparative between cost prediction using statistical methods and neural networks
امیر محمدزاده نسرین مهدی پور آرش محمدزاده

Prediction of total cost of water helps the Isfahan municipality to optimize thewater usage in its 14 urban zone. The total cost of water, basically, depends ondifferent parameters. Generally, the analytically prediction of the total cost is verydifficult if not impossi More

Prediction of total cost of water helps the Isfahan municipality to optimize thewater usage in its 14 urban zone. The total cost of water, basically, depends ondifferent parameters. Generally, the analytically prediction of the total cost is verydifficult if not impossible. Thus, applying intelligent systems such as neural networkmodels can be a good alternative. In this paper, using multi-layer perceptron neuralnetwork and error back propagation algorithm, the total cost of municipal water in theIsfahan municipality is calculated based on parameters such as per capita populationand area of each urban zone. In this paper, a model for simulation and prediction ofthe annual total cost of water in Isfahan municipality is developed. The simulationmodel is developed using the regression and the neural network model is built usingdata from 2004 to 2009. Finally, the neural network method is selected as the mainsimulation method for forecasting the total cost of water in the 14 urban zones ofIsfahan. Manuscript profile

Open Access Article

26 - Providing a neural network model to predict the profits of companies listed on the Tehran Stock Exchange and comparing its accuracy with HDZ and ARIMA models‏‏
masoud asadi seyedmozaffar mirbargkar Ebrahim Chirani

10.30495/jma.2022.21080

Profit forecasting is an important criterion for companies and companies listed on the Tehran Stock Exchange must be very careful in forecasting their profits. This study aims to provide a neural network model to predict the profits of companies listed on the Tehran Sto More

Profit forecasting is an important criterion for companies and companies listed on the Tehran Stock Exchange must be very careful in forecasting their profits. This study aims to provide a neural network model to predict the profits of companies listed on the Tehran Stock Exchange and compare its accuracy with ARIMA and HDZ models. The research method is an applied research in terms of purpose, an inductive research in terms of logic and a quantitative research in terms of data nature. In order to collect data, the basic financial statements of companies in the period 1398-1393 were used. In this study, neural network method was used to predict corporate profits and two models, ARIMA and HDZ, were evaluated. The results show that the rate of data convergence and regression in the first phase and in the HDZ method equal to 0.79087, in the second phase, in the ARIMA method, it is equal to 0.79184, and in the artificial neural network method, it is equal to 0.79464, which has a higher degree of convergence and regression coefficient. Based on the results, it can be seen that the designed neural network has the ability to predict stock price trends using general and industry indicators, and this, in addition to confirming the neural network's ability to predict financial areas and profitability it also confirms strategy of the price forecast on the Tehran Stock Exchange.&rlm; &rlm;&rlm; Manuscript profile

Open Access Article

27 - Application of Artificial Neural Network and Genetic Algorithm for Predicting three Important Parameters in Bakery Industries
H. Abbasi Z. Emam-Djomeh S. M. Seyedin

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Open Access Article

28 - Comparison of artificial neural network and multivariate linear regression (MLR) models to predict cover percentage Artemisia aucheri from some soil properties
Mansoreh Kargar Zeynab Jafarian

Soil ecosystems management for different purposes requires accurate and quantitative understanding of the soil characteristics and their processes. This study was aimed to predict Artemisia aucheri cover though some soil physical and chemical properties in Vavsar rangel More

Soil ecosystems management for different purposes requires accurate and quantitative understanding of the soil characteristics and their processes. This study was aimed to predict Artemisia aucheri cover though some soil physical and chemical properties in Vavsar rangeland, Kiasar, Mazandaran province. Random systematic sampling was used. Five transects with 100 m length and 10 plots 4 m2 on each transect were established. Then cover (%) of A. aucheri and 50 soil sample from 0-15 cm depth was estimated in each plot. Soil properties including soil organic carbon, total nitrogen, EC, water percentage, CaCo3 percentage, soil texture, and pH were measured. Data were divided in two series: a series for analysis including 70% of the data for and 30% for evaluation of customized models. Result showed that soil water, silt and sand percentages were the most important soil properties for prediction A. aucheri cover in the study area. Prediction of the statistical models in the study area resulted in mean error and root mean square error values of 0.25, 0.06 for ANN equation and 0.43, 0.12 for MLR, respectively. Therefore, the ANN model could provide superior predictive performance when was compared with MLR model. Manuscript profile

Open Access Article

29 - Optimizing the exploitation of the Shahryar plain aquifer by simulating the groundwater flow using the combined modeling method
Nima Salehi Shafa Hossein Babazadeh Fayaz Aghayari Ali Saremi MohammadReza Ghafouri Masoud Safavi Ali Panahdar

10.30495/jrnr.2022.70366.10272

In this study, a multi-objective simulation model was presented and six scenarios were defined based on the groundwater budget for optimal exploitation of Shahryar plain aquifer. For this purpose, first, using the GIS based models and using fuzzy and weight methods, the More

In this study, a multi-objective simulation model was presented and six scenarios were defined based on the groundwater budget for optimal exploitation of Shahryar plain aquifer. For this purpose, first, using the GIS based models and using fuzzy and weight methods, the regions with high water and low water in the study area were identified in order to prioritize resources and uses. Then the desired scenarios were simulated and evaluated by the (GMS) model. Finally, in order to increase the accuracy of the research results, the (TDS) concentration and the groundwater budget were simulated using the artificial neural network model (ANN). According to the results of fuzzy and weighted methods, the areas of Rabat Karim, then Islamshahr and finally Shahryar were identified as low water areas. Based on the obtained results, the unsteady state budget and verification were calculated as -344.68 and -109.98 (MCM), respectively. The third scenario with a budget of 203.33 (MCM) was chosen as the best scenario and the budget resulting from that, ratio to the budget resulting from the model (GMS) and the neural network for the year 95, has increased by 284.87 percent and 284.83 percent respectively. Also, the concentration of total dissolved solids in the groundwater obtained from the desired models in the entire study period was estimated by 655 and 651 mg/liter on average. The criteria of correlation coefficient and determination coefficient obtained from neural network models for groundwater budget and total data were estimated to be equal to one and for the total dissolved solids concentration of groundwater were estimated to be 0.997 and 0.994, respectively. In the present research, the multi-objective simulation pattern as a comprehensive and practical method by providing new simulation methods has the ability to support several effective scenarios and leads to increase the stability of the groundwater system. Manuscript profile

Open Access Article

30 - Structural Relationship Study of Octanol-Water Partition Coefficient of the Compounds in kesum Essential Oil Using GA-MLR and GA-ANN Methods
Atefehsadat Navabi Tahereh Momeni Isfahani

10.30495/jptc.2021.20873

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Open Access Article

31 - Estimating Daily Maximum Temperatures Using Artificial Networks (Case study: Kerman)
Shokoufeh Omidi ghaleh mohammadi Ahmad Mazidi Sodabh Karemi Najmeh Hassani sadi Mahboobeh Omidi ghaleh mohammadi hassan kharajpor

10.30495/sarzamin.2022.35670.1604

Considering the capability of the artificial neural networks in simulating sophisticated processes, it is being used in estimation and computation of climatic parameters. The goal of this research is to estimate the daily maximum temperature in Kerman province. To this More

Considering the capability of the artificial neural networks in simulating sophisticated processes, it is being used in estimation and computation of climatic parameters. The goal of this research is to estimate the daily maximum temperature in Kerman province. To this aim, daily climatic parameters as input to the neural networks and daily maximum temperature as the output during a statistical period of 24 years (1989-2013) were used, the findings revealed that the output of the multi-layer perceptron neural network, considering the error amount and correlation among data, is more precise and shows lower error and more correlation in relation to the expected output (daily maximum temperature). Also, among other climatic parameters, minimum temperature and the average of the wet temperature indicated the estimation of the daily maximum temperature with lower error and more correlation in comparison to other climatic parameters. Manuscript profile

Open Access Article

32 - Climate Signals applied to the prediction of evaporation in west of Iran
Enayatolah Rahmati Majid Montazeri Amir Gandomkar Mehran Lashani Zand

Evaporation is one of the climatic variables that predict significant role in the planning of water. Due to the relatively high rainfall in areas of West Iran, awareness of the evaporation rate of water in these areas is essential for proper management.The factors influ More

Evaporation is one of the climatic variables that predict significant role in the planning of water. Due to the relatively high rainfall in areas of West Iran, awareness of the evaporation rate of water in these areas is essential for proper management.The factors influencing rate of evaporation, which are climatic signals according to their role in predicting enables evaporation. Evaporation prediction was performed using artificial neural network model based on climatic signals. the data of evaporation at three synoptic stations and the most important climate signals whit at least 20 years of monthly analysis using NeuroSolution software. The results show that the most Important signals affecting the evaporation areas include; Nina3, Nina1, Sw monsoon, Mei and Nina4.Comparison of observed data with a high correlation between the ANN output data shows. So that the correlation of the Kermanshah station is 71%, Hamedan 82% and Sanandaj 80%.The output data of the neural network and climatic signals, can accurately predict the top 97% of the areas evaporation. Manuscript profile

Open Access Article

33 - A novel method for forecasting the Malmquist productivity index by artificial neural network: Evidence from Iranian commercial bank
Elsa Shokrollahpour Fariba Salahi

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Open Access Article

34 - Performance Evaluation of Decision Making Units Using Data Envelopment Model and Artificial Neural Network (Case Study: Fars Regional Water Corporation)
Morteza shafiee Saeedeh Akbarpoor Sara Salari Dargi

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Open Access Article

35 - Application of Machine Learning Models for flood risk assessment and producing map to identify flood prone areas: Literature Review
Parisa Firoozishahmirzadi Shaghayegh Rahimi Zeinab Esmaeili Seraji

10.30495/ijdea.2022.68987.1181

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36 - Development and combination of soft computing and geostatistical models in estimation of spatial distribution of groundwater level
سامان معروف پور احمد فاخری فرد جلال شیری

One of the most important issues in groundwater resources quantitative management is estimating water table level using observation wells network data. The purpose of this study is to estimate the groundwater level using the combination of the geostatistics and soft com More

One of the most important issues in groundwater resources quantitative management is estimating water table level using observation wells network data. The purpose of this study is to estimate the groundwater level using the combination of the geostatistics and soft computing methods. Bam Normashir and Rhmtabad plains (Kerman province) with an area of 1928 km2 was selected as a case study of this work. In this study, Kriging and IDW methods were used along with the data driven ANN, ANFIS and GEP models for predicting the spatial distribution of groundwater level, then, the best model was selected for further sampling in the studied region. Data from 65 wells during the period of 2002 to 2011 were used. RMSE, R2, AARE and MAE statistical indices were used for comparing the applied models. Results showed that for all of the models with two input parameters (including longitude and latitude), ANN and IDW models presented the most accurate results with the lowest RMSE (7.138 and 15.062m, respectively) and AARE (33 and 44%, respectively), and the highest R2 (0.606 and 0.596, respectively) for the point and regional estimation of groundwater table level. Finally, ANN-IDW hybrid model was selected for estimation and zoning the groundwater level for the future investigations. Manuscript profile

Open Access Article

37 - Autoregressive simulation of Zarrinehrud river basin runoff using Procrustes analysis method and artificial neural network and support vector machine models
بهروز سبحانی Mohammad Isazadeh منیر شیرزاد

Rivers flow prediction in river basins has an important role in the operation and correct management of water resources. Determining type and number of estimator models inputs is one of the important steps in rivers flow prediction. Therefore, The Procrustes analysis (P More

Rivers flow prediction in river basins has an important role in the operation and correct management of water resources. Determining type and number of estimator models inputs is one of the important steps in rivers flow prediction. Therefore, The Procrustes analysis (PA) method for determining the number of effective inputs was used. In this study, flow prediction was done using the flow data obtained from the Safakhaneh and Santeh hydrometric stations. The Artificial Neural Network (ANN) and The Support Vector Machine (SVM) models was used for flow prediction. The best estimation of flow is done using the MLP and SVM models in Safakhaneh hydrometric station with RMSE equal to 5.68 (m3/s) and 4.85 (m3/s), respectively, and CC equal to 0.73 and 0.78, respectively. While in Santeh hydrometric station RMSE was equal to 6.44 (m3/s) and 6.36 (m3/s) respectively, and CC was equal to 0.78 and 0.79 respectively for MLP and SVM models. PA-SVM model showed better results than SVM model in estimating Safakhaneh hydrometric stations flow with RMSE equal to 5.45 (m3/s) and CC equal to 0.73 during the test period. The results also indicated that SVM and PA-SVM models estimated the flow of Santeh station with RMSE equal to 6.85 (m3/s) and 7.03 (m3/s) respectively. Basically, results indicated that the Procrustes analysis method can be used as one of the Efficient and suitable methods for determining the number of effective inputs. Comparison of the ANN and SVM results indicated that ANN model has more accuracy than SVM model.  Manuscript profile

Open Access Article

38 - Prediction of soil texture using artificial neural networks
علی محمدی ترکاشوند Elnaz Khanbabakhani Mohammad Ali Mahmoodi

Soil texture is one of the most important soil properties that affect many physico-chemical properties such as water storage, cation exchange capacity (CEC), soil fertility and soil ventilation. Today, artificial intelligence technology such as neural and neuro-fuzzy ne More

Soil texture is one of the most important soil properties that affect many physico-chemical properties such as water storage, cation exchange capacity (CEC), soil fertility and soil ventilation. Today, artificial intelligence technology such as neural and neuro-fuzzy networks is used to solve problems in modeling systems and processes. For this purpose, 150 soil samples from a depth of 0 - 15 cm of Gavshan Dam watershed in the Kurdistan province were collected. The geographic locations, height and slope percent of every sampling point were recorded. The particle size distribution of samples was measured in the laboratory using hydrometer method. The longitude and latitude, height, slope percent and soil texture particles of training points were introduced to artificial neural networks to estimate soil texture particles by MATLAB software. The accuracy of model was evaluated by scoring, using statistical indicators such as root mean square error (RMSE), the ratio of geometric mean error (GMER) and correlation coefficient (R2). According to the results, the values for estimating sand and clay are approximately the same and for predicting the silt, less than sand and clay, and 37.0, although less error. The accuracy and accuracy of the model show that the neural network does not have any accuracy and accuracy in estimating the percentage of soil texture components and the soil texture mapping. Manuscript profile

Open Access Article

39 - Modeling Estimation of Suspended Sediment Rate in Pasikhan River Using Decision Tree Artificial Neural Network
سیدسامان Nasiri Ebrahim Amiri محبوبه shadabi

Accurate estimation of sediment transport in rivers due to erosion is an important factor for the management of hydrological projects. Artificial neural networks are of great importance for many reasons, such as the ability to detect patterns, the good relationship betw More

Accurate estimation of sediment transport in rivers due to erosion is an important factor for the management of hydrological projects. Artificial neural networks are of great importance for many reasons, such as the ability to detect patterns, the good relationship between input and output, and the need for less input data to predict suspended sedimentation. Accordingly, the present study attempts to model the estimation of suspended sediment content in the Pasikhan River using the artificial neural network of the M5 decision tree. The amount of sediment in rivers is subject to many parameters of river geometry, hydraulic flow and sediment properties. For this reason, in this study, it has been tried to reduce the number of effective parameters by first dimensioning the effective parameters on sediment transport capacity. The results showed that the initial decision tree, the M5 tree, does not require pruning and is suitable for use. Three parameters of determination coefficient (R2), mean relative error (ME) and mean squared error (RMSE) were used to evaluate the accuracy of the prediction model. The obtained values for these three parameters were 0.851, 1037.64 and 941.32, respectively, indicating the suitability of these three parameters. Comparison of suspended sediment yield from decision tree model with Pasikhan River measurement data showed that the coefficient of determination was 0.8953 which is a very good value. The results showed that this model is effective in predicting suspended sediment content in the Pasikhan River. Manuscript profile

Open Access Article

40 - Rainfall-Runoff modeling using Deep Learning model (Case Study: Galikesh Watershed)
Razieh Tatar Khalil Ghorbani mehdi Meftah halghi meysam salarijazi

Artificial neural networks (ANN) are one of the data mining methods applied by many researchers in different fields of studies such as rainfall runoff modeling. To improve the performance of these networks, deep learning neural networks were developed to increase modeli More

Artificial neural networks (ANN) are one of the data mining methods applied by many researchers in different fields of studies such as rainfall runoff modeling. To improve the performance of these networks, deep learning neural networks were developed to increase modeling accuracy. This study evaluated deep learning networks to improve the performance of artificial neural networks in Galikesh watershed and to predict discharge for 1, 3, 6 and 12-month time scale based on 1 to 5 month time scale lags made in precipitation and temperature data. Based on 70% and 30% of the data used for training and test respectively the results demonstrated that in all time steps, the deep learning neural network improved the performance of artificial neural network and on average RMSE decreased in both training and test from 0.68 to 0.65 and 0.84 to 0.73 respectively. Moreover, R-square was increased on average from 0.57 to 0.62 and 0.51 to 0.67 respectively in training and test. We can also denote the effect of temperature on the increase of accuracy of rainfall-runoff modeling. Manuscript profile

Open Access Article

41 - Application of Hybrid Model of Artificial Neural Networks and Firefly Algorithm to Predict the Amount of TDS in River Water
Farahnaz Sabzevari Behrouz Yaghoubi Saeid Shabanlou

10.30495/wsrcj.2023.71849.11356

Background and Aim: Estimation and forecasting of qualitative parameters along with quantitative parameters of water alongside the river to make correct managerial decisions is one of the objectives of managers and planners of the water industry should be accurately sim More

Background and Aim: Estimation and forecasting of qualitative parameters along with quantitative parameters of water alongside the river to make correct managerial decisions is one of the objectives of managers and planners of the water industry should be accurately simulated. Most of the models for qualitative parameter estimations require very large input parameters that are either difficult to access or require much time and money to determine. Therefore, the use of data-driven models in this field has been developed to save time and money.Method: In this paper, the application of artificial neural networks and its combination with the firefly algorithm to predict the amount of Total dissolved solids (TDS) of water in the Gavehrood River located in Iran, Kermanshah has been trained and validated. with this purpose, water quality data of hydrometric station upstream of the Gavoshan reservoir dam are used for the statistical period (1991-2010). Based on different inputs, the multilayer perceptron (MLP) artificial neural network and its combination with the firefly algorithm are tested. The best algorithm of the inputs, the number of hidden layers and the number of neurons in each layer in the artificial neural network are determined. The input data imported to the models include the flow rate (Q), Sodium (Na), Magnesium (Mg), Calcium (Ca), Sulfate (So4), Chloride (Cl), Bicarbonate (Ho3), Electrical conductivity (EC) and Total Dissolved Solides of the river in the previous period (TDSt-1) and the output data of TDS. The number of hidden layers is obtained to be 1 and the number of hidden layer neurons is achieved to be 9. Also, the neural network function in this study is considered as a waterfall type and the results are compared by combining artificial neural networks with the firefly algorithm. The model outputs are compared with measurement data using the error measurement criteria.Results: In this regard, the values of the used error evaluation indices including the observed standard deviation (RSR), Nash Sutcliffe coefficient (NSC), correlation coefficient (R) and root mean square error (MSE) for artificial neural network are yielded 0.154, 0.976, 0.989 and 25.27, respectively and in the case of the neural network combination with the firefly algorithm, are achieved to be 0.129, 0.983, 0.992 and 17.8, respectively.Conclusion: Therefore, the performance of the hybrid method of artificial neural networks by using the firefly algorithm in predicting TDS is more appropriate than artificial neural networks. Manuscript profile

Open Access Article

42 - Comparison of Data Mining Models Performance in Rainfall Prediction Using Classification Approach (Case Study: Hamedan Airport Synoptic Weather Station)
Morteza Salehi Sarbijan Hamidreza Dezfoulian

10.30495/wsrcj.2024.75668.11411

Background and Aim: Rainfall is one of the complex natural phenomena and one of the most crucial component of the water cycle, playing a significant role in assessing the climatic characteristics of each region. Understanding the amount and trends of rainfall changes is More

Background and Aim: Rainfall is one of the complex natural phenomena and one of the most crucial component of the water cycle, playing a significant role in assessing the climatic characteristics of each region. Understanding the amount and trends of rainfall changes is essential for effective management and more precise planning in agricultural, economic, and social sectors, as well as for studies related to runoff, droughts, groundwater status, and floods. Additionally, rainfall prediction in urban areas has a significant impact on traffic control, sewage flow, and construction activities. Method: The objective of this study is to compare the accuracy of classification models, including Chi-squared Automatic Interaction Detector (CHAID), C5 decision tree, Naive Bayes (NB), Quest tree, and Random Forest, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) in predicting rainfall occurrence using 50 years of data from the synoptic station at Hamedan Airport. In this study, 80% of the data is used for training the models, and 20% for model validation and the results obtained from the model executions are compared using metrics such as confusion matrix, Receiver Operating Characteristic (ROC) curve, and the Area Under the Curve (AUC) index. To create the classification variable for rainfall and non-rainfall data, based on rainfall data, the days of the year are categorized into two classes: days with rainfall (y) and days without rainfall (n). Data preprocessing is performed using Automatic Data Preprocessing (ADP). Then, Principal Component Analysis (PCA) is employed to reduce the dimensions of the variables. Results: In this study, the PCA method reduces the dimensions of the variables to 5. Also, approximately 80% of the available data corresponds to rainless days, while 20% corresponds to rainy days. The research results indicated that the KNN model with an accuracy of 91.9% for training data and the SVM model with 89.13% for test data exhibit the best performance among the data mining models. The AUC index for the KNN model is 0.967 for training data and 0.935 for test data, while for the SVM algorithm, it is 0.967 for training data and 0.935 for test data. According to the ROC curve for Hamedan rainfall data, the KNN model outperforms other models. Considering the sensitivity index in the confusion matrix, the KNN and SVM models perform better in predicting non-rainfall occurrence for training data. In terms of the precipitation occurrence prediction, the RT and KNN models show better results according to the specificity index. Conclusion: The results demonstrated that for the RT, C5, ANN, SVM, BN, KNN, CHAID, QUEST, accuracy metrics was obtained 86.82%, 89.78%, 89.55%, 89.96%, 88.06%, 91.9%, 88.29%, 87.46%, 91.9%, respectively for training data. Moreover, for test data, the accuracy metrics for this model was obtained 83.82%, 87.9%, 88.12%, 89.13%, 87.12%, 89.13%, 87.12%, 88.19%, 86.93%, 86.76%, respectively. The AUC index in the training data for RT, C5, ANN, SVM, BN, KNN, CHAID QUEST models was 0.94%, 0.99%, 0.94%, 0.94%, 0.93%, 0.97%, 0.93%, 0.89%, respectively. In addition, for the test data, this metric was evaluated 0.89%, 0.89%, 0.93%, 0.94%, 0.92%, 0.90%, 0.92%, 0.88% respectively. As observed, considering accuracy metric and AUC index for training data KNN model and for test data SVM model were more sufficient in rainfall prediction.  Manuscript profile

Open Access Article

43 - Predicting soil–water distribution coefficients of heavy metals using artificial neural networks
Amin Falamaki Mahnaz Eskandari

Contamination of soil and water resources is a major concern for optimal use of these resources worldwide. The so-called distribution coefficient (Kd) is an applied parameter not only for modeling contaminant transport in soil but also for risk analysis of soil and wate More

Contamination of soil and water resources is a major concern for optimal use of these resources worldwide. The so-called distribution coefficient (Kd) is an applied parameter not only for modeling contaminant transport in soil but also for risk analysis of soil and water resources contamination. The most common quantitative model for estimating Kd is parametric method. The correlation coefficient of this model is usually low, however, the predicted Kd values may cause significant inaccuracy in predicting the impacts of contaminant migration or siteremediation options. The objective of this study was to investigate application of artificial neural networks (ANN) for improving Kd prediction of heavy metals. Consequently, three ANN types including multi layer perceptron (MLP), redial basis function (RBF) and hierarchical networks (HN) and two heavy metals of Chromium (VI) and cadmium were used for modeling purposes. The collected data were first divided into two training and test groups. The first group was used to train ANN and the second to evaluate generalized ANN models. The most suitable geometry of networks were obtained with trial and error procedure. The results of modeling Kd(Cr) revealed that both MLP and RBF networks are reasonable tools, but MLP was more accurate than RBF. Although the applied input data for training networks were not so much (at least 9 and the maximum of 16), but they were sufficient for modeling Kd(Cr). This finding is a promising result because direct measurement of Kd is expensive and time consuming. Further, usually limited numbers of available data are existing in each case. The results of predicting Kd(Cd) approved the preferences of MLP for modeling purposes. The ANN model can significantly enhance the correlation coefficient between predicted and measured data form 0.37 of parametric method to 0.63. Manuscript profile

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44 - Modeling of Physicochemical Properties of Eggplant Slices Coated with Basil Seed Gum during Frying Process
F. Salehi M. A. Asadnahal

Introduction: Fried food products are very popular due to their unique characteristics such as color, smell, taste and desirable texture. Controlling frying conditions and using edible hydrocolloid coatings (gums) is one of the best methods to reduce the oil uptake, moi More

Introduction: Fried food products are very popular due to their unique characteristics such as color, smell, taste and desirable texture. Controlling frying conditions and using edible hydrocolloid coatings (gums) is one of the best methods to reduce the oil uptake, moisture retention, and improving the appearance properties of fried foods.Materials and Methods: In this study, different concentrations of basil seed gum (0.0, 0.5, 1 and 1.5% w/w) were used to coat the eggplant slices during deep-frying at 150, 175 and 200°C and the relationship between process parameters and the quality of final product were modeled by genetic algorithm-artificial neural network method.Results: The results of this study showed that coating with basil seed gum reduced the oil uptake of the final product. Coating pretreatment maintained the final product moisture and moisture content of the sample coated with 1.5% gum was higher than the other samples (64.05%). This process was modeled by genetic algorithm-artificial neural network method with 2 inputs that included basil seed gum concentration and frying temperature and 5 outputs that included oil percentage, moisture content, and three main color indexes (yellowness(b*), redness (a*), and lightness (L*)). The results of modeling showed that a network with 4 neurons in a hidden layer and using the hyperbolic tangent activation function can predict the physicochemical properties of fried eggplant slices.Conclusion: The coating containing 1.5% of basil seed gum retained moisture content and reduced oil absorption by the fried samples, and this coating is recommended as a suitable edible coating for coating of eggplant slices before the frying process. Sensitivity analysis results showed that the changes in the concentration of basil seed gum had the highest effect on the lightness index and then on the oil content of fried eggplant slices. The change of frying temperature also had the highest effect on the lightness index of fried samples. Manuscript profile

Open Access Article

45 - Oil Extraction from Pistacia Khinjuk - Experimental and Prediction by Computational Intelligence Models
Y. Vasseghian Gh Zahedi M Ahmadi

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46 - A Comparative Study Concerning Linear and Nonlinear Models to Determine Sugar Content in Sugar Beet by Near Infrared Spectroscopy (NIR)
S. Minaei H. Bagherpour M. Abdollahian Noghabi M.E. Khorasani Fardvani F. Forughimanesh

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47 - The Application of Neural Network Method for the Prediction of the Osmotic Factors of Crookneck Squash
M. Mokhtarian H. Tavakolipour

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48 - Prediction of the results of implementation of air pollution control strategies using the Geo-Artificial Neural Network for Tehran metropolis
Mehran Ghoddousi Farideh Atabi Jafar Nouri Alireza Gharagozlu

10.22034/jest.2019.23618.3275

Background and Objective: Predicting the results of the implementation of strategic air pollution control policies is the first and most important challenge for Tehran municipality. The main objective of this study was to define a specific method for assessing the resul More

Background and Objective: Predicting the results of the implementation of strategic air pollution control policies is the first and most important challenge for Tehran municipality. The main objective of this study was to define a specific method for assessing the result of urban air pollution controlling strategies in Tehran metropolis using a multi-dimensional decision support system. Method: First, the most appropriate air pollution control strategies were selected based on existing conditions and structures in each zone of the city and then weighed according to selected criteria. Based on the spatial monitoring of air pollution formation patterns in the past and present time, as well as the analysis of their effects, the results of implementing air pollution control strategies were simulated using Geo-Artificial Neural Network models. In the next step, variables of time series and uncertainty variables were simulated for predicting the potential future air pollution patterns and finally, the results of the defined control strategies were evaluated based on spatial thematic layers. Findings: Definition of final clusters of air quality control strategies, weighting and ranking of the selected policies based on defined criteria have been the first findings of this research. Also, extraction of time series zoning based on the data collected during a four-year period, as well as simulation of the baseline scenario models and spatial data layers of their output were among the achievements of this study. Finally, the modeling of the predictive variables, design of the air quality control software and the prediction of the results of the the implementation of air pollution control strategies were presented. The results showed that by applying the Geo-Artificial Neural Network models (GANN), the urban managers could effectively predict the results of implementing the air pollution control strategies. Discussion and Conclusion: The results of this study showed that the spatio-temporal analysis supports the process of evaluation and prediction of the effects of pollution and can be used to determine the best pollution control strategies for the zones affected by air pollution. The final results of GANN models indicate that if the selected strategies are implemented based on the scenarios defined, in the "optimistic scenario", air quality in all areas of Tehran is completely stable and remains healthy, while in the "ordinary scenario" will reduce the level of air pollution up to 70 percent in the autumn and winter season if the selected strategies are implemented compared to the lack of implementation of control plans. The final model of the verification process model also confirmed that the pattern of pollution predicted by the model in each of the urban areas had a proper trend and adaptation compared to the pattern of contamination obtained from the actual results of the field data.   Manuscript profile

Open Access Article

49 - Forecasting Municipal Solid Waste Quantity by Intelligent Models and Their Uncertainty Analysis
Maryam Abbasi Malihe Fallah Nezhad Rooholah Noori Maryam Mirabi

10.30495/jest.2020.11144

Background and Objective: The first step in design of municipal waste management systems is complete understanding of waste generation quantity. Forecasting waste generation is one of the most complex engineering problems due to the effect of various and out of control More

Background and Objective: The first step in design of municipal waste management systems is complete understanding of waste generation quantity. Forecasting waste generation is one of the most complex engineering problems due to the effect of various and out of control parameters on waste generation. Therefore, it is obvious that it is necessary to develop approaches to a model such complex events. The objective of this study is forecasting waste generation quantity using intelligent models as well as their comparisons and uncertainty analysis.Method: In this study, Mashhad city was selected as a case study and waste generation time series of waste generation in 1380 to 1390 were used for weekly prediction. Intelligent models including artificial neural network, support vector machine, adaptive neuro-fuzzy inference system as well as K-nearest neighbors were used for modelling. After optimizing the models’ parameters, models’ accuracy were compared by statistical indices. Finally, result uncertainty of the models was done by Mont Carlo technique.Findings: Results showed that coefficient of determination (R2) of artificial neural network adaptive neuro-fuzzy inference system, support vector machine, and K-nearest neighbor models were 0.67, 0.69, 0.72 and 0.64 respectively. Uncertainty analysis was also justified the results and demonstrates that support vector machine model had the lowest uncertainty among other models and the lowest sensitivity to input variables.Conclusion: Intelligent models were successfully able to forecast waste quantity and among the studied models, support vector machine was the best predictive model. Moreover, support vector machine produced the results with the lowest uncertainty the other models. Manuscript profile

Open Access Article

50 - Evaluation and Optimization of Waste Collection and Transportation System in Urmia by Combining the Response Surface and Artificial Neural Network
Saeid JafarzadehGhoushchi Shabnam Hamidi- Moghaddam

10.22034/jest.2020.27028.3603

Background and Objective: Optimization of urban waste collection and transportation system has the largest part of waste management costs. Therefore, improving this system and reducing its operating costs as a necessity in urban waste management has always been consider More

Background and Objective: Optimization of urban waste collection and transportation system has the largest part of waste management costs. Therefore, improving this system and reducing its operating costs as a necessity in urban waste management has always been considered. Method: Due to the high volatility, changes in the size of the waste, climate change and demographic and substructure tissue, the use of artificial neural network system (ANN) is a suitable method for predicting the production waste size, and on the other hand, for The optimization of the management system of these wastes is also used by the surface response method (RSM). Findings The results of this combined method show that the best combination of factors affecting urban waste transport system was proposed by RSM considering the largest loaded pack with about 26 workers, 10 pickups and 6 trucks. This combination is capable of carrying around 34836 tons of cargo at a cost of 596696000 Rials, which represents a high efficiency over actual values. Also, to predict load, the back propagation algorithm (BP) with 9 neurons in the hidden layer was selected as the best model with a predictive power of 99/19% in prediction of weight and 96/62% in cost prediction. Discussion and Conclusion: The results showed that using the combination of two methods of surface response as a statistical method and artificial neural network as a mathematical method, we can find suitable results for evaluation and optimization of waste collection and transportation system. Manuscript profile

Open Access Article

51 - Evaluation of Suspended Sediment Load by Sediment Rating Curves and Comparing with Artificial Neural Network and Regression Methods (Case study: Babolrud River Mazandaran Province)
Alireza Mardookhpour Hosein jamasbi Omid Alipour

10.22034/jest.2018.22430.3152

Background and Objective: In this research the object is prediction of suspended sediment load by and artificial neural network (ANN), Sediment Rating Curves (SRC) and regression methodfor BabolrudRiver in Mazandaran province. Method: The inputs conclude discharge and t More

Background and Objective: In this research the object is prediction of suspended sediment load by and artificial neural network (ANN), Sediment Rating Curves (SRC) and regression methodfor BabolrudRiver in Mazandaran province. Method: The inputs conclude discharge and the output is sediments concentration in time series. The input and output of river have positive procedure for (1979-2013) and 75% of data utilized for training and 25% for tests. For training the network, data that recognize issue conditions were selected and some data for testing, Findings: The results show the concentration of sediment suspended load derived artificial neural network and is close together and regression coefficient is 92.8%, while regression coefficient is 83% for sediment rating curves and 90% for statistical method respectively. Discussion and Conclusion: In conclusion, artificial neural network (ANN) has more workability and flexibility for prediction of suspended sediment load to sediment rating curves and statistical methods. Manuscript profile

Open Access Article

52 - Proper Orthogonal Decomposition Performance to Determine the Inputs to the Artificial Neural Network for Prediction of Inflow into Alavian Dam
Saber Moazami Roohollah Noori Mohammad Reza Vesali Naseh Abbas Akbarzadeh

10.22034/jest.2019.21920.3101

Background and Objective: Dams play an important role in development of countries by drinking and agricultural water supply, flood control, hydropower energy supply and recreational purposes. Constructing a dam and making an artificial lake has an important effect on su More

Background and Objective: Dams play an important role in development of countries by drinking and agricultural water supply, flood control, hydropower energy supply and recreational purposes. Constructing a dam and making an artificial lake has an important effect on surrounding environment, so being able to forecast the inflow to the dam is an important issue for water resource management. Method: In this study artificial neural network (ANN) was applied to forecast the monthly inflow from Soofichai River to Alavian Dam. Regarding the huge amount of input data to ANN model and for optimizing its application, proper orthogonal decomposition (POD) was used in order to determine the best inputs for ANN model . Finally, the application of ANN and POD-ANN models was evaluated by determination coefficient (R2), mean absolute error (MAE) and average of absolute relative error (AARE). Findings: Results of ANN and POD-ANN models indicated that although ANN output is close to the observed values of inflow to the dam, but it has significant errors. POD-ANN model showed better results than ANN model for high values of inflow. In generall, comparing R2, MAE and AARE values of two models revealed that POD-ANN model had better performance in both calibration and verification steps in comparison with ANN model. R2, MAE and AARE in verification step of POD-ANN model were 0.93, 0.79, and 0.54, respectively. Discussion and Conclusion: Preprocessing data contributes to better performance of  POD-ANN than ANN model, especially in high values of inflow. Therefore, it can be concluded that applying data preprocessing and reducing inputs to ANN model enhances its performance. Manuscript profile

Open Access Article

53 - Turbidity Removal from Water Using Graphene Oxide as Coagulant and Modeling with Artificial Neural Network
nazila rezania Maryam Hasani Zonoozi motahareh Saadatpour

10.30495/jest.2020.48158.4851

Background and Objective: In recent years, applications of carbon-based nanomaterials have been developed in various fields such as water and wastewater industry. One of these compounds is graphene oxide (GO), which has attracted a lot of attention due to its high speci More

Background and Objective: In recent years, applications of carbon-based nanomaterials have been developed in various fields such as water and wastewater industry. One of these compounds is graphene oxide (GO), which has attracted a lot of attention due to its high specific surface two-dimensional structure and various surface groups. In this regard, the main purpose of this study is to investigate the coagulation properties of graphene oxide in removing turbidity from water and modeling the process using artificial neural network (ANN).Material and Methodology: The samples were prepared by using garden soil and tap water and the GO was purchased in the form of suspension. Jar tests were performed to assess the influence of pH, GO dosage, initial turbidity, settling time and other parameters on the turbidity removal efficiency. In order to simulate the process, Perceptron neural network was used.Findings: Under acidic pH conditions and with increasing the GO dosage from 2.5 mg/L to 40 mg/L, the removal efficiency increased considerably. However, the initial turbidity did not show a clear effect on the process performance. Much of the turbidity removal occurred within the first 10 minutes of the settling time and the flocs’ exhibited higher settling rates at higher GO dosages and acidic pH condition. According to the results obtained from the created ANN model, the coefficient of determination (R2) and the correlation coefficient (R) between the observed and predicted values of the test data were 0.9492 and 0.974, respectively, which reveal the model’s high capability in predicting the process results.Discussion and Conclusion: GO showed high capability in turbidity removal from water. The pH and GO dosage were recognized as the process controller parameters. The ANN data mining model showed good performance in predicting process efficiency.  Manuscript profile

Open Access Article

54 - Investigation of the accuracy of multilayer perceptron network and radial base function in estimating river sediment (Case study: Zayandehrud)
Ramtin Sobhkhiz Alireza Mardookhpour

10.22034/jest.2021.56444.5208

Background and Objective: Estimating the amount of sediment by the river is one of the topics that has been considered by many researchers since the past. Reduction of the dam reservoir capacity because of sediments has different effects on different sections and causes More

Background and Objective: Estimating the amount of sediment by the river is one of the topics that has been considered by many researchers since the past. Reduction of the dam reservoir capacity because of sediments has different effects on different sections and causes adverse effects on the water rights that were initially agreed upon, which will impose several economic and specific consequences. This study aims to model and estimate the amount of suspended sediment using existing experimental equations and new methods called black box. Material and Methodology: The discharge (volumetric flow rate) related to Zayandehrud River in Eskandari station, one of the hydrological measuring stations, has been used to estimate the amount of sediment. For this purpose, water discharge and sediment rate are used as input and output, respectively. Findings: According to the obtained results, it is concluded that the RBF network has better performance due to less error in the test stage, but the MLP network seems to have a better performance considering other parameters and the error in the TRAIN stage. Discussion and Conclusion: Finally, after modeling by using neural networks, the Einstein relationship, and the sediment measurement curve, it is inferred that neural networks are more accurate to estimate the amount of sediment. Manuscript profile

Open Access Article

55 - Comparison of Bayesian Neural Networks and Artificial Neural Network to Estimate Suspended Sediments in the RiverS (Case Study: Simineh Rood)
Mohammad Ali Ghorbani Reza Dehghani

10.22034/jest.2017.10622

Background and Purpose: Simulation and evaluation of sediment are important issues in water resources management. Common methods for measuring sediment concentration are generally time consuming and costly and sometimes does not have enough accuracy.  Materials an More

Background and Purpose: Simulation and evaluation of sediment are important issues in water resources management. Common methods for measuring sediment concentration are generally time consuming and costly and sometimes does not have enough accuracy.  Materials and Methods: In this research, we have tried to evaluate sediment amounts, using bayesian neural network for Simineh-Rood, West Azerbaijan, Iran, and compare it with common artificial neural networks. Monthly river discharge, temperature and total dissolved solids for time period (1354-1383) was used as input and sediment discharge for output. Criteria of correlation coefficient, root mean square error and Nash Sutcliff bias coefficient were used to evaluate and compare the performance of models. Results: The results showed that three models smart estimate sediment discharge with acceptable accuracy, but in terms of accuracy, the bayesian neural network model had the highest correlation coefficient (0.832), minimum root mean square error (0.071ton/day) and the Nash Sutcliff (0.692) and the bias (0.0001) and hence was chosen the prior in the verification stage. Discussion and conclusions: Finally, the results showed that the bayesian neural network has great capability in estimating minimum and maximum sediment discharge values. Manuscript profile

Open Access Article

56 - Estimation of Aquifer Qualitative Parameters in Guilans Plain Using Gamma Test and Support Vector Machine and Artificial Neural Network Models
Mohammad Isazadeh seyedmostafa Biazar Afshin Ashrafzadeh Rezvan Khanjani

10.22034/jest.2019.13946

Abstract Background and Objective: Having information about qualitative and quantitative parameters distribution of groundwater supplies is one of most important parameters in integrated groundwater management. Thus, in this study it has been attempted to determine a pr More

Abstract Background and Objective: Having information about qualitative and quantitative parameters distribution of groundwater supplies is one of most important parameters in integrated groundwater management. Thus, in this study it has been attempted to determine a proper model and input combination for estimation of quality parameters including electrical conductivity (EC), calcium (Ca) and sodium (Na) ions in aquifers of Guilans plain. Method: In this study, the data from 132 observation wells during 2001 to 2013 were used and artificial neural network (ANN) and support vector model (SVM) were applied. In the first approach, estimations were conducted according to five different combinations, including water level, distance from see, total precipitation of six months and coordinates of observation wells. In the second approach, estimations were conducted based on combination of the selected qualitative parameters of gamma test with combinations of the best input in the first part. Findings: Comparison of the results from the first part indicated that SVM model outperformed the ANN mode in the estimation of Ca, Na and EC parameters. Support vector machine error values for estimating Ca, Na and EC variables at the test period were 1.218 (meq/l), 0.867(meq/l), and 175.742 (µmos/cm), while for artificial neural network these values were 1.268 (meq/l), 0.933 (meq/l), and 186/448 (µmos/cm) respectively. The results from this part showed that adding the distance from see input improves the estimation of models in all cases. In the second part, using gamma test for measuring the nine quality parameters, the best combination of quality parameters was determined to estimate the three parameters: Ca, Na and EC. The results from the second part show that both ANN and SVM models have an excellent performance in the estimation of the three qualitative parameters. ANN model error values in estimating Ca, Na and EC variables in validation period were 0.662 (meq/l), 0.305(meq/l), and 47.346 (µmos/cm), while these values were 0.671 (meq/l), 0.356 (meq/l), and 55.412 (µmos/cm) for SVM model respectively.  Obviously, the results from ANN model in this section were better than those from SVM model. Discussion and Conclusion:Results showed that both ANN and SVM models have a great ability in predicting qualitative parameters in the aquifers. Also, in less inputs, the results of SVM model are better than those of ANN model and in more inputs it is vice versa. Results of the second section showed that gamma test is fully practical and accurate in determining the effective input combinations. Manuscript profile

Open Access Article

57 - Prediction of Carbon Monoxide Concentration in Tehran using Artificial Neural Networks
Hamid Reza Jeddi Rahim Ali Abbaspour Mina Khalesian Seyed Kazem Alavipanah

Background and Objective: Nowadays, air pollution is one of the most important problems almost all over the world. There are many strategies to control and reduce air pollution, one of which is prediction of this event and getting ready to deal with the negative effects More

Background and Objective: Nowadays, air pollution is one of the most important problems almost all over the world. There are many strategies to control and reduce air pollution, one of which is prediction of this event and getting ready to deal with the negative effects of it. The aim of this study is to provide a multi-layer structure of artificial neural networks (ANN) for predicting of carbon monoxide pollution at subsequent 24 hours in Tehran metropolis. Method: To predict the amount of CO emissions in near future (subsequent 24 hours), wind speed and direction, temperature, relative humidity, and barometric pressure characteristics are used as meteorological data, and concentration of carbon monoxide is considered as a pollution parameter. To eliminate the noise of data, wavelets transform method and determining the threshold with normal distribution are used before training the ANN. Finally, two neural networks as two general models are proposed and used for modelling. Findings: The results show that the correlation coefficient, index of agreement, accuracy of prediction, and root mean square error for model no. 1 with duplicate data are 0.9012, 0.915, 0.848, and 0.1012 and for model no. 2 with duplicate data are 0.9572, 0.978, 0.963, and 0.0385 respectively. Moreover, the results of listed parameters for model no. 1 with new data are 0.9086, 0.89, 0.885, and 0.0825 and for model No. 2 with new data are 0.8678, 0.928, 0.932, and 0.1163 respectively. Conclusion: Results showed that there is a good agreement between predicted and observed values, hence the proposed models have a high potential for air pollution prediction. Manuscript profile

Open Access Article

58 - Applying ANN and GIS for estimation of effective parameters in determination of plant pattern (Case Study: Nahavand City)
Hossein Banejad Hamid Mohebzadeh Ehsan Olyaie

AbstractOne of the most important issues in irrigated agriculture is determination of optimum plant pattern.Therefore, estimation of effective parameters in quality and quantity of available water is significantand is one of the most important components in adoption of More

AbstractOne of the most important issues in irrigated agriculture is determination of optimum plant pattern.Therefore, estimation of effective parameters in quality and quantity of available water is significantand is one of the most important components in adoption of management decisions in development ofsustainable agriculture. In this study, Artificial Neural Networks technique has been used forestimation of piezometer wells water level and also effective factors for water quality used inagriculture (EC, SAR). For this purpose, monthly recorded data for piezometer wells water levelduring a seven year and data related with water quality during a four years period in Nahavand plainwere used. Also, a groundwater level in Nahavand in year of 1385-86 was drawn. Efficiency of modelwas evaluated by statistical criteria including coefficient of determination (R2), root mean square error(RMSE) and mean absolute error (MAE). The derived results showed that R2 value for estimation ofpiezometer wells water level is 0.98 and for SAR and EC is 0.991 and 0.990 respectively. The aboveresults indicated the appropriate ability of Artificial Neural Networks as superior technique forsimulation of effective quality and quantity parameters in determination of plant pattern. Also theresults from spatial drowning of groundwater level by Geographic Information System indicated theshortage of water resource in this region Manuscript profile

Open Access Article

59 - Assessment of Intelligent models for Estimating the Electrical Conductivity in Groundwater (Case study: Mazandaran plain)
Isa Hazbavi Reza Dehghani

10.22034/jest.2018.13753

Abstract Background and Objective: Groundwater resources along with surface water supply the needs for municipal, industrial and agriculture uses, and their quantity and quality should be investigated. Salinity is one of the most important parameters in assessing the qu More

Abstract Background and Objective: Groundwater resources along with surface water supply the needs for municipal, industrial and agriculture uses, and their quantity and quality should be investigated. Salinity is one of the most important parameters in assessing the quality of groundwater. Method: In this study, application of artificial neural networks and Bayesian network in predicting the electrical conductivity in 8 observation wells in Mazandaran plain was investigated. For this purpose, hydrogen carbonate, chloride, sulfate, calcium and magnesium were selected as input and output parameters for electrical conductivity at monthly a scale during 2003-2013. The criteria of correlation coefficient, mean absolute error and Nash Sutcliff coefficient were used to evaluate the performance of the model. Findings: The results showed that artificial neural network model has the highest correlation coefficient (0.989), the lowest mean absolute error (0.019 ds/m) and the highest standard of Nash Sutcliffe (0.970) ranked the first priority in the validation phase. Discussion and Conclusion: The results indicate acceptable capability of artificial neural network models to estimate the electrical conductivity of groundwater.   Manuscript profile

Open Access Article

60 - Determining the optimal method for classification and mapping of land use/land cover through comparison of artificial neural network and support vector machine algorithms using satellite data (Case study: International Hamoun wetland)
amir houshang ehsani Mojtaba Shakeryari

10.22034/jest.2019.13711

Background and Objective: Images classification is one of the important techniques for interpretation of satellite images that is widely used in survey of earth changes. In the meantime, satellite data has been recognized as the best tool for detection and evaluation of More

Background and Objective: Images classification is one of the important techniques for interpretation of satellite images that is widely used in survey of earth changes. In the meantime, satellite data has been recognized as the best tool for detection and evaluation of changes due to its update information, low costs and variety of forms. Therefore, land use/land cover map is one of the most important information required by the environmental managers and planners. On the other hand, in recent years, artificial neural network method has been used widely for the classification of satellite data. The aim of this study is to compare three different methods for land cover classification using 2014 OLI image over a 26-year period. Method: In this study, digital data of OLI (2014) sensor was used in order to optimize image classification method. Initially, the image was corrected in terms of geometry and radiometry in the ENVI software. Then IDRISI software was used for image classification using three different methods: fuzzy artmap, multilayer perceptron artificial neural networks and support vector machine. Finally, land cover maps were classified into five categories: water, vegetation, canebrake, barren lands and saline lands. To evaluate accuracy with the help of user accuracy, producer accuracy, overall accuracy, kappa coefficient and error matrix, the created map was compared with the ground reality map created by GPS, Google Earth images and field observations. Discussion and Conclusion: The results of image accuracy evaluation showed that among the applied methods the fuzzy artmap algorithm had the highest accuracy in classification of satellite data with an overall accuracy of 94.68 and kappa coefficient of 0.91 compared to both multilayer perceptron artificial algorithm with an overall accuracy of 92.99 and kappa coefficient of 0.89 and support vector machine with an overall accuracy of 90.93 and kappa coefficient of 0.85. This study showed that classification of fuzzy artmap artificial neural network algorithm has a high capability to create the land cover map with high accuracy. Manuscript profile

Open Access Article

61 - Exploring Land Cover Changes in Arak Using GIS and Remote Sensing
Mozhgan Ahmadi Nadoushan Alireza Soffianian Sayed Jamaleddin Khajeddin

Introduction: Land cover changes are among the most important alterations of the Earth’s land surface which affect the environment and environmental processes. Material and Methods: In this study, aerial photos from 1956 and 1972, Landsat TM and IRS-P6 LISS-III im More

Introduction: Land cover changes are among the most important alterations of the Earth’s land surface which affect the environment and environmental processes. Material and Methods: In this study, aerial photos from 1956 and 1972, Landsat TM and IRS-P6 LISS-III images obtained in 1980 and 2006 as well as the post-classification method were used to detect land cover changes and to evaluate Arak and its periphery during 1956-2006. At first, geometric correction was done to prepare aerial photos and satellite images. For this purpose, topographic maps at scales of 1:50000 and 1:25000 and nearest neighbor method and resampling method were applied. Root mean square error for all aerial photos and satellite images was less than one pixel. Afterwards, all georefrenced photos were mosaicked and land cover maps with 4 classes (urban areas, vegetated areas, barren lands, and rocks) were generated using visual interpretation of aerial photos. Following satellite images geometric correction, topographic correction was applied to images using DEM and Lambert model. In the next step, thye artificial neural networks classification method was implemented after producing false color composite images and image fusion. Results: Land cover maps in four classes were generated with overall accuracy of over 90%. To detect the land cover changes during 4 periods between 1956 and 2006, land cover maps of 1956, 1972, 1990 and 2006 were compared, and change maps and Tables were made. The results showed significant urban expansion, vegetated and barren lands losses and stability in rocks and mountainous areas during 1956-2006. Manuscript profile

Open Access Article

62 - Developing an Optimal Method for Financial Distress Prediction of the Firms (Case Study: Tehran Stock Exchange)
Mansour Soufi Mahdi Homayounfar Mehdi Fadaei

One of the most important issues in the field of financial management is how the investors distinguish between favorable investment opportunities and undesirable ones. One of the ways to help investors is to provide financial distress prediction models. According to the More

One of the most important issues in the field of financial management is how the investors distinguish between favorable investment opportunities and undesirable ones. One of the ways to help investors is to provide financial distress prediction models. According to the various studies have been made to develop these type of models, in this study the combination of artificial neural networks (ANN) and genetic algorithm (GA) techniques based on Zimensky prediction ratios is used for modeling financial distress. The research statistical population includes public companies in Tehran stock exchange which admitted between October 2013 to October 2015 and among them 66 distressed and 150 going concern companies were selected as the research sample using screening method. The results indicate that the power of both artificial neural network and genetic algorithm models in financial distress prediction are equal (95 percent), however, the prediction error of neural network is relatively low compared to genetic algorithm. Manuscript profile

Open Access Article

63 - Optimization of Network-Based Matrix Investment Portfolio and Comparison with Fuzzy Neural Combination Pattern and Genetic Algorithm(ANFIS)
ALI SheidaeiNarmigi Fraydoon Rahnamay Roodposhti Reza Radfar

Researchers have been researching portfolio optimization issues for several years. One of the main issues is to determine the optimization method, which is to form an optimal investment portfolio, ie to minimize investment risk and maximize investment profit. The aim of More

Researchers have been researching portfolio optimization issues for several years. One of the main issues is to determine the optimization method, which is to form an optimal investment portfolio, ie to minimize investment risk and maximize investment profit. The aim of this study is to investigate the strategic capability of network matrix and fuzzy genetic neural model (ANFIS) in optimizing the investment portfolio among companies on the Tehran Stock Exchange. Grouping stocks by network matrix based on new variables including aggressive, indifferent and defensive stocks provided by Roodpashti (2009) and traditional variables including growth, growth-value and value stocks and classification of companies based on their market value and use. From the law of quarters and finally their weighting is considered in proportion to the return of that share. The design and presentation of a stock portfolio optimization model using adaptive fuzzy neural inference system and its combination with genetic algorithm (ANFIS) in which two different categories of technical and fundamental variables are used as model inputs. Research outputs show that these systems have the necessary ability to optimize the stock portfolio. Therefore, a combined model of neural networks and fuzzy reasoning theory with genetic algorithm has been used to weight the factors affecting stock portfolio optimization in the 7 years leading up to 1398. Manuscript profile

Open Access Article

64 - Application of Genetic Algorithm, Particle Swarm and Artificial Neural Networks in Predicting Profit Manipulation
Morteza Hoseinalinezhad Seyed Mohamad Hassan Hashemi Kucheksarai Ali Jafari

Profit management has been one of the most controversial topics in recent research. Most research on earnings management has examined the linear relationship between independent variables and earnings management using statistical methods but they did not use these varia More

Profit management has been one of the most controversial topics in recent research. Most research on earnings management has examined the linear relationship between independent variables and earnings management using statistical methods but they did not use these variables to predict earnings management. Today, with the growth of information technology and the introduction of artificial intelligence, including artificial neural networks into the field of scientific research, it has become possible to study nonlinear relationships between variables. In this study, an attempt was made to estimate optional accruals for predicting earnings management using artificial neural networks. Also in this research, the genetic algorithm optimizer model and Particle swarm has been used to optimize the weights of the artificial neural network model to enhance the predictive power. Then, the ability to predict profit management was evaluated using the modified Jones statistical model, artificial neural network and the combined technique of genetic algorithm, Particle swarm and neural network. The sample used in this study included 150 companies listed on the Tehran Stock Exchange between 2015 and 2020. Findings showed that the artificial neural network has a high ability to predict profit management, compared to the modified Jones linear model. The findings also indicate that the accuracy and ability of the combined model of genetic algorithm, particle swarm and neural network in predicting profit management is higher than the combined model of genetic algorithm-artificial neural network. Manuscript profile

Open Access Article

65 - Developing a model for predicting the Tehran Stock Exchange index using a combination of artificial neural network and Markov hidden model
Leila Talaie Kakolaki Mehdi Madanchi Taghi Torabi Farhad Ghaffari

The purpose of this study was to design a new model for predicting the Tehran Stock Exchange index using pattern recognition in a combination of hidden Markov model and artificial intelligence. The present study is an applied type and mathematical analytical method. Its More

The purpose of this study was to design a new model for predicting the Tehran Stock Exchange index using pattern recognition in a combination of hidden Markov model and artificial intelligence. The present study is an applied type and mathematical analytical method. Its location is the Tehran Stock Exchange and during the years 2010 to 2020. Findings showed that the prediction error rate with artificial neural network has a higher accuracy than Markov's hidden model. Also, the prediction error of the hybrid model is much lower than the other two models for predicting the total stock index of Tehran Stock Exchange, so it has higher accuracy for forecasting stocks. According to the MAPE index, the hybrid model method could improve the predictive power of the artificial neural network by 0.044% and also improve the predictive power of the hidden Markov model by 0.70%. Manuscript profile

Open Access Article

66 - Presenting the developed model of Benish by using tunneling phenomena based on artificial neural network technique and particle swarm optimization algorithm to identifying profit manipulating companies
Farhad Azadi Mehrdad GhanbarI Babak Jamshidi navid Javad Masodi

Today, profit rates and the possibility of managing and manipulating the profits are clear to all, and researchers have always sought solutions to remove the uncertainties facing investors and stakeholders when making their financial decisions. To clarify users' decisio More

Today, profit rates and the possibility of managing and manipulating the profits are clear to all, and researchers have always sought solutions to remove the uncertainties facing investors and stakeholders when making their financial decisions. To clarify users' decision path of financial data users, Beneish (1999) has developed a profit-management predicting model that has yielded different results in different societies. Thus, this article aims to optimize and localize Beneish’s model by adding the Tunneling variable to Beneish’s variable and using a modern neural network and particle swarm algorithms. The statistical research population consisted of 196 companies listed at the Tehran Stocks Exchange from 2014 to 2019. The research method was a descriptive-library method in which the variables are interrelated through the causal-correlational method. From an objective point of view, it is an Ex-Post Facto research design. To analyze the data, the regression method and artificial neural and the PSO algorithms were used. The model analysis results suggested that all financial ratios had significant effects on Beneish’s profit management, as the Tunneling phenomenon and the financial leverage had the highest and lowest effects on predicting Beneish’s profit management, respectively. Manuscript profile

Open Access Article

67 - Earnings Per Share Forecast: the Combination of Artificial Neural Networks and Particle Swarm Optimization Algorithm
Dariush Forougi Heidar Foroughnejad Manochehr Mirzaei

Expectations about earning have significant effects on managers and investors’ decisions. Today, one of the measures that are takenin to consideration as an indicator ofcompanies’profitability is the concept of earningpershare.Also earningper share has major More

Expectations about earning have significant effects on managers and investors’ decisions. Today, one of the measures that are takenin to consideration as an indicator ofcompanies’profitability is the concept of earningpershare.Also earningper share has major effectson stock price of companies. Hence, forecastingearning per shareisof great importance forbothinvestorsandmanagers. The aimof thisstudy is to modelearning pershareforecast of listed companies in Tehran Stock Exchange(TSE) by using the combination ofartificial neural networksand particle swarm optimizationalgorithmbased onunivariate andmultivariate models. To do this,the data of114 companies among the existing listed onesinTehran Stock Exchange was usedduring1380-1389(2001-2010).The results showed that univariate model with 78.5% accuracy and multivariate models with 91.7% accuracy, forecast earning per share. Manuscript profile

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68 - Credit rating of manufacturing corporations in Tehran stock exchange withmulti-criteriadecision-makingandartificial neural network models
Maghsoud Amiri Morteza Bakyhoskoie Mehdi Biglari Kami

This paper is investigated the credit rating of manufacturing firms in Tehran Stock Exchange. In this regard, have been extracted  financial ratios of  public stock companies during the three years from the financial statements. This financial ratios indicates More

This paper is investigated the credit rating of manufacturing firms in Tehran Stock Exchange. In this regard, have been extracted  financial ratios of  public stock companies during the three years from the financial statements. This financial ratios indicates the ability to pay principal and interest of loan. Initially, 50 companies were selected and ranked by the TOPSIS method. Financial ratios are as a criterion and weight of the each criterion are determined by using Shannon entropy method. Then the ranking, companies are classified into four categories. The artificial neural network is trained to classify and after training the neural network are tested. Statistical results show robust classification of neural network. Then all the companies included in this study are classified by neural network. Manuscript profile

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69 - Comparing the performance Of Artificial Neural Networks(ANN) and Auto Regressive Moving Average(ARIMA) Model in Modeling and Forecasting Short-term Exchange Rate Trend in Iran
Abbas Ali Abunoori Fardad Farokhi Seyedeh Fatemeh Shojaeyan

Exchange rate and its related fluctuation plays a significant role as one of the most important issues of each country's foreign trade sector. Many factors such as economic, politics, and psychological factors impress on exchange rates and these factors create more unce More

Exchange rate and its related fluctuation plays a significant role as one of the most important issues of each country's foreign trade sector. Many factors such as economic, politics, and psychological factors impress on exchange rates and these factors create more uncertainty situations. Policymakers’ attempt is to reduce this uncertainty via forecasting this variable with minimal error.Artificial neural networks have high potential in modeling complex processes and forecasting dynamic nonlinear paths .Therefore, in this study has tried to use the  artificial neural network (ANN) In addition to modeling and forecasting daily exchange rates during the period of  March 2002 to March 2005, and minimizing the forecast error by this method, its results were compared with that of ARIMA based on forecasting accuracy evaluation criteria , and to examine the sensitivity of model results toward exchange rates.Estimation of the model with the same method for three data sets exchange rate including dollar,euro and pound have been performed .Results indicate that used neural network has better predictive power in comparison with arima model while  pound and Euro exchange rates’ prices are function of their yesterday prices and dollar exchange rate price is a function of its price over the past 6 days .   Manuscript profile

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70 - Predicting bankruptcy of companies listed on the Stock Exchange using the artificial neural network
Mohsen Vaez-Ghasemi Saeid Ramezanpour Chardeh

Being informed of capital market’s companies financial situation is one of the shareholders and economic analysts’ perturbation. Thus, financial market analysts and researchers were looking for methods to predict capital market’s company’s future More

Being informed of capital market’s companies financial situation is one of the shareholders and economic analysts’ perturbation. Thus, financial market analysts and researchers were looking for methods to predict capital market’s company’s future conditions. This research is finding a model to predict bankruptcy of stock exchanges market’s companies with using the artificial neural network. In this research we used Zemijewski financial ratios with one macro – economic variable to predict companies’ bankruptcy. Population of study was selected from the accepted companies in Iran’s stock and exchanges organization. Financial ratios have been extracted from companies’ financial statement in a five years’ period between 2010 and 2014, finally we choose 84 companies that divided to salubrious and bankrupt equal number in each. We used multi-layer perceptron (MLP) with back propagation algorithm to create predictor model and data analysis. The network has been trained once with financial ratios and again with additional macro – economic variable to confirm that the accuracy of network model will increase by additional macro – economic variable. Ultimately the designed model in total mode has 92.95 percent of accuracy and 85 percent correct prediction of bankrupted companies for one year earlier of bankruptcy.   Manuscript profile

Open Access Article

71 - Promotion of Effective Level of Investment Management in Iran Capital Market using Artificial Neural Network and Fuzzy Logic
Hossein Amouzad Mahdiraji

One of the most important problems in capital market is allocating financial resources in an optimal fashion. In an effective capital market, from an operational point of view, the capital is allocated for the best investment option. Therefore, in order to establish mor More

One of the most important problems in capital market is allocating financial resources in an optimal fashion. In an effective capital market, from an operational point of view, the capital is allocated for the best investment option. Therefore, in order to establish more output, making use of appropriate management tools is a step toward more effective market management of transactions. Regarding backgrounds of applying Artificial Neural Networks and Fuzzy Logic in stocks investment and financial prediction, applying them in selecting an appropriate portfolio can lead to desired results for investors. The major goal of the current research is to achieve an optimal investment portfolio in capital market by applying Artificial Neural Network and Fuzzy Logic. Accompanied by Markowitz Model, models were used which were created through Artificial Neural Network. In order to establish investment portfolio, some of those companies were selected which were active in Tehran stock exchange, and which have had positive efficiency from the year 1386 to 1395. In order to evaluate the suggested portfolios in different conditions, the output of different portfolios based on the monthly and yearly output of the member companies were compared and optimization of suggested portfolios using genetic algorithm were carried out. The study shows that using the Fuzzy models versus mentioned models would provide higher output for the investors.     Manuscript profile

Open Access Article

72 - یک روش ترکیببی جدید بر اساس تحلیل پوششی داده ها و شبکه عصبی برای بهینه سازی ارزیابی عملکرد
علی نمکین سید اسماعیل نجفی محمد فلاح مهرداد جوادی

در این مقاله ، یک روش جدید ترکیبی از شبکه های عصبی پرسپترون چند لایه و تحلیل پوششی داده ها ارائه می شود که در آن مقادیر ورودی و خروجی برای تعداد زیادی واحد تصمیم گیرنده به عنوان ورودی های شبکه عصبی تعیین می شود. می توان دید که با بکارگیری شبکه عصبی برای حل مسائل تحلیل پ More

در این مقاله ، یک روش جدید ترکیبی از شبکه های عصبی پرسپترون چند لایه و تحلیل پوششی داده ها ارائه می شود که در آن مقادیر ورودی و خروجی برای تعداد زیادی واحد تصمیم گیرنده به عنوان ورودی های شبکه عصبی تعیین می شود. می توان دید که با بکارگیری شبکه عصبی برای حل مسائل تحلیل پوششی داده ها نیاز به حل مدل مورد نظر برای هر واحد تصمیم گیرنده نیست و لذا الگوریتم ارائه شده زمان پردازش و استفاده از حافظه را نسبت به آنچه مورد نیاز روش متعارف در تحلیل پوششی داده ها است، به مقدار زیادی کاهش می دهد.جهت بررسی دقت شبکه ارائه شده،  چندمطالعه موردی از جمله مجموعه ای از  500شعبه بانک  مورد استفاده قرار می گیرد.نتایج نشان دهنده دقت بالا وزمان محاسباتی کمتر(اعتبارلازم) مدل ترکیبی پیشنهادی است. Manuscript profile

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73 - Modeling of Groundwater Quality Parameters Using Artificial Neural Network and Geostatistics Models (Case Study: Zeidoun plain)
Abdol Amir Echreshzadeh Aslan Egdernezhad

Background and Aim: One of the obstacles to develop sustainable is the poor quality of water. The assessment of water quality is usually based on chemical decomposition and measurement of chemical parameters of water. Measuring these parameters in big area is costly and More

Background and Aim: One of the obstacles to develop sustainable is the poor quality of water. The assessment of water quality is usually based on chemical decomposition and measurement of chemical parameters of water. Measuring these parameters in big area is costly and time-consuming, as result it required to estimating methods for prediction of those parameters. The purpose of this study is to model the groundwater quality parameters of Zeydoon plain using ANN+PSO and geostatistics models. Methods: For this purpose, the information of 42 observation wells in Zeidoon plain on a monthly basis for 7 years has been used. Neural network model inputs including qualitative parameters SO42- ، pH ، HCO32-،  Na+، Mg2+، Ca2+، TDS، SAR and EC were considered. Findings: The results of simulation of groundwater quality parameters using ANN + PSO model showed that in SAR simulator model the highest simulation accuracy is related to the model with sigmoid logarithm function, in EC simulator model the highest accuracy is similar. The construction is related to the model with the stimulus function of the sigmoid tangent. Also, in the TDS simulator model, the highest simulation accuracy of the model with the sigmoid tangent stimulus function was obtained. As RMSE and MAE have the lowest value and R2 index has the highest value. The results of simulation of groundwater quality parameters using the geostatistical model showed that the highest accuracy of the kriging model in the simulation is related to EC, SAR and TDS parameters, respectively. Discussion and Conclusion: Finally, comparing the results of comparing the results of ANN + PSO model and Kriging model showed that ANN + PSO model is more accurate in simulating groundwater quality parameters of Zidon plain than Kriging model. Also, the results of this research showed that the combination of intelligent models with optimization algorithms with correct architecture and complete model inputs are used as a useful tool for simulating groundwater quality parameters. Manuscript profile

Open Access Article

74 - Optimization of Oleuropein Extraction from Olive Leaves using Artificial Neural Network
Mahnaz Yasemi

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75 - Application of Differential Kinetic Method Using ANN with a New Synthetic Reagent for Simultaneous Spectrophotometric Determination of Mercury and Palladium
Tayyebeh Madrakian Abbas Afkhami Masoumeh Mohammadnejad Sayyed Javad Sabounchei Sepideh Samiee

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76 - Prediction Micro-Hardness of Al-based Composites by Using Artificial Neural Network in Mechanical Alloying
R, M Babaheydari S, O Mirabootalebi

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77 - Short-Term Load Forecasting using an Ensemble of Artificial Neural Networks: Chaharmahal Bakhtiari Case
E. Faraji M. Mirzaeian H. Parvin A. Chamkoorii Majid Mohammadpour

Short-term load forecasting is very important in electrical marketing. Load forecasting is dependent on climatic condition of every region and the previous structures of electrical consumption in that region; so we have accomplished this through employing climatic data More

Short-term load forecasting is very important in electrical marketing. Load forecasting is dependent on climatic condition of every region and the previous structures of electrical consumption in that region; so we have accomplished this through employing climatic data (including temperature and pressure) and real load consumption of Chaharmahal Bakhtiari. We have evaluated our method using four machine learning algorithms: artificial neural networks (multilayer perceptron), ensemble of artificial neural networks, support vector machine and ensemble of support vector machine. Experimental results indicates that ensemble of artificial neural networks is superior to the others in the field of load consumption forecasting of Chaharmahal Bakhtiari. Manuscript profile

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78 - Modeling and quantitative structure-activity study of some carboxylate derivatives as anticancer drugs using multivariate linear regression and artificial neural networks
mehdi nekoei Mahmood Ebrahimi پرستو فتاحی Behzad Chahkandi

Chemotherapy is one of the most effective treatments for cancers, but many cancers become resistant to the therapeutic effects of a drug during treatment with chemotherapy, which is called Multi Drug Resistance. Currently, some new drugs, including carboxylate derivativ More

Chemotherapy is one of the most effective treatments for cancers, but many cancers become resistant to the therapeutic effects of a drug during treatment with chemotherapy, which is called Multi Drug Resistance. Currently, some new drugs, including carboxylate derivatives, have been used to reduce drug resistance. In the present study, a structure-activity quantitative relationship (QSAR) study was performed to predict the drug activity of some carboxylate derivatives using multivariate linear regression (MLR) and artificial neural networks (ANN). First, the structure of drug compounds, drawing and appropriate group of descriptors were calculated. Then, the step selection method was used to obtain the best descriptors that were most related to the drug activity of the compounds. First, the linear model of multiple linear regression (MLR) was developed. ANN was then used to obtain better results. Statistical data show the superiority of ANN method over MLR method. Manuscript profile

Open Access Article

79 - The application of multiple linear regression and artificial neural networks to study the quantitative structure-activity relationship of a group of chemokine derivatives
mehdi nekoei محمدرضا کیانسب مجید محمدحسینی بهنام مهدوی تهمینه باهری

A quantitative structure-activity relationship (QSAR) study was conducted to predict the pharmacological activity of some chemokine derivatives using multiple linear regression and artificial neural networks (ANN). At first, the structure of pharmaceutical compounds was More

A quantitative structure-activity relationship (QSAR) study was conducted to predict the pharmacological activity of some chemokine derivatives using multiple linear regression and artificial neural networks (ANN). At first, the structure of pharmaceutical compounds was drawn and optimized with the help of Hypercam software. Then, a wide range of molecular descriptors were calculated by Dragon software. After reducing the number of descriptors that had a correlation above 0.9 and the descriptors that were more than 90% similar, stepwise regression was used to obtain the best descriptors that were most related to the pharmacological activity of the target compounds. became 7 descriptors including MATS2p, PCWTe, RDF045m, RDF065m, RDF115m, C-003 and C-040 were selected. Then, multiple linear regression (MLR) and artificial neural networks (ANN) methods were used to model and predict the activity of test series compounds. The obtained results show that both methods provide acceptable results that can be used to predict new pharmaceutical compounds. Manuscript profile

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80 - Application of multivariate linear regression and artificial neural networks to predict the antimicrobial activity of some anilide derivatives by quantitative structure-activity relationship (QSAR) method
mehdi nekoei Parastoo pourali Syed Hamed Mppsavi

Quantitative structure-activity relationship (QSAR) study was performed to predict the antimicrobial activity of some anilide derivatives using multivariate linear regression (MLR) and artificial neural networks (ANN). First, the structure of the compounds, the drawing More

Quantitative structure-activity relationship (QSAR) study was performed to predict the antimicrobial activity of some anilide derivatives using multivariate linear regression (MLR) and artificial neural networks (ANN). First, the structure of the compounds, the drawing and the appropriate group of descriptors were calculated. Then, the step selection method was used to obtain the best descriptors that were most associated with the antimicrobial activity of the compounds. With this method, 5 descriptors were selected and first the linear MLR model was constructed. Then, artificial neural network was used to obtain better results. The values of coefficient of determination (R2) and root mean square error (RMSE) for the test series were 0.07 and 0.073 for the MLR linear model and 0.613 and 0.021 for the nonlinear ANN model, respectively. Statistical data show the superiority of ANN method over MLR method. Manuscript profile

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81 - Modeling and quantitative structure-property relationship studying to predict the half-life of polychlorinated biphenyls using multivariate linear regression and artificial neural networks
سکینه بهرامی نسب مهدی نکوئی سیدعباس طاهری

Quantitative structure-property relationship (QSPR) study was performed to predict the half-life of some polychlorinated biphenyl derivatives using multivariate linear regression (MLR) and artificial neural networks (ANN). First, the structure of the compounds, the draw More

Quantitative structure-property relationship (QSPR) study was performed to predict the half-life of some polychlorinated biphenyl derivatives using multivariate linear regression (MLR) and artificial neural networks (ANN). First, the structure of the compounds, the drawing, and the appropriate group of descriptors were calculated. Then, the step-wise method was used to obtain the best descriptors that were most related to the half-life of the compounds. With this method, 6 descriptors including Lop, GATS5m, GATS8m, LDip, RDF020u, R2v + were selected from the types of topological descriptors, charge, three-dimensional representation of molecules based on electron diffraction and radial distribution function. First, a multiple linear regression linear model was constructed. Then, artificial neural network was used to obtain better results. The values of coefficient of determination (R2) and root mean square error (RMSE) for the test series were equal to 0.716 and 0.050 for the MLR linear model and 0.896 and 0.030 for the nonlinear ANN model, respectively. Statistical data show the superiority of ANN method over MLR method. Manuscript profile

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82 - Genetic Algorithm and ANN for Estimation of SPIV of Micro Beams
M. Heidari

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83 - Estimation of Surface Roughness in Turning by Considering the Cutting Tool Vibration, Cutting Force and Tool Wear
A. Salimi A. Ebrahimpour M. Shalvandi E. Seidi

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84 - Designing an Artificial Neural Network Based Model for Online Prediction of Tool Life in Turning
A. Salimiasl A. Özdemir I. Safarian

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85 - Modelling of Friction Stir Extrusion using Artificial Neural Network (ANN)
Mohammad Ali Ansari Reza Abdi Behnagh Dong Lin Sarang Kazeminia

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86 - Experimental and Numerical Investigation of the Arms Displacement in a New Electrothermal MEMS Actuator
M. Kolahdoozan A. Rouhani Esfahani M. Hassani

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87 - Prediction of Residual Stresses by Radial Basis Neural Network in HSLA-65 Steel Weldments
M. Heidari

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88 - Experimental Study and Modeling of Friction Stir Welding Process of Aluminum 1100 Alloys, using Artificial Neural Network with Taguchi Method
V. Zakeri Mehrabad Ali Doniavi A. Gholipoor

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89 - Comparative Study and Robustness Analysis of Quadrotor Control in Presence of Wind Disturbances
Reham Mohammed

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90 - Design of an Intelligent Adaptive Control with Optimization System to Produce Parts with Uniform Surface Roughness in Finish Hard Turning
vahid pourmostaghimi Mohammad Zadshakoyan

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91 - A Survey on Face Recognition Based on Deep Neural Networks
mohsen Norouzi Ali Arshaghi

10.30486/mjtd.2023.1977609.1016

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92 - Distinction of Target and Chaff Signals by Suggesting the Optimal Waveform in Cognitive Radar using Artificial Neural Network
Seyed Mohammad Mahdi Ziaei Pouriya Etezadifar Yaser Norouzi Nadali Zarei

10.30486/mjtd.2023.1980623.1027

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93 - Improving Students' Performance Prediction using LSTM and Neural Network
Hussam Abduljabar Salim Ahmed Razieh Asgarnezhad

10.30486/mjtd.2023.1995783.1050

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94 - Assessment of efficiency of artificial neural network in predicting the trend of desertification processes by using GIS (Case study: Dehloran plain, Ilam)
Soraya Yaghoobi Marzban Faramarzi Haji Karimi Javad Sarvarian

Desertification is recognized as a main problem in the arid and semi-arid areas. Therefore, identification and prediction of the effective factors in development of desertification are very important for better management of these areas. The main purpose of this study w More

Desertification is recognized as a main problem in the arid and semi-arid areas. Therefore, identification and prediction of the effective factors in development of desertification are very important for better management of these areas. The main purpose of this study was evaluating the accuracy of an artificial neural network model for predicting the desertification process and selects the most effective criteria on desertification in the Dehloran plain by using the Iranian model for desertification potential assessment (IMDPA). In IMDPA model, water and climatic were selected as effective factors in desertification. In this model, three indicators for climate criteria; annual precipitation, drought index (Standardized precipitation index; SPI and continued drought and for water criteria; ground water table depletion, sodium absorption ratio, Cl, electrical conductivity (EC) and total dissolved solids were evaluated. Each index was rated using of IMDPA model. Then desertification intensity and criteria maps were prepared using a geometric average for predicting period in ArcGIS®9.3. Final data were entered into neural network to predict. The results showed that the neural network model has a high efficiency for predicting the desertification process in the study area. The accuracy of the model was about 80% and mean square error (MSe) was less than one. In addition, the climate factor and the index of EC were found the most effective variables for predicting the desertification process. In 2015-2016 predicted the most important probable criteria affecting the intensity of desertification were  climate  and water with weighted average 2 (moderate in sub-class1, 2 and 3), 1.84 (moderate in sub-class 1and 2), respectively. Manuscript profile

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95 - Forecasts of climate change effects on Amygdalus scoparia potential distribution by using ensemble modeling in Central Zagros
Maryam Haidarian Aghakhani Reza Tamartash Zeinab Jafarian Mostafa Tarkesh Esfahani Mohammad Reza Tatian

Predicting the potential distribution of plants in response to climate change is essential for their conservation and management. Amygdalus scoparia is a wild almond species native to Iran Therefore, this study aimed at predicting the effect of climate change on the geo More

Predicting the potential distribution of plants in response to climate change is essential for their conservation and management. Amygdalus scoparia is a wild almond species native to Iran Therefore, this study aimed at predicting the effect of climate change on the geographical distribution of A. scoparia in Chaharmahal and Bakhtiari province in the central Zagros region. In this regard, we used 5 modeling approaches, Generalized Linear Model (GLM), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Generalized Boosting Method (GBM) and Random Forest (RF) to determine relationships between the occurrence of species and environmental factors under the ensemble framework by using Biomod and R software. The results showed that AUC values greater than 0.9 and functioning of all models been excellent. The mean temperature of the driest quarter and Annual precipitation had the most important role for habitat suitability of this species and (85%) changes in A. scoparia distribution was justified. The results of the model showed that 9%, (148680 ha) of in Chaharmahal and Bakhtiari province for the A. scoparia have had high habitat suitability. Area of suitable habitat was calculated by ArcGIS software on current and future climate conditions. Under RCP4.5 and RCP8.5 climate scenario A. scoparia might lose (Respectively 43% and 59%) of its climatically suitable habitats due to climate change factors, by 2050, while in a number of areas (135% and 140%), the current unsuitable habitats may be converted to suitable. The results of this study can be used in planning, conservation and rehabilitation of A. scoparia. Manuscript profile

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96 - Spectral discrimination of important orchard species using hyperspectral indices and artificial intelligence approaches
Mohsen Mirzaie Mozhgan Abbasi Safar Marofi Eisa Solgi Roohollah Karimi

Study spectral reflectance through spectral indices allows the optimal use of the wide range of spectral wavelengths in hyperspectral data. The purpose of this study was to introduce and evaluate the performance of spectral indices to discriminate dominant orchard speci More

Study spectral reflectance through spectral indices allows the optimal use of the wide range of spectral wavelengths in hyperspectral data. The purpose of this study was to introduce and evaluate the performance of spectral indices to discriminate dominant orchard species in Chaharmahal Bakhtiari province. In this study, 150 spectral curves were measured in the range of 350 to 2500 mm, from grapes, walnuts and almond trees. After the initial correction, 30 of the most important spectral indices were extracted. Analysis of variance and comparisons of meanings was applied to identify the optimal indices for species discrimination at a 99% confidence level. Then, an artificial neural network (ANN) and support vector machine (SVM) approaches were used to evaluate the performance of indices in species discrimination. ANOVA results indicated that the Moisture Stress Index (MSI), Band ratio at 1,200 nm, normalized phaepophytiniz index (NPQI) and cellulose absorption index (CAI) indices are optimal for discrimination of the studied species. The performance evaluation of the introduced indicators in some of the ANN and SVM enhancement structures has been associated with 100% accuracy in both education and testing, which shows the effectiveness of these studies in distinguishing orchard species. The performance evaluation of the introduced indicators has been validated at 100% in both training and testing stages. This result emphasizes the necessity of performing spectroscopic studies to separate the orchard species before analyzing the hyperspectral images due to their large data volume, high cost and huge data analysis. Manuscript profile

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97 - Modeling and zoning water quality parameters using Sentinel-2 satellite images and computational intelligence (Case study: Karun river)
Kazem Rangzan Mostafa Kabolizade Mohsen Rahshidian Hossein Delfan

Considering the progress made in remote sensing technology, collecting information on the quality of surface water resources by this technology, while reducing the cost and time of traditional sampling, can monitor all surface water zones. In this study, the Sentinel-2 More

Considering the progress made in remote sensing technology, collecting information on the quality of surface water resources by this technology, while reducing the cost and time of traditional sampling, can monitor all surface water zones. In this study, the Sentinel-2 satellite images were used to estimate the concentration of acidity, bicarbonate and sulfate parameters. Initially, Sentinel-2 satellite images were pre-processing and then bands and spectral indexes were determined to identify the significant relationship between the parameter values of water quality and images using the multivariate regression method. In the next stage, using Artificial neural network (ANN) and Adaptive Neuro fuzzy inference system (ANFIS) models, the relationship between Sentinel-2 satellite images and water quality parameters were modeled and then their accuracy was calculated for real values. The results showed that in the modeling of sulfate parameter using Sentinel-2 satellite, ANFIS model with relative error equal to 0.0773 and RMSe equal to 0.8014 has a higher accuracy compared to ANN models with relative error equal to 0.1581 and RMSe equal to 1.2477. While, the relative error of the results of the ANN model are obtained 0.0064 and 0.0556 for acidity and bicarbonate parameter, respectively, and RMSe is equal to 0.0702 and 0.2691, respectively.  The ANFIS model has a relative error of 0.0165 and 0.0722, and RMSe is 0.1975 and 0.3037 for acidity and bicarbonate parameter, respectively. Finally, using satellite images, the mentioned models were applied to prepare a qualitative map of each parameter along the part of the Karun river. Manuscript profile

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98 - Study and prediction of land surface temperature changes of Yazd city: assessing the proximity and changes of land cover
Mohammad Mansourmoghaddam Iman Rousta Mohammadsadegh Zamani Mohammad Hossein Mokhtari Mohammad Karimi Firozjaei Seyed Kazem Alavipanah

10.30495/girs.2021.682083

Background and Objective The expansion of urbanization has increased the scale and intensity of thermal islands in cities. Investigating how cities are affected by these thermal islands plays an important role in the future planning of cities. For this purpose, this stu More

Background and Objective The expansion of urbanization has increased the scale and intensity of thermal islands in cities. Investigating how cities are affected by these thermal islands plays an important role in the future planning of cities. For this purpose, this study examines and predicts the effect of land cover (LC) changes in the three classes of LC including urban areas, barren lands, and vegetation on land surface temperature (LST) in the city of Yazd during the last 30 years using Landsat 5 and 8 images. This study also examines the effect of the ratio of proximity to the barren land and vegetation classes during this period to examine how the recorded LST is affected by the mentioned ratio.Materials and Methods The LC maps of Yazd city were extracted using a supervised Artificial Neural Network classifier for 1990, 2000, 2010, and 2020. Terrestrial data, google earth, and ground truth maps were used to derive training data. The LST of Yazd was obtained from the thermal band of Landsat 5 and Landsat 8. After that, the LST was classified into six available classes, including 16-20, 21-25, 26-30, 31-35, 36-40, and 41-46°C which has shown that the four last classes play an important role in LST changes in Yazd city during last 30 years. To evaluate the effects of the proximity of barren land and vegetation LC classes on the LST recorded by the sensor, firstly the proximity ratio was calculated in 5×5 kernels for all image pixels. Then the mean of LST was derived based on this ratio of barren and vegetation lands.Results and Discussion The results of this study showed that in Yazd city, from 1990 to 2020, the area of the urban area has grown 91.5 % (33.6 km2) over the last 30 years. Barren and vegetation land, have negative growth in the area over the same period. From 1990 to 2020, barren lands in Yazd experienced a growth -79.4% (21.3 km2), which the sharp growth of urban areas justifies this negative growth in barren lands. Vegetation classes in Yazd from 1990 to 2020, have experienced a growth -68.5% (12.2 km2). The average ground temperature of this city has been constantly increasing during these 30 years. By 2020, the city of Yazd, reaching an average of 38.1°C compared to 29.2°C in 1990, has experienced a 30.4% increase in its average LST. The temperature classes of this city have also moved towards warmer temperature classes in these 30 years. As the main part of the LST area of Yazd, in 1990, in the first place, the class of 26-30 °C with 47 km2 and at the second place the class of 31-35 °C with 26.4 km2 are classified. In 2000, in a reverse trend, the main LST class was 31-35°C with 52.8 km2 as the first place and the 26-30°C class with 20 km2 as the second place. With an increased class, the LST class of 36-40 °C for both 2010 and 2020 with 40.2 and 63 km2 respectively has been recorded as the largest LST class. The LST class of 31-35 °C has been recorded as the second LST class of both years with 33.2 and 9.7 km2, respectively. The difference between these two years is in the growth -70.7% (23.5 km2) of the class area of 31-35°C and the increase of 10.3% (0.8 km2) of the hottest class of the statistical period, 41-46°C, in 2020, compared to 2010. The results of this study also showed that the highest average temperature in all year was recorded for barren lands at 37.3°C. Also, a positive correlation (mean correlation 0.95) was shown between the proximity to barren land cover and the mean LST. However, the sharp upward trend of urban areas in the whole statistical period (91.5% with 33.6 km2) as the second class with the highest average LST after the barren lands with an average of 34.1 °C versus a downward trend of 79.4% (21.3 km2) of barren lands has increased the average LST over a statistical period of 30 years. It is because the decrease of 68.5% (12.2 km2) of vegetation areas as an LC class with the lowest average LST (32.2°C) in the same period, neutralized the effect of decreasing barren lands and intensified the trend of increasing the LST. Meanwhile, a negative correlation (mean correlation -0.97) was established between the ratio of proximity to vegetation and the average LST. The results of forecasting land cover changes in 2030 in the city of Yazd indicate that in a process similar to previous periods, the class of urban areas will increase. This growth will not be significant compared to 2020, with 1.6% (1.1 km2). However, a significant decrease in green areas (vegetation) by -19.6% (1.1 km2) in the same period, along with a slight decrease in barren lands -1.8% (0.1 km2) will cause the earth’s surface to become warmer, and the area of LST classes will be increased by the year. Accordingly, the main area of the LST class in 2030 for the city of Yazd, as in 2020, is forecasted 36-40°C with 58.2 km2 (-7.6% growth compared to 2020). But the dramatic growth of the hottest class of LST over the statistical period (41-46°C) with 166.3% (14.3 km2) growth as the second major class of LST in this year (2030), as well as the negative and dramatic growth of the relatively cooler class 31-35°C with -97.9 % (9.5 km2) in this year indicates the warmer ground surface temperature in 2030.Conclusion The results of this study indicate that in 30 years in Yazd city, the decrease in vegetation in the first place, along with the increase in urban areas in the second place, has caused an increase in LST. Thus, the vegetation class reduces the LST due to its cooling effect considering its water content. In this study, it was shown that by taking all factors into account, the reduction of barren lands will lead to a decrease in LST, and also increasing urban areas with a lower impact factor than barren lands will increase the LST. However, the decrease in the area of green lands (vegetation) in recent years, along with the sharp increase in the area of urban areas has caused an increase in LST. Increasing the proximity to vegetation by creating green areas by increasing the ratio of vegetation in the vicinity of different LC and also reducing the area of barren lands, can be a good solution to deal with the impact of urbanization in recent years on ground surface temperature. Manuscript profile

Open Access Article

99 - Evaluate the accuracy of Unmanned aerial vehicles (UAV) data on the survey of dieback Buxus hyrcana (Case study: Sisangan forest park-Mazandaran)
Mohammadreza Kargar Younes Babaei Amir Eslam Bonyad

10.30495/girs.2021.679275

Background and ObjectiveSisangan forest park is one of the important habitats of Buxus Hyrcana in Iran. Unfortunately, the park has suffered from dieback in recent years, and many Box trees have been destroyed. Monitoring and management of this zone can be effective in More

Background and ObjectiveSisangan forest park is one of the important habitats of Buxus Hyrcana in Iran. Unfortunately, the park has suffered from dieback in recent years, and many Box trees have been destroyed. Monitoring and management of this zone can be effective in controlling, protecting, and supporting it. However, due to the destruction of Box trees, on a large scale, it is not possible to accurately estimate the area using the available data. On the other hand, manual measurements are also very time-consuming and tedious. Therefore, a way must be found to do this process accurately and automatically. Unmanned aerial vehicles (UAV) have made this possible by using highly accurate sensors (spatial resolution). Another solution that can be used to automatically separate dieback trees from green trees is to use different classification methods. The aim of this study is to prove the ability of low-cost UAV data with conventional sensors to detect and zoning areas that have suffered Dieback. Since the cost of UAVs with multispectral sensors (red edge band and near infrared) is very high, it should be possible to reduce this cost. Since the cost of UAV with multispectral sensors (red-edge and near-infrared band) is very high, it should be possible to reduce this cost. Materials and Methods Sisangan Forest Park has located 30km to the east of Nowshahr County, Mazandaran province, at latitude 36º33′30″ to 36º35′30″ N, and longitude 51º47′ to 51º49′30″E. This park is both a tourist destination and many important plant species of the country grow in it. One of the most important of these species is the Buxus Hyrcana. But unfortunately, in recent years they have become snag due to pests and insect infestations. Multirotor UAVs have been used in this research. The camera installed on this device is capable of capturing 20 megapixel images. Imaging operations were performed on December 28, 2017, at 10:00 AM, which lasted 45 minutes. The study area was visited for field sampling and its different points were identified in terms of density of snags and preserved Buxus Hyrcana. Then, three circular pieces with a radius of 60 meters and an area of 1.13 hectares were designed in the zone and the density of snag stands and preserved Buxus Hyrcana stands were determined in these three samples. In each plot, 50 training points were recorded in the places where the Buxus Hyrcana stands were located and also 50 points were recorded in the places where the preserved Buxus Hyrcana stands, floor grass cover, and blackberry was located. In this study, in order to evaluate the accuracy of UAV images in identifying and classifying zones covered with Dieback, the smallest Dieback stands with the smallest canopy width were also recorded. Because UAV images require geometric corrections, they were first corrected geometrically and geographically. They were classified with ENVI software. According to the above explanations, 100 points were recorded in each sample plot, 75 of which were monitored for the classification process and 25 of which were used to evaluate the classification accuracy. Three monitored artificial neural network classification algorithms, maximum likelihood and minimum distance were used to classify these images. Finally, after performing each of the classification steps, a low-pass filter with a size of 3 by 3 pixels was used for smoothing the images. Kappa coefficients and overall accuracy indices were also used to evaluate the results. Results and Discussion In this number of sample plots, 579 stands were measured. Buxus Hyrcana was by far the most frequent in the zone. European hornbeam, Parrotia persica, and Oak were in the next ranks, respectively. The results showed that the artificial neural network algorithm had the best results compared to the other two algorithms. But the results of the artificial neural network also fluctuate according to the condition of the sample piece. This algorithm with an overall accuracy of 97.47% and a kappa coefficient of 0.94 had the best results in the separation and detection of the Buxus Hyrcana snags in the sample plot with the dominance of Buxus Hyrcana snags. After the artificial neural network algorithm, the maximum likelihood algorithm showed more favorable results in separating the Buxus Hyrcana snag stands. The minimum distance algorithm showed good results, but it was not as accurate as of the previous two algorithms. All three algorithms showed poorer results in separating the bases in the sample plot with the dominance of live bases in the sample than the other two sample plots. The sample piece with the predominance of live and green bases compared to the other two sample pieces has more phenomena and effects and in terms of image texture, there are many significant differences compared to the other two sample pieces. All three algorithms showed poorer results in separating the stands in the sample plot by dominance the preserved stands in the sample than the other two sample plots. The sample plot with the predominance of preserved stands compared to the other two sample plots has more phenomena and in terms of image texture compared to the other two sample plots has a lot of significant differences. In this sample plot, in addition to the presence of preserved and snag stands, grass cover and blackberry accessions can also be seen. In this study, the results of classification and detection of Buxus Hyrcana snags using an artificial neural network algorithm were much better than the maximum likelihood and minimum distance algorithms. One of the reasons for the better results of the artificial neural network algorithm is its nonlinearity and non-parametricity. But in classification by traditional algorithms such as statistical methods, they have lower accuracy because they have less flexibility. Parametric types of traditional methods, such as the maximum likelihood algorithm, due to depending on Gaussian statistics, if the data are not normal, cannot have the desired accuracy in classifying and separating classes from each other. In traditional algorithms such as maximum likelihood and minimum distance algorithms, training data play a vital role. In these methods, it is assumed that the distribution within the training samples should be normal so that if this condition cannot be met, the classification accuracy will be greatly reduced. While artificial neural network methods operate based on the characteristics and structure of the data itself. Conclusion The results of this study showed that using the data and ordinary images of a low-cost UAV, it is possible to study the condition of Dieback after the outbreak of the disease and determine its area. Despite the high cost of purchasing expensive sensors to monitor vegetation status, these methods presented in this article can be done at a much lower cost. This method can be of great help to the relevant institutions in determining the area of snag coatings. Manuscript profile

Open Access Article

100 - Comparing artificial neural network, support vector machine and object-based methods in preparation land use/cover mapsusing landSat-8 images
Farnoush Aslami Ardavan Ghorbani Behrouz Sobhani Mohsen Panahandeh

Preparing the maps of land use/cover for spatial planning and management is essential. Nowadays, satellite images and remote sensing techniques have widespread applications according to their capabilities to produce the updated data and analyze the images in all discipl More

Preparing the maps of land use/cover for spatial planning and management is essential. Nowadays, satellite images and remote sensing techniques have widespread applications according to their capabilities to produce the updated data and analyze the images in all disciplines such as agriculture and natural resources. In the present study, Artificial Neural Network, Support Vector Machines and Object-Based techniques wereutilized for drawing the land use and vegetation maps in Ardabil, Namin, and Nir counties. The images of LandSat-8 Operational Land Imager (OLI) (2013) were usedafter geometric correction and topographic normalization and classified into 9 land use/cover classes including water bodies, irrigated farming, rainfed farming, meadows, outcrops, forests, rangelands, residential and airport areas. After the accuracy assessment, overall accuracy for the produced maps of ANN, Support Vector Machine (SVM) and Object-based (OB) techniques was estimated as 89.91, 85.68 and 94.37%, respectively and Kappa's coefficients were 0.88, 0.82 and 0.93, respectivelyindicating that the object-based method in comparison with two other methods has more advantages;on the other hand, all three methods could provide the desirable accuracy for the land use/covermaps. Overally, three advanced classification methods were examined in the heterogeneous area with elevation changes up to 3600m using the images of new lunched Landsat 8 and the most appropriate land use/cover mapping method was introduced. Manuscript profile

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101 - Landslide hazard zonation using artificial neural network (Case study: Sepiddasht-Lorestan, Iran)
Syamak Bharvand Salman Soori

This study was carried out to determine the relative hazard zonation of the slope instabilities and landslide occurrence in Sepiddasht, Iran. The method of Artificial Neural Network with the multiple-layer percepteron structure and the back propagation learning algorith More

This study was carried out to determine the relative hazard zonation of the slope instabilities and landslide occurrence in Sepiddasht, Iran. The method of Artificial Neural Network with the multiple-layer percepteron structure and the back propagation learning algorithm were used. In order to study the stability of the slopes, the landslides of the region were initially identified and recorded using satellite images of TM and ETM+, aerial images of 1:50,000, and field surveys (year, 2014). The impact of each factor including slope, aspect, land use, elevation, lithology, precipitation, the distance from the fault road and drainage on the slope instabilities was estimated using the ArcGIS®10.1 software via combining the map of the factors influencing the landslide with the landslide distribution map. Then a proper structure (1-13-9) for the landslide hazard zonation of Sepiddasht region was obtained through training the artificial neural network by MATLAB software. Based on the results of the landslide hazard zonation, 0.18, 12.41, 14.09, 29.85, and 43.52 percent of the region were located in very low, low, medium, high, and very high risk classes respectively. Manuscript profile

Open Access Article

102 - Estimating of evapotranspiration using remote sensing, artificial neural network and comparison with the experimental method (Penman-Monteith-FAO)
Aziz Azimi Kazem Rangzan Mostafa Kaboulizade Mohammad Khoramian

Evaporation waste of water is one of the most important factors. Because evapotranspiration is a complex phenomenon that depends on many factors and data, accurate estimation of evaporation and transpiration, is very difficult and costly. Therefore, the purpose of this More

Evaporation waste of water is one of the most important factors. Because evapotranspiration is a complex phenomenon that depends on many factors and data, accurate estimation of evaporation and transpiration, is very difficult and costly. Therefore, the purpose of this study was to estimate evapotranspiration using the surface energy balance algorithm for land (SEBAL) and also evaluate the performance of artificial neural networks. To estimates the Evapotranspiration rate the method of SEBAL Algorithmby using satellite images was applied. For this purpose, four images of Landsat 8 in this study were used that by comparing the results from the two methods, Remote Sensing and Penman-Monteith- FAO Equation presented MSE and MAE as respectively 1.54 and 1.04 per day. To solve the complexity of the evaporation process, Artificial Neural Networks was used for forecasting evaporation pan based on meteorological data. Perceptron with Back-propagation algorithm was applied for training it in this study. It used daily climate data that collected during 13 years from a Safi Abad station in Dezful city for network training. The results showed that the best network was the network with all inputs along with a hidden layer and 28 Neurons in the middle layer. The implementation results of this network presented that statistical Indicators were as MSE (0.0032), MAE (0.0445), R2 (0.9609). Comparing the results from Artificial Neural Networks and Penman-Monteith- FAO as reference method showed that MSE and MAE were 1.11 and 0.52 mm per day, respectively. These results presents that the performance of Artificial Neural Networks was better than the remote sensing method in the estimation of evapotranspiration rate. Manuscript profile

Open Access Article

103 - Land use change modeling using artificial neural network and markov chain (Case study: Middle Coastal of Bushehr Province)
Mehdi Gholamalifard Mohsen Mirzayi Sharif Joorabian Shooshtari

Coastal lands of Bushehr Province has a high importance in terms of marine exporting and importing, oil and gas reserves, agriculture,  nuclear plant, suitable condition for fishing and tourist attractions. Therefore new desirable methods for monitoring and modelin More

Coastal lands of Bushehr Province has a high importance in terms of marine exporting and importing, oil and gas reserves, agriculture,  nuclear plant, suitable condition for fishing and tourist attractions. Therefore new desirable methods for monitoring and modeling changes are required to be used in these areas. This study was performed with the aimed of monitoring and modeling land use changes using Artificial Neural Network (ANN) and Markov Chain in Land Change Modeler (LCM) in 23 years period (1990-2011). After model accuracy assessment using kappa coefficient, land cover map of the year 2016 was predicted by the 2006-2011 calibration period. The results indicated that two trends include changes from open lands to agricultural and then quitting these agricultural lands have been observed during the study period. Such that, the agricultural area has increased to 19715.76 hectares from 1990 to 2006,but between 2005 to 2011, only 14.48% of agricultural lands has remained unchanged and the large area  of those were finally left. In this study, LCM was able to predict 0.76 of changes correctly. So that it was predicted 12000 hectares increasing of extent urban development in the coastal lands of Bushehr Province in 2016. Manuscript profile

Open Access Article

104 - Double Cracks Identification in Functionally Graded Beams Using Artificial Neural Network
F Nazari M.H Abolbashari

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Open Access Article

105 - Free Vibrations of Three-Parameter Functionally Graded Plates Resting on Pasternak Foundations
J.E Jam S Kamarian A Pourasghar J Seidi

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Open Access Article

106 - Cell Deformation Modeling Under External Force Using Artificial Neural Network
M.T Ahmadian G.R Vossoughi A.A Abbasi P Raeissi

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107 - Optimization of Functionally Graded Beams Resting on Elastic Foundations
M.H Yas S Kamarian J.E Jam A Pourasghar

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108 - Dynamic Analysis of Multi-Directional Functionally Graded Panels and Comparative Modeling by ANN
H Khoshnoodi M.H Yas A Samadinejad

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109 - Sensivity analysis of the effective input parameters upon the population flactuation of the sunn pest adult insect using Artificial Neural Network
Zahra Dustiy Naser Moeini naghadeh Abbas Ali Zamani Leila Naderloo

The Sunn pest, Eurygaster integriceps Put. is the most important pest of wheat and barley in Iran. Many studies have shown that various biotic and abiotic environmental factors affect the population of this pest. In this study, the relationship between population densit More

The Sunn pest, Eurygaster integriceps Put. is the most important pest of wheat and barley in Iran. Many studies have shown that various biotic and abiotic environmental factors affect the population of this pest. In this study, the relationship between population density of the Sunn pest adult insect with different environmental factors including sampling date, average daily temperature, average relative humidity, wind speed, wind direction, height from sea level and degree-day was investigated. Field data were collected from two wheat farm of one-hectare in the city of Chadegan, Isfahan province. The used network type was multilayer perceptron with back propagation algorithm and the learning algorithm was Levenberg-Markvart. After sensitivity analysis due to the ease of the model and extraction of effectiveness of factors including four factors of sampling date, temperature, humidity and wind speed were selected. The results showed that a neural network with two hidden layer, 7 neuron in the first hidden layer and three neuron in the second hidden layer, as a sigmoid activation function, and a data percentage of 60, 30, 10 for training, testing and validation for prediction of population fluctuation of the Sunn pest adult insect is used (R2= 0.94). Manuscript profile

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110 - The Predictability Power of Neural Network and Genetic Algorithm from Fiems’ Financial crisis
Nader Rezaei Maryam Javaheri

10.22034/amfa.2019.1863963.1195

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111 - Forecasting the Tehran Stock market by Machine ‎Learning Methods using a New Loss Function
Mahsa Tavakoli Hassan Doosti

10.22034/amfa.2020.1896273.1399

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112 - Prediction the Return Fluctuations with Artificial Neural Networks' Approach
Masoud Taherinia Mohsen Rashidi Baghi

10.22034/amfa.2019.580643.1149

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113 - Estimating Efficiency of Bank Branches by Dynamic Network Data Envelopment Analysis and Artificial Neural Network
Javad Niknafs Mohammad Ali Keramati Jalal Haghighatmonfared

10.22034/amfa.2019.1585957.1192

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114 - Measurement of Bitcoin Daily and Monthly Price Prediction Error Using Grey Model, Back Propagation Artificial Neural Network and Integrated model of Grey Neural Network
Mahdi Madanchi Zaj Mohammad Ebrahim Samavi Emad Koosha

10.22034/amfa.2020.1881110.1315

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115 - Investigating the Market Efficiency in Tehran Stock Exchange through Artificial Intelligence
mohammad jouzbarkand Hossein Panahian

10.22034/amfa.2021.1934511.1613

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Open Access Article

116 - Option pricing with artificial neural network in a time dependent market
Mehran Araghi Elham Dastranj Abdolmajid Abdolbaghi Ataabadi Hossein Sahebi Fard

In this article, the pricing of option contracts is discussed using the Mikhailov and Nogel model and the artificial neural network method. The purpose of this research is to investigate and compare the performance of various types of activator functions available in ar More

In this article, the pricing of option contracts is discussed using the Mikhailov and Nogel model and the artificial neural network method. The purpose of this research is to investigate and compare the performance of various types of activator functions available in artificial neural networks for the pricing of option contracts. The Mikhailov and Nogel model is the same model that is dependent on time. In the design of the artificial neural network required for this research, the parameters of the Mikhailov and Nogel model have been used as network inputs, as well as 700 data from the daily price of stock options available in the Tehran Stock Exchange market (in 2021) as the net-work output. The first 600 data are considered for learning and the remaining data for comparison and conclusion. At first, the pricing is done with 4 commonly used activator functions, and then the results of each are com-pared with the real prices of the Tehran Stock Exchange to determine which item provides a more accurate forecast. The results obtained from this re-search show that among the activator functions available in this research, the ReLU activator function performs better than other activator functions. Manuscript profile

Open Access Article

117 - Financial Reporting Readability: A new Artificial Neural Network and Multi-Indicator Decision Making Approach
Ali Asghar Khazaei Harivand Arash Naderian Majid Ashrafi Ali Khozin

The desirability of the financial reporting can greatly help the users of finan-cial information in making investment decisions. The purpose of this re-search is to measure the readability of financial reporting using a multi-indicator decision-making model and the arti More

The desirability of the financial reporting can greatly help the users of finan-cial information in making investment decisions. The purpose of this re-search is to measure the readability of financial reporting using a multi-indicator decision-making model and the artificial neural network method and the role of information presentation time in its improvement. In this research, various indicators have been used to measure the readability of financial reporting, and the quality of reporting is obtained through the rank-ing of companies by the stock exchange. In this research, the number of 149 companies admitted to the Tehran Stock Exchange in the period of 2010-2020 was examined, and to measure the financial readability through struc-tural equations and Stata software, and to test the hypothesis of the research, the regression model and Eviews econometrics software were used. In this study, we have tried to Use machine learning techniques and optimization tools as a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. The findings of the research show that the time of providing information has an impact on the readability of financial reporting. The obtained outputs from the estimation of the artificial neural networks and results obtained from estimation, using of this method with evaluation scales concerning random amount and comparing it with adjusted R, we found that there is meaningful relation between the associated variables and return. However, such network has the least error than other networks. The results show an overall improvement in forecasting using the neural network as compared to linear regression method. In other words, our proposed system displays an extremely higher profitability potential. The obtained result can be argued that the more the company's information is provided by the managers to the company's shareholders and investors on time and at the right time, the more readable and understandable the financial reports will be. Manuscript profile

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118 - ANN-DEA Approach of Corporate Diversification and Efficiency in Bursa Malaysia
Meysam Doaei Seyed Hashem Davarpanah Mahdi Sabzi

10.22034/amfa.2017.529058

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119 - Forecasting the Profitability in the Firms Listed in Tehran Stock Exchange Using Data Envelopment Analysis and Artificial Neural Network
Maryam Saberi Mohammad Reza Rostami Mohsen Hamidian Nafiseh Aghami

10.22034/amfa.2016.527823

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120 - Prediction of LD50 for aniline derivatives (Including some herbicide compounds) using computational methods
E. Mohammadinasab M. Rezaei

In recent decades, computational methods with regard to accurate validation parameters for the determination of the physical- chemistry properties of compounds have been considered by many researchers and have been used as an economic and environmental alternative to sa More

In recent decades, computational methods with regard to accurate validation parameters for the determination of the physical- chemistry properties of compounds have been considered by many researchers and have been used as an economic and environmental alternative to saving time and eliminating high costs.  In this study, the relationship between the logarithmic values of LD50,) log (LD50)(molkg-1) and molecular descriptors has been investigated for 60 types of aniline derivatives(including some herbicides compounds). At first, the structure of the compounds were drawn by Gauss view05 software and optimized using Gaussian 09 software with B3LYP/6-311++G** method, and then were extracted molecular descriptors. Then inappropriate descriptors were eliminated by genetic algorithm method and the best ones were used for multiple linear regression (MLR) and artificial neural networks (ANN) models. The results showed that the ANN method with the lowest error and the highest coefficient of determination was higher than the MLR method to predicting the log(LD50)(molkg-1) of studied aniline derivatives.   Manuscript profile

Open Access Article

121 - The study of relationship between molecular descriptors and LD50 of organophosphate pesticides
E. Mohammadinasab M. kianpour

The organophosphate pesticides are considered as dangerous chemical pesticides for human health. The possibility of absorbing these toxins through the skin is accessible to many researchers who have been studying the toxicity of organophosphate compounds. Experimental m More

The organophosphate pesticides are considered as dangerous chemical pesticides for human health. The possibility of absorbing these toxins through the skin is accessible to many researchers who have been studying the toxicity of organophosphate compounds. Experimental methods are time-consuming and high cost, and they come with a lot of dangers. Quantitative structure activity/property relationship studies provide the capability to access data, information and physico-chemical properties of chemical compounds, using the methods and modeling.  In this study, the multiple regression linear method and the artificial neural network with multi-layer perceptron (MLP) model were used to investigate the quantitative relationship of LD50 (mgkg-1) toxicity index with some molecular descriptors of some organophosphate compounds.  Investigation of correlation coefficients and root mean square errors values of final models in this study showed that ANN method using the MLP model was higher than the MLR method for prediction of LD50(mgkg-1) of organophosphates compounds. Manuscript profile

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122 - Prediction of LD50 for carboxylic acid derivatives using multiple linear regression and artificial neural networks models
E. Mohammadinasab f. Mohammaei

In this research, Quantitative Structure–Activity Relationship (QSAR) study has been used for prediction of toxicity values of carboxylic acid derivatives. Firstly, the toxicity (LD50) values of data set of studied compounds were taken from the scientific web book More

In this research, Quantitative Structure–Activity Relationship (QSAR) study has been used for prediction of toxicity values of carboxylic acid derivatives. Firstly, the toxicity (LD50) values of data set of studied compounds were taken from the scientific web book and the their structures were drawn with the Gauss view 05 program and optimized at Hartree–Fock level of theory and 3-21G basis set by Gaussian 09 software. Then the dragon software was used for the calculation of molecular descriptors. The unsuitable descriptors were deleted with the aid of the genetic algorithm (GA) and backward techniques, and the best descriptors were used for multiple linear regression (MLR) and artificial neural network (ANN) models. The prediction accuracy of the final model was discussed using the statistical parameters. Leave-one-out cross-validation and external test set of the predictive models demonstrated a high-quality correlation between the observed and predicted toxicity values of all, training, test and validation sets in GA-ANN method. The model by ANN algorithm due to the lower error and higher regression coefficients was clearly superior to those models by MLR algorithm. The proposed model may be useful for predicting log LD50 of new compounds of similar class. Manuscript profile

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123 - Modeling the Amount of Required Energy and Kinetics of Lavender Drying Using Artificial Neural Network
Mohammad Younesi Alamooti Hamid Khafajeh Mohammad Zarein

10.30495/sarj.2023.1982366.1125

Lavender with the scientific name Lavandula stricta Del is a perennial medicinal plant with a height of about half a meter that grows in different regions of Iran. Drying is one of the oldest methods of preserving materials. The use of neural networks can be used in the More

Lavender with the scientific name Lavandula stricta Del is a perennial medicinal plant with a height of about half a meter that grows in different regions of Iran. Drying is one of the oldest methods of preserving materials. The use of neural networks can be used in the design and selection of optimal working conditions and dryer control. In this study, various parameters of drying, evaluation of mathematical models to determine the best model, evaluation of different topologies of MLP artificial neural network to determine the best network for lavender plant with microwave dryer with power range of 100-1000 watts and The frequency of 2450 MHz is provided in four power levels of 300, 500, 700 and 900 watts. MLP artificial neural network was used to predict the relationship between drying kinetic parameters (moisture ratio and drying rate) and efficiency of energy consumption with changes in microwave power consumption using Statistica software. Among the fitted models, the Midili model was chosen as the best model according to R 2, χ 2 and RMSE criteria. Microwave power levels had an effect on drying time, with drying times of 3 minutes for 900 W power and 11 minutes for 300 W power. In order to predict drying kinetic parameters and energy consumption efficiency, MLP network with one input and three outputs was successfully used. The results generally showed that the MLP artificial neural network is a very powerful tool in predicting drying kinetic parameters and energy efficiency of lavender medicinal plant based on microwave power consumption values. Manuscript profile

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124 - Quantitative structure–activity relationship on a series of imidazole [1, 2-a] pyridinecarboxamide derivatives as anti-tuberculosis agents
Mohsen Nekoeinia Saeed Yousefinejad

Tuberculosis drug resistance is still one of the most important challenges in the treatment of this infectious disease, and therefore the discovery and development of new effective anti-tuberculosis drugs are always of interest to researchers. In this study, Quantitativ More

Tuberculosis drug resistance is still one of the most important challenges in the treatment of this infectious disease, and therefore the discovery and development of new effective anti-tuberculosis drugs are always of interest to researchers. In this study, Quantitative structure – activity relationship (QSAR) analysis was applied on a series of imidazole[1,2-a] pyridinecarboxamide derivatives as anti-tuberculosis agents. The biological activity of the 18 derivatives were estimated by multiple linear regression and artificial neural network approaches. The four molecular descriptors (nCl, MATS8m, BELe4 and GATS8e) were selected by using stepwise multiple linear regression. The best results of artificial neural network were obtained with a 5-5-1 architecture trained with the feed forward backpropagation algorithm. An external test set containing 5 compounds for evaluating the model's predictive ability was used. The results showed that the artificial neural network approach provides better predictive power compared with multiple linear regression. According to the results of this study, electronegativity, atomic masses and molecular geometry have been found to be important factors controlling the anti-tuberculosis activity. Manuscript profile

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125 - Design and Implementation of Organizational Architecture in Organizations in Charge of Combating Smuggling of Goods and Currency with the Aim of Improving the Management of Organizational Networks
Abdolreza Yari Mohammadali Keramati Ahmadreza Etemadi Abdollah Kouloubandi

10.30495/jsm.2021.1923975.1443

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126 - Designing Cell Production Arrangement Scenarios with the Approach of Artificial Neural Networks
Mahdi Ahmadipanah Kamyar Chalaki Roya Shakeri

10.30495/jsm.2022.1964485.1673

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127 - Designing an Optimal Model Using Artificial Neural Networks to Predict Non-Linear Time Series (case study: Tehran Stock Exchange Index)
Bahman Ashrafijoo Nasser Fegh-hi Farahmand Yaghoub Alavi Matin kamaleddin rahmani

10.30495/jsm.2022.1965914.1679

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128 - ارزیابی تصفیه‎خانه فاضلاب شهر خرمشهر با استفاده از شبکه‎های عصبی و مصنوعی و ماشین‎بردار پشتیبان و امکان استفاده از آن برای مصارف کشاورزی
علی ایران فر علیرضا نیکبخت شهبازی رضا جلیل زاده ینگجه

در این تحقیق از شبکه عصبی و مصنوعی و ماشین&lrm;بردار پشتیبان جهت ارزیابی فاضلاب تصفیه&lrm;خانه شهر خرمشهر استفاده شده است. همچنین امکان استفاده فاضلاب این شهر برای مصارف کشاورزی بررسی گردید. در این تحقیق از مقادیر ماهیانه BOD، COD، TS و TSS که مورد نیاز در این پژوهش بود More

در این تحقیق از شبکه عصبی و مصنوعی و ماشین&lrm;بردار پشتیبان جهت ارزیابی فاضلاب تصفیه&lrm;خانه شهر خرمشهر استفاده شده است. همچنین امکان استفاده فاضلاب این شهر برای مصارف کشاورزی بررسی گردید. در این تحقیق از مقادیر ماهیانه BOD، COD، TS و TSS که مورد نیاز در این پژوهش بودند مورد استفاده قرار گرفت و همچنین از این مقادیر جهت ارزیابی فاضلاب تصفیهخانه شهر خرمشهر جهت استفاده در مصارف کشاورزی استفاده گردید. کاربرد مدل شبکه عصبیمصنوعی، برای پیشبینی کیفیت پساب خروجی تصفیهخانههای فاضلاب نیز امکان پذیر میباشد. مدل منتخب ANN (LM) از دقت مناسبی در تخمین به&lrm;هنگام BOD5 برخوردار بود. به هر حال این مدل در پیشبینی مقادیر حدی بیشینه از عملکرد ضعیفتری برخوردار بود. با استفاده از الگوریتم بهینهسازی جست&lrm;وجوی شبکه دو مرحلهای، مقادیر بهینه مشخصههای مدل SVM یعنی ɛ، C و γ به ترتیب معادل 037/0، 13 و 472/1 به دست آمد. در نهایت با توجه به نتایج به دستآمده در این تحقیق مدل SVM برای پیشبینی به&lrm;هنگام BOD5  برای تصفیهخانه شهر خرمشهر توصیه شد. با توجه به نتایج به&lrm;دست آمده از آنالیز کیفی فاضلاب ورودی پساب خروجی تصفیه شده راندمان حذف BOD5 برابر با 88 درصد، COD برابر با 92 درصد، TDS برابر با 70 درصد و حذف TSS برابر با 27 درصد میباشد. شوری پساب تصفیهخانه فاضلاب شهر خرمشهر  با مینیمشوری 208، ماکزیمم 3050 و میانگین 1544 میکروموس بر سانتیمتر اندازهگیری شد. بنابراین پساب تصفیهخانه فاضلاب این شهر در گروه C3، آبهای قابل&lrm;قبول قرار دارد. براساس مقدار سدیم پساب خروجی فاضلاب این تصفیه&lrm;خانه برای آبیاری گندم، جو، سویا، انجیر، زیتون، صنوبر و امثال آن&lrm;ها، بر اساس نمودار ویلکاکس هیچ محدودیتی در استفاده از این پساب وجود ندارد. Manuscript profile

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129 - تعیین کیفیت آب در طول مسیر رودخانه با استفاده از شبکه‌های عصبی مصنوعی تکاملی (مطالعه موردی رودخانه کارون بازه شهیدعباسپور- عرب اسد)
محمد نیکو مهدی نیکو تیمور بابائی نژاد آزاده امیری قدرت الله رستم پور

رودخانه‌ها به عنوان اصلی ترین منبع تأمین کننده نیاز شرب، کشاورزی و صنعت از اهمیت خاصی برخوردار هستند. از طرفی کیفیت آب از لحاظ شرب نیز در بین پارامترهای کیفی مهم ترین متغیر می‌باشد. لذا بررسی و پیش بینی تغییرات پارامترهای کیفی در طول یک رودخانه، یکی از اهداف مدیران و بر More

رودخانه‌ها به عنوان اصلی ترین منبع تأمین کننده نیاز شرب، کشاورزی و صنعت از اهمیت خاصی برخوردار هستند. از طرفی کیفیت آب از لحاظ شرب نیز در بین پارامترهای کیفی مهم ترین متغیر می‌باشد. لذا بررسی و پیش بینی تغییرات پارامترهای کیفی در طول یک رودخانه، یکی از اهداف مدیران و برنامه ریزان منابع آب، می‌باشد. در این راستا تعداد زیادی مدل‌های کیفیت آب، در زمینه مدیریت بهتر برای حفظ کیفیت آب، گسترش یافته است. در این میان مدل‌های شبکه عصبی مصنوعی که با الهام از ساختار مغز بشر عمل می‌نمایند، به عنوان گزینه‌ای برتر، مورد تحقیق و بررسی قرار می‌گیرد. این تحقیق بر روی رودخانه کارون، بزرگترین رودخانه کشور و با استفاده از پارامترهای اندازه گیری شده در ایستگاه‌های موجود در طول رودخانه (بازه شهیدعباسپور- عرب اسد) انجام شده است. بدین منظور، دبی، ماه، طول رودخانه و پارامترهدایت الکتریکی اندازه گیری شده در ایستگاه‌های شهیدعباسپور، پل شالو، گتوندو عرب اسد به عنوان ورودی‌های مدل، در نظر گرفته شد. با استفاده از مدل شبکه عصبی، نسبت جذب سدیم (SAR) و کل املاح محلول (TDS) اندازه گیری شده در همان ایستگاه‌ها نیز پیش بینی می‌گردد. از جمله مواردی که در این تحقیق به عنوان یک روش جدید استفاده شده است،تعیین شاخص‌های کیفی آب، در چند ایستگاه به صورت هم زمان می‌باشد. به منظور بهینه کردن هرکدام ازمدل‌های شبکه عصبی مصنوعی، از الگوریتم ژنتیک استفاده گردید. نتایج نشان می‌دهد که مدلشبکه عصبی مصنوعی انتخاب شده،  نسبت به مدل‌های آماری رگرسیون غیرخطی از توانایی، انعطاف پذیری و دقت بیشتری در پیش بینی کیفیت آب در رودخانه برخوردار می‌باشد. Manuscript profile

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130 - پیش بینی ابعاد آبشستگی در حوضچه ی استغراق سرریزهای سرویس با ‌روش‌های هوش مصنوعی.
علی لشکرآرا سارا خرم زاده

پیش &lrm;بینی دقیق ابعاد حفره آبشستگی در پایین دست سازه های هیدرولیکی از جمله سرریزهای جامی&lrm; شکل، به دلیل پیچیدگی ‌های ناشی از بررسی همه جانبه و همزمان جریان حاوی آب و رسوب و اعمال کلیه متغیرهای مؤثر در پدیده آبشستگی به سادگی میسر نمی ‌باشد. ابعاد حفره آبشستگی اغلب More

پیش &lrm;بینی دقیق ابعاد حفره آبشستگی در پایین دست سازه های هیدرولیکی از جمله سرریزهای جامی&lrm; شکل، به دلیل پیچیدگی ‌های ناشی از بررسی همه جانبه و همزمان جریان حاوی آب و رسوب و اعمال کلیه متغیرهای مؤثر در پدیده آبشستگی به سادگی میسر نمی ‌باشد. ابعاد حفره آبشستگی اغلب با استفاده از معادلات تجربی تعیین می&lrm;گردد که این روابط در محدوده خاصی از داده ‌ها و شرایط آزمایش پاسخگو می&lrm; باشد. از آنجایی که ساخت مدل فیزیکی مشکلات و محدودیت هایی به همراه دارد و معمولا در تعیین نگاشت میان پارامتر های مؤثر بر آبشستگی نمی&lrm; توان اثر دقیق همه پارامترها را در نظر گرفت، لذا در مقاله حاضر بهینه یابی ابعاد حفره آبشستگی برای مجموعه ‌ای از مشاهده‌ ها آزمایشگاهی محققان قبلی طراحی شده است. در این تحقیق ازشبکه عصبی مصنوعی و سیستم تطبیقی عصبی- فازی بهره گیری شده و نتایج آن با معادله حاصل از روش رگرسیون غیرخطی بین داده ‌های مشابه و همچنین فرمول های تجربی پیش &lrm;بینی حداکثر عمق آبشستگی مقایسه شده است. نتایج این تحقیق حاکی از دقت و برتری قابل ملاحظه سیستم تطبیقی عصبی - فازی با حداکثر خطای 2/5 درصد نسبت به نتایج حاصل از مدل شبکه عصبی و معادله رگرسیون غیرخطی و فرمول تجربی با حداکثر خطا به ترتیب 38/10، 42/12 و 05/14 درصد می&lrm;باشد. Manuscript profile

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131 - پیش بینی و آنالیز حساسیت تبخیر ماهانه از مخزن سد سیاه بیشه با استفاده از شبکه‌های عصبی مصنوعی در ترکیب با الگوریتم ژنتیک
آزاده محمدیان شوئیلی حسن فتحیان مهدی اسدی لور

فرآیند تبخیر، یکی از مؤلفه‌های اصلی چرخه آب در طبیعت است که نقش اساسی در مطالعات کشاورزی، هیدرولوژی و هواشناسی، بهره برداری از مخازن، طراحی سیستم‌های آبیاری و زهکشی، زمان بندی آبیاری و مدیریت منابع آب ایفا می‌کند. روش‌های زیادی از جمله روش‌های بیلان آب، تبخیر از تشت و ر More

فرآیند تبخیر، یکی از مؤلفه‌های اصلی چرخه آب در طبیعت است که نقش اساسی در مطالعات کشاورزی، هیدرولوژی و هواشناسی، بهره برداری از مخازن، طراحی سیستم‌های آبیاری و زهکشی، زمان بندی آبیاری و مدیریت منابع آب ایفا می‌کند. روش‌های زیادی از جمله روش‌های بیلان آب، تبخیر از تشت و روش‌های تجربی برای تخمین تبخیر از سطح آزاد، ارائه شده است که هر کدام از این روش‌ها،  با محدودیت و خطای اندازه گیری توأم می‌باشد. امروزه تکنیک جدید استفاده از شبکه‌های عصبی مصنوعی که مبتنی بر هوش مصنوعی می‌باشد کاربرد گسترده ای در زمینه‌های مختلف علمی به ویژه مهندسی آب پیدا کرده است. در این تحقیق با استفاده از مدل شبکه عصبی مصنوعی پرسپترون چند لایه(MLP)، شبکه تابع پایه شعاعی (RBF) و شبکه پیش رونده(FF)،میزان تبخیر ماهانه از مخزن سد سیاه بیشه تا 3 ماه آیندهپیش بینی شد. برای تعیین متغیرهای ورودی مؤثر در مدل‌های شبکه عصبی مصنوعی و تعداد نرون‌ها در لایه میانی هر یک از مدل‌ها، از قابلیت بهینه سازی الگوریتم ژنتیک استفاده شد. نتایج نشان می‌دهد که ضریب همبستگی بین مقادیر اندازه گیری شده و محاسبه شده با مدل‌های RBF ، MLPو  FFدر برآورد و پیش بینی تبخیر ماهانه از مخزن سد سیاه بیشه به ترتیب برابر با 92/0، 90/0 و 88/0 می‌باشد. بنابراین مدل RBF از دقت بیشتری نسبت به دو مدل MLP وFFدر برآورد و پیش بینی میزان تبخیر ماهانه از مخزن سد،  برخوردار می‌باشد. نتایج حاصل از آنالیز حساسیت نشان می‌دهد که تبخیر ماهانه از مخزن سد تا 3 ماه آینده به ترتیب نسبت به زمان وقوع تبخیر بر حسب ماه، فشار هوا در سطح زمین در 1 ، 3 و2 ماه قبل، سرعت باد در سطح 1000 میلی بار در 3 و 2 ماه قبل و دمای هوا در سطح 300 میلی بار در زمان حال بیشترین حساسیت را دارد. Manuscript profile

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132 - Using Artificial Neural Network Methodology and Fuzzy Logic to Design an Intelligent Model for Optimizing and Preventive Maintenance in Interaction with Production in the Textile and Clothing Industry
Sayyed Shahram fatemi Mehrdad Javadi Amir Azizi Sayyed Esmail Najafi

10.30495/imj.2023.1991238.1860

In this research, the intelligent model of preventive maintenance and repairs based on artificial neural network methodology - fuzzy logic with the help of artificial intelligence environment of MATLAB software based on the structure of Falcon's five-layer model of arti More

In this research, the intelligent model of preventive maintenance and repairs based on artificial neural network methodology - fuzzy logic with the help of artificial intelligence environment of MATLAB software based on the structure of Falcon's five-layer model of artificial neural networks is presented, the research method is based on systems thinking. After determining the most important factors affecting preventive maintenance and repairs with the help of a questionnaire and based on a dataset of 2,000 samples of data and reports of the Director General of Textile and Clothing Industries of the Ministry of Safety during the years 1396 to 1401 (in the form of six and a half years) and validity Data evaluation by the maintenance and repair experts of 240 industrial units, a smart model was designed, which after the implementation of the model in Borujerd textile factories as the place of implementation of the plan can be claimed if (If); Five "technology" factors have values of 0.9129; Good condition (upper bound of good membership function), "Employees" has values of 0.9239; good condition (upper bound of good membership function), "working environment" has values of 0.8859; relatively good (lower limit of the membership function), "quality" has values of 0.9999; Perfect condition (highest function), "strategy" has values of 0.9999; good status (upper limit) in preventive maintenance and repairs, then: the status of the output variable of the research, i.e."Optimization of preventive maintenance and repairs performance (Y)" will be at its fifth level, i.e. very good, equal to 0.882. Manuscript profile

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133 - Identifying the influencing factors in customer churn of Kurdistan Telecommunications Company and presenting models for predicting churn using machine learning algorithms
vida sadeghi Anvar Bahrampour Seyed Ali Hosseini

10.30495/imj.2023.1987440.1850

The main sources of income and assets are important for any organization. With this view, companies have started to do more to maintain health. Since in many companies the cost of acquiring a new customer is much higher than actual customer satisfaction, customer churn More

The main sources of income and assets are important for any organization. With this view, companies have started to do more to maintain health. Since in many companies the cost of acquiring a new customer is much higher than actual customer satisfaction, customer churn has become the main area of evaluation for these companies. Client-facing companies, including those active in the technology industry, are facing a major challenge due to customer attrition. With the rapid development of the telecommunications industry, dropout prediction becomes one of the main activities in gaining a competitive advantage in the market. Predicting customer churn allows operators a period of time to remediate and implement a series of preventative measures before customers migrate to other operators. In this research, a decision support system for predicting and estimating the churn of customers of Kurdistan Telecommunication Company (with 52,900 subscribers) with different data-mining and machine methods (including simple linear regression (SLR), multiple linear regression (MLR). Polynomial regression. (PR), logistic regression, artificial neural networks, Adabust and random forest) are presented. The results of the evaluations carried out on the data set of the Kurdistan Province Telecommunication Company, the high performance of artificial neural network methods with 99.9% accuracy, Adabust with 99.9% accuracy, 100% accuracy and random forest It shows 100% with accuracy. Manuscript profile

Open Access Article

134 - A Model to Predict the Profitability of Pharmaceutical Companies Using Artificial Neural Network
Ali Habibi Ghaffar Tari

The aim of this paper is to develop a model to predict the profitability of pharmaceutical companies using the artificial neural network. The research method was descriptive-modeling and statistical population included all active pharmaceutical companies of the Stock Ex More

The aim of this paper is to develop a model to predict the profitability of pharmaceutical companies using the artificial neural network. The research method was descriptive-modeling and statistical population included all active pharmaceutical companies of the Stock Exchange including 21 companies. Due to the limited population, no sampling was performed and the whole community was surveyed. The documents of pharmaceutical companies were used to data collection and artificial neural networks were used for data analysis. The results showed that the best model to predict the profitability of pharmaceutical companies is obtained by considering the variables of working capital to total assets, cumulative profit (loss) to total assets, earnings before interest and taxes to total assets, the market value of equity to the book value of debts, sales to total assets and liquidity. Manuscript profile

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135 - Performance prediction of a steam single-effect absorption chiller by the artificial neural network
Farshad Panahizadeh Mahdi Hamzehei Mahmood Farzaneh-Gord

Depending on the temperature and pressure of the heat source, single-effect absorption chillers are categorized in two types of hot water and steam single-effect chillers. Due to the ability to use the waste steam in oil, gas and petrochemical industries for air conditi More

Depending on the temperature and pressure of the heat source, single-effect absorption chillers are categorized in two types of hot water and steam single-effect chillers. Due to the ability to use the waste steam in oil, gas and petrochemical industries for air conditioning and process cooling purposes, the steam type chiller is more widely used. In this study, the artificial neural network is exploited in the prediction of the steam single-effect absorption chiller performance since it is faster and has lower computational cost compared to thermodynamic modeling methods. The perceptron multilayer neural network with the error backpropagation algorithm, the hyperbolic tangent excitation function and the Levenberg-Marquardt learning method with 15285 data points and also the mean squared error estimation index are used. Inputs of the artificial neural network are the inlet cooling tower water temperature, inlet chilled water temperature, inlet steam temperature, outlet chilled water temperature and the solution heat exchanger efficiency respectively. Also, outputs of the neural network are the coefficient of performance and thermal energy consumption of the chiller. Results of this study show that the artificial neural network is capable to predict the coefficient of performance and the thermal energy consumed by the single-effect absorption chiller while the values of mean squared error are 3.183×10^(-7) and 7.466×10^(-8) respectively which verify the accuracy of the method proposed here in absorption chiller performance prediction. Manuscript profile

Open Access Article

136 - Remaining useful life estimation of mechanical systems by mixed method of mathematical method and evolutionary computational framework
fatemeh mehregan

An accurate prediction of the remaining useful life of the equipment is necessary for use, repairs and maintenance. Useful life prediction has been widely used, while the data obtained from it is not functional in different conditions. Many data-driven algorithms have b More

An accurate prediction of the remaining useful life of the equipment is necessary for use, repairs and maintenance. Useful life prediction has been widely used, while the data obtained from it is not functional in different conditions. Many data-driven algorithms have been proposed and good results have been obtained in the field of predictive troubleshooting. Therefore, in this article, the relevant parameters are optimized using the meta-heuristic algorithm, so that the moving time window is used along with the mathematical model. Setting parameters related to data in the optimization framework allows the use of simple models such as neural networks with a small number of hidden layers and a small number of neurons in each layer, which can be used in environments with limited resources such as embedded systems. To evaluate the effectiveness of the proposed method, the root mean square error scoring index and useful life health score have been used. For this purpose, a random data set has been considered and the results show the acceptability of the method. Manuscript profile

Open Access Article

137 - Assessment of Adaptive neural fuzzy inference systems and support vector regression in runoff estimation(A case study:Dez Basin)
Ghazaleh Ahmadian Ahmadabad Mahmoud Zakeri Niri Saber Moazami Goudarzi

Estimation of discharge flow in basin due to impact on water resource management can have an important economic role.In this research several computationals intelligence techniques suchas:ANN,SVR and ANFIS have been used to prediction the runoff dez basin.correlation be More

Estimation of discharge flow in basin due to impact on water resource management can have an important economic role.In this research several computationals intelligence techniques suchas:ANN,SVR and ANFIS have been used to prediction the runoff dez basin.correlation between stations was investigated and stations of kamandan,zoorabad and daretakht were eliminated due to small correlation with around stations.then due to lack of human intervention with using xlstat software were evaluated  trend of stations and were selected stations without trend.Inorder to evaluate the performance of  models were used correlation,RMSE and NSE.Results of this research showed that ANFISwith clustering approach gives better estimation than grid partitioning approach.ANN, ANFIS and SVR have agood ability to simulate the flow of dez basin. Manuscript profile

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138 - Proposing a New Method to Optimize the Routing in the Distribution of Vendors' Goods Using the Internet of Things (IoT)
Mohammad Sadegh Jahan

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139 - Comparison and Prediction of the Experimental Data for Thermal Efficiency of a Double-Pipe Heat Exchanger with Fe3O4 Nanofluid Using Artificial Neural Networks
محمد اختری مجتبی میرزایی داریوش خسروی مهد

In this study, the thermal efficiency of a double-pipe heat exchanger with Fe3O4-water nanofluid in Reynolds numbers between 2000-21000 and volume fractions between (0.1-0.4% v / v) using artificial neural networks and correlation with experimental data has been evaluat More

In this study, the thermal efficiency of a double-pipe heat exchanger with Fe3O4-water nanofluid in Reynolds numbers between 2000-21000 and volume fractions between (0.1-0.4% v / v) using artificial neural networks and correlation with experimental data has been evaluated and predicted. Iron oxide nanoparticles were about 20 nm in size. SEM photography of nanoparticles is provided to show the stability and homogeneity of suspension. Different Reynolds numbers and volume fractions of iron oxide nanofluid are used as the training data for ANN. A two-layer feed-forward neural network with back-propagation Levenberg-Marquardt learning algorithm (BP-LM) was used for heat transfer pre-parameters. Moreover, 70% of data were used in training set and 15% of data were used in evaluation set and remaining data were used as test data to prevent preprocess of network and to study the final efficacy of the network. In addition, based on the experimental data and the use of artificial neural network, data predicted by the neural network are in good agreement with experimental data measured by the double-pipe heat exchanger. The overall verification by the mean squared error (MSE) and correlation coefficient (R2) for the thermal efficiency of a double-pipe heat exchanger is 0.0001 and 0.996, respectively, indicating that prediction is successful. Manuscript profile

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140 - Study on Young's Modulus, Fracture toughness & Energy of Composites Reinforced by ZnO Nanoparticles through Regression Tree, ANN & RSM
abdorreza alavi gharahbagh Ali dadrasi sasan folladpanjeh

A study on fracture toughness (KIC), fracture energy (GIC) and Young's modulus of styrene acrylonitrile composites by two volume content of 24% and 34% acrylonitrile has been conducted. ZnO nanoparticles were added to composites up to 1 vt%. Volume percent parameter has More

A study on fracture toughness (KIC), fracture energy (GIC) and Young's modulus of styrene acrylonitrile composites by two volume content of 24% and 34% acrylonitrile has been conducted. ZnO nanoparticles were added to composites up to 1 vt%. Volume percent parameter has been described as a sensational parameter. The experimental results indicate that adding ZnO nanoparticles increase the mechanical properties and in some cases, it decreases them. Also the experimental results and the results of modeling show that the second order response surface method makes the best prediction. Additionally, the best value for Fracture toughness is 2.283 MPa.m1/2 when the volume percent of styrene acrylonitrile is 34% and volume percent of ZnO is 0.1%. Also, the best value for fracture energy is equal to 1101 J/m2, when the volume percent of styrene acrylonitrile is 34% and the volume percent of ZnO is 0.33%. And finally, this method shows that the best value for Young's modulus is 4.281 GPa when the volume percent of styrene acrylonitrile is 31% And volume percent of the particle is 0.5%. Manuscript profile

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141 - Experimental Condition Monitoring of Unbalanced Rotary Shaft Based on ANFIS by Using Piezoelectric Sensor
Mojtaba Hasanlu

Todays, there are using hybrid methods in order to reach high level degree of accuracy and reliability for engineering systems. According to more reality modelling of system, it was mixed three strategies such as fuzzy, artificial neural networks and adaptive method. Th More

Todays, there are using hybrid methods in order to reach high level degree of accuracy and reliability for engineering systems. According to more reality modelling of system, it was mixed three strategies such as fuzzy, artificial neural networks and adaptive method. This mixed methods is presenting and analyzing each of engineering problem. Adaptive-Neural-Fuzzy (ANF) can be showed in which a robustness and reliable model by designer who assess that in order to make decision as well. In this paper, main aim is experimental condition and health monitoring of rotary system is including shaft, bearings, electromotor which are main components of system and using piezoelectric as sensor by ANF method. Firstly, by using LabVIEW software, experimental data of flexural vibration was recorded with piezoelectric sensor where were fixed on top of bearings, and secondly we used MATLAB software for analyzing experimentation for presentation of ANF model in order to curve fitting of data. Manuscript profile

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142 - Fault Detection of Cylinder- head automotive Using Mechanical Waves and Intelligent Techniques
Mojtaba Doustmohammadi Morteza Mohammadzaheri Ehsan Jamshidi

Safety of machinery parts, it is important for users and defects as one of the most important parts of threats to the health of the pieces have always been considered. In this paper, a new method based on the use of artificial neural networkDetection Pridecylinder head More

Safety of machinery parts, it is important for users and defects as one of the most important parts of threats to the health of the pieces have always been considered. In this paper, a new method based on the use of artificial neural networkDetection Pridecylinder head into mechanical waves is presented. Then, for verification and validation of the finite element model , modal test piece , and after confirming the fault simulation is performed on a finite element model. Force simulation model (FEM) acceleration signal - the healthy and faulty models calculated for each fault. And with the signing of mechanical defects ( acceleration difference between healthy and faulty models ) and to train a multilayer perceptron neural network (MLP) , the difference between the acceleration signal to the associated fault location. Detectionautomotive cylinder head (estimated fault location) has been. The results of the neural network capabilities designed to estimate the fault location on a good show.  Manuscript profile

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143 - Determining the amount of total organic carbon using satellite imagery and artificial neural network model (Case study area: Mountain Mish, Gachsaran city)
kamran mojard

The amount of Total Organic Carbon (TOC) is one of the most important parameter in evaluating hydrocarbon source rocks. Therefore, it is necessary to estimate of source rock by a method. There are several methods for estimating the of source rocks. One of the earliest m More

The amount of Total Organic Carbon (TOC) is one of the most important parameter in evaluating hydrocarbon source rocks. Therefore, it is necessary to estimate of source rock by a method. There are several methods for estimating the of source rocks. One of the earliest methods is the traditional method, which is very expensive and time consuming, so researchers are looking for more efficient methods. Given the high potential of remote sensing and spectral VIR spectra, the VNIR makes it possible to estimate the characteristics of the origin rock at a lower cost basis. Different quantitative and qualitative methods are used to establish a relationship between the concentration of rock elements and the spectrum obtained from remote sensing data. In this study, we tried to estimate the total organic carbon content of the total origin rock using the OLI Landsat 8 image sensor and using the MLP artificial neural network model. For this purpose, the band of 5 spectral ranges (0.845-0.8585) with Pearson correlation coefficient of 0.62 was chosen for the neural network. An artificial neural network with neurons in the secret layer with R2 = 0.79 and RMSE = 0.0008 were selected to generate a total organic carbon map. Manuscript profile

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144 - Studying of Central Alborz's crustal velocity by using ANN method
Tohid malekzadeh Dilmaghani

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145 - Time series modeling of Alborzs crustal velocity by using artificial neural networks
Tohid malekzadeh Dilmaghani

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146 - Modeling of time series of Earth crust velocity field in Azarbaijan using multilayer neural network with PSO training algorithm
Tohid malekzadeh Dilmaghani

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147 - Face Detection with methods based on color by using Artificial Neural Network
Reza Abbasgolizadeh Habib Izadkhah Ramin Meshkabadi

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148 - Geoid Determination Based on Log Sigmoid Function of Artificial Neural Networks: (A case Study: Iran)
Omid Memarian Sorkhabi

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149 - Using Neural Network to Control STATCOM
Mozhgan Balavar

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150 - Neural Networks in Electric Load Forecasting:A Comprehensive Survey
Vahid Mansouri Mohammad Esmaeil akbari

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151 - Classification of Brain Tumor Grades by MRI Images using Artificial Neural Network
Melika Aboutalebi Rezvan Abbasi

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152 - Optimization ELM neural network in prediction problem: case study forecasting demand steel in Iran
Jalal Rezaeenour Mansoureh Yari eili Esmaiel roozbahani Mohammad hossein Roozbahani

Prediction of supply and demand, is a crucial issue for supply chain partners. With the accurate forecasted supply and patterns that indicate the sizes and directions of future production flow, the government and suppliers can have a well-organized strategy and provide More

Prediction of supply and demand, is a crucial issue for supply chain partners. With the accurate forecasted supply and patterns that indicate the sizes and directions of future production flow, the government and suppliers can have a well-organized strategy and provide a better infrastructure for improving industrial sector.With the aim of developing accurate forecasting tool in steel industry, this study present a new optimized neural network, by combination of Extreme Learning Machine and genetic algorithm. We employed our model on a dataset for steel supply - demand in Iran from jul-2009 to jan2013 to estimating the performance. The results show that prediction accuracy and performance relatively better than other classical approaches, according to RMSE and MAPE evaluations. In our model. Based on statistical tests, our new model is better than other model in accuracy, so can be used in as a suitable forecasting tool in steel supply forecasting problems. Manuscript profile

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153 - Evaluate the performance of the organization using two integrated approaches the DEA-BSC and ANN-DEA
Reza Ehtesham Rasi Eisa Naji

Considering the importance of efficiency in advancing communities and the place that today's organizations have embraced, the use of performance evaluation has become an inevitable necessity. Therefore, we are trying to investigate the effectiveness of organizations by More

Considering the importance of efficiency in advancing communities and the place that today's organizations have embraced, the use of performance evaluation has become an inevitable necessity. Therefore, we are trying to investigate the effectiveness of organizations by combining the two DEA-BSC systems and neural networks in this study. In this regard, first, the performance indicators were determined by using the Balanced Scorecard technique in four dimensions of customer, internal processes, learning and growth and financial, then using the data envelopment analysis method and non-radial SBM model and GAMS software, the organization's efficiency we calculated. In the next step, with the help of the indicators derived from the DEA-BSC method and using the combination of two systems of data envelopment analysis and neural network and using the MATLAB software, the efficiency was re-calculated. In the last step, we compared the two methods of DEA-BSC and ANN-DEA. The results of the comparison of the two methods indicated the compatibility of the two models in discussing the efficiency and superiority of the ANN-DEA method in terms of the short response time and the determination of the efficiency and the possibility of using Its trained algorithm to measure the performance of organizational units in the future. Manuscript profile

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154 - Developing a New Decision Support System to Manage Human Reliability based on HEART Method
Rasoul Jamshidi

10.22094/joie.2019.553983.1521

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155 - Using neural network to estimate weibull parameters
Babak Abbasi behrouz Afshar nadjafi

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156 - Estimation of Products Final Price Using Bayesian Analysis Generalized Poisson Model and Artificial Neural Networks
Marjan Niyati Amir Masud Eftekhari Moghadam

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157 - Forecasting the Cost of Water Using a Neural Network Method in the Municipality of Isfahan
Amir Mohammadzadeh Nasrin Mahdipour Arash Mohammadzadeh

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158 - Time Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry
Behnam Vahdani Seyed Meysam Mousavi Morteza Mousakhani Hassan Hashemi

10.22094/joie.2016.231

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159 - Seismic Fragility Analysis of RC Continuous Girder Bridges Using Artificial Neural Network
Alireza Yazdankhah Araliya Mosleh Fatemeh Pouran Manjily Mehran Seyedrazzaghi

10.22094/jseg.2022.1943961.1116

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160 - Modeling of Accumulated Energy Ratio (AER) for Estimating LiqueFaction Potential Using Artificial Neural Network (ANN) and Gene Expression Programming (GEP) (using data from Tabriz)
Armin Sahebkaram Alamdari Rouzbeh Dabiri Rasoul Jani Fariba Behrouz Sarand

10.22094/jseg.2021.688673

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161 - Comparison Study on Neural Networks in Damage Detection of Steel Truss Bridge
Hassan Aghabarati Mohsen Tabrizizadeh

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162 - Modeling Effective Teaching of Faculty Members Based on Artificial Neural Network Analysis
Maryam Sameri

The aim of this study was to Modeling Effective Teaching of Faculty Members Based on Artificial Neural Network Analysis. So this study has practical purpose and in terms of research methods was combined and Hybrid-type exploration projects. In this study, all students o More

The aim of this study was to Modeling Effective Teaching of Faculty Members Based on Artificial Neural Network Analysis. So this study has practical purpose and in terms of research methods was combined and Hybrid-type exploration projects. In this study, all students of Islamic Azad university of Urmia were considered as the population. on qualitative section; we interviewed 24 students from Islamic Azad University of Urmia. Participants were selected using typical sampling until we research theoretical saturation while conducting deep and semi-structured interview. Using a random stratified sample size formula Cochran,361 students were selected. In this study, to collect data and information semi-structured interview and questionnaire have been used. Formal and content validity of the questionnaire was approved. Coronbach's alpha coefficient was 0/81. To analyze the data, descriptive and inferential statistical techniques were used. Based on the findings of the interview, it was determined that effective factors on effective teaching in 5 categories of personality and personal traits, scientific characteristics and literacy, ability in human relationships and communication, teaching and classroom management skills and evaluation skills. The quantitative findings also showed that from the perspective of students, the best structure for effective teaching, had an input layer with 5 input variables and a hidden layer with 3 neurons and an output layer with an output variable. Studying the importance of independent variables in predicting effective teaching showed that from the viewpoint of students, the most important is the personality and personality traits, and the least important is the academic characteristics and literacy of the teacher. Manuscript profile

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163 - Application of Artificial Neural Networks to Recognize the Relationship between Social Capital and Customer Satisfaction
Houshang Taghizadeh Mohammad Sadeg Zeinali Kermani

The purpose of the present research was to develop a neural networks model to identify the relationship between social capital and customer satisfaction. For this purpose, the effective factors were investigated by reviewing the literature and the related concepts in so More

The purpose of the present research was to develop a neural networks model to identify the relationship between social capital and customer satisfaction. For this purpose, the effective factors were investigated by reviewing the literature and the related concepts in social capital and customer satisfaction. The present research is based on information obtained from two different data sets. The first data set was built up from the responses of 100 managers of the companies' active in the field of automobile parts manufacturing industries in East Azarbaijan. This sample was chosen by Bartlett charts. For gathering information about SC and its dimensions, we used a questionnaire. The second data set resulted from the responses of customers to a questionnaire survey formulated according to five-point Likert scale items ranging from strongly disagree to strongly agree. The method of research is correlation. Spearman correlation coefficient and Artificial Neural Networks were used for data analysis. Multi-layer perceptron (MLP) neural networks with hyperbolic tangent function, trained by feed forward algorithm, were utilized to build the identification model. Testing the questions showed that there is a significant relation between social capital and customer satisfaction.   Manuscript profile

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164 - Improving the Efficiency of Forecasting Productivity, Using a Taguchi Experiment Design Approach (Case Study: Food Industries in Iran)
Seyed mahmon Zanjirchi Mehdi Hatamimanesh Hamedreza Kadkhodazadeh Seyedali Mohammadbanifatmi

Productivity forecasting is a key factor in strategy planning in an organization. Artificial neural networks method is one of the productivity estimating methods whose users must have enough experience and skill because of its adjustable parameters. Trial and Error is m More

Productivity forecasting is a key factor in strategy planning in an organization. Artificial neural networks method is one of the productivity estimating methods whose users must have enough experience and skill because of its adjustable parameters. Trial and Error is mostly used to find the proper levels of these parameters. This article presents a seven step pattern for selecting proper adjustable parameters for neural network, using Taguchi experiment design method to improve the efficiency of productivity forecasting. As a result, the optimum parameters levels that lead to the most desirable forecasting in neural network are as follows: the number of hidden layers: 2 layers, the number of neurons in each hidden layer: 7 neurons, learning rate: 0.9 and the number of neural network inputs:  productivity indicators with more than 0.85 degree of correlation. Among the above mentioned factors, the number of hidden layers with 71.18% of contribution rate in experiment results is the most important factor in neural network design to forecast the productivity of Iranian food industry. Finally, the overall results of the study showed that using this pattern provides the possibility of choosing competitive strategies besides decreasing forecasting time and cost. Moreover, this pattern helps decision makers with the extent of the consideration that must be put into each adjustable parameter by determining the contribution rate of each parameter in the experiment results. Manuscript profile

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165 - The Prediction of the Success of New Product Development Using the Combination of Factor Analysis and Artificial Neural Network
Gholamraza Soltani Fesaghandis Alireza Pooya Mostafa Kazemi Zahra Naji Azimi

The success of new products is as the greatest element for the company's success and even their survival. However, numerous studies show that the failure rate of new product development as a fundamental problem in many companies has been raised. Many companies are tryin More

The success of new products is as the greatest element for the company's success and even their survival. However, numerous studies show that the failure rate of new product development as a fundamental problem in many companies has been raised. Many companies are trying to predict the success of the development of new product before its development. The aim of this study was to predict the success of new product development using a combination of factor analysis and artificial neural network in the food and beverage industry. This research based on purpose is practical and based on method is descriptive. The population of this research is the food and beverage manufacturers in the province of East Azerbaijan in 1394. In order to collect data two questionnaires have been distributed among the population after the validity and reliability assessments. In order to analyze the data in this study, factor analysis and artificial neural network is used. Analysis of the data revealed the presence of six main structures as factors in the success of new product development: conceptualization of new product, market orientation, design orientation, technology orientation, use of sources, and management of new product development as the effective elements in the development of the success of new product. Furthermore, the prediction of the success of new product’s development using neural networks shows that the designed network was able to predict the success of the development of new product correctly. Manuscript profile

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166 - Entrepreneurship policy and innovative indicators of industrial companies: Evaluation by MCDM and ANN Methods
mehdi karimi farshid namamian farhad vafaei Alireza Moradi

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167 - مدل سرمایه گذاری مناسب در سبد سهام با رویکرد تحلیل پوششی داده‌ها- شبکه عصبی
مصطفی کاظمی محمد اسفندیار حدیث نجاریان

در سال‌های اخیر با ورود سرمایه گذاران خصوصی به بازار سرمایه، رقابت موجود بین شرکت‌های سرمایه گذاری افزایش چشمگیری داشته است. شرکت‌های بزرگ و قدرتمند، اهداف پیش بینی شده خود را با جدیت پیگیری می‌کنند تا توان رقابتی خود را بالا ببرند. برای تجزیه و تحلیل کارایی شرکت‌های سر More

در سال‌های اخیر با ورود سرمایه گذاران خصوصی به بازار سرمایه، رقابت موجود بین شرکت‌های سرمایه گذاری افزایش چشمگیری داشته است. شرکت‌های بزرگ و قدرتمند، اهداف پیش بینی شده خود را با جدیت پیگیری می‌کنند تا توان رقابتی خود را بالا ببرند. برای تجزیه و تحلیل کارایی شرکت‌های سرمایه گزاری از روش‌های پارامتریک و نا‌پارامتریک استفاده می‌شود. با توجه به ضعف قدرت تفکیک‌پذیری و حساسیت مرز کارایی به داده‌های پرت در روش تحلیل پوششی داده‌ها، در این پژوهش کارایی 31 شرکت‌های سرمایه‌گذاری در بورس اوراق بهادار تهران با استفاده از مدل‌های تحلیل پوششی داده‌ها و مدل ترکیبی شبکه‌های عصبی و تحلیل پوششی داده‌ها به عنوان دو روش نا‌پارامتریک، مورد ارزیابی قرار می‌گیرند. با استفاده از مدل‌های BCC و CCR تحلیل پوششی داده‌ها کارایی شرکت‌های سرمایه گذاری در بازه زمانی 1388ـ1390 محاسبه و نتایج تجزیه و تحلیل گردید. با توجه به ضعف تحلیل پوششی داده‌ها در رتبه‌بندی واحد‌های کارا، با استفاده از روش اندرسون و پیترسون واحد‌های کارا رتبه‌بندی شده است.در روش ترکیبی شبکه‌های عصبی و تحلیل پوششی داده‌ها از شبکه پرسپترون چند لایه با الگوریتم آموزش لونبرگ ـ مارکوآرت (LM) استفاده شده است. مقایسه نتایج مدل ترکیبی با روش تحلیل پوششی داده‌ها نشان‌دهنده قدرت بالای شبکه‌های عصبی برای اندازه‌گیری کارایی میباشد.  Manuscript profile

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168 - Prediction of forging force and barreling behavior in isothermal hot forging of AlCuMgPb aluminum alloy using artificial neural network
hamidreza Rezaei Ashtiani p shahsavari

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169 - Estimation of the mean grain size of mechanically induced Hydroxyapatite based bioceramics via artiﬁcial neural network
Mohammad Fahami Majid Abdellahi

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170 - Damage detection and structural health monitoring of ST-37 plate using smart materials and signal processing by artificial neural networks
Hamid Reza Mirdamadi Farshad Ghasemi Javad Jafari

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171 - Prediction of Corrosion Rate for Carbon Steel in Soil Environment by Artificial Neural Network and Genetic Algorithm
Amir Akhtari-Goshayeshi Moslem Ghobadi Ehsan Saebnoori Alireza Zarezadeh Mohammad Rostami Mohammad Nematollahi

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172 - Evaluation of environmental sustainability in urban areas with the approach of fine dust problem using multi-criteria decision making technique of linear allocation and artificial neural network (case study: Ahvaz)
yahya abdolkarim neysi Mohammad Ebrahim Afifi marzeyeeh mogholi

Environmental sustainability assessment is one of the most important tools in the process of sustainable development planning and therefore attention to it in policy-making and planning is inevitable. The purpose of this paper is to evaluate the environmental sustainabi More

Environmental sustainability assessment is one of the most important tools in the process of sustainable development planning and therefore attention to it in policy-making and planning is inevitable. The purpose of this paper is to evaluate the environmental sustainability in urban areas with the approach of the dust problem using multi-criteria decision-making technique of linear allocation and artificial neural network in Ahvaz metropolis. In this research, first, by studying the theoretical foundations of the research, the criteria that are effective for environmental sustainability were identified. Then, the documentary and library method and referring to different departments and organizations were used to collect information in the theoretical part. However, the main information of the research was collected using field studies (completing questionnaires, observations and field studies). The research process was such that a questionnaire was designed to implement the methodology through the criteria obtained from the study of the problem literature and distributed among the people in 8 regions by stratified random sampling method. The sample size of the total areas was determined based on the unlimited Cochran's formula in the form of population classes of the areas. Keywords: Environmental stability, particulate matter, linear allocation, artificial neural network, Ahvaz Manuscript profile

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173 - land use change modeling using LCM module (Case study: NEKA region)
seyede massoomeh fathollahi roudbary Kamran Nasirahmadi mehrdad khanmohamadi

Land use/cover changes, especially human urbanization Cause destruction of natural habitats and threaten biodiversity. Regularly, Land use/cover models are one the most important methods for evaluating this trend. The objective of this study is the investigation of land More

Land use/cover changes, especially human urbanization Cause destruction of natural habitats and threaten biodiversity. Regularly, Land use/cover models are one the most important methods for evaluating this trend. The objective of this study is the investigation of land use/cover change and modeling in the Neka city using Land Change Modeler (LCM). Landsat TM (١٩٨8), ETM+ (٢٠٠2), and OLI (2016) data was used for land use/cover classification and change. In addition, transition potential modeling was conducted using an artificial neural network. In this method, 5 sub-models and 9 variables were used. Then calibration period (1988-2002) was used by Markov chain and hard prediction for extrapolating the 2016 land use/cover changes. Finally, land use/cover maps for 2002 and 2016 were used for land use/cover map extending prediction to the year 2030. The accuracy assessment of model was conducted by Error Matrix. The results of this study showed the annual rate of decline in the forest was 2297 Hectare during the period 1988-2016. The biggest changes were in the conversion of forest lands to agriculture. Modeling results using artificial neural network also showed acceptable accuracy (69%). The results of modeling for 2030 also showed that the area of the forest is decreasing, Agricultural lands and urban areas are increasing. Manuscript profile

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174 - Assessing the development level of rural areas of central district of Falavarjan County: The application of Artificial Neural Network
Hamid Rastegari Mehdi Nooripoor

Planning of development programs either at macro or at micro levels, needs to obtain a proper understanding about the differences between rural areas in terms of their infrastructure, social, economic and environmental aspects; so that this understanding can contributed More

Planning of development programs either at macro or at micro levels, needs to obtain a proper understanding about the differences between rural areas in terms of their infrastructure, social, economic and environmental aspects; so that this understanding can contributed to policymakers and decision-makers the ability to recognize abilities, differences and deprivations of rural areas in order to organize a balanced development in the supposed areas. Therefore, the aim of this research was to assess the level of development of rural areas of central district of Falavarjan County. A survey research method was used in this study. Research population included villages with 20 and more households (26 villages) that about 376 household of these villages were selected as research sample based on Cochran Sampling. A researcher constructed questionnaire and secondary data taken from the village ID in 2010 was used to collect data that its face validity procedure was verified by faculty members of the Rural Development Management Department of Yasouj University and its reliability was also verified calculating Cronbach's Alpha reliability (0.65 ≤&prop;≤ 0.95). In order to assess and prioritization rural development, 24 indices in four dimensions were used: social-cultural, economic, environmental and physical-infrastructure. In order to analyze the data, the raw data were normalized and then using Artificial-Neural Network (ANN) of multi-layer feed-forward back-propagation, weight of each rural development index was obtained, respectively. Data processing was done using MATLABR2015a and SPSS22‌ software. The results showed that Jojil, Jowlorestan and Zefreh ranked first to third respectively and Mehrenjan Atrak and Mehrenjan ranked last in terms of rural development. The overall results of this study showed that the rural development is in a relatively favorable situation in the studied villages. Manuscript profile

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175 - Modeling the degradation of Sunset Yellow FCF azo dye by Fe2O3/Bentonite catalyst using artificial neural networks
Mohammad Ehsan Mosayebian Reza Moradi Kazem Mahanpoor

10.22034/jna.2021.685871

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176 - Intelligent and Optimal Control of Air Conditioning ‎Systems by Achieving Comfort and Minimize Energy
Yazdan Daneshvar Majid Sabzehparvar Seyed Amir Hossein Hashemi

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177 - Stock Price Forecasting through Using ANN and ARIMA Techniques: A Case Study of Pars Petroleum Company
Seyed Nezame aldin Makian Fateme sadat Mousavi

Stock exchange market is one of the important ways to investment. In this market, the investors are looking for the best securities to maximize the profit. Therefore, forecasting the stock price of next day has a vital role in purchasing such securities. To do this, app More

Stock exchange market is one of the important ways to investment. In this market, the investors are looking for the best securities to maximize the profit. Therefore, forecasting the stock price of next day has a vital role in purchasing such securities. To do this, application of Neural Networks financial forecasting has become very popular over the last few years. In this paper, for predicting the next day's close stock price of Pars Petroleum Company, Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) will be developed, used and compared. The data are daily collected and analyzed during 2009-2011. The findings indicate that forecasting the price by Neural Network is superior to ARIMA due to its less error coefficients and high explanatory ability. Manuscript profile

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178 - Improving the Performance of Forecasting Models with Classical Statistical and Intelligent Models in Industrial Productions
Maryam Bahrami Mehdi Khashei Atefeh Amindoust

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179 - A New Hybrid Prediction Reduces the Bullwhip Effect of Demand in a Three-level Supply Chain
Afshin Yousefi Ayub Rahimzadeh Alireza Moradi

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180 - Using Artificial Neural Networks to Predict Rolling Force and Real Exit Thickness of Steel Strips
Mohammad Heydari Vini1

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181 - Optimum Designing of Forging Preform Die for the H-shaped Parts Using Backward Deformation Method and Neural Networks Algorithm
Afshin Naeimi Mohsen Loh Mousavi Ali Eftekhari

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182 - Designing a model for predicting the financial bankruptcy of companies listed in the Tehran Stock Exchange using artificial neural networks and comparing it with the logit regression model
farhad sanchooli

Considering the concerns that investors have about the return of principal and capital gains and the consequences and costs that bankruptcy can cause for companies and the country's economy and other individuals and institutions, the design of a reliable model In order More

Considering the concerns that investors have about the return of principal and capital gains and the consequences and costs that bankruptcy can cause for companies and the country's economy and other individuals and institutions, the design of a reliable model In order to predict the probability of bankruptcy of companies, it seems necessary to guide decision makers such as investment companies, banks and the government.In this research, the artificial neural network method and the logit regression method were used to predict the bankruptcy of a number of companies admitted to the Tehran Stock Exchange during the years 2015 to 2019, and the results were compared with the logit regression method. The overall prediction accuracy of the artificial neural network method for each of the years t, t-1, t-2 and t-3 is equal to 96.55%, 96.55%, 92.24% and 24/2 respectively. 92% and for the logit regression method for the same years, it is 94%, 94.82%, 90.51% and 87.06% respectively, which showed that the artificial neural network method has a higher accuracy than the logit regression method. is. Therefore, it can be concluded that the artificial neural network method provides a more appropriate tool for predicting the bankruptcy of companies. Manuscript profile

Open Access Article

183 - Design of a Two-Stage Operational Amplifier Using Artificial Neural Network
Alireza Pourkhalili Sayed Mohammad Ali Zanjani

Design of complex analog integrated circuits requires the appropriate choice of various design parameters such as MOSFET’s aspect ratio, compensation capacitance and load capacitance in a way that improves user’s desired parameters like gain, bandwidth, powe More

Design of complex analog integrated circuits requires the appropriate choice of various design parameters such as MOSFET’s aspect ratio, compensation capacitance and load capacitance in a way that improves user’s desired parameters like gain, bandwidth, power dissipation and phase margin. Considering previous works, in this paper, a two-stage miller compensated operational amplifier with PMOS input pair is designed using artificial neural network. The inputs of the neural network are design parameters including DC gain, bandwidth, power dissipation and phase margin and in its output, the sizing of transistors and the amounts of reference current supply, compensation capacitance and load capacitance are acquired. In this design method, a sampling method based on parallel HSPICE simulations is employed for data acquisition from the 15-dimensional design space which results in simplicity and automation of the dataset collecting procedure and reduces the total sampling time and then this data is used for training the neural network model. In the next stage, a range sampling method is applied for making new designs from the trained model which has facilitated the design procedure and made the user-desired tradeoffs between different performance parameters of the operational amplifier possible. Moreover, if the amplifier performance figure of merit (FOM) is defined as the result of the multiplication of unity gain bandwidth and load capacitance divided by power consumption, the comparison between obtained designs of this paper’s proposed method and the results of some other methods applied for designing operational amplifiers with relatively similar topologies in previous works, indicates that this parameter has increased by 154% at the minimum. Manuscript profile

Open Access Article

184 - Reduction of Sub-synchronous Resonances with D-FACTS Devices using intelligent Control ,
Zahra Amini Abbas Kargar

When a turbine–generator set connect to a long transmission line, may results side effects such as Sub-Synchronous Resonances (SSR). The capabilities of the Distributed Static Series Compensator (DSSC) as a member of the family of D-FACTS can be used to reduce the More

When a turbine–generator set connect to a long transmission line, may results side effects such as Sub-Synchronous Resonances (SSR). The capabilities of the Distributed Static Series Compensator (DSSC) as a member of the family of D-FACTS can be used to reduce these SSR. To achieve this desired goal, the fuzzy controller, Particle Swarm Optimization (PSO) and artificial neural network is used to control of the DSSC. Particle swarm optimization is designed Based on the Conventional Damping Controller (CDC) and fuzzy logic is designed based on damping controller (FLBDC) and damping control based on artificial neural network trained using the fast pace of changes has been designed. Stability of the system is analysed by simulations in the time domain with performance index (PI). All simulations are done using Matlab / Simulink software. Case studies show that proposed algorithms can reduce SSR in the system.All simulations are done using Matlab / Simulink software. Case studies show that proposed algorithms can reduce SSR in the system. Manuscript profile

Open Access Article

185 - Partial Discharge Analysis in Generator Stator Winding Using Artificial Neural Network
Seyed Meisam Ezzati Faramarz Faghihi Ali Moarefian poor

This paper analyses the Partial Discharge (PD) on the stator terminals of synchronous generator. This is necessary to collect experimental data for the analysis. First, exploiting the measurement devices, special signals that describe the partial discharges are repeated More

This paper analyses the Partial Discharge (PD) on the stator terminals of synchronous generator. This is necessary to collect experimental data for the analysis. First, exploiting the measurement devices, special signals that describe the partial discharges are repeatedly collected. Then, based on the current standards, the collected empirical data are subjected to interpretation. To ease the interpretation process, an Artificial Neural Network is trained and validated. We have used a double layers forward perceptron neural network which is trained by Levenberg–Marquardt algorithm that utilizes least square method as the performance index. As the case study, three gas turbine-generators located in Shahre-Rey power plant (Rey Power Generation Management Company) have been subjected to repeatedly data collection. The mentioned generators are manufactured by Mitsubishi with 85 MW of nominal power. Generally, partial discharge analysis has the following practical implication about the probable defects: lamination of the internal terminal, mobility within the main insulation and discharge into the groove in stator of synchronous generator. Manuscript profile

Open Access Article

186 - Adaptive Load Shedding Analysis in Power Systems by Using Artificial Neural Network
Rahmatolah Hooshmand Majid moazzami

The stability of frequency and voltage is one of the basic principles in the power systems. One of the latest control measures for power system frequency control and stability is load shedding. A fast and optimal adaptive load shedding method using Artificial Neural Net More

The stability of frequency and voltage is one of the basic principles in the power systems. One of the latest control measures for power system frequency control and stability is load shedding. A fast and optimal adaptive load shedding method using Artificial Neural Networks (ANN) is presented in this paper. In this paper, the total power generation and the total existing load in power system were selected as the ANN inputs. This method has been tested on theNew England test system. The simulation results show the ability of this frequency control algorithm for optimal solving problem related to conventional method. Manuscript profile

Open Access Article

187 - Application of hierarchy-fuzzy analysis models and artificial neural networks in locating urban waste burial (Case Study: Lali city)
Fatemeh Amiri Ladan Khedri Gharibvand Asghar Khedrifar

10.30495/jonass.2023.1974777.1061

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Open Access Article

188 - Predicting Hook -Shaped and Concrete Steel Fibers Adhesion Parameters Using Artificial Neural Networks
amir ebrahim akbari baghal Amir ebrahim akbari bagal

Given the importance of using steel fibers in reinforcement of concrete, in the present study using artificial neural networks to predict the behavior of hook -shaped steel fibers from concrete. Due to the constraint of comprehensive laboratory data, data obtained from More

Given the importance of using steel fibers in reinforcement of concrete, in the present study using artificial neural networks to predict the behavior of hook -shaped steel fibers from concrete. Due to the constraint of comprehensive laboratory data, data obtained from limited element analysis has been used as neural network input. The fibers are used to simulate the fiber and the Abacus software. In the limited element model, the interactions between fibers and concrete are simulated using the concept of the transitional area of the common surface whose parameters are obtained using the reversed limited element method and the use of the out -of -the -way experimental test results on a fiber sample. After assessment of the numerical model results with the empirical results, the results were extracted for effective parameters of the fibers and based on them using neural networks. Forecasting of the outburst has been carried out by the Multi-Layer Artificial Neural Networks and the Rear Publishing Algorithm, with Marcoradet-Clberg optimization techniques. The results show that the neural network model presented in this study, due to the ability to use more variables in modeling and more accurate results, is an effective way to predict the fiber's extrusion force. Manuscript profile

Open Access Article

189 - مدل ترکیبی شبکه‌ی‌ عصبی و تحلیل پوششی داده ها برای ارزیابی کارایی عملکرد واحدها
صادق حیدری احسان زنبوری حمید پروین

کایی و ارزیابی یکی از اصلی‌ترین و مهم‌ترین نیاز های سازمان‌ها، شرکت‌ها و موسسات می‌باشد و این سازمان ها چون با حجم زیادی از داده سر و کار دارند. تحلیل پوششی داده‌ها روشی مناسب برای کارایی و ارزیابی عملکرد سازمان‌ها می‌باشد. این تحقیق برای ارزیابی عملکرد و کارایی واحدهای More

کایی و ارزیابی یکی از اصلی‌ترین و مهم‌ترین نیاز های سازمان‌ها، شرکت‌ها و موسسات می‌باشد و این سازمان ها چون با حجم زیادی از داده سر و کار دارند. تحلیل پوششی داده‌ها روشی مناسب برای کارایی و ارزیابی عملکرد سازمان‌ها می‌باشد. این تحقیق برای ارزیابی عملکرد و کارایی واحدهای تصمیم گیرنده انجام گرفته است، ابتدا رویکردی با مدل BCC خروجی محور رتبه‌بندی واحدهای کارا در قالب مدل‌های تحلیل پوششی داده‌ها مورد بررسی قرار گرفت و ضعف مدل، از نظر محاسبه و تفکیک کارایی مشخص گردید سپس برای از بین رفتن این مشکلات از روش ترکیبی تحلیل پوششی داده‌ها مدل BCC خروجی محور و شبکه عصبی مصنوعی به منظور ارزیابی کارایی این واحدها استفاده گردید تا بتوان این مشکل را بر طرف نمود. در پایان نیز مقایسه‌ای بین نتایج حاصل از دو مدل انجام گرفته است. با توجه به مقدار کارایی بدست آمده با روش bcc خروجی محور، مشاهده می گردد تعدادی از واحدها مقدار کارایی آنها برابر با یک است که این باعث می‌گردد نتوانیم این واحدها رتبه بندی نماییم. اما با استفاده از روش پیشنهادی Neuro-DEA هیچ دو شعبه ای دارای مقدار کارایی برابر نبوده و با توجه به کارایی بدست آمده به راحتی می توان این واحد ها را ارزیابی و رتبه بندی نمود. Manuscript profile

Open Access Article

190 - An Artificial Neural Network Method to Predict the COVID-19 Cases in Iran
Meisam Shamsi رضا بابازاده Mohsen Varmazyar

The sudden emergence of a Coronavirus and its rapid spread due to the globalization factors, especially the airline network, provoked the reaction of countries. Governments attempt to use all available means, including prediction methods, to control the spread of the Co More

The sudden emergence of a Coronavirus and its rapid spread due to the globalization factors, especially the airline network, provoked the reaction of countries. Governments attempt to use all available means, including prediction methods, to control the spread of the Coronavirus. In this article, we have developed various models based on artificial neural networks, including multi-layer perceptron, radial basis function, and adaptive-network-based fuzzy inference system with different learning algorithms, transfer functions, membership functions, hidden layers, hidden neurons, and kernels. We have identified five factors influencing the Coronavirus outbreak based on the Pearson correlation coefficient approach. These factors are gasoline consumption, internet pressure, number of wedding ceremonies, online transactions, and mask consumption. The accuracy of the developed models is identified by calculating three types of statistical errors, including root mean square error, mean absolute error, and mean absolute percentage error. The results show that the radial basis function model predicts the number of Covid-19 cases for the one month (mid-term) with an accuracy of over 97%. This study provides an efficient approach to predict the number of COVID-19 cases which help policymakers to make strategic decisions, including closing borders, designing supply chains for medical and health equipment, and enacting new laws. Manuscript profile

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191 - Prediction of Egg Production Using Artificial Neural Network
S. Ghazanfari K. Nobari M. Tahmoorespur

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Open Access Article

192 - Estimating Heritabilities and Breeding Values for Real and Predicted Milk Production in Holstein Dairy Cows with Artificial Neural Network and Multiple Linear Regression Models
M. Nosrati S.H. Hafezian M. Gholizadeh

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Open Access Article

193 - تعیین اندازه گل و رنگ پوست بره های زندی با استفاده از پردازش تصویر و شبکه عصبی مصنوعی
م. خجسته کی ع.ا. اسلمی نژاد ع.ر. جعفری اروری

در این مطالعه، روشی بر مبنای استفاده از پردازش تصویر و شبکه عصبی مصنوعی برای تعیین رنگ و نوع گل پوست در بره های نوزاد گوسفند زندی معرفی شده است. داده ها از 300 بره نوزاد در مرکز پرورش گوسفند زندی خجیر تهران جمع آوری شد. در ابتدا، اندازه و شکل گل پوست More

در این مطالعه، روشی بر مبنای استفاده از پردازش تصویر و شبکه عصبی مصنوعی برای تعیین رنگ و نوع گل پوست در بره های نوزاد گوسفند زندی معرفی شده است. داده ها از 300 بره نوزاد در مرکز پرورش گوسفند زندی خجیر تهران جمع آوری شد. در ابتدا، اندازه و شکل گل پوست بره های تازه متولد شده توسط ارزیاب های با تجربه ثبت شد و به طور همزمان، چندین عکس دیجیتال از نمای جانبی هر بره گرفته شد. ویژگی های مربوط به اندازه گل و رنگ پوست بره ها از تصاویر دیجیتال با استفاده از ابزار پردازش تصویر (IPT) نرم افزار MATLAB استخراج شد. برای تعیین رنگ پوست، طبقه بندی پوست براساس اندازه گل و نیز برای برآورد اندازه گل پوست بره ها سه شبکه عصبی مصنوعی مجزا طراحی شد. رنگ پوست بره ها با استفاده از شبکه عصبی مصنوعی با دقت 100 درصد تعیین شد. دقت شبکه عصبی آموزش دیده برای طبقه بندی پوست بره ها بر اساس اندازه گل آنها 87/94 درصد بود. همچنین دقت شبکه عصبی سوم برای برآورد اندازه گل های پوست 44/98 درصد بود. همبستگی بین اندازه گل برآورد شده با استفاده از شبکه عصبی مصنوعی و اندازه گل تعیین شده توسط ارزیاب 4/96 درصد (0.01>P) بود. نتایج این مطالعه نشان داد که امکان استفاده از هوش مصنوعی به عنوان جایگزین ارزیابی انسانی در ثبت صفات پوست وجود دارد. Manuscript profile

Open Access Article

194 - کاربرد مدل خطی و شبکه عصبی مصنوعی برای پیش‌بینی عملکرد رشد در جوجه‌های گوشتی
ش. غضنفری

این مطالعه به منظور پیش‌بینی عملکرد رشد با استفاده از مدل خطی و شبکه عصبی مصنوعی در جوجه‌های گوشتی انجام شد. شبکه عصبی مصنوعی ابزار قدرتمندی برای سیستم مدلینگ در دامنه وسیعی از کاربردها است. مدل شبکه عصبی مصنوعی با الگوریتم پس انتشار به طور موفقیت آمیزی ارتباط بین ورودی More

این مطالعه به منظور پیش‌بینی عملکرد رشد با استفاده از مدل خطی و شبکه عصبی مصنوعی در جوجه‌های گوشتی انجام شد. شبکه عصبی مصنوعی ابزار قدرتمندی برای سیستم مدلینگ در دامنه وسیعی از کاربردها است. مدل شبکه عصبی مصنوعی با الگوریتم پس انتشار به طور موفقیت آمیزی ارتباط بین ورودی‌ها (انرژی قابل سوخت و ساز (کیلوکالری/کیلوگرم) و پروتئین خام (گرم/کیلوگرم) و خروجی‌ها (مصرف خوراک، افزایش وزن و ضریب تبدیل خوراک) را آموزش داد. ارزش R2و T بالا برای مدل شبکه عصبی مصنوعی در مقایسه با مدل خطی نشان داد که شبکه عصبی مصنوعی یک روش مؤثر برای پیش‌بینی عملکرد رشد در دوره آغازین برای جوجه‌های گوشتی است. همچنین، گسترش آزمایش با سطوح بیشتری از ورودی‌ها برای پیش‌بینی عملکرد با استفاده از بهترین مدل شبکه عصبی مصنوعی انجام شد. Manuscript profile

Open Access Article

195 - مقایسه شبکه عصبی مصنوعی و مدل‌های رگرسیونی برای پیش‌بینی وزن بدن در بز کرکی راینی
م. خورشیدی-جلالی م.ر. محمدآبادی ع. اسمعیلی‌زاده ا. برازنده ُ.ا. بابنکو

شبکههای عصبی مصنوعی الگوریتمهای آموزشی و مدلهای ریاضی هستند که توانایی تقلید از مغز انسان در پردازش اطلاعات را دارند و میتوانند دادههای پیچیده و غیر خطی را مورد استفاده قرار دهند. هدف این پژوهش مقایسه شبکه عصبی مصنوعی و مدلهای رگرسیونی برای More

شبکههای عصبی مصنوعی الگوریتمهای آموزشی و مدلهای ریاضی هستند که توانایی تقلید از مغز انسان در پردازش اطلاعات را دارند و میتوانند دادههای پیچیده و غیر خطی را مورد استفاده قرار دهند. هدف این پژوهش مقایسه شبکه عصبی مصنوعی و مدلهای رگرسیونی برای پیش‌بینی وزن بدن در بز کرکی راینی بود. دادههای 1389 بز برای وزن بدن، ارتفاع جدوگاه، طول بدن و قفسه سینه مورد استفاده قرار گرفت. مدلهای رگرسیونی مختلف با تمام فاکتورهای ثابت برای بیشتر حالتهای ممکن و با درجههای مختلف محاسبه شدند و دو شبکه عصبی مصنوعی با لایههای مخفی متفاوت، توابع آموزش و توابع انتقال گوناگون استفاده شدند. در نهایت، مدل پرسپترون چند لایه با یک لایه مخفی به همراه نرونها انتخاب و استفاده شد. همبستگی بین وزن بدن و اندازه‌گیریهایش نشان داد که میتوان از اندازههای بدن برای پیش‌بینی وزن بدن استفاده کرد و هرچه اندازههای بیشتری استفاده شوند پیش‌بینی دقیقتری انجام خواهد شد. براساس پارامترهای R2و MSE، بهترین معادله رگرسیون فیت شده برای پیش‌بینی وزن بدن با استفاده از اندازه‌گیریهای ابعاد بدن انتخاب شد. در حالیکه هر سه اندازه در مدل اثر معنی‌داری داشتند (0001/0P<)، ارتفاع جدوگاه بالاترین ضریب را داشت (65/0)، بنابراین میتواند بیشترین اثر را در پیش‌بینی داشته باشد. مقایسه دو مدل نشان داد که هر دو مدل میتوانند به خوبی وزن بدن را، نزدیک به وزن واقعی آن پیش‌بینی کنند، اما توانایی شبکه عصبی مصنوعی بالاتر است (R2 برای شبکه عصبی مصنوعی 86/0 و برای مدلهای رگرسیونی 76/0) و به ورن واقعی بدن نزدیکتر میباشد. با این وجود، اگر اندازههای مرتبط بیشتری رکورد‌برداری شوند میتوان نتایج مطلوبتری را با شبکه عصبی مصنوعی به دست آورد. بنابراین، از شبکه عصبی مصنوعی میتوان به جای روشهای سنتی مرسوم برای پیش‌بینی وزن واقعی بدن با استفاده از اندازههای بدن استفاده کرد. Manuscript profile

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196 - کاربرد مدل‌های ریاضی برای تخمین میزان انرژی قابل متابولیسم اقلام خوراکی انرژی‌زا در طیور
M. Sedghi K. Tayebipoor B. Poursina M. Eman Toosi P. Soleimani Roudi

این مطالعه با هدف بررسی امکان پیش‌بینی انرژی قابل متابولیسم ظاهری (AME) در طیور با استفاده از آنالیز تقریبی در نمونه‌های گندم (51 نمونه)، جو (56 نمونه) و یولاف (34 نمونه) انجام شد. از آنالیز رگرسیون گام به گام برای ارزیابی رابطه بین AME با نشاسته، عصاره اتری (EE)، فیبر More

این مطالعه با هدف بررسی امکان پیش‌بینی انرژی قابل متابولیسم ظاهری (AME) در طیور با استفاده از آنالیز تقریبی در نمونه‌های گندم (51 نمونه)، جو (56 نمونه) و یولاف (34 نمونه) انجام شد. از آنالیز رگرسیون گام به گام برای ارزیابی رابطه بین AME با نشاسته، عصاره اتری (EE)، فیبر خام (CF)، قندهای محلول (SS)، خاکستر (ash) و پروتئین خام (CP) در نمونه‌های گندم و جو، و ماده خشک (DM) ، CF، Ash و CP در نمونه‌های یولاف استفاده شد. براساس نتایج رگرسیون گام به گام، SS، CF و Ash، بهترین متغیرهای ورودی برای پیش‌بینی AME در نمونه‌های گندم بودند. بعلاوه، نشاسته،CF  و EE در نمونه های جو و CF و CP در نمونه‌های یولاف بهترین پارامترهای ورودی جهت تخمین AME شناسایی شدند. همچنین برای بدست آوردن بهترین مدل تخمین زننده AME در این غلات با استفاده از ورودی‌های انتخاب شده از رگرسیون خطی چندگانه (MLR) و شبکه عصبی مصنوعی (ANN) استفاده شد. نتایج MLR و ANN نیز نشان داد که SS، CF و ash فاکتورهای مؤثری جهت تخمین AME درگندم می‌باشند. برای پیش‌بینی AME نمونه‌های جو CF، EE و نشاسته متغییرهای مستقل خوبی به شمار می‌آیند. همچنین CF و CP پارامترهای خوبی برای پیش‌بینی AME در نمونه‌های یولاف می‌باشند. در ارتباط با کارایی مدل‌ها، دقت مدل ANN بالاتر از مدل MLR بود. بر اساس این نتایج، می‌توان چنین نتیجه‌گیری کرد که استفاده از ترکیبات شیمیایی موجود در نمونه‌های گندم، جو و یولاف، همراه با مدل ANN، روش کاربردی جهت پیش‌بینی AME این غلات در تغذیه طیور می‌باشد. Manuscript profile

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197 - Performance of Artificial Neural Networks Model under Various Structures and Algorithms to Prediction of Fat Tail Weight in Fat Tailed Breeds and Their Thin Tailed Crosses
ک. نوبری S.D. Sharifi N. Emam Jomea Kashan M. Momen A. Kavian

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198 - مقایسه کارآیی شبکه عصبی مصنوعی و رگرسیون چندگانه در پیش‌بینی وزن دنبه گوسفند
م.ع. نوروزیان م. وکیلی علویجه

در این مطالعه ارتباط بین وزنهای تولد، از شیرگیری و پایان پروار با وزن دنبه 69 رأس گوسفند بلوچی توسط روشهای شبکه عصبی مصنوعی و رگرسیون چندگانه بررسی شد. هر دو روش با دقت بالایی وزن دنبه را پیشبینی کردند. هر چند که میانگین خطا به صورت معنیداری در روش ش More

در این مطالعه ارتباط بین وزنهای تولد، از شیرگیری و پایان پروار با وزن دنبه 69 رأس گوسفند بلوچی توسط روشهای شبکه عصبی مصنوعی و رگرسیون چندگانه بررسی شد. هر دو روش با دقت بالایی وزن دنبه را پیشبینی کردند. هر چند که میانگین خطا به صورت معنیداری در روش شبکه عصبی مصنوعی کمتر از رگرسیون چندگانه بود. ضریب تعیین برآورد شده در روش شبکه عصبی مصنوعی (93/0) بالاتر از رگرسیون چندگانه (81/0) به دست آمد. استفاده از شبکه عصبی مصنوعی میانگین خطای استاندارد را 59 و ضریب تعیین را 15 درصد بهبود داد. به نظر میرسد که بتوان با استفاده از شبکه عصبی مصنوعی وزن دنبه را از صفات وزن بدن پیشبینی کرد. Manuscript profile

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199 - Estimation of evaporation from Dez regulatory dam station pan using artificial neural network
mehdi najafvand derikvandi hossein eslami

More rainfall in arid and semi-arid just evaporate into the atmosphere and so estimates the amount of water vapor in the water cycle will be important. Evaporation is dependent on various parameters and to its estimate needs for a different climate variables and the int More

More rainfall in arid and semi-arid just evaporate into the atmosphere and so estimates the amount of water vapor in the water cycle will be important. Evaporation is dependent on various parameters and to its estimate needs for a different climate variables and the interaction of these variables is very complex, so it must be accurate methods to be used in the evaporation study. In this study, artificial neural networks were used to estimate the pan evaporation of Dez regulating dam station. As ANN hyperbolic tangent function and the learning momentum was used. Multilayer Perceptron structure which used a network of six input neurons, three hidden layer and an output neuron was formed. Input layers include maximum temperature, minimum temperature, sunshine hours, average wind speed, relative humidity and an average rate of evaporation from water surface to the output layer. The relationship between climatic factors showed that the average temperature on the surface evaporation caused more than sunshine and wind speed. High coefficient of determination (92/0) between the actual data with simulated data with artificial neural network plus a small error (RMSE = 1.41) showed that the estimate accuracy is very high. Verification by t-test revealed no significant (P> 0.01) differences were between actual and estimated values. Manuscript profile

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200 - Hargreaves Method Improves Accuracy in Estimating Reference Evapotranspiration Adjustment Weight With the Help of Artificial Neural Network and Decision Tree
omid mohtarami Mohammad Reza Hosseini Ruhollah Fattahi تیمور سهرابی

One of the most important components of the hydrological cycle is evapotranspiration which plays an important role in water resource management. In the present study the accuracy of evapotranspiration estimation by Hargreaves method and correction factor K was improved More

One of the most important components of the hydrological cycle is evapotranspiration which plays an important role in water resource management. In the present study the accuracy of evapotranspiration estimation by Hargreaves method and correction factor K was improved using the neural network and decision tree model M5. This coefficient is the ratio of Penman-Monteith evapotranspiration model is the method of Hargreaves. The data used in this study are the maximum and minimum temperatures and relative humidity in the period 2004-2013 Farokhshahr stations and airports in the region ShahrKord is cold and arid. Network Levenberg-Marquardt training algorithm is designed with a feedforward network and sigmoid tangent function is hidden in layers. Decision tree model was designed to help software WEKA. The results show that the neural network and decision tree model to model good performance, but the performance of the neural network model is more accurate correction factor. The results showed that the correction factor carefully before using the Hargreaves RMSE = 0.90 (Root Mean Square Error) Penman-Monteith than that this value after the correction factor to help RMSE = 0.69 and with the use of neural networks the correction factor to help decision tree to reach RMSE = 0.72. The results showed that after using a correction factor to the improved performance of Hargreaves. Manuscript profile

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201 - Utilization of Artificial Neural Networks for Determining the Overflow Discharge of Marun Dam
Ebrahim Nohani valiolah partovi zia

For more accurate measurement of the water flow, it has been always attempted to design structures with least errors and highest accuracy. Nowadays, the use of artificial neural networks (ANN) models has been rapidly grew mainly due to the fact that these models are not More

For more accurate measurement of the water flow, it has been always attempted to design structures with least errors and highest accuracy. Nowadays, the use of artificial neural networks (ANN) models has been rapidly grew mainly due to the fact that these models are not confined to the physical parameters. Artificial neural networks are based on use of embedded knowledge between input and output variables of a problem, regardless of physical aspects and these networks are able to extract inherent relation of the input and output and they can generalize the obtained relation to other situations and cases. In the present research, the information related to the overflow of Marun Storage Dam was adopted. The input parameters of ANN model are as follows: day, month, water surface elevation, water sharing percent and output parameters overflow discharge of storage dam. The models employed in artificial neural networks include FF, JEN, MLP and RBF. Moreover, the genetic algorithm (GA Manuscript profile

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202 - Investigation and Comparison of Performance of Modern Intelligent tTechniques in Groundwater Nitrate Simulation
Jafar Seraji

Today, due to recent drought, one of the main sources of drinking water in the country is underground resources, and also nitrate is one of the most important pollutants of groundwater resources, which has adverse effects on people's health. The present study seeks to c More

Today, due to recent drought, one of the main sources of drinking water in the country is underground resources, and also nitrate is one of the most important pollutants of groundwater resources, which has adverse effects on people's health. The present study seeks to compare and provide an efficient and innovative technique for simulating and predicting nitrate in these resources. Therefore, three artificial neural networks (ANN) models of neuro-fuzzy inference system (ANFIS) and vector-supported vector (SVM) are compared in simulation as a data-driven tool. Simulation based on observation samples from wells in the aquifer under study for 13 years and the modeling period has been selected monthly. Estimates of model simulations include magnesium (Mg), bicarbonate (Hco3), calcium (Ca), sodium (Na). First, the heterogeneous simulation of heterogeneity is carried out on different makeup. Based on the results of the evaluation of the neo-Frazi system The correlation coefficient of R2 = 9978/0 and MS2 = 0002 have better capability and capability. Manuscript profile

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203 - Development of a Wavelet Hybrid Models for Estimating Regional Droughts in Siminehroud Basin
Erfan Rostam Zade alireza parvishi

In the present study, the drought of Siminehroud basin was investigated by intelligent Support Vector Machine (SVM) models, Artificial Neural Network (ANN) and Wavelet theory (W). Data from six rain gauge stations in the region were used and drought index was calculated More

In the present study, the drought of Siminehroud basin was investigated by intelligent Support Vector Machine (SVM) models, Artificial Neural Network (ANN) and Wavelet theory (W). Data from six rain gauge stations in the region were used and drought index was calculated in four time scales. The first-order autocorrelation was also selected as the optimal delay. Then the appropriate structure of the Artificial Neural Network was determined using Trial and Error Method and the three coefficients of the SVM model were determined and modeled. The results of evaluating individual models showed that there is no significant difference between two methods in predicting droughts. Then WANN and WSVM hybrid models were prepared. The results showed that the application of Wavelet theory greatly improved the performance of individual models and the amount of RMSE and MAE indices decreased by 19% and 21% and the correlation coefficient increased by 30%, respectively. Manuscript profile

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204 - Development and Implementation of Principal Component Analysis Method for Monitoring of Gas Turbine
Samira Piri Niaragh Elham Ghanbari

10.30495/jmemiau.2023.707350

Gas turbines are complex and expensive machines that the cost of repairing unexpected failures is very high. There are many sensors installed in each gas turbine that record and collect large amounts of data. With the data mining of such big data, failure prediction is More

Gas turbines are complex and expensive machines that the cost of repairing unexpected failures is very high. There are many sensors installed in each gas turbine that record and collect large amounts of data. With the data mining of such big data, failure prediction is possible before the occurrence. The data set for the present study is the recorded quantities of sensors mounted on a 9-frame gas turbine in one of the country's power plants. The one column of data matrix rows was first labeled to identify healthy and defective row in each data sample. Then, by using the Principal Component Analysis method, the dimensions of the data matrix were reduced from seven to four dimensions and the main features were extracted. Following this, a model was developed by applying Artificial Neural Network method that was able to identify fault rows in the data matrix and identify the class of the data samples as healthy or defective. Accuracy, precision, and convergence of the model for two-to-six-dimensional model reductions were studied after machine learning was performed on 80% of the data. After matrix dimensionality reduction, and feature extraction by using "Principal Component Analysis" method, our well-designed model was also able to identify and classify the fault by using "Artificial Neural Network" method. In this thesis, it was found that our mode l by combining "Principal Component Analysis" method with "Artificial Neural Network" was able to show more than 90% precision with good accuracy and maximum degree of data matrix convergence. Moreover, it was able to specify the gas turbine fault class.     Manuscript profile

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205 - Applying Adaptive Network-based fuzzy Inference System to Predict Travel Time in Highways for Intelligent Transportation Systems
Rouhollah Maghsoudi Behzad Moshiri

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206 - An Efficient Model for Lip-reading in Persian Language Based on Visual Word and Fast Furrier Transform Combined with Neural Network
Khadijeh Mirzaei Talarposhti Mehrzad Khaki Jamei

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207 - An Optimal Configuration of Neural Networks by Multi-Objective Genetic Algorithm and Ensemble-Classifier Approach for Evaluation Trust in the Single Web Service
baharak shakeri aski Abolfazl Haghighat mehran mohsenzadeh

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208 - Multivariate Time Series Prediction Considering Intra-Time-Series and Inter-Time-Series Dependencies
Parinaz Eskandarian Jamshid Bagherzadeh Habibollah Pirnejad Zahra Niazkhani

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209 - Multi-layer Perceptron Neural Network Training Based on Improved of Stud GA
Firozeh Razavi Faramarz Zabihi Mirsaeid Hosseini Shirvani

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210 - New Approach with Hybrid of Artificial Neural Network and Ant Colony Optimization in Software Cost Estimation
Nader Ebrahimpour Farhad Soleimanian Gharehchopogh Zeinab Abbasi Khalifehlou

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211 - Artificial Neural Networks endowed with External Factors for Forecasting Foreign Exchange Rate
Zabihallah Pargam Yazdan Jamshidi

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212 - A Novel Approach for Discrimination Magnetizing Inrush Current and Internal Fault in Power Transformers Based on Neural Network
Mehran Taghipour-Gorjikolaie Mohammad Yazdani-Asrami S. Asghar Gholamian S. Mohammad Razavi

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213 - Discrimination between Iron Deficiency Anaemia (IDA) and β-Thalassemia Trait (β-TT) Based on Pattern-Based Input Selection Artificial Neural Network (PBIS-ANN)
Mehrzad Khaki Jamei Khadijeh Mirzaei Talarposhti

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214 - Load and Harmonic Forecasting for Optimal Transformer Loading and Life Time by Artificial Neural Network
S.Mohammad Bagher Sadati Jamal Moshtagh Abdollah Rastgou

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215 - Ovarian Cancer Classification Using Hybrid Synthetic Minority Over-Sampling Technique and Neural Network
Moshood A. Hambali Morufat D. Gbolagade

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216 - Assessment of Citizens' Satisfaction with Intra-City Transportation System in Hamedan City
Hamed Abbasi

Transportation is one of the most important constituent parts of urban life that determines the shape and way of social and economic development of the city. In this regard, the quality of transportation in Hamadan city, which can be very useful in its development, was More

Transportation is one of the most important constituent parts of urban life that determines the shape and way of social and economic development of the city. In this regard, the quality of transportation in Hamadan city, which can be very useful in its development, was considered. The nonparametric regression model and artificial neural network were used to assess the citizens' satisfaction from the transportation system. For this purpose, first, a questionnaire was developed based on three main indicators (equipment and facilities status, physical status, management status, and service delivery). Citizens' perspective was gathered. Then, using a nonparametric regression model, independent indicators and satisfaction as an independent variable were used. The correlation and root mean square of the output errors of this model were 0.914 and 0.334 respectively. In another approach, using an artificial neural network, a model with three intrinsic neuron structures, one hidden layer and one output neuron was constructed. The output correlation of this model was 0.998 and the mean square error of the error was about 6 times lower than regression model. The results showed that the neural network model with both linear and nonlinear correlation estimates is more versatile and more suitable than nonparametric regression. On the other hand, price indices with coefficient (0.885), equality and welfare with (0.795) and decrease in demand for travel (0.790) are the most effective indicators for citizens' satisfaction with urban transport network. Manuscript profile

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217 - Evaluating the Components of Social and Economic Resilience against earthquake in the 3rd Municipal District of Shiraz City
Parisa Moshksar Yaghoob Peyvastehgar Ali Shamsoddini

Today, local communities are struggling to find conditions that will allow them to return quickly to the pre-crisis situation in the event of a crisis. In recent years, emphasis has been placed on the issue of resilience rather than vulnerability. resilience is the abil More

Today, local communities are struggling to find conditions that will allow them to return quickly to the pre-crisis situation in the event of a crisis. In recent years, emphasis has been placed on the issue of resilience rather than vulnerability. resilience is the ability of a system to absorb perturbation, or the magnitude of disturbance that can be absorbed before a system changes its structure by changing the variables. Shiraz is located in the Zagros seismic zone with high seismicity. Considering the importance of existing land uses in the 3rd municipal district of Shiraz city, the aim of this study was an evaluation of social and economic resilience in this district. This applied research is using descriptive and analytical methods. The indicators of social and economic resilience were identified from the literature, and then data were collected through a field study using questionnaires. Data were analysed using multiple linear regression and feedforward multilayer perceptron artificial neural network. Linear regression indicated that a decrease in share of income spent on necessities could result in an increase in social and economic resilience of the households under study. Neural network analysis revealed that social capital and employment recovery are the most and least effective factors. In the population under study, social component, was the most important determinant of resilience. Manuscript profile

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218 - Application of artificial neural network and Cellular AutomataIn modeling and predicting land use changes in Bavanat city
Marzieh Mogholi

Introduction: Today, due to the high value of land and the limitation of natural resources in the city of Bowanat, it is very important to predict land use changes in this city.Research Aime: determining the level of ability in modeling the localization phenomena in the More

Introduction: Today, due to the high value of land and the limitation of natural resources in the city of Bowanat, it is very important to predict land use changes in this city.Research Aime: determining the level of ability in modeling the localization phenomena in the city of Bowanat is one of the main goals of the research. Methodology: considering the practicality and development in this research of artificial neural networks for calibrating the model for the effective factors in the city. Bowanat has been used and ENVI and Arc GIS image processing software have been used.Methodology: Due to practicality and development in this research, artificial neural networks were used to calibrate the model for effective factors in the city of Bowanat, and ENVI and Arc GIS image processing software were used.Studied Areas: Bowanat city is located 240 km from Shiraz city with an area of 4992.2 square kilometers, which is located at 30.46 degrees north and 53.67 degrees east.Results: In the design of urban growth modeling in Bowanat between 2003 and 2018 using artificial neural network, it was observed that for two main reasons, the mentioned model is suitable for predicting land use changes in Bowanat city, the first reason being the ability of the CA model and the reason The second is to achieve a model for urban change and expansion by changing urban land use.Conclusion: After examining the findings, it was found that the road network is one of the most important factors in the growth and expansion of Bowanat city, and in addition, the percentage of land slope is one of the effective parameters in the modeling of Bowanat city.Keywords: Land use, Fuzzy Logic, Artificial neural network, Bavanat city. Manuscript profile

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219 - Application of artificial neural networks in modeling urban physical development (Case study: Rasht city)
tala abedi golamreza miri parviz rezaei reza zarei

Introduction: The physical development of cities is increasing day by day. Correct management of this development from various aspects is among the important issues that must be considered. There are many methods for predicting and determining the direction of urban dev More

Introduction: The physical development of cities is increasing day by day. Correct management of this development from various aspects is among the important issues that must be considered. There are many methods for predicting and determining the direction of urban development, one of these methods for determining suitable areas is the method based on neural networks.The purpose of the research: The purpose of this research is to model the development of the city of Rasht in the last 20 years and predict the directions of development of this city until 2032.Research methodology :By using ETM+ Landsat 7 and 8 satellite images of 2002, 2012 and 2021 of Rasht city and with GIS software, images with suitable band composition are prepared and then the images are classified using Multi Layer Perceptron Artificial Neural Network (MLP) method. The indicators considered for the neighborhood model of urban areas are the distance from urban points, the distance to the central areas of the city, and the distance to the main streets and roads.The geographical scope of the research:Rasht city, the capital of Gilan province, is located at 49 degrees 35 minutes 45 seconds east longitude and 37 degrees 16 minutes 30 seconds north latitude from the Greenwich meridian, and its area is about 10,240 hectares.Findings and discussion:In this model, in the training mode of the first stage (input, applying 4 indexes on the images of 2002), the network performed 104 iterations, and the lowest error rate, which is evaluated by the crossentropy criterion, was equal to 0.058526 in the 98th iteration. In the second step, the input of the model was to apply 4 indicators on the images of 2012, and the lowest error rate was evaluated as 0.076657.Results :In total, the model has been able to predict the development of Rasht city in 2012, 95.9% and for 2021, 93.8%, which can be acceptable. The model error in this first part was 1.4% and in the second part was 2.6%. By examining the 20-year period of physical development, the development directions of Rasht city in 2032 were predicted. Manuscript profile

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220 - Expectation of Chamomile Fundamental Oil Abdicate by Using the Artificial Neural Network System
Nazanin Khakipour Mahtab Payandeh

The aim of this research was to forecast the proportion and production of chamomile essential oils by employing an artificial neural network system reliant on specific soil physicochemical characteristics. Various chamomile cultivation sites were explored, and 100 soil More

The aim of this research was to forecast the proportion and production of chamomile essential oils by employing an artificial neural network system reliant on specific soil physicochemical characteristics. Various chamomile cultivation sites were explored, and 100 soil samples were transported to the greenhouse. The pH, EC, K, OM (organic matter), CCE (calcium carbonate equivalent), and clay content in the soils ranged from 8.75 to 7.94, 1.6 to 1.0, 381 to 135, 2.30 to 0.22, 69 to 16, and 55.6 to 32.0, respectively. Growth parameters, essential oil percentage, and yield were measured. The artificial neural network modeling aimed to predict essential oil concentration and yield using three sets of soil properties as predictors: Nitrogen (N), phosphorus (P), potassium (K), and clay; pH, EC, organic matter (OM), and clay; CCE, clay, silt, sand, N, P, K, OM, pH, and EC. Consequently, three pedotransfer functions (PTFs) were formulated using the multi-layer perceptron (MLP) with the Levenberg-Marquardt training algorithm to estimate chamomile essential oil content. The evaluation of results indicated that the third PTF (PTF3), developed using all independent variables, exhibited the highest accuracy and reliability. Furthermore, the findings suggested the feasibility of predicting chamomile essential oil concentration and yield based on soil physicochemical properties. This has significant implications for land suitability assessments, identifying areas conducive to chamomile cultivation, and planning for essential oil yields. Manuscript profile

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221 - شناسایی گیاهان آپارتمانی بر اساس ویژگی های تصویری با کمک شبکه عصبی
نرگس قانعی قوشخانه عباس روحانی محمودرضا گلزاریان فاطمه کاظمی

در این مقاله سامانه بینایی ماشینی مبتنی بر شبکه عصبی برای شناسایی 12 گیاه آپارتمانی توسعه داده شد. از سامانه پردازش تصویر  برای استخراج 41 ویژگی رنگی، بافتی و شکلی از تصاویر رو و پشت برگ گیاه  استفاده گردید. ویژگیهای استخراج یافته به عنوان معیار تشخیص و و More

در این مقاله سامانه بینایی ماشینی مبتنی بر شبکه عصبی برای شناسایی 12 گیاه آپارتمانی توسعه داده شد. از سامانه پردازش تصویر  برای استخراج 41 ویژگی رنگی، بافتی و شکلی از تصاویر رو و پشت برگ گیاه  استفاده گردید. ویژگیهای استخراج یافته به عنوان معیار تشخیص و ورودی به شبکه عصبی داده شد. شبکه عصبی پرسپترون چند لایه (MLP) با الگوریتم آموزش، الگوریتم فاکتور کاهش نرخ یادگیری (BDLRF) به عنوان طبقهبندی کننده استفاده گردید. طبقهبندی در سه مرحله براساس قابلیت و قدرت ویژگی‌ها در شناسایی گیاهان انجام شد. معیار قابلیت داشتن در هر مرحله با استفاده از قدرت تفکیک پذیری کلاسی گیاهان بررسی گردید. در این روش طبقهبندی، هر مرحله نیاز به تعداد کمی از ویژ‌گی‌ها دارد؛ در نتیجه سرعت و دقت آن می‌تواند بسیار بالا باشد. نتایج نشان داد که دقت طبقهبندی گیاهان در سه مرحله به 100% می‌رسد. همچنین ویژگی‌های بهینه برای طبقهبندی شامل سه مرحله‌ی ورودی از ویژگی‌های موفولوژیکی (شکلی)، ویژگی‌های رنگی HSI استخراج یافته از پشت برگ و ویژگی‌های بافتی  HSI  استخراج یافته از پشت برگ‌ها می‌شود. Manuscript profile

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222 - Predicting rainfed barley crop yield using Artificial neural network and fuzzy neural systems in Khorasan provinces-Iran
Ahad Madani Abbas Khasheyi َAlireza khakzad sivaki

10.2./jpps.2022.691244

In this research, we try to predict the yield of rainfed barley in Khorasan provinces using climatic parameters and two methods of artificial nervous netwework (Ann) and fuzzy neural system (Anfis). Calculations were performed with MATLAB software and then the statistic More

In this research, we try to predict the yield of rainfed barley in Khorasan provinces using climatic parameters and two methods of artificial nervous netwework (Ann) and fuzzy neural system (Anfis). Calculations were performed with MATLAB software and then the statistical indices of correlation coefficient (R2), root mean square error (RMSE) and full mean error (MAE) were used to evaluate the performance of the models. Last year's yield and rainfall had an effective role in reducing prediction error and increasing correlation coefficient in both Ann and Anfis methods. Last year's yield and evapotranspiration made the Anfis method more accurate than the Ann method. The results of both Anfis and Ann methods for model L inputs, which included rainfall, relative humidity and last year's yield, showed that this model achieved the highest accuracy among the input models. However, in the Anfis method for model E inputs, which included evapotranspiration, rainfall, relative humidity and minimum temperature, the results showed that it was more accurate than the Ann method. The greatest difference in accuracy in estimating yield between the two Anfis and Ann methods was observed with R inputs model, which includes moisture inputs, Dew point temperatures and maximum temperatures. The presence of radiation parameters at the inputs reduced the accuracy of yield estimation in both methods. Overall, the Anfis method was more accurate in estimating yield than Ann. Manuscript profile

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223 - مدل سازی جریان انرژی و ارزیابی اقتصادی تولید هندوانه در استان فارس در ایران
Sajad Rostami Maryam Lotfalian Bahram Hosseinzadeh مهدی قاسمی ورنامخستی

این مطالعه با هدف بررسی بهره‌وری انرژی مصرفی و آنالیز اقتصادی روش‌های مختلف کشت هندوانه در استان فارس، کشور ایران انجام شد. روش‌های تولید هندوانه به پنج گروه تقسیم شدند. این گروه‌ها عبارت بودند از: خاکورزی مرسوم (گروه 1)، کشاورزی حفاظتی (گروه 2)، پاشش کود مکانیزه (گروه More

این مطالعه با هدف بررسی بهره‌وری انرژی مصرفی و آنالیز اقتصادی روش‌های مختلف کشت هندوانه در استان فارس، کشور ایران انجام شد. روش‌های تولید هندوانه به پنج گروه تقسیم شدند. این گروه‌ها عبارت بودند از: خاکورزی مرسوم (گروه 1)، کشاورزی حفاظتی (گروه 2)، پاشش کود مکانیزه (گروه 3)، کاشت نیمه مکانیزه (گروه 4) و کاشت تمام مکانیزه (گروه 5). داده‌ها از 317 نفر از تولید کننده‌های هندوانه از مناطق مختلف استان به صورت چهره به چهره جمع‌آوری شد. از شبکه‌های عصبی مصنوعی چند لایه برای مدل‌سازی جریان انرژی تولید هندوانه استفاده شد. نتایج نشان داد که بیشترین مصرف انرژی متعلق به روش کاشت تمام مکانیزه با ارزش MJ.ha-172/81317 و با بهره‌وری kg.ha-1 61/0 و کارایی مصرف انرژی 17/1 بوده است. نتایج خوشه‌بندی با سه ورودی (منابع انسانی، ماشین‌آلات و سوخت دیزل) نشان داد که تفاوت بین گروه‌های 2 و 4 بیشتر از گروه‌های دیگر است. کمترین مصرف انرژی نیز برای گروه کشاورزی حفاظتی به میزان MJ.ha-1 86/78163 ، با بهره‌وری kg.ha-1 64/0 و راندمان انرژی 22/1 برآورد شد. نتایج مدل‌سازی انرژی نشان داد که مدل ANN با ساختار 1-10-9 برای مدل‌سازی انرژی جریان انرژی این سیستم است. به طور کلی، نتیجه‌گیری شد که مدل‌های شبکه عصبی مصنوعی میتواند برای پیش‌بینی جریان‌های انرژی هندوانه استفاده شود. از منظر اقتصادی نیز کمترین سود خالص متعلق به روش کاشت کاملاً سنتی به میزان 82784 هزار ریال در هکتار و بیشترین آن نیز متعلق به گروه کاشت تمام مکانیزه به میزان 87092 هزار ریال در هکتار محاسبه شد. Manuscript profile

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224 - تجزیه و تحلیل و مدل‌سازی عملکرد، انتشارات CO2 و میزان انرژی برای تولید ریحان در ایران با استفاده از شبکه‌های عصبی مصنوعی
سجاد رستمی سمیه چوبین بهرام حسین‌زاده سامانی زهرا اسمعیلی حماد ذرعی‌فروش

این مطالعه با هدف بررسی رابطه بین انرژی‌های ورودی و عملکرد تولید ریحان گلخانه‌ای و همچنین گازهای گلخانه‌ای انتشار یافته از این محصول انجام شد. داده‌ها از24 گلخانه به روش پرسشنامه‌ای و بصورت چهره به چهره با کشاورزان جمعآوری گردید. نتایج حاصل از این مطالعه More

این مطالعه با هدف بررسی رابطه بین انرژی‌های ورودی و عملکرد تولید ریحان گلخانه‌ای و همچنین گازهای گلخانه‌ای انتشار یافته از این محصول انجام شد. داده‌ها از24 گلخانه به روش پرسشنامه‌ای و بصورت چهره به چهره با کشاورزان جمعآوری گردید. نتایج حاصل از این مطالعه نشان داد که انرژی ورودی کل 119852.9مگاژول بر هکتار وانرژی خروجی کل 61040مگاژول بر هکتار می‌باشد. بالاترین سهم از مصرف انرژی مربوط به الکتریسیته با 52200 مگاژول برهکتار و به دنبال آن پلاستیک با 23220 مگاژول بر هکتار و کودهای شیمیایی با 13894مگاژول بر هکتار قرار گرفتند. شاخص نسبت انرژی و بهره‌وری به ترتیب 45/. و21/. محاسبه شد که هر دو نشان می‌دهند کارایی انرژی در بخش کشاورزی پایین می‌باشد همچنین انرژی خالص 72706.9- برآورد شد و کل گازهای گلخانه‌ای منتشر شده از تولید ریحان  9595.6کیلوگرم معادل  Co2 محاسبه شد. بیشترین انتشار گاز‌های گلخانه‌ای در این مطالعه مربوط به الکتریسیته با 2.216کیلوگرم معادل  Co2بود. نتایج مدلسازی ثابت کرد که شبکه‌های عصبی مصنوعی می‌تواند عملکرد ریحان و انتشار گازهای گلخانه‌ایCo2 با درجه بالایی از دقت و صحت R2=0.99)  و(MSE= 0.00023 پیش‌بینی کند. Manuscript profile

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225 - ارائه یک مدل شبکه عصبی RBF برای پیش‌بینی روزهای کاری عملیات خاک‌ورزی تولیدات محصولات
ارمغان کوثری مقدم عباس روحانی Lobat Kosari-Moghaddam مهدی اسماعیل پور تروجنی

هدف از این مطالعه تعیین احتمال روزهای کاری (PWD) برای عملیات خاک‌ورزی با استفاده از داده‌های هواشناسی و به کمک روش مدلسازی رگرسیونی خطی چندگانه (MLR) و شبکه عصبی مصنوعی توابع پایه‌ای شعاعی (RBF) بوده است. در هر دو مدل، هفت متغیر شامل دمای متوسط، بیشینه و کمینه، رطوبت نس More

هدف از این مطالعه تعیین احتمال روزهای کاری (PWD) برای عملیات خاک‌ورزی با استفاده از داده‌های هواشناسی و به کمک روش مدلسازی رگرسیونی خطی چندگانه (MLR) و شبکه عصبی مصنوعی توابع پایه‌ای شعاعی (RBF) بوده است. در هر دو مدل، هفت متغیر شامل دمای متوسط، بیشینه و کمینه، رطوبت نسبی، بارندگی، سرعت باد و تبخیر و تعرق بر پایه روزانه به عنوان پارامترهای ورودی در نظر گرفته شدند. احتمال روزهای کاری نیز به عنوان خروجی مدل‌های ایجاد شده، در نظر گرفته شد. معیارهای عملکردی شامل مجذور مربعات خطا (RMSE)، میانگین درصد خطای مطلق (MAPE) و ضریب تبیین (R2) بودند. نتایج نشان داد که مقادیر R2 برای مدل‌های MLR و RBF به ترتیب برابر 78/0 و 99/0 بوده است. هر دو مدل عملکرد قابل قبولی داشتند؛ اما مدل RBF عملکرد دقیق‌تری نسبت به مدل MLR داشت. مقادیر RMSE و MAPE نیز برای مدل RBF کمتر از مدل MLR بدست آمد. بنابراین مدل RBF به عنوان بهترین مدل برای پیش‌بینی احتمال روزهای کاری انتخاب شد. علاوه بر این، نتایج این مدل‌ها با یک مدل رطوبت خاک که پیش‌تر ارائه شده بود، مقایسه شد. نشان داده شد که نتایج مدل‌های مورد مطالعه با نتایج مدل رطوبت خاک سازگاری خوبی داشته است، اگرچه که مدل RBF بالاترین ضریب تبیین را داشت (R2=99%). در نهایت می‌توان بیان نمود که مدل RBF ایجاد شده می‌تواند برای پیش‌بینی احتمال روزهای کاری در راستای سیاست‌گذاری‌های مدیریتی در بخش کشاورزی مورد استفاده قرار گیرد.  Manuscript profile

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226 - Application of Artificial Neural Networks (ANN) to Predict Geomechanical Properties of Asmari Limestones
Mahdi Razifard Mashallah Khamechiyan ‪Mohammad Reza Amin‐Naseri

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227 - Delineation of groundwater recharge potential zones using weighted linear combination method (case study: Kuhdasht plain, Iran)
Siamak Baharvand Jafar Rahnamarad Soori Salman

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228 - Introducing a New Artificial Neural Network Model for prediction of the Pressuremeter Modulus in soils of Tehran
Shahin Razavi Kamran Goshtasbi Ali Noorzad Kaveh Ahangari

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229 - Town trip forecasting based on data mining techniques
Mohammad Fili Majid Khedmati

10.30495/jiei.2020.678774

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230 - An integrated data envelopment analysis–artificial neural network approach for benchmarking of bank branches
Elsa Shokrollahpour Farhad Hosseinzadeh Lotfi Mostafa Zandieh

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231 - On the use of back propagation and radial basis function neural networks in surface roughness prediction
Angelos P. Markopoulos Sotirios Georgiopoulos Dimitrios E. Manolakos

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232 - Modeling and forecasting US presidential election using learning algorithms
Mohammad Zolghadr Seyed Armin Akhavan Niaki S. T. A. Niaki

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233 - A neuro-data envelopment analysis approach for optimization of uncorrelated multiple response problems with smaller the better type controllable factors
Mahdi Bashiri Amir Farshbaf-Geranmayeh Hamed Mogouie

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234 - Forecasting S&P 500 index using artificial neural networks and design of experiments
Seyed Taghi Akhavan Niaki Saeid Hoseinzade

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235 - A hybrid computational intelligence model for foreign exchange rate forecasting
M Khashei F Mokhatab Rafiei M Bijari S.R Hejazi

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236 - An application of artificial neural network to maintenance management
V. O. Oladokun O. E. Charles-Owaba C. S. Nwaouzru

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237 - Comparison of the performances of neural networks specification, the Translog and the Fourier flexible forms when different production technologies are used
R Feki

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238 - Estimation of Discharge over the Submerged Compound Sharp-Crested Weir using Artificial Neural Networks and Genetic Programming
A. Abbaspour S. Hashemikia

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239 - Evaluation of Artificial Intelligent Methods to Release Sediments from Reservoirs by Pressurized Flushing
Milad Abdolahpour Ali Hosseinzadeh Dalir Hadi Sanikhani

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240 - Long-term Streamflow Forecasting by Adaptive Neuro-Fuzzy Inference System Using K-fold Cross-validation: (Case Study: Taleghan Basin, Iran)
Reza Esmaeelzadeh Alireza Borhani Dariane

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241 - A comparison between,CAPM,Fama and French,s models and artificial neural networks in predicting the Iranian stock Market
S.M Jafari جواد Misaghi میثم Ahmadvand

Comparison between the Capital Asset Pricing model,Fama and Ferench three factors model and Artificial Neural Network model in predicting Tehran stock Exchange returns is discussed in this research.the first two models are linear and the following are nonlinear.Four hyp More

Comparison between the Capital Asset Pricing model,Fama and Ferench three factors model and Artificial Neural Network model in predicting Tehran stock Exchange returns is discussed in this research.the first two models are linear and the following are nonlinear.Four hypotheses have been designed for this purpose.To examine these hypotheses,the expected return was calculated daily during 1383 to 1387 for 110 companies.companies in each quarter have divided to 6 portfolios by size and book to market value factors. Results showed that the performance of Fama &Ferench three factors model is better than Capital Asset pricing model.Also Univariable and Multyvariable Artificial Neural Network models have better performance in compare with their corresponding nonlinear models. Manuscript profile

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242 - Artificial neural networks:a modle for prediction
Hossein Pourshahriar Kzaem R. Tabatabaiei M. Karim Khodapanahi A. Kazemnejad Soraya Khafri

Taking into account the ambiguities and limitations of prevailing statistical models, such as losing data related to complicated and nonlinear interactions between psychological constructs and some of the assumptions like homogeneity of variances and normal distribution More

Taking into account the ambiguities and limitations of prevailing statistical models, such as losing data related to complicated and nonlinear interactions between psychological constructs and some of the assumptions like homogeneity of variances and normal distribution, the present research investigated the capability of Artificial Neural Networks Model for con ducting predictive studies. A sample of 456 male senior high school students responded to the California Personality Inventory (CPI; Gaff, 1975) and Adjustment Inventory for School Students (AISS; Sinha & Singh, 1993), and were categorized into five levels of adjustment (from maladjusted to completely adjusted). Factor analysis of various combinations of personality traits suggested that some of the networks could not predict adjustment due to non conformity between the number of variables and network architectures. However, a revision of the architectures and repetition of new networks significantly increased the proportion of correct predictions (the proportion of participants categorized into the indicated levels of adjustment based on AISS). The most appropriate network for predicting adjustment included a combination of the cognitive variables of flexibility, femininity, communality and tolerance.        Manuscript profile

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243 - Computational Intelligence Methods for Facial Emotion Recognition: A Comparative Study
Fatemeh Shahrabi Farahani Mansour Sheikhan

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244 - Predicting the Risk of Diabetes in Iranian Patients with β-Thalassemia Major / Intermedia Based on Artificial Neural Network
Fatemeh Yousefian Touraj Banirostam Azita Azarkeivan

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245 - Persian Speech Recognition Through the Combination of ANN/HMM
Ladan Khosravani pour Ali Farrokhi

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246 - .Application of Meta-Heuristic Algorithms in Predicting Financial Distress using intra-corporate (Financial and non-financial) and Economic Variables (Grasshopper Optimization and Ant Colony Algorithms)
فریدون مرادی احمد یعقوب نژاد آزیتا جهانشاد

10.30495/fed.2023.1962198.2734

. Abstract The purpose of this study is investigating the capability of Grasshopper Optimization Algorithm (GOA) in more accurately predicting the financial distress by-using intra-corporate (financial and non-financial) and economic variables. The method of this rese More

. Abstract The purpose of this study is investigating the capability of Grasshopper Optimization Algorithm (GOA) in more accurately predicting the financial distress by-using intra-corporate (financial and non-financial) and economic variables. The method of this research is improving the performance of the basic model of Multilayer Perceptron Artificial Neural Network (ANN-MLP) by-using a hybrid model with GOA (MLP-GOA) and Ant Colony Optimization Algorithm (MLP-ACO). The statistical research population of companies active in Tehran Stock Exchange during a 7-year period (from 1391 to 1397) included 476 companies, and finally, after systematic elimination, there were 289 qualified companies (including 2023 observation year-company). Checked and screened. The results showed the ability of ANN-MLP model to predict financial distress by-using financial and non-financial variables, and in addition the hybrid models (MLP-GOA and MLP-ACO) had been improved this ability. The accuracy of the MLP-GOA model for the year t, year t-1and year t-2 (before financial distress occurs), respectively are 97.30%, 94.53% and 91.30% that higher than the accuracy of the basic model and the hybrid MLP-ACO model. Although, entering the economic variables has increased the capability of all models significantly but the results showed that the financial distress is more affected by intra-corporate variables and the effect of economic variables has already been considered through the effect on financial events recorded in the accounting system. The results of this study can be used by company managers, banks and rating and credit institutions, insurance companies, financial analysts, investors and investment companies in assessing the risk of financial distress to make appropriate decisions and actions. Manuscript profile

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247 - ارزیابی مدل‏های خطی و غیرخطی در پیش‏بینی شاخص قیمت سهام در بورس اوراق بهادار تهران
علی اکبر خسروی نژاد مرجان شعبانی صدر پیشه

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248 - Predictability Test of Stock Market Price Index in Iran Investment Market and comparing Linear and Nonlinear models predictability potentials
Karim Emami Ghodratollah Emamverdi

Since the highly complicated Time Series such as Stock Market Prices are usually stochastic, their changes are assumed to be unpredictable. Some tests which have been used to study the statistical observations related to the economical variables e.g. Stock Market Price, More

Since the highly complicated Time Series such as Stock Market Prices are usually stochastic, their changes are assumed to be unpredictable. Some tests which have been used to study the statistical observations related to the economical variables e.g. Stock Market Price, are often go wrong while encountering the chaotic data and recognize them as stochastic ones, though these data are actually generated from the deterministic systems which bear few tribulations. For this reason the predictable and non-linear tests such as HURST, BDS, Runs Test, and Correlation Dimension have been used to study the existence of deterministic chaotic trend and non-linear process in Time Series of Daily Stock Market Price Index of TEHRAN STOCK EXCHANGE from 23 rd October, 2000 to 24 th September, 2002. The result of the above mentioned tests shows the predictability and the existence of a non-linear process in the sample data. After the illustration of predictability and the non-linear process in daily stock index data, then the linear time series models (AR), non-linear (GARCH) and Artificial Neural Network (ANN) have been estimated to present a suitable model for predicting the Stock Price Index. Comparing the potential of predictability of these models by such criteria as: CDC, RMSE, MAE, MAPE and U-THEIL inequality coefficient, it has been revealed that there is the highest potential of predictability in Artificial Neural Network models than the other ones Manuscript profile

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249 - Presenting a Comprehensive Model for Measuring the Liquidity Risk of Banks Listed on the Tehran Stock Exchange (Case Study: Mellat Bank)
Toraj Azari Mojtaba Tastori Reza Tehrani

10.30495/fed.2022.694721

 AbstractLack of liquidity management of banks is one of the most important risks for any bank and lack of attention to liquidity risk leads to irreparable consequences. Preventing liquidity risk requires a comprehensive measurement method but liquidity risk is com More

 AbstractLack of liquidity management of banks is one of the most important risks for any bank and lack of attention to liquidity risk leads to irreparable consequences. Preventing liquidity risk requires a comprehensive measurement method but liquidity risk is complicated issue, and this complexity makes it difficult to provide a proper definition. In addition, defining liquidity risk determinants and formulation of the related objective function to measurement its value is a difficult task. To address these problems and assess liquidity risk and its key factors, in this study we propose a model that uses artificial neural networks and Bayesian networks. Design and implementation of this model includes several algorithms and experiments to validate the model. In this paper, we have used Levenberg-Marquardt and Genetic optimization algorithms to teach artificial neural networks. We have also implemented a case study in Bank Mellat to demonstrate the feasibility, efficiency, accuracy and flexibility of the research liquidity risk measurement model.  Manuscript profile

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250 - Dynamic Prediction of Financial Distress: A Case Study
Hamid Rahimi Mehrzad Minooei mohammad reza fathi

10.30495/fed.2024.709360

Abstract Considering the current economic conditions of the country, the number of helpless companies and the importance of financial helplessness are increasing day by day. The increase in economic factors affecting financial helplessness has also increased the comple More

Abstract Considering the current economic conditions of the country, the number of helpless companies and the importance of financial helplessness are increasing day by day. The increase in economic factors affecting financial helplessness has also increased the complexity of investment decisions for these companies. For this purpose, the approach presented in this research, taking into account various financial criteria, provides the possibility of dynamic forecasting of Financial Distress for these decision makers. makes The approach introduced in this research is first by clustering the companies in the proportional cluster of financially helpless and non-helpless with the help of artificial neural network method, self-organizing mapping (SOM) and then by using the data envelopment analysis method based on the worst performance (WPF-DEA). A dynamic forecast of the financial helplessness of the companies admitted to the Tehran Bahadur Stock Exchange was carried out. Using the mentioned method, 105 companies were evaluated and the result of the inefficiency of these companies was predicted during 5 time periods from 2015 to 2019. The dynamic data coverage analysis model based on the worst performance has the ability to evaluate the inefficiency of the examined units, including companies that are members of the Stock Exchange and Securities Organization. Data envelopment analysis has been able to successfully identify the financial helplessness of companies as inefficient decision units. Manuscript profile

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251 - Estimation of Daily Evaporation Using of Artificial Neural Networks (Case Study; Borujerd Meteorological Station)
A. Ariapour M. Nassaji Zavareh

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252 - Analysis of knowledge-based organizational culture components using artificial neural network
Somayeh hosseinzadeh Toraj Mojibi Sayyedmahdi alvani javad rezaiyan

This study analyzes the components of knowledge-based organizational culture in order to achieve performance effectiveness using artificial neural network.The present study, from an application point of view, is Applied Research, which was applied using a mixed Explorat More

This study analyzes the components of knowledge-based organizational culture in order to achieve performance effectiveness using artificial neural network.The present study, from an application point of view, is Applied Research, which was applied using a mixed Exploratory Research type method. To prepare the research literature, the method of studying documents and their results was applied and also intensive face-to-face interviews with 20 university experts were accomplished. After recording the interviews, the data were analyzed qualitatively using a context analysis method. The research population, in the quantitative part, consisted of 50 administrative experts of related field who were selected using objective sampling. To measure the effectiveness of each the components of knowledge-based organizational culture and analysis of their sensitivity a Multilayer feedforward neural networks with back propagation model was used.Findings showed that the sensitivity of performance effectiveness to knowledge-based organizational culture is 4.39%. Also, knowledge-based organizational culture was identified with five components, and among its components, the two components of knowledge-based leadership and trust culture were identified as the components to which performance effectiveness is most sensitive. Manuscript profile

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253 - Modeling to Predict the Liquidity Risk of Iran's Government Banks Using Artificial Neural Networks and Accounting Indicators
Mahdi Khosroyani Farzaneh Heydarpoor

10.30495/faar.2022.697091

AbstractOne of the most important risks of bank is liquidity risk, so banks must have appropriate information systems to measure, predict and control liquidity risk. Banks manage their liquidity risk using different tools and methods, depending on the conditions and typ More

AbstractOne of the most important risks of bank is liquidity risk, so banks must have appropriate information systems to measure, predict and control liquidity risk. Banks manage their liquidity risk using different tools and methods, depending on the conditions and type of activity. Despite the fundamental differences in the size, type of activity and structure of Government owned banks,is it possible to model and forecast the liquidity risk of state banks? To answer this question in this study, using the accounting information of Government banks in Iran, and the research accounting indicators were calculated and liquidity risk was modeled by the multilayer perceptron neural network. Then, the difference between the results of the model and the real data was measured by MSE. The research results showed that the designed model can be used to predict the liquidity risk of Iran's Government owned banks. Manuscript profile

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254 - Microgrid Planning Including Renewables Considering Optimum Compressed Air Energy Storage Capacity Determination Using HANN-MDA Method
Seyedamin Saeed Tahere Daemi Zohreh Beheshtipour

Microgrids, with their ability to integrate renewable energy sources, play a crucial role in achieving sustainable and resilient energy systems. Effective planning and optimization of microgrids, particularly considering the inclusion of compressed air energy storage (C More

Microgrids, with their ability to integrate renewable energy sources, play a crucial role in achieving sustainable and resilient energy systems. Effective planning and optimization of microgrids, particularly considering the inclusion of compressed air energy storage (CAES) systems, are essential for maximizing their benefits. This study proposes a novel approach, the Hybrid Artificial Neural Network-Modified Dragonfly Algorithm (HANN-MDA), for determining the optimum capacity of CAES in microgrid planning. The HANN-MDA method combines the learning capabilities of artificial neural networks with the optimization power of the modified dragonfly algorithm. The proposed method aims to minimize the overall cost of microgrid operation while considering the integration of renewable energy sources and the storage capabilities of CAES. Simulation results demonstrate the effectiveness of the HANN-MDA method in accurately determining the optimal CAES capacity, leading to improved microgrid performance and cost savings. The findings highlight the importance of considering CAES in microgrid planning and the potential of the HANN-MDA method for achieving efficient and economically viable microgrid designs. Manuscript profile

Open Access Article

255 - Artificial Intelligence Based Approach for Identification of Current Transformer Saturation from Faults in Power Transformers
A. R Moradi Y Alinejad Beromi K Kiani Z Moravej

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Open Access Article

256 - Hourly Wind Speed Prediction using ARMA Model and Artificial Neural Networks
Farzaneh Tatari Majid Mazouchi

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Open Access Article

257 - Effective Feature Selection for Pre-Cancerous Cervix Lesions Using Artificial Neural Networks
Farnaz Rouhbakhsh Fardad Farokhi Kaveh Kangarloo

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Open Access Article

258 - Designing non-linear pattern contagious influence of the Tehran Price Index from the physical assets market (Application of NARX artificial neural network model)
mahdi shaban habibollah nakhaei Ghodrat Alloh Talebnia nazanin bashirimanesh

The present study examines the contagiousness of the Tehran Stock Exchange from the price of parallel assets using the dynamic neural network. To perform calculations, the time series of coin price variables as a representative of the gold market, the average price per More

The present study examines the contagiousness of the Tehran Stock Exchange from the price of parallel assets using the dynamic neural network. To perform calculations, the time series of coin price variables as a representative of the gold market, the average price per square meter of residential building as a representative of the housing market. The price of each barrel of Iranian crude oil and the US dollar exchange rate and their conditional fluctuations as explanatory variables and the total index of Tehran Stock Exchange and its conditional fluctuation as the target variable from 1387 to 1397 are examined daily .The dynamic neural network is evaluated with four input variables and one target variable with different neurons with the MSE criteria, and the models with 20 neurons and 10 neurons have the lowest MSE, .Research results show that the stock exchange has a maximum of two lag from competing markets has become contagious, indicating the poor performance of the Tehran Stock Exchange. The results show that the proposed neural network patterns have a high power in predicting the index of Tehran Stock Exchange and its fluctuations from 1387 to 1397 as in-sample forecast and in 1398 as extra-sample forecast. Manuscript profile

Open Access Article

259 - Stock Price Prediction in Tehran Stock Exchange Using Artificial Neural Network Model and ARIMA Model: A Case Study of Two Active Pharmaceutical Companies in Stock Exchange
Ahmad Chegeni AZIZ GORD

In This Study We Compare the Efficiency of Both Artificial Neural Network Prediction Methods (ANN) and Traditional Method of Auto Regressive Integrated Moving Average (ARIMA) in Predicting Stock Prices in Iranian Stock Market. For This Purpose, Four Pharmaceutical Compa More

In This Study We Compare the Efficiency of Both Artificial Neural Network Prediction Methods (ANN) and Traditional Method of Auto Regressive Integrated Moving Average (ARIMA) in Predicting Stock Prices in Iranian Stock Market. For This Purpose, Four Pharmaceutical Companies, Alborz Drug, Iran Drug, Pars Drug, and Jam Drug Were Selected and ARIMA Model and Artificial Neural Network Model Were Estimated For All Four Companies. In Order to Estimate Artificial Neural Network Model, Stock Price Variable as Dependent Variable and Stock Trading Volume, Drug Industry Index, OPEC Oil Price, Exchange Rate and Gold Price are Considered as Independent Variables. MSE, RMSE, MAD, R2 and MAPE Criteria Were Used to Compare Two Models. In Order to Estimate the Stock Price Forecast Regression Model, Use of Auto Regressive Integrated Moving Average (ARIMA) Regression Is Used and Estimation of the Coefficients of the Model is Performed Using the EVIEWS Statistical Software. An Suitable ANN Model Was Created For Predicting Stock Prices Using MATLAB Software. The Results of the Research Showed That the Research Hypothesis is Correct and the Artificial Neural Network Model (ANN) Has a Better Predictor of Stock Price in the Iranian Stock Market Than the ARIMA Method. Manuscript profile

Open Access Article

260 - Comparison of the Predictive Accuracy of Artificial Neural Network Systems Based on Multilayer Perceptron Approach and Falmer Binary-Logistics Model in Order to Predict Bankruptcy
Somieh Saroei Hamid Reza Vkili Fard, Ghodratolah Taleb Nia

Financial analysts and other users need relevant and reliable information to predict corporate bankruptcy, which should be distributed symmetrically to all users. Accordingly, the purpose of this study is to compare the prediction accuracy of Artificial Neural Network ( More

Financial analysts and other users need relevant and reliable information to predict corporate bankruptcy, which should be distributed symmetrically to all users. Accordingly, the purpose of this study is to compare the prediction accuracy of Artificial Neural Network (ANN) systems based on the Multilayer Perceptron Approach and Falmer Binary-Logistics Model in order to predict bankruptcy. To test the hypotheses, the combined data of 172 companies listed on the Tehran Stock Exchange in the period 2007-2016 were used. The results of the analysis of the research data show that the ANN system can identify of the factors affecting on bankruptcy of Iranian companies in the year before bankruptcy by Precision equal 98%. Findings from the binary-logistic model showed that the forecasting model designed based on the Falmer regression method is able to predict with 82% accuracy the bankruptcy of the sample companies. Therefore, the use of artificial neural networks can more powerfully and accurately predict bankruptcy than regression models. Manuscript profile

Open Access Article

261 - Stock price prediction using artificial neural networks on lowest price range data
Bahman Ashrafijoo Nasser Fegh-hi Farahmand yagoub Alavi matin Kamaleddin Rahmani

Today, one of the most important challenges in the capital market is stock price prediction. Stock price data represents a financial time series whose trend is very difficult to predict due to its characteristics and dynamic nature. One of the most recent methods used i More

Today, one of the most important challenges in the capital market is stock price prediction. Stock price data represents a financial time series whose trend is very difficult to predict due to its characteristics and dynamic nature. One of the most recent methods used in predicting financial time series is ANN with back propagation of error. In this article, artificial neural networks based on three different Levenberg-Marquardt learning algorithms, scaled conjugate gradient and Bayesian regularization were used to predict the stock market based on the data of the lowest price range as well as the 30-minute data of the stock market index and compared their results together. We compare. All three algorithms provide a 99.9% estimate using the lowest price range data. But when using 30-minute data, the estimation accuracy decreases to 96.2%, 97.0%, and 98.9% for Levenberg-Marquarat algorithm, scaled conjugate gradient, and Bayesian regularization, respectively, which compares with the results Obtained by using the data of the lowest price range, the accuracy of the prediction is significantly reduced. Finally, the optimal neural network is compared with the regression method to determine that the results of the ANN in complex nonlinear time series are more efficient than linear methods. Manuscript profile

Open Access Article

262 - Fuzzy – neural model with hybrid genetic algorithms for stock price forecasting in auto industry in Tehran security exchange
ehsan Sadeh reza Ehtesham Rasi ali Sheidaei Narmigi

Selection of appropriate time and price in trading stocks has an important role in investment decisions on profit and loss of investors in capital markets. Nonlinear intelligent systems, such as artificial neural networks, fuzzy- neural networks and genetic algorithms, More

Selection of appropriate time and price in trading stocks has an important role in investment decisions on profit and loss of investors in capital markets. Nonlinear intelligent systems, such as artificial neural networks, fuzzy- neural networks and genetic algorithms, would be used to forecast stock prices motions. In this article,a model of stock prices motions has been designed using Adaptive Neuro- Fuzzy Inference System (ANFIS)integrated with genetic algorithm, in which two different groups of fundamental and technical variables have been employed as model inputs. According to Model outputs,the rate of forecasting errors in both groups of inputs is not significant and these systems are able to forecast daily stock prices. The Mann-Whitney test has been used to measure the accuracy of models and it was found that there is no significant difference between results of prices forecasted in both methods. Both methods are able to forecast next day price with an insignificant error provided that at least one of the inputs in both methods has a linear dependence with price, .  Also, results show that  these systems do not work properly to forecast prices of high volatility stocks Manuscript profile

Open Access Article

263 - Forecast earnings management based on adjusted Jones model using Artificial Neural Networks and Genetic Algorithms
Khosro Faghani Makrani S. Hasan Salehnezhad Vahid Amin

In recent years, earnings management in university research has attracted much attention. The aim of this study is to predict earnings management through discretionary accruals based on adjusted Jones model. In this study, two models of artificial neural networks and ge More

In recent years, earnings management in university research has attracted much attention. The aim of this study is to predict earnings management through discretionary accruals based on adjusted Jones model. In this study, two models of artificial neural networks and genetic algorithms - neural network hybrid model as a successful model to predict earnings management based on adjusted Jones model were used in the Tehran Stock Exchange. The sample used in this study is consisted of 570 firm-year between 2008 to 2013. The results showed that neural networks have a high ability to predict earnings management rather than the adjusted Jones linear model. The findings also suggest that, the genetic algorithm through optimizing artificial neural network weights is able to increase power of artificial neural network to predict earnings management. Manuscript profile

Open Access Article

264 - Optimization of technical indicators’ parameters for intraday data using optics – inspired optimization (OIO): a case study of Tehran stock exchange
Mohammad Ali Rastegar Farah Ashuri

In this paper a stock trading system based on the combination of six technical indicators is designed. The indicators are combined using an artificial neural network and their parameters are optimized using convex combination-based optics-inspired optimization (COIO) al More

In this paper a stock trading system based on the combination of six technical indicators is designed. The indicators are combined using an artificial neural network and their parameters are optimized using convex combination-based optics-inspired optimization (COIO) algorithm. In the proposed model the technical indicators’ optimized parameters are obtained using both COIO and genetic algorithms with the aim of maximization of modified Sharpe ratio. The presented paper uses stock intra-day prices as input data and considers the transaction costs. The designed strategy is compared against several other approaches including: using the indicators’ default parameters, buy and hold strategy and optimization using genetic algorithm, for both daily and intra-day prices and due to a greater modified Sharpe ratio for the proposed model, its superiority is shown in all cases. Moreover, in a comparison based on end- of- period returns, it is shown that without considering the transaction costs the results of the intra-day data beats the results of the daily data while no superiority is observed when considering the transaction costs. So reducing the transaction costs is recommended to motivate traders to trade on an intra-day basis. Manuscript profile

Open Access Article

265 - The Application of Robust Optimization and Goal Programming in Multi Period Portfolio Selection Problem
Saghar Homaeifar Emad Roghanian

Portfolio selection is one of the most important area in financial world. Investors always want to make the best decisions which are compatible with conditions of real world. In the real world, data are usually under uncertainty. On the other hand, the most of strategie More

Portfolio selection is one of the most important area in financial world. Investors always want to make the best decisions which are compatible with conditions of real world. In the real world, data are usually under uncertainty. On the other hand, the most of strategies for portfolio selection are multi-period. Therefore, investors should rebalance their portfolios during investment horizon. In this research we present a multi-period portfolio optimization model which considers transaction costs and deal with uncertainty by application of robust programming. This model is a mean-CVaR multi objective model that is solved by goal programming. Furthermore, most of previous researches have used regression or time series models to forecast future returns of stocks for solving numerical examples, however, in this paper we forecast future returns by using Artificial Neural Networks (ANNs). Finally, solutions of robust model are compared with results of nominal one. These results show that consideration of data uncertainty and other real assumptions lead to more practical solutions.    Manuscript profile

Open Access Article

266 - Forecasting Stock Price Trend by Artificial Neural Networks (Case Study: Isfahan Oil Refinery Company)
hossein badiei Ruhollah Rezazadeh Hadi Mahmoudi

Artificial neural networks (ANN) are mathematical models inspired by human’s neural and brain system. This research deals with the next day price forecasting in Tehran’s stock market by MLP, and attempts, by various methods, to reduce the prediction error. H More

Artificial neural networks (ANN) are mathematical models inspired by human’s neural and brain system. This research deals with the next day price forecasting in Tehran’s stock market by MLP, and attempts, by various methods, to reduce the prediction error. High pricing of stocks may lead to low demand for negotiable stocks and the failure of privatization. Raising various doubts in the negotiation of public properties, low pricing results in the long-term failure of negotiation policies. With respect to the importance of this issue, the newness of stock market and the lack of financing institutes and investment banks in Iran, prediction of stock price trend and its ascending and descending order can influence the decisions and strategies of managers. Various variables affect stock prices among which the role of economic indices, such as exchange rate / oil price and gold price is significant. The purpose of the present study is to predict the final prices of stocks by utilizing daily data through neural networks. The results indicate that the ANN model has low error and high explanatory and thus considerable forecasting power. Manuscript profile

Open Access Article

267 - Analysis of Most Important Variables Affecting TEPIX and Modeling Them with Artificial Neural Networks and Comparing Results with Technical Analysis and Elliott Waves
Mohammad Kamravafar S. Zabihollah Hashemi

The main goal of this research is to studying an identifying the main influencing variables on the TEPIX (Tehran Stock Price Index) and modelling them using artificial neural networks and comparing results with technical analysis and Elliot waves. Independent variables More

The main goal of this research is to studying an identifying the main influencing variables on the TEPIX (Tehran Stock Price Index) and modelling them using artificial neural networks and comparing results with technical analysis and Elliot waves. Independent variables used are dollar exchange rate, inflation, GDP, unemployment and liquidity and dependent variable is TEPIX. In this study, artificial neural networks (NLP and GMDH), technical analysis tools (Elliot waves and regression channel) are used that they show between independent variables in GMDH, unemployment is unneeded variable and have low influence, but others have high influence in the model. Further the study shows that technical analysis and artificial neural networks may have same results, but ANN have more power to predict the TEPIX Manuscript profile

Open Access Article

268 - Predict the risk of falling stock prices by using meta-innovative methods (Cumulative particle motion optimization algorithm) and comparison with logistic regression
Esfandiar Malekian hossin fakhari jamal ghasemi Sarveh Farzad

The Crash, which indicates how much specific stock prices are at risk of collapse. Accordingly, the purpose of this research is to model the risk of falling stock price of listed companies in Tehran Stock Exchange using a multivariate optimization algorithm for particle More

The Crash, which indicates how much specific stock prices are at risk of collapse. Accordingly, the purpose of this research is to model the risk of falling stock price of listed companies in Tehran Stock Exchange using a multivariate optimization algorithm for particle cumulative movement and comparing results with logistic regression. For this purpose, a hypothesis was developed for the study of this issue and the data for 106 members of the Tehran Stock Exchange for the period of 2010-2010 were analyzed. First, 14 independent variables were introduced as inputs of the combined genetic algorithm and artificial neural network, which was considered as a feature selection method, and 7 optimal variables were selected. Then, using particle cumulative algorithm and logistic regression, predicted The risk of falling stock prices. The stock price collapse criterion has been used to calculate the risk of falling stock prices. The research findings show that the particle agglomeration algorithm is more likely than traditional logistic regression to predict the risk of falling stock prices. These findings underscore the need for managers to use meta-metric methods for forecasting. Manuscript profile

Open Access Article

269 - A Comparison between Fama and French five-factor model and artificial neural networks in predicting the stock price
reza tehrani Milad Heyrani Samira Mansuri

One of the most important issues of financial markets is the prediction of price and stock returns. In this paper, we try to find the best model and stock price prediction approach based on the mean square error (MSE), root-mean-square error (RMSE), R-squared, standard More

One of the most important issues of financial markets is the prediction of price and stock returns. In this paper, we try to find the best model and stock price prediction approach based on the mean square error (MSE), root-mean-square error (RMSE), R-squared, standard deviation (SD), Mean absolute error and the mean absolute percent error (MAPE) for the Fama and French five-factor model. For this purpose, after the formation of a portfolio based on the Fama and French model during the period from 2009 to 2017, stock price is estimated by econometric model, neural network and Fuzzy Neural Networks, so the accuracy of each approach was compared. The results of the prediction the efficiency of the generated portfolios show that the prediction accuracy of the radial base function network (RBF) is very high compared to other ARMA models and other neural networks. Manuscript profile

Open Access Article

270 - The use of Firefly Algorithm and Bayesian Regulation technique of optimized Artificial Neural Network to predict stock price in Iran Stock Market
seyyed alireza mosavi Afsaneh Gholami

Predicting the future stock price has always been considered as an important issue by both buyers and sellers. Hence, Artificial Neural Network (ANN) was used in this study to develop a model pertaining to artificial intelligence in order to predict stock price in Iran More

Predicting the future stock price has always been considered as an important issue by both buyers and sellers. Hence, Artificial Neural Network (ANN) was used in this study to develop a model pertaining to artificial intelligence in order to predict stock price in Iran Stock Market. Since artificial neural networks should consist of the best network topology to achieve the highest performance, Firefly Algorithm (FA), a meta-heuristic Algorithm, was used to find the optimal structure of network. Finally, Bayesian regulation technique, rather than the conventional teaching techniques, was applied to maintain the more generalized network. In general, Data from three big companies: Iran Khodro Company, Shiraz Petrochemical Company, and Isfahan Steel Companywere gathered in span of three years. This paper profited from some parameters, including high price, low price, the opening price, closing price, EMA(5) ،EMA(10) ،RSI ،William R% ،Stochastic k% ،Stochastic D% و ،ROCas network inputs and benefited from the closing stock price in the next days as the neural network as well. After developing a model associated with each company, some parameters such as the root-mean-square error (RMSE), Standard Deviation of error(SD), Absolute average relative deviation (AARD), the regression coefficient (R2) as well as the graphical analysis of relative deviation have been used to examine the accuracy of the developed network. The outcomes of the analysis of the developed neural networks revealed that the mentioned models with great accuracy are able to predict stock price in the subsequent day for the corporations mentioned above. Manuscript profile

Open Access Article

271 - Tehran Stock Exchange Overal Index Prediction using Combined Approach of Metaheuristic Algorithms, Artificial Intelligence and Parametric Mother Wavelet
Alireza Saranj Madjid Ghods reza tehrani

Understanding and the investigating the behavior of stock prices, has always been one of the major topics of interest to the investors and finance scholars. In recent years, various models for prediction using neural network and hybrid models have been proposed which ha More

Understanding and the investigating the behavior of stock prices, has always been one of the major topics of interest to the investors and finance scholars. In recent years, various models for prediction using neural network and hybrid models have been proposed which have a better performance than the traditional models. Here a hybrid model of neural network and wavelet transform is proposed in which genetic algorithm has been used to improve the performance of wavelet transform in optimizing the wavelet function. Daily stock exchange rates of TSE from April 21, 2012 to April 19, 2017 are used to develop a prediction model. The results show that it is possible to find a wavelet basis, which will be appropriate to the intrinsic characteristics of time series for prediction and the prediction error in this model is reduced comparing to the neural network and hybrid neural network and wavelet models. Manuscript profile

Open Access Article

272 - Comparison of the Efficiency of Statistical Learning Algorithms and Artificial Neural Networks to Predict Stock Prices
Alireza Sadat Najafi Soheila Sardar

10.30495/ijm2c.2022.1948716.1241

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Open Access Article

273 - AN INTELLIGENT FAULT DIAGNOSIS APPROACH FOR GEARS AND BEARINGS BASED ON WAVELET TRANSFORM AS A PREPROCESSOR AND ARTIFICIAL NEURAL NETWORKS
Mahmuod Akbari Hadi Homaei Mohammad Heidari

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Open Access Article

274 - The Modeling of Exchange Rate Predict in Iran by Using Neural Network Based on Genetic Algorithms and Particle Swarm Algorithm
ali jamali saeed daie karimzadeh

In recent years the use of artificial intelligence techniques in the financial and investment markets instead of customary quantitative methods has been increasing and gives better performance towards classic methods usually. Artificial Neural Network (ANN), has weakn More

In recent years the use of artificial intelligence techniques in the financial and investment markets instead of customary quantitative methods has been increasing and gives better performance towards classic methods usually. Artificial Neural Network (ANN), has weaknesses points despite its enormous benefits also. In this study, in order to overcome the weaknesses of the network consists of combining artificial intelligence methods with Evolutionary algorithms, means of artificial neural network combined with genetic algorithm (GA) and Particle Swarm algorithm (PSO) to model and daily predict of nominal exchange rates or the exchange rate dollar by Rial in Iran in the period 21.03.2013 to 22.12.2019 is used. This combined model with neural networks method as one artificial intelligence model according to the criteria of MSE , RMSE, MAE, U.Theil compared. The results of this research show the superiority of synthetic neural network model -Particle Swarm algorithm compare to other models of investigation. Manuscript profile

Open Access Article

275 - Theoretical Study of Relation among Structural Parameter and Water Decontamination Behaviors of some Drugs in Presence of Carbon Nanotube
Vahhab Fattahimehr Farhad Khamchin Moghadam Hadi Khatami Mashhadi

10.22034/jchr.2018.544201

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Open Access Article

276 - Ability of Machine Learning Algorithms and Artificial Neural Networks in Predicting Accounting Profit Information Content Before Announcing
Hossein Alizadeh Majid Zanjirdar Gholam Ali Haji

10.30495/afi.2023.1962857.1137

Purpose: The aim of this research is to investigate the capability of artificial neural networks and machine learning algorithms, including Support Vector Machine and Random Forest, in predicting the information content of accounting profits before its announcement in a More

Purpose: The aim of this research is to investigate the capability of artificial neural networks and machine learning algorithms, including Support Vector Machine and Random Forest, in predicting the information content of accounting profits before its announcement in accepted companies on the Tehran Stock Exchange during the period from 2015 to 2020.Methodology: Daily data required for the research were collected using Rahnaward-e-Novin software, and a systematic random sampling method was used to select 88 companies. MATLAB was used for modeling artificial neural networks and machine learning algorithms, and Python code was employed to calculate abnormal returns in neural networks and machine learning algorithms. The information content of profits was measured through the test of the relationship between profits and abnormal returns, based on the model by Porti et al. (2018). The input variables for artificial neural networks and machine learning algorithms are technical indicators. Accuracy, precision, recall, and F-score metrics were used for performance evaluation.Findings: The results of predicting with three models of artificial neural networks, Support Vector Machine, and Random Forest showed that Support Vector Machine and Random Forest had higher accuracy than artificial neural networks in predicting buy, sell, and hold strategies, and only Support Vector Machine had the ability to predict the information content of profits among the three models.Originality / Value: Designing a predictive model for stock price movements in the next trading day using artificial neural networks, Support Vector Machine, and Random Forest as the main innovation of the research. The research findings can increase the speed of information dissemination to the market and attract it, which will reduce the impact of informational asymmetry and information-based trading and ultimately enhance market efficiency. Manuscript profile

Open Access Article

277 - On the dynamic stability of a flying vehicle under the follower and transversal forces
Omid Kavianipour Majid Sohrabian

This paper deals with the problem of the instability regions of a free-free uniform Bernoulli beam consisting of two concentrated masses at the two free ends under the follower and transversal forces as a model for a space structure. The follower force is the model for More

This paper deals with the problem of the instability regions of a free-free uniform Bernoulli beam consisting of two concentrated masses at the two free ends under the follower and transversal forces as a model for a space structure. The follower force is the model for the propulsion force and the transversal force is the controller force. The main aim of this study is to analyze the effects of the concentrated masses on the beam instability. It is determined that the transverse and rotary inertia of the concentrated masses cause a change in the critical follower force. This paper also offers an approximation method as a design tool to find the optimal masses at the two tips using an artificial neural network (ANN) and genetic algorithm (GA). The results show that an increase in the follower and transversal forces leads to an increase of the vibrational motion of the beam which is not desirable for any control system and hence it must be removed by proper approaches. Manuscript profile

Open Access Article

278 - Application of Artificial Neural Network to Estimate the Strategic Value Creation Via Relative Efficiency in the Automotive Industry
Akbar Valizadeh Oghani Nasser Fegh-hi Farahmand Farzin Modarres Khiabani Majid Bagherzadeh

  The purpose of this study is to investigate the effect of relative efficiency of companies on value creation in the automotive industry accepted in Tehran Stock Exchange. The data were extracted from the financial statements of selected companies during in the 2 More

  The purpose of this study is to investigate the effect of relative efficiency of companies on value creation in the automotive industry accepted in Tehran Stock Exchange. The data were extracted from the financial statements of selected companies during in the 2013-2017. Initially, with the implementation of the DEA model with a native model, the relative efficiency is determined for each company. Then the strategic value creation of the companies is measured by the average of the factors such as return on equity, Q Tobin ratio, return on investment, and wealth creation for shareholders. The neural network model used in this study is a multilayer perceptron with back propagation error training pattern. The results show that the implementation of the artificial neural network model in the automotive industry explains the strategic value of the companies to a satisfactory level through the relative efficiency index and other input variables. Although some of the companies are efficient, such as Rana Investments Co., Khawar Spring Co., Saipa Diesel, Bahman Group and Charkheshgar Co., But in recent years, the automotive industry has been inefficient. At the same time, companies in this industry have somehow been able to strategically create value for their shareholders and their owners.   Manuscript profile

Open Access Article

279 - Prediction of Ground-Level Air Pollution Using Artificial Neural Network in Tehran
Afshin Khoshand Mahshid Shahbazi Sehrani Hamidreza Kamalan Siamak Bodaghpour

10.22034/apj.2017.1.1.6167

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Open Access Article

280 - Predict the success of students in math’s course in final exams in Arak city with neural networks
Maryam Sadat Abbas Toloie Ashlaghi Reza Radfar

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Open Access Article

281 - Prediction of Adhesion Parameters of Hook-shaped Steel Fibers and Concrete Using Artificial Neural Networks
Amir ebrahim akbari bagal

As steel fibers are important reinforcement materials in concrete, in this study, the behavior of hook-shaped steel fibers from concrete is predicted through the use of artificial neural networks. In the absence of comprehensive laboratory data, data obtained from finit More

As steel fibers are important reinforcement materials in concrete, in this study, the behavior of hook-shaped steel fibers from concrete is predicted through the use of artificial neural networks. In the absence of comprehensive laboratory data, data obtained from finite element analysis was used for modeling. The simulations are carried out using ABAQUS software's finite element method in 3D. Using the concept of the transition zone of the interface, whose parameters were obtained by inverse finite element analysis and experimental tests conducted on a sample of fibers, this model has been developed to simulate the interaction between fibers and concrete. On the basis of the results of the numerical model validated against the experimental results, the effective parameters of the fibers were extracted, and a neural network was then constructed based on the results. A multilayer forward perceptron artificial neural network and back-propagation training algorithm are used to predict pull-out force, with Marquardt-Lonberg optimization applied. The results demonstrate that the neural network model presented in this research is an effective method for predicting the pull-out force of fibers from concrete, in part because it allows the use of more variables in modeling, as well as delivering more accurate results. Manuscript profile

Open Access Article

282 - An artificial neural network comparison with logistic regression in predicting post-traumatic mental disorders in mild brain injury patients
Elham Shafiei arash nademi Esmaeil l Fakharian abdollah omidi

Although severe brain injury can make people susceptible to mental disorders, there is still debate about traumatic brain injury. The purpose of this study was to compare the power of artificial neural network in predicting post-traumatic mental disorder in mild brain i More

Although severe brain injury can make people susceptible to mental disorders, there is still debate about traumatic brain injury. The purpose of this study was to compare the power of artificial neural network in predicting post-traumatic mental disorder in mild brain injury patients and logistic regression. For this purpose, in a prospective cohort study, 100 trauma patients referred to the trauma center of Shahid Beheshti Hospital of Kashan during 6 months were compared with 100 healthy people. For modeling, the data were randomly divided into two educational groups (100) and experimental (100 people). The Rock's curve and classification accuracy were used to estimate the predictive power of mental disorder. The results showed that there is a significant difference between the two groups of mild traumatic patients and healthy subjects in terms of mental disorders, and artificial neural network models have better efficiency than logistic regression models. This study showed that in order to predict mental disorder, the diagnostic indices of this factor should be considered at the beginning of the traumatic brain injury patients and then, using the artificial neural network model, predict this factor. The necessity of using this technology in demographic screening is useful in treating patients with trauma and preventing possible problems for such patients. Manuscript profile

Open Access Article

283 - Prediction of compressive strength of concretes containing micro silica subject to carbonation using neural network
Ali Delnavaz

Concrete materials are exposed to special weather conditions, corrosion and significant damage. For this purpose, the effect of 28-day compressive strength changes on the samples studied in this study was investigated by considering the simultaneous effect of chloride i More

Concrete materials are exposed to special weather conditions, corrosion and significant damage. For this purpose, the effect of 28-day compressive strength changes on the samples studied in this study was investigated by considering the simultaneous effect of chloride ion penetration and carbonation phenomenon. For this reason, in the first case, the samples are exposed to carbon dioxide once and then to chloride ions. In the latter case, only samples under the influence of chloride infiltration are examined. To make the samples, which include 9 mixing designs, three water-to-cement ratios of 0.35, 0.4 and 0.5 and three percent of 0%, 7% and 10% silica fume have been used. Finally, an optimal model is introduced to predict the compressive strength of concrete containing micro silica exposed to carbonation using artificial neural network. Also, a relation for estimating compressive strength based on the ratio of water to cement and the amount of silica is presented. Manuscript profile

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