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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 FatehiMammography 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 MoreMammography 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 HajigholamiThis 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 MoreThis 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 - Forecasting of the Students’ Performance in Military Higher Education Using Artificial Neural Network Prediction Algorithm (Case study: A military organization)
Mohammad Fallah Hamideh ReshadatjooBackground: 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 MoreBackground: 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
4 - Estimating Energy Consumption of Educational Spaces Using Artificial Neural Networks (ANNs)
مهسا فلاح نیاSize 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 MoreSize 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
5 - Estimating Energy Consumption of Educational Spaces Using Artificial Neural Networks (ANNs)
Mahsa FallahniaSize 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 MoreSize 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
6 - A new method for ranking of Z-numbers
M. Matinfar S. ezadiIn 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 MoreIn 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
7 - Solving of stochastic Voltaire integral equations by fuzzy artificial neural network method
Hadi Abtahi Hamid Reza Rahimi Maryam moslehVoltaire 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 MoreVoltaire 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
8 - Using Neural Network to Determine Input Excesses, Output Shortfalls and Efficiency of Dmus in Russell Mode
D. Modhej M. Sanei N. ShojaData 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 MoreData 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
9 - Reviews of Manipulating Prices using QDF & ANN-GA Models in Tehran's Stock Exchange
M. Hossein Poustfroush Alireza Naser Sadrabadi Mahmood MoeinaddinIn 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 MoreIn 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
10 - پیشبینی روند تغییرات قیمت سهام با بهکارگیری شاخصهای تحلیل تکنیکی و استفاده از روش ترکیبی الگوریتم ژنتیک و شبکه عصبی مصنوعی: مطالعه موردی سهام ایران خودرو
زینب آذریان سید مهدی همایونی -
Open Access Article
11 - Estimated Demand for Medical services Case Study of Shiraz and Arsanjan Cities "Generalized Logit and ANN Method
morteza hasanshahiIntroduction: The doctor is a health care provider which is depreciated due to, work, accidents, environmental pollution and age and increases the demand for health services; many factors have an impact on the demand for health services; this study attempts to measure t MoreIntroduction: The doctor is a health care provider which is depreciated due to, work, accidents, environmental pollution and age and increases the demand for health services; many factors have an impact on the demand for health services; this study attempts to measure the impact of 18 of the most important ones. Methodology: The model used is the Generalized Logit and ANN. The population includes those who referred to Shiraz and Arsanjan hospitals in 1994. The sample consists of 100 patients and 100 patient patients (non-patient) and data Collected through questionnaires. Findings: The results of model validity tests including fit of Goodness (Pearson and Deviance index), Parallel Regression, Maximum likelihood, and Newton-Raphson Algorithm indicate that the validity of the model is up to 84% confidence. According to the results, increase of 1% in visit, 2% of demand for services is reduced. The initial health and beliefs has the same interpretation of visit. An increase of one percent in premiums caused a decrease of 3.11% and increases one unit in health index, decrease of 1.3% and a one year increase in age, increase of 10%, and daily consumption of one cigarette, increases 0.04% in demand Health care. Conclusion: According to the results, health, insurance, education and awareness of body anatomy have the greatest impact and smoking and job have the least impact on the demand for health care. With increasing age, education, insurance coverage, awareness and income, the demand for treatment increases. On the other hand, over time, education, awareness and per capita income are rising, so, the demand for these services will increase in the future. Manuscript profile -
Open Access Article
12 - Data Mining as an Intangible Model of Information Therapy and Seeking Behaviors in Immune Deficiency Disease Specialists
Sedigheh Mohammadesmaeil Shiba KianmehrIntroduction: 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 MoreIntroduction: 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
13 - Forecasting the Type of Audit Opinions: A Data Mining Approach
محمدحسین ستایش فهیمه ابراهیمی سیدمجتبی سیف مهدی ساریخانیData 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 MoreData 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
14 - Development of a Fraud Detection Model Using an Integrated Approach Based on the Factor Analysis Model and the Artificial Neural Network Method in Firms Listed in Tehran Stock Exchange
Jaber Mohammadmoosaee Babak Jamshidinavid Mehrdad Ghanbari Farshid KheirollahiThe main purpose of this research is to propose a fraud detection model using an integrated approach based on the factor analysis model and the artificial neural network method. The network used to predict corporate financial fraud has 17 neurons (selected financial rat MoreThe main purpose of this research is to propose a fraud detection model using an integrated approach based on the factor analysis model and the artificial neural network method. The network used to predict corporate financial fraud has 17 neurons (selected financial ratios) in the input layer and 1 neuron (corporate fraud status) in the output layer. The conversion function used in the output layer is linear and for the middle layer a non-linear sigmoid function is selected. The neural network used in this research is a feed forward neural network with back propagation algorithm. The statistical population of this study is comprised of the companies listed in Tehran Stock Exchange in the time interval from1392 to 1393. Out of these companies, 140 have been selected as the research sample. The Beneish M-Score model has been used in order to classify the companies with the likelihood of fraudulent and non-fraudulent reporting. According to the Beneish M-Score Model, 78 companies were fraudulent in terms of their reports and 62 were non-fraudulent. For the final selection of the input variables (financial ratios) in the artificial neural network, the confirmatory factor analysis model and the principal component analysis model have been used. The results obtained from the aforementioned models have shown that the reported structure of the neural network model has 7 hidden layer neurons and the momentum learning algorithm has been used for training the network. This algorithm was more precise and functioned better than other reviewed structures. Therefore, it was selected as the final adjustment of the neural network.. The obtained results indicated that the artificial neural network method had a higher performance in this regard; in that the precision of classification of fraudulent and non-fraudulent firms and the overall performance of the artificial neural network method was57and 69%,72,73%,61,62% respectively. Manuscript profile -
Open Access Article
15 - Comparative between cost prediction using statistical methods and neural networks
امیر محمدزاده نسرین مهدی پور آرش محمدزادهPrediction 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 MorePrediction 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
16 - 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 ChiraniProfit 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 MoreProfit 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.‏ ‏‏ Manuscript profile -
Open Access Article
17 - Comparison of artificial neural network and multivariate linear regression (MLR) models to predict cover percentage Artemisia aucheri from some soil properties
Mansoreh Kargar Zeynab JafarianSoil 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 MoreSoil 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
18 - 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 PanahdarIn 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 MoreIn 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
19 - Climate Signals applied to the prediction of evaporation in west of Iran
Enayatolah Rahmati Majid Montazeri Amir Gandomkar Mehran Lashani ZandEvaporation 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 MoreEvaporation 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
20 - Analysis and investigation of landslide hazard zoning using of hierarchical analysis and artificial neural network models in the southern foothills of the Alborz (Tajrish of Tehran)
Mohsen Ranjbar Asal FadakIn this research, by review of previous works and field works, the Nine factors identifiedeffective factors in landslide hazard and used for analysis risk by GIS software. the occurredland slides in the study area were gathered and rectified by GPS. These Nine maps were MoreIn this research, by review of previous works and field works, the Nine factors identifiedeffective factors in landslide hazard and used for analysis risk by GIS software. the occurredland slides in the study area were gathered and rectified by GPS. These Nine maps werecrossed with the occurred landslide map and Landslides amounts and their areas werecomputed in each class. After determining the rate of each factor (element), land slidezonation was performed in GIS by artificial neural network and AHP Models. The efficiencyof output results of models was assessed by DR and QS indices. The results of DR indexshowed The map was produced using a neural network than maps produced using the analytichierarchy higher accuracy for the study area. Manuscript profile -
Open Access Article
21 - Analysis of lake water level between the climatic signals
Morteza Beitari Khakedi Ebrahim FatahiAmong the important characteristics of each lake, its water level. Understanding of how waterlevel fluctuations in an effective change-related issues including water storage changes - byshoreline construction and environmental issues. In this study, the effect of fluctu MoreAmong the important characteristics of each lake, its water level. Understanding of how waterlevel fluctuations in an effective change-related issues including water storage changes - byshoreline construction and environmental issues. In this study, the effect of fluctuating lakewater discharge and meteorological signals lake basin are discussed. The data used in thisstudy due to high volume in the interval from 1986 to 2008 and from 1951 to 2011 at differentstations. In this study, monthly data signals NAO - Nino1 +2 - Nino3 Nino4 Nino3.4 NOI-NP- PDO - SOI is used. All the above data were obtained from the NCEP. After explaining therelation and the prediction model using artificial neural networks for the same time interval,three months and six months respectively. The results of this model were evaluated andanalyzed. Model output of the neural network software Neurosolutions6 that all stations in thestate, ranging from the time delay of the quarter and half-year delay of the effective signalswing and lake water flows into the lake basin Order 1-NINO3 2 - NINO 3 +4 3 - NINO1 +24-NINO4 is the least of the order of 1 - NAO 2 - NOI 3 - PDO 4 - SOI 5 - NP is. Manuscript profile -
Open Access Article
22 - Development and combination of soft computing and geostatistical models in estimation of spatial distribution of groundwater level
سامان معروف پور احمد فاخری فرد جلال شیریOne 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 MoreOne 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
23 - 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 شیرزادRivers 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 MoreRivers 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
24 - Prediction of soil texture using artificial neural networks
علی محمدی ترکاشوند Elnaz Khanbabakhani Mohammad Ali MahmoodiSoil 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 MoreSoil 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
25 - Modeling Estimation of Suspended Sediment Rate in Pasikhan River Using Decision Tree Artificial Neural Network
سیدسامان Nasiri Ebrahim Amiri محبوبه shadabiAccurate 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 MoreAccurate 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
26 - Rainfall-Runoff modeling using Deep Learning model (Case Study: Galikesh Watershed)
Razieh Tatar Khalil Ghorbani mehdi Meftah halghi meysam salarijaziArtificial 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 MoreArtificial 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
27 - Comparison of Data Mining Models Performance in Rainfall Prediction Using Classification Approach (Case Study: Hamedan Airport Synoptic Weather Station)
Morteza Salehi Sarbijan Hamidreza DezfoulianBackground 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 MoreBackground 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
28 - Modeling of Physicochemical Properties of Eggplant Slices Coated with Basil Seed Gum during Frying Process
F. Salehi M. A. AsadnahalIntroduction: 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 MoreIntroduction: 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
29 - Modeling of Soybean Snack Roasting by Infrared Heating Using Artificial Neural Network (ANN)
H. Bagheri M. Kashani Nejadntroduction: Soybean is recognized as a good source of essential nutrients including protein, oil and several bioactive compounds and soybean has the potential to be used as snack and roasted nut, but most significant factor responsible for such limitation is probably c Morentroduction: Soybean is recognized as a good source of essential nutrients including protein, oil and several bioactive compounds and soybean has the potential to be used as snack and roasted nut, but most significant factor responsible for such limitation is probably considered as the characteristic flavor of soybean. Raw soybean has beany, bitter and astringent flavors. Therefore to improve its consumption, the particular flavor of raw soybean must be removed. Roasting might be considered as one of the best methods for this object. Materials and Methods: In this study, the infrared roaster is designed and soybean has been prepared and roasted according to the experimental condition. In this work, an artificial neural network model was developed for modeling of moisture content of soybean snack during infrared roasting. In order to do this, infrared lamp powers of 250, 350 and 450 W, distance between lamp and sample of 4, 7 and 10 cm and roasting time of 30 min were considered as the inputs and the amount of moisture ratio (MR) was estimated as the output. In addition, three different mathematical models were fitted to the experimental data and compared with the ANN model. Results: Based on these results, artificial neural network model for MR with one hidden layer, Sigmoid function as the transfer function, Levenberg-Marquardt method as the learning rule, 4 hidden neurons, 55% for training subset and 25 and 20 percent for each of validation and test subsets respectively had the best over fitting. The determination coefficient (R2) and root mean square error (RMSE) computed for the ANN model were 0.9992 and 0.01099and for the best mathematical model (Two term model) were 0.9776 and 0.02758, respectively. Conclusion: It was concluded that the artificial neural network model satisfied the work better than the mathematical model concerned with soybean snack roasting. Manuscript profile -
Open Access Article
30 - Forecasting Municipal Solid Waste Quantity by Intelligent Models and Their Uncertainty Analysis
Maryam Abbasi Malihe Fallah Nezhad Rooholah Noori Maryam MirabiBackground 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 MoreBackground 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
31 - Evaluation and Optimization of Waste Collection and Transportation System in Urmia by Combining the Response Surface and Artificial Neural Network
Saeid JafarzadehGhoushchi Shabnam Hamidi- MoghaddamBackground 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 MoreBackground 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
32 - Comparison of Procedure of Artificial Neural Networks, Logistic Regression and Similarity Weighted Instance-Based Learning in Modeling and Predicting the Destruction of the Forest (Case Study: Gorgan-Rood Watershed- Golestan Province)
zeynab moradi Ali Reza Mikaeili-TBackground and Objective: The change in forest cover plays a vital role in ecosystem services, atmospheric carbon balance and thus climate change. The goal of this study is comparison of three procedure of Artificial Neural Network, Logistic regression and Similarity we MoreBackground and Objective: The change in forest cover plays a vital role in ecosystem services, atmospheric carbon balance and thus climate change. The goal of this study is comparison of three procedure of Artificial Neural Network, Logistic regression and Similarity weighted Instance-based Learning (SIM Weight) to predict spatial trend of forest cover change. Method: In this study, land use maps for the periods 1984 and 2012 derived from Landsat TM satellite imagery, was used. Transition potential modeling using artificial neural network, Logistic regression and Similarity weighted Instance-based Learning and prediction based on the best model using Markov chain model was performed. In order to assess the accuracy of modeling, statistics of relative performance characteristic (ROC), ratio Hits/False Alarms and figure of merit was used. Findings: The results show the accuracy of artificial neural network with the ROC equal to 0.975, the ratio Hits/False Alarms equal to 63 percent and the figure of merit is equal to 12 percent. Discussion and Conclusions: Artificial Neural Networks in comparison with Logistic Regression and Similarity weighted Instance-based Learning has higher accuracy and less error in modeling and predicting of forest changes. Manuscript profile -
Open Access Article
33 - 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 AkbarzadehBackground 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 MoreBackground 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
34 - Estimation of Effluent TSS of Ahvaz Wastewater Treatment Plant Using Inelegant Models
Mojtaba Ghaed Rahmati Hadi Moazed Parvaneh TishehzanIntroduction: The limitation of fresh water resources in the world, especially in arid and semi-arid regions such as Iran, has inevitably led to the reuse of urban wastewater. One of the most important indicators of sewage pollution and comparison with different standar MoreIntroduction: The limitation of fresh water resources in the world, especially in arid and semi-arid regions such as Iran, has inevitably led to the reuse of urban wastewater. One of the most important indicators of sewage pollution and comparison with different standards for reuse or discharge to the water resources is TSS. The present study was conducted in 2016 with the aim of estimation of effluent TSS of Ahvaz wastewater treatment plant using inelegant models. Material and methods: Regard to costly and time-consuming measurement tests of TSS, the capability of multivariate linear regression model, Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) was studied to estimate (TSS) in wastewater treatment plant output by MATLAB and SPSS 21 software. Accordingly, various compounds of sewage quality parameters were evaluated during the 8-year statistical period (2008-2015) as input of models in two daily and monthly modes. Results: The results of the regression model indicated that the maximum R2 for training and verification were 0.75 and 0.67 in daily and 0.68 and 0.66 in monthly period, respectively. The root mean square error (RMSE) in this test was 0.033 and 0.025 in the daily period and 0.053 and 0.053 in the monthly period. The maximum R2 in ANN for training and verification were 0.87 and 0.79 in daily and 0.87 and 0.85 in monthly period, respectively. The RMSE in this test was 0.030 and 0.023 in the daily period and 0.034 and 0.031 in the monthly period. Meanwhile, the maximum R2 in ANFIS for training and verification were 0.91 and 0.83 in daily and 0.89 and 0.87 for monthly period, respectively. The RMSE in this test was 0.026 and 0.025 in the daily period and 0.031 and 0.028 in the monthly period. Conclusion: The results confirmed the application of three models is appropriate, but the ANFIS was considered as a more appropriate model. Manuscript profile -
Open Access Article
35 - Turbidity Removal from Water Using Graphene Oxide as Coagulant and Modeling with Artificial Neural Network
nazila rezania Maryam Hasani Zonoozi motahareh SaadatpourBackground 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 MoreBackground 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
36 - Comparison of Bayesian Neural Networks and Artificial Neural Network to Estimate Suspended Sediments in the RiverS (Case Study: Simineh Rood)
Mohammad Ali Ghorbani Reza DehghaniBackground 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 MoreBackground 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
37 - A Neural Network Model for Prediction of Tri-Halo-Methane Concentration in Drinking Water
Mohammad Javad Zoqi Mohammad Ali JafariIn this study a neural network model is proposed for modeling tri-halo-methane concentration indrinking water. After training, the neural network model predicts tri-halo-methane concentration basedon input data. Parameters such as pH, Temperature, free chlorine residue MoreIn this study a neural network model is proposed for modeling tri-halo-methane concentration indrinking water. After training, the neural network model predicts tri-halo-methane concentration basedon input data. Parameters such as pH, Temperature, free chlorine residue and TOC were used as inputdata. To validate the proposed method, a case study was carried out, based on the data obtained fromGuilan grand treatment plant (Sangar). The Levenberg-Marquardt algorithm was selected as the bestof thirteen back-propagation algorithms. The optimal neuron number for Levenberg-Marquardtalgorithm is 8 neurons. The performance of modeling was determined. The trends of the forecast andmeasured data were in good agreement. Manuscript profile -
Open Access Article
38 - Comparison of Autoregressive Static and Artificial Dynamic Neural Network for the Forecasting of Monthly Inflow of Dez Reservoir
Mohammad Ebrahim Banihabib Mohammad Valipoor S. Mahmood BehbahaniIn this paper, the capability of autoregressive static and artificial dynamic neural networks models was compared for forecasting of monthly inflow of Dez reservoir. In previous researches, static and artificial dynamic neural networks models have not been compared MoreIn this paper, the capability of autoregressive static and artificial dynamic neural networks models was compared for forecasting of monthly inflow of Dez reservoir. In previous researches, static and artificial dynamic neural networks models have not been compared for above-mentioned propose. In addition, using artificial neural network model as an autoregressive model is innovation point of this research. Monthly flow data of Dez station in Dez River in years1955 to 2001 is used in this research. Data of 42 former years and 5 recent years are used for Training and testing data set, respectively. Different structure for the static and artificial dynamic neural network models were evaluated by comparing the root-mean-square error (RMSE) of the models. First, static and artificial dynamic neural network models were selected in training phase using data from October 1955 to September 1997. Then, using the selected structures, the monthly forecasted inflow of reservoir was compared with observed data from October 1997 to September 2001. Also, two types of radial and sigmoid activation function and the number of neurons in the hidden layer were investigated in this study. Results showed that the best model to forecast the reservoir inflow is autoregressive artificial neural network model associated with the sigmoid activation function and 17 neurons in the hidden layers. Artificial dynamic neural network model with sigmoid activation function can forecast reservoir inflow for 5 years better than static artificial neural networks model Manuscript profile -
Open Access Article
39 - 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 KhanjaniAbstract 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 MoreAbstract 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
40 - Prediction of Carbon Monoxide Concentration in Tehran using Artificial Neural Networks
Hamid Reza Jeddi Rahim Ali Abbaspour Mina Khalesian Seyed Kazem AlavipanahBackground 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 MoreBackground 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
41 - Assessment of Intelligent models for Estimating the Electrical Conductivity in Groundwater (Case study: Mazandaran plain)
Isa Hazbavi Reza DehghaniAbstract 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 MoreAbstract 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
42 - 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 ShakeryariBackground 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 MoreBackground 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
43 - Exploring Land Cover Changes in Arak Using GIS and Remote Sensing
Mozhgan Ahmadi Nadoushan Alireza Soffianian Sayed Jamaleddin KhajeddinIntroduction: 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 MoreIntroduction: 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
44 - 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 GhaffariThe 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 MoreThe 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
45 - 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 MasodiToday, 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 MoreToday, 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
46 - Credit rating of manufacturing corporations in Tehran stock exchange withmulti-criteriadecision-makingandartificial neural network models
Maghsoud Amiri Morteza Bakyhoskoie Mehdi Biglari KamiThis 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 MoreThis 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 -
Open Access Article
47 - Trained Radial Neural Networks Based on Variables of Statistical Models and Their Comparison in Bankruptcy Prediction
Alireza Mehrazin Ahmad Zenedel Mohammad Taghipour Omid ForoutanNowadays artificial neural networks have found a special position among these methods. this study seeks to find a better method of building and training artificial neural networks which leads to more accurate predictions of bankruptcy. Meanwhile, three neural networks o MoreNowadays artificial neural networks have found a special position among these methods. this study seeks to find a better method of building and training artificial neural networks which leads to more accurate predictions of bankruptcy. Meanwhile, three neural networks of radial basis function type were built and trained separately by Altman model (1983), Zmijewski model (1984) and combinatory models’ variables. After evaluating the ability of these three models of bankruptcy prediction, their accuracy has been compared. Generally, this study is based on these hypotheses: First, artificial neural network models can predict bankruptcy using Altman, Zmijewski, and combinatory variables. Second, Type I and Type II error rates are equal in the aforementioned artificial neural network models. Time span of 2004 to 2012 (eight years) has been used to select samples from the listed companies in Tehran Stock Exchange. Results show that all three models have the ability of predicting bankruptcy and the model trained with Altman Model’s variables is more accurate than the other two models in this regard. Manuscript profile -
Open Access Article
48 - Predicting bankruptcy of companies listed on the Stock Exchange using the artificial neural network
Mohsen Vaez-Ghasemi Saeid Ramezanpour ChardehBeing 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 MoreBeing 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
49 - Promotion of Effective Level of Investment Management in Iran Capital Market using Artificial Neural Network and Fuzzy Logic
Hossein Amouzad MahdirajiOne 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 MoreOne 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
50 - Day-ahead stock price forecasting using hybrid model
Vahid Vafaei Ghaeini Alimohammad KimiagariForecasting financial markets is an important issue in finance area and research studies. Importance of forecasting on one hand and its complexity, on the other hand, researchers have done much work in this area and proposed many methods. In this research, we propose a MoreForecasting financial markets is an important issue in finance area and research studies. Importance of forecasting on one hand and its complexity, on the other hand, researchers have done much work in this area and proposed many methods. In this research, we propose a hybrid model include wavelet transform, ARMA-EGARCH and NN for day-ahead forecasting of stock market price in different markets. At first WT is used to decompose and reconstruct time series into detailed and approximated parts. And then we used ARMA-EGARCH and NN models respectively for forecasting details and approximate series. In this model we used technical index by approximate part to the improvement of our NN model. Finally, we combine prediction of each model together. For validation, proposed model compare with ANN, ARIMA-GARCH and ARIMA-ANN models for forecasting stocks price in UA and Iran markets. Our results indicate that proposed model has better performance than others model in both markets. Manuscript profile -
Open Access Article
51 - 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 جوادیدر 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 Moreدر 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 -
Open Access Article
52 - حل مسایل کنترل بهینه فازی با استفاده از شبکه عصبی بهبود یافته و اصل مینیمم پونتریاگین
S. Askari S. Abbasbandyدر این مقاله سعی بر آن است که بر اساس قابلیت شبکه عصبی مصنوعی و اصل مینیمم پونتریاگین، یک روش جدید جهت حل مسئله کنترل بهینه فازی ارائه شود.در این مقاله سعی بر آن است که بر اساس قابلیت شبکه عصبی مصنوعی و اصل مینیمم پونتریاگین، یک روش جدید جهت حل مسئله کنترل بهینه فازی ارائه شود. Manuscript profile -
Open Access Article
53 - Modeling of Groundwater Quality Parameters Using Artificial Neural Network and Geostatistics Models (Case Study: Zeidoun plain)
Abdol Amir Echreshzadeh Aslan EgdernezhadBackground 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 MoreBackground 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
54 - Modeling quality parameters EC, SAR and TDS in groundwater using artificial neural network (case study: Mehran Plain and DEHLORAN)
Mir Mehrdad Mirsanjari Fatemeh Mohammadyari Reza Basiri Fatemeh Hamidi pourGiven the importance of ground water for drinking and agriculture sector, simulation and forecasting changes its quality is an increasing human needs. In this study, the modeling of water quality parameters TDS and EC based on other chemical components of the major an MoreGiven the importance of ground water for drinking and agriculture sector, simulation and forecasting changes its quality is an increasing human needs. In this study, the modeling of water quality parameters TDS and EC based on other chemical components of the major anions and cations, SAR and pH have been carried out. In addition to modeling the sodium adsorption ratio as the dependent variable parameters latitude, electrical conductivity, total dissolved elements and pH values were used as independent variables. The neural network to predict Marquardt Levenberg- groundwater quality parameters were selected. Results showed that high performance neural network to predict the groundwater quality parameters. High levels of correlation coefficient obtained between the values of parameters modeled closely reflects anticipated the measured data and the ability and accuracy of the relationships between input variables with output. Coefficient of determination of all three elements were modeled in three phases: training, validation and testing is over 90 percent Which indicate acceptable accuracy and good learning neural network and efficient network using the learning algorithm and data provided to the network. The results of great importance for the planning and integrated management of water resources and conservation and better utilization of it is important in the study area. Manuscript profile -
Open Access Article
55 - Parameters of predicted changes in the Electrical Conductivity of groundwater in Tehran city with the help of neural network
Naser Mehrdadi Gholam Reza Nabi Bidhendi Akbar Baghvand Hamid Zare Abyaneh Pouyan Abbasi MaedehIn an attempt to examine the quality of ground water in Tehran with respect to the consumption pattern in the last ten years for 71 examination point, three distinct neural networks of different Electrical Conductivity (EC), input and output parameters were MoreIn an attempt to examine the quality of ground water in Tehran with respect to the consumption pattern in the last ten years for 71 examination point, three distinct neural networks of different Electrical Conductivity (EC), input and output parameters were set out . It is observed that, in order to forecast with a great deal of trial and error, the tangent algorithms with the momentum-training algorithm turns out to be less error. As the number of the input parameters is reduced and the training algorithm is fixed with momentum and the stimulating algorithm gives way to the tangent algorithm, error falls off. Finally, three model with one hidden layer, the momentum training algorithms and the stimulating tangent was constructed. The maximum error occurring implies the maximum determination coefficient of 0.986 that its connected to models 1 and 3. Moreover, in line with the neural network laid out in one layer, the minimum normal root mean square error (NRMSE) is supposed to run out at 0.110 in models 1 and 3. According to lesser input parameter of model number 2 and very close approximation to this two models (1and 3) with maximum determination coefficient of 0.96 and the minimum normal root mean square error (NRMSE) 0.176 can be a very close approximation and can decrease inputs parameters and experience for Measurement of input parameters and the estimate is supposed to be excellently acceptable. As regards the effect of the parameters on the forecast made, the neural network involves the predominance of the two sulphate and chloride ions over the sodium parameter. Manuscript profile -
Open Access Article
56 - شبیهسازی اثر باکتریهای محرک رشد بر ذرت با استفاده از شبکه عصبی مصنوعی
علی رضا رضایی معصومه نژاد علی علی غفوریان -
Open Access Article
57 - 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 ChahkandiChemotherapy 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 MoreChemotherapy 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
58 - 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 MppsaviQuantitative 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 MoreQuantitative 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 -
Open Access Article
59 - 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 طاهریQuantitative 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 MoreQuantitative 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 -
Open Access Article
60 - 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 SarvarianDesertification 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 MoreDesertification 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 -
Open Access Article
61 - 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 TatianPredicting 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 MorePredicting 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 -
Open Access Article
62 - Spectral discrimination of important orchard species using hyperspectral indices and artificial intelligence approaches
Mohsen Mirzaie Mozhgan Abbasi Safar Marofi Eisa Solgi Roohollah KarimiStudy 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 MoreStudy 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 -
Open Access Article
63 - 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 DelfanConsidering 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 MoreConsidering 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 -
Open Access Article
64 - 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 BonyadBackground 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 MoreBackground 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
65 - 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 PanahandehPreparing 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 MorePreparing 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 -
Open Access Article
66 - Landslide hazard zonation using artificial neural network (Case study: Sepiddasht-Lorestan, Iran)
Syamak Bharvand Salman SooriThis 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 MoreThis 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
67 - Land use change modeling using artificial neural network and markov chain (Case study: Middle Coastal of Bushehr Province)
Mehdi Gholamalifard Mohsen Mirzayi Sharif Joorabian ShooshtariCoastal 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 MoreCoastal 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
68 - 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 NaderlooThe 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 MoreThe 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 -
Open Access Article
69 - Prediction of LD50 for aniline derivatives (Including some herbicide compounds) using computational methods
E. Mohammadinasab M. RezaeiIn 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 MoreIn 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
70 - The study of relationship between molecular descriptors and LD50 of organophosphate pesticides
E. Mohammadinasab M. kianpourThe 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 MoreThe 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 -
Open Access Article
71 - Prediction of LD50 for carboxylic acid derivatives using multiple linear regression and artificial neural networks models
E. Mohammadinasab f. MohammaeiIn 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 MoreIn 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 -
Open Access Article
72 - Modeling the Amount of Required Energy and Kinetics of Lavender Drying Using Artificial Neural Network
Mohammad Younesi Alamooti Hamid Khafajeh Mohammad ZareinLavender 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 MoreLavender 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 -
Open Access Article
73 - Quantitative structure–activity relationship on a series of imidazole [1, 2-a] pyridinecarboxamide derivatives as anti-tuberculosis agents
Mohsen Nekoeinia Saeed YousefinejadTuberculosis 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 MoreTuberculosis 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 -
Open Access Article
74 - حساسیت به حرکات توده ای خاک با استفاده از شبکه عصبی مصنوعی،منطق فازی و رگرسیون چند متغیره (مطالعه موردی: حوضه گرماب رود ساری)
محمد ابراهیم عفیفی ابوالفضل بهنیافر -
Open Access Article
75 - 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 ینگجهدر این تحقیق از شبکه عصبی و مصنوعی و ماشین‎بردار پشتیبان جهت ارزیابی فاضلاب تصفیه‎خانه 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 Moreدر این تحقیق از شبکه عصبی و مصنوعی و ماشین‎بردار پشتیبان جهت ارزیابی فاضلاب تصفیه‎خانه 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 به‎هنگام BOD5 برخوردار بود. به هر حال این مدل در پیش­بینی مقادیر حدی بیشینه از عملکرد ضعیف­تری برخوردار بود. با استفاده از الگوریتم بهینه­سازی جست‎وجوی 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 به‎هنگام BOD5 برای تصفیه­خانه شهر خرمشهر توصیه شد. با توجه به نتایج به‎دست 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 قابل‎قبول قرار دارد. براساس مقدار سدیم پساب خروجی فاضلاب این تصفیه‎خانه برای آبیاری گندم، جو، سویا، انجیر، زیتون، صنوبر و امثال آن‎ها، بر اساس نمودار ویلکاکس هیچ محدودیتی در استفاده از این پساب وجود ندارد. Manuscript profile -
Open Access Article
76 - 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 پوررودخانهها 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 Moreرودخانهها 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 -
Open Access Article
77 - پیش بینی ابعاد آبشستگی در حوضچه ی استغراق سرریزهای سرویس با روشهای هوش مصنوعی.
علی لشکرآرا سارا خرم زادهپیش ‎بینی دقیق ابعاد حفره آبشستگی در پایین دست سازه های هیدرولیکی از جمله سرریزهای جامی‎ 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 Moreپیش ‎بینی دقیق ابعاد حفره آبشستگی در پایین دست سازه های هیدرولیکی از جمله سرریزهای جامی‎ 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 می‎گردد که این روابط در محدوده خاصی از داده ها و شرایط آزمایش پاسخگو می‎ 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 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 ‎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 می‎باشد. Manuscript profile -
Open Access Article
78 - 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 لورفرآیند 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 Moreفرآیند 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 -
Open Access Article
79 - 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 NajafiIn 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 MoreIn 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 -
Open Access Article
80 - A Model to Predict the Profitability of Pharmaceutical Companies Using Artificial Neural Network
Ali Habibi Ghaffar TariThe 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 MoreThe 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 -
Open Access Article
81 - Performance prediction of a steam single-effect absorption chiller by the artificial neural network
Farshad Panahizadeh Mahdi Hamzehei Mahmood Farzaneh-GordDepending 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 MoreDepending 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
82 - 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 GoudarziEstimation 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 MoreEstimation 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 -
Open Access Article
83 - Predicting distribution pattern of Bemisia tabaci G. ( (Hem.: Aleyrodidae) by Hybrid neural network With Particle Swarm Optimization Algorithm
Alireza Shabaninejad Bahram TafaghodiniyaToday, with the Advance statistical techniques and neural networks, predictive models of distribution was rapidly developed in Ecology. Purpose of this study was predict and Mapping distribution of Bemisia tabaci G. using MLP neural networks combined with Particle Swarm MoreToday, with the Advance statistical techniques and neural networks, predictive models of distribution was rapidly developed in Ecology. Purpose of this study was predict and Mapping distribution of Bemisia tabaci G. using MLP neural networks combined with Particle Swarm Optimization in surface of cucumber field. Population data of pest was obtained in 2017 by sampling in 100 fixed points in a fallow field in Ramhormoz, to evaluate the ability of neural networks combined with Particle Swarm Optimization to predict the distribution used statistical comparison parameters such as mean, variance, statistical distribution and coefficient determination of linear regression among predicted values and actual values. Results showed that in training and test phases of neural network combined Particle Swarm Optimization algorithm, was no significant effect between variance, mean and statistical distribution of actual values and predicted values. Our map showed that patchy pest distribution offers large potential for using site-specific pest control on this field. Manuscript profile -
Open Access Article
84 - 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 مهدIn 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 MoreIn 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 -
Open Access Article
85 - 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 folladpanjehA 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 MoreA 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 -
Open Access Article
86 - Clearance Prediction of Rotary System with and without Mechanical Diagnosis by Using Artificial Neural Networks and Particle Swarm Optimization
Mojtaba Hasanluلقی تکیه گاه های موتور و یاتاقان ها سیستم را با کوپلینگ 4 نوع مختلف عیب ابتدا با استفاده از روش تبدیل سریع فوریه فرکانس ها و جابجایی های عمودی شفت در محل دو یاتاقان استخراج نموده و سپس اثر لقی تکیه گاه ها را در حالت حضور و عدم حضور عیوب دیگر مورد بررسی قرار میگیرد. حال Moreلقی تکیه گاه های موتور و یاتاقان ها سیستم را با کوپلینگ 4 نوع مختلف عیب ابتدا با استفاده از روش تبدیل سریع فوریه فرکانس ها و جابجایی های عمودی شفت در محل دو یاتاقان استخراج نموده و سپس اثر لقی تکیه گاه ها را در حالت حضور و عدم حضور عیوب دیگر مورد بررسی قرار میگیرد. حال برای دستیابی به یک مدل بهینه از شبکه عصبی بهمراه الگوریتم بهینه سازی ازدحام ذرات تک هدفه استفاده می کنیم بدین صورت که یکبار فرکانس های سیستم معیوب و بدون بعنوان ورودی شبکه عصبی معرفی میگردند و خروجی مطلوب آن فرکانس سیستم در حالتی که سیستم هیچ گونه عیبی ندارد مدلسازی می شود و سپس در مرحله بعد فرآیند قبل جهت مدل سازی بیهنه با شبکه عصبی را با استفاده از جابجایی های معیوب(وروی شبکه عصبی) و جابجایی سیستم (ورودی مطلوب) مورد ارزیابی قرار میگیرد. Manuscript profile -
Open Access Article
87 - مدلسازی لوله های انتقال گاز با شبکه های عصبی مصنوعی به منظور تشخیص عیوب آنها
علی جودکی مرتضی محمدظاهری احسان جمشیدی -
Open Access Article
88 - Fault Detection of Cylinder- head automotive Using Mechanical Waves and Intelligent Techniques
Mojtaba Doustmohammadi Morteza Mohammadzaheri Ehsan JamshidiSafety 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 MoreSafety 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 -
Open Access Article
89 - آشکارسازی تغییرات کاربری اراضی و عوامل مؤثر بر آن با استفاده از شبکه عصبی مصنوعی(مورد مطالعه: شهرستان تالش)
شهرام امیرانتخابی فرهاد جوان حسن حسنی مقدم -
Open Access Article
90 - پهنه بندی مناطق در معرض پیشروی سطح آب دریا در اثر تغییر اقلیم (مطالعه موردی: بندر شهید رجایی)
حمید گوهرنژاد -
Open Access Article
91 - Determining the amount of total organic carbon using satellite imagery and artificial neural network model (Case study area: Mountain Mish, Gachsaran city)
kamran mojardThe 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 MoreThe 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 -
Open Access Article
92 - Evaluate the performance of the organization using two integrated approaches the DEA-BSC and ANN-DEA
Reza Ehtesham Rasi Eisa NajiConsidering 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 MoreConsidering 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 -
Open Access Article
93 - 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 MohammadbanifatmiProductivity 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 MoreProductivity 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 -
Open Access Article
94 - 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 AzimiThe 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 MoreThe 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 -
Open Access Article
95 - 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 mogholiEnvironmental 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 MoreEnvironmental 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 -
Open Access Article
96 - Evaluating the effects of climate change on Lar Basin Water Resources Using SWAT Model and comparing its results with Bayesian Networks and Hybrid Intelligent Models
Mahsa Solimani Puor Amirpouya SarrafIran's location on the arid and semi-arid belt of the world, as well as the mismanagement of water resources, has created a warning situation of water shortage in many parts of the country. The present research evaluates the effects of climate change on temperature, rai MoreIran's location on the arid and semi-arid belt of the world, as well as the mismanagement of water resources, has created a warning situation of water shortage in many parts of the country. The present research evaluates the effects of climate change on temperature, rainfall and runoff in future periods with the help of LARS-WG statistical model and SWAT hydrological conceptual model for Lar Basin. To estimate the flow rate of the river, the performance of Bayesian network and the combined wavelet-neural network model are also examined. After entering the rainfall and temperature information of the region, runoff was simulated for two hydrometric stations of Gozeldareh and Plour and the outflow runoff of Plour station between 1979 to 2018 was calibrated and validated as a control point. In order to evaluate the efficiency of the models, the criteria of Nash-Sutcliffe and explanation coefficient are used. According to climate models, the highest temperature increase in the final period and under the RCP8.5 climate scenario shows about 10% increase in temperature in spring and winter. Finally, among these models, the physical model with an average annual prediction of 6.04 cubic meters per second according to the observation period, showed a decrease in runoff. Manuscript profile -
Open Access Article
97 - Change Time Study Pricipitain of Hamadan Province Using Statistic’s and Neural Network Methods
Zohreh Maryanaji Hamed AbbasiClimate change is one of the common problems in today’s societies and considerate as threat for earth. Increasing of earth temperature is cased to extensive changes in earth climates and also it laded to same variations of spatial and temporal of precipitation so MoreClimate change is one of the common problems in today’s societies and considerate as threat for earth. Increasing of earth temperature is cased to extensive changes in earth climates and also it laded to same variations of spatial and temporal of precipitation so that these variations cased to a lot of damages especially in last decade. This study to identify of variations and the trend of season and duration of precipitation in different times intervals. Then it is predicted same changes in the future by the method of artificial neural network. In this study we have us of the data from two synoptic stations Hamadan and Nojeh .The statistic’s years in this study; it seems the rainfall season in the central part of Hamadan province in started later and ended later in last decade. In other words the starter of rainfall season in Hamadan which occurred in the fall season, at present tend to ward the winter season and the fall season is more dryer than before ages. This shows that the rainfall season is interchanged in this district. in using of the method of artificial neural network we should consider to two main points in the predicting of precipitation the first one, the low attention of this method in the long –term predicting of precipitation and the second one, the exaggerate in the minimum and maximum amount of precipitation in different seasons of year. Manuscript profile -
Open Access Article
98 - پیشبینی بارش فصلی با حداقل متغیرهای اقلیمی مطالعه موردی: ایستگاه کرمان
Fateme Bayatani غلام عباس فلاح قالهری غلام عباس فلاح قالهری الهام فهیمی نژاد الهام فهیمی نژاد پیش­بینی بارش و برآورد نزولات جوی، به عنوان یکی از مهمترین پارامترهای اقلیمی در حوزه مدیریت منابع آبی، از اهمیت ویژه­ای برخوردار است. بنابراین در این مقاله، امکان کاربرد شبکه عصبی در برآورد بارش با حداقل پارامترهای اقلیمی مورد بررسی قرار گرفت. به این منظو More پیش­بینی بارش و برآورد نزولات جوی، به عنوان یکی از مهمترین پارامترهای اقلیمی در حوزه مدیریت منابع آبی، از اهمیت ویژه­ای برخوردار است. بنابراین در این مقاله، امکان کاربرد شبکه عصبی در برآورد بارش با حداقل پارامترهای اقلیمی مورد بررسی قرار گرفت. به این منظور از شبکه عصبی پرسپترون چند لایه با قانون پس انتشار خطا و الگوریتم سیگموئید همراه با داده های میانگین رطوبت نسبی(meanHR)، کمینه رطوبت نسبی (minHR)، بیشینه رطوبت نسبی (maxHR)، میانگین دما (meanT)، کمینه دما (minT)، بیشینه دما (maxT)، میانگین فشار (meanP)، کمینه فشار (minP) و بیشینه فشار (maxP) ماه اکتبر ایستگاه هواشناسی سینوپتیک کرمان، طی دوره آماری 2014-1969 به عنوان ورودی مدل استفاده گردید. نتایج نشان داد در صورت کمبود پارامترهای اقلیمی، تنها با اندازه گیری minT و meanT میتوان با خطایی معادل 8/9 میلیمتر، برآورد مناسبی از بارش با استفاده از شبکه­های عصبی مصنوعی در منطقه مورد مطالعه به دست آورد. Manuscript profile -
Open Access Article
99 - land use change modeling using LCM module (Case study: NEKA region)
seyede massoomeh fathollahi roudbary Kamran Nasirahmadi mehrdad khanmohamadiLand 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 MoreLand 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 -
Open Access Article
100 - Investigating of Hydro-geochemical of Groundwater in Shiramin Plain using Artificial Neural Networks
Reza Norouzi Somayeh Emami Hamed ShiralizadhehGroundwater is one of the most important water resources in arid and semi-arid regions. Increased water consumption due to population growth, has a great impact on quality and quantity of water supply. The main aim of this study was to evaluate the accuracy of interpola MoreGroundwater is one of the most important water resources in arid and semi-arid regions. Increased water consumption due to population growth, has a great impact on quality and quantity of water supply. The main aim of this study was to evaluate the accuracy of interpolation methods for predicting the spatial distribution of some groundwater quality indices for Shiramin plain. In this study, hydro-geochemical quality of groundwater resources for drinking, agricultural and industry purposes was investigated with the Wilcox and Schoeller Standards in Shiramin plain East Azerbaijan province, Iran. Further in the research, the modeling of quality parameters (TDS), EC and SAR is done with using artificial neural network. According to Schuler and Wilcox groundwater quality index, water was moderately suitable and unsuitable for agriculture and drinking, respectively. The most of the samples were in C3-S1 category. The results are representative of the acceptable performance of ANNs to predict groundwater quality. . Manuscript profile -
Open Access Article
101 - Assessing the development level of rural areas of central district of Falavarjan County: The application of Artificial Neural Network
Hamid Rastegari Mehdi NooripoorPlanning 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 MorePlanning 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 ≤∝≤ 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 -
Open Access Article
102 - پیشبینی سرمای دیررس بهاره با استفاده از شبکهی عصبی پرسپترون چند لایه (MLP) و تاثیر آن در حمل و نقل شهر خرمآباد
Saeid Taghavi Haniyeh Omidzadeh -
Open Access Article
103 - Stock Price Forecasting through Using ANN and ARIMA Techniques: A Case Study of Pars Petroleum Company
Seyed Nezame aldin Makian Fateme sadat MousaviStock 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 MoreStock 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 -
Open Access Article
104 - تخمین استحکام فشاری ماسه ریختهگری در مقادیر مختلف رطوبت با استفاده از شبکه عصبی مصنوعی
رامین مشک آبادی غلامرضا مرامی کمال جهانی -
Open Access Article
105 - 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 sanchooliConsidering 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 MoreConsidering 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
106 - Reduction of Sub-synchronous Resonances with D-FACTS Devices using intelligent Control ,
Zahra Amini Abbas KargarWhen 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 MoreWhen 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
107 - Partial Discharge Analysis in Generator Stator Winding Using Artificial Neural Network
Seyed Meisam Ezzati Faramarz Faghihi Ali Moarefian poorThis 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 MoreThis 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
108 - Predicting Hook -Shaped and Concrete Steel Fibers Adhesion Parameters Using Artificial Neural Networks
amir ebrahim akbari baghal Amir ebrahim akbari bagalGiven 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 MoreGiven 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
109 - Predication of Some Physiochemical Properties of Low Calorie Cake Containing Apple Fiber Using Artificial Neural Networks
Maryam Sabet Ghadam Mohammad Reza Saeedi Asl Akram Sharifi Ahmad Pedram Nia Mohammad Armin -
Open Access Article
110 - مدلسازی استخراج روغن از دانه کتان با پیش تیمار میدان الکتریکی پالسی با استفاده از شبکه عصبی مصنوعی
شکوفه غراوی مسعود بذرافشان معصومه مقیمی -
Open Access Article
111 - پیشگویی فعالیت رادیکالگیرندگی، شمارش آغازگرها و خواص حسی ماست پروبیوتیک حاوی عصارههای هیدروالکلی اسپیرولینا پلاتنسیس و گیاه چویل با شبکه عصبی مصنوعی
عبد الرضا آقاجانی سید علی مرتضوی فریده طباطبایی یزدی -
Open Access Article
112 - بهینهسازی فرآیند آبگیری اسمزی شلیل و مدلسازی پارامترهای آبگیری با کمک شبکهی عصبی مصنوعی
حمید بخش آبادی معصومه مقیمی زهرا دولت آبادی سحر اصغری پور -
Open Access Article
113 - مدل ترکیبی شبکهی عصبی و تحلیل پوششی داده ها برای ارزیابی کارایی عملکرد واحدها
صادق حیدری احسان زنبوری حمید پروینکایی 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 Moreکایی 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
114 - 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 اروریدر 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 Moreدر 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
115 - کاربرد مدل خطی و شبکه عصبی مصنوعی برای پیشبینی عملکرد رشد در جوجههای گوشتی
ش. غضنفریاین 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 Moreاین 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
116 - 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. بابنکوشبکه­های 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 Moreشبکه­های 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 -
Open Access Article
117 - 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 Roudiاین 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 Moreاین 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 -
Open Access Article
118 - مقایسه کارآیی شبکه عصبی مصنوعی و رگرسیون چندگانه در پیشبینی وزن دنبه گوسفند
م.ع. نوروزیان م. وکیلی علویجهدر 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 Moreدر 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 -
Open Access Article
119 - Estimation of evaporation from Dez regulatory dam station pan using artificial neural network
mehdi najafvand derikvandi hossein eslamiMore 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 MoreMore 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 -
Open Access Article
120 - 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 سهرابیOne 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 MoreOne 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 -
Open Access Article
121 - Utilization of Artificial Neural Networks for Determining the Overflow Discharge of Marun Dam
Ebrahim Nohani valiolah partovi ziaFor 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 MoreFor 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 -
Open Access Article
122 - Development of a Wavelet Hybrid Models for Estimating Regional Droughts in Siminehroud Basin
Erfan Rostam Zade alireza parvishiIn 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 MoreIn 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 -
Open Access Article
123 - Development and Implementation of Principal Component Analysis Method for Monitoring of Gas Turbine
Samira Piri Niaragh Elham GhanbariGas 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 MoreGas 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 -
Open Access Article
124 - Assessment of Citizens' Satisfaction with Intra-City Transportation System in Hamedan City
Hamed AbbasiTransportation 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 MoreTransportation 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 -
Open Access Article
125 - Application of artificial neural network and Cellular AutomataIn modeling and predicting land use changes in Bavanat city
Marzieh MogholiIntroduction: 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 MoreIntroduction: 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 -
Open Access Article
126 - Application of artificial neural networks in modeling urban physical development (Case study: Rasht city)
tala abedi golamreza miri parviz rezaei reza zareiIntroduction: 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 MoreIntroduction: 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 -
Open Access Article
127 - Expectation of Chamomile Fundamental Oil Abdicate by Using the Artificial Neural Network System
Nazanin Khakipour Mahtab PayandehThe 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 MoreThe 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 -
Open Access Article
128 - شناسایی گیاهان آپارتمانی بر اساس ویژگی های تصویری با کمک شبکه عصبی
نرگس قانعی قوشخانه عباس روحانی محمودرضا گلزاریان فاطمه کاظمیدر 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 Moreدر 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 -
Open Access Article
129 - 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 تروجنیهدف 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 Moreهدف 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 -
Open Access Article
130 - پیش بینی تراز آب زیرزمینی با استفاده از مدل شبکه های عصبی مصنوعی (مطالعه موردی: دشت شبستر)
زینب مختاری امیرحسین ناظمی عطااله ندیری -
Open Access Article
131 - بررسی معیارهای متفاوت برای منظم کردن اجزاهای اصلی به منظور ایجاد یک مدل QSPR برای پیش بینی نقطه های ذوب
ولی زارع شاه­ آبادی فاطمه عباسی­ تبار -
Open Access Article
132 - کاربرد شبکه عصبی مصنوعی در پیشگویی بازده استخراج یونهای روی از نمونههای حقیقی با استفاده از مولکول نگاری بسپاری
سید حسین هاشمی مسعود کیخوایی مجید میرمقدم محمد شاکری -
Open Access Article
133 - مدلسازی فرایند تبدیل خشک متان بهکمک پلاسما با استفاده از شبکه عصبی مصنوعی و الگوریتم ژنتیک
سیدمرتضی فاضلی فاطمه راوری حمیدرضا بزرگ زاده جعفر صادق زاده اهری -
Open Access Article
134 - روشهای خطی و غیرخطی ارتباط کمیساختار- فعالیت جهت پیشبینی فعالیت دارویی برخی از مشتقات آمینواسیدها
مهدی نکویی مجید محمدحسینی مهدی رحیمی عبدالرضا علویقرهباغ -
Open Access Article
135 - بکارگیری شبکههای عصبی Fuzzy ARTMAP برای تشخیص صعود ناگهانی سیگنالهای EEG با استفاده از استخراج ویژگی توسط موجک
فاطمه صفری علی فرخی نعمت طالبی این مقاله در پی معرفی دو سیستم کلاسبندی مبتنی بر شبکههای عصبی Fuzzy Artmap برای تشخیص اتوماتیک حدودهای ناگهانی در امواج الکترد آلفا نوگرافی (EEG) 19 کانال اشخاص میباشد. این الگوریتم سریع و نتایح قابل قبولی عرضه مینمایند. سیگنالهای EEG به چهار زیر باند با استف More این مقاله در پی معرفی دو سیستم کلاسبندی مبتنی بر شبکههای عصبی Fuzzy Artmap برای تشخیص اتوماتیک حدودهای ناگهانی در امواج الکترد آلفا نوگرافی (EEG) 19 کانال اشخاص میباشد. این الگوریتم سریع و نتایح قابل قبولی عرضه مینمایند. سیگنالهای EEG به چهار زیر باند با استفاده از تبدیل ویولت گسسته تقسیم بندی شدهاند. وروردیهای شبکه شامل دو ویژگی متفاوت هستند که از زیرباندهای 3 و 4 استخراج میشوند. عملکرد این کلاسبندی کنندهها در این مقاله معرفی شده و باهم و دیگر سیستمها مشابه مطابق با مقادیر حساسیت، ویژگی و انتخاب پذیری مقایسه گشتهاند. Manuscript profile -
Open Access Article
136 - .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. 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 -
Open Access Article
137 - پیشبینی شاخص سهام با استفاده از ترکیب شبکه عصبی مصنوعی و مدلهای فرا ابتکاری جستجوی هارمونی و الگوریتم ژنتیک
مریم دولو تکتم حیدری -
Open Access Article
138 - بررسی اثر روابط اعطای تسهیلات بر هزینه مبادله تسهیلات بانکی: مطالعه موردی بانک کشاورزی
عباس عرب مازار مهرداد نعمتی امیر درویشی -
Open Access Article
139 - Predictability Test of Stock Market Price Index in Iran Investment Market and comparing Linear and Nonlinear models predictability potentials
Karim Emami Ghodratollah EmamverdiSince 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, MoreSince 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 -
Open Access Article
140 - 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 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 -
Open Access Article
141 - Analysis of knowledge-based organizational culture components using artificial neural network
Somayeh hosseinzadeh Toraj Mojibi Sayyedmahdi alvani javad rezaiyanThis 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 MoreThis 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 -
Open Access Article
142 - 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 bashirimaneshThe 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 MoreThe 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
143 - 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 GORDIn 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 MoreIn 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
144 - Modeling for Measuring Corporate Financial Sustainability Using the Econophysics and Bayesian Method
moloud soleimani Faegh ahmadi Mohammad Hossein Ranjbar Hamid Reza vakilifardThe concept of financial sustainability has been in the economic literature for nearly two decades. In the theoretical financial literature, firm financial sustainability can be described as a financial system consisting of financial intermediaries, markets, and market MoreThe concept of financial sustainability has been in the economic literature for nearly two decades. In the theoretical financial literature, firm financial sustainability can be described as a financial system consisting of financial intermediaries, markets, and market infrastructures that are capable of withstanding risk shocks and resolving financial imbalances. Therefore, according to the above argument, the purpose of the present study is to present a proposed model for measuring the financial sustainability of the company by using Econophysics and artificial neural network using a sample of 132 companies listed in Tehran Stock Exchange during 2015-2019. The results of the first hypothesis show that the prediction of financial sustainability based on the Econophysics method can provide better results. Also, based on the result of the second hypothesis, the Bayesian method can predict better financial sustainability. Finally, by comparing the Econophysics and Bayesian approaches to predicting financial sustainability of the firm, it can be concluded that the prediction of financial sustainability based on the economophysical method yields better results than the Bayesian method. Manuscript profile -
Open Access Article
145 - 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 NiaFinancial 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 ( MoreFinancial 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
146 - ارائه مدلی برای شناسایی عوامل موثر بر قیمت آتی سکه به روش شبکه عصبی مصنوعی و مقایسه آن با مدلهای رگرسیونی
میلاد گودرزی بهزاد امیری -
Open Access Article
147 - مقایسه عملکرد مدلهای رگرسیونی ARIMA وشبکه عصبی باالگوریتم ژنتیک (GMDH) درپیش بینی قیمت نفت خام ایران
عباسعلی ابونوری ناهید خدادادی -
Open Access Article
148 - Forecast earnings management based on adjusted Jones model using Artificial Neural Networks and Genetic Algorithms
Khosro Faghani Makrani S. Hasan Salehnezhad Vahid AminIn 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 MoreIn 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
149 - پیشبینی بازده صندوقهای سرمایهگذاری مشترک
کاظم چاوشی ابراهیم صابر -
Open Access Article
150 - Optimization of technical indicators’ parameters for intraday data using optics – inspired optimization (OIO): a case study of Tehran stock exchange
Mohammad Ali Rastegar Farah AshuriIn 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 MoreIn 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
151 - Forecasting Stock Price Trend by Artificial Neural Networks (Case Study: Isfahan Oil Refinery Company)
hossein badiei Ruhollah Rezazadeh Hadi MahmoudiArtificial 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 MoreArtificial 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
152 - 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 FarzadThe 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 MoreThe 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
153 - The Modeling of Exchange Rate Predict in Iran by Using Neural Network Based on Genetic Algorithms and Particle Swarm Algorithm
ali jamali saeed daie karimzadehIn 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 MoreIn 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
154 - Precipitation-runoff Simulation with Neural Network(Case study: Nasa Bam Plain)
mehdi shahrokhi sardoo mojtaba jafari kermanipourShort-term runoff forecasting is of particular importance due to its direct relationship with how managers interact with life risks caused by floods. In this research, by using artificial neural networks, simulation of rainfall-runoff process has been done on a daily ba MoreShort-term runoff forecasting is of particular importance due to its direct relationship with how managers interact with life risks caused by floods. In this research, by using artificial neural networks, simulation of rainfall-runoff process has been done on a daily basis in the Nasa Bam watershed. In order to predict the future process of using the water resources of the mentioned plain, different combinations of rainfall and temperature data and discharge and discharge difference of two consecutive days were used. The number of hidden layer neurons in the neural network varied between 2 and 10 neurons. The statistical criteria of root mean square error RMSE, mean absolute value of error MAE and correlation coefficient R were used to evaluate and compare the performance of neural networks in runoff forecasting. The results showed that by having 2 inputs and feedforward neural network or 1 input and newrbe network, the best performance was achieved and the rainfall-runoff process was predicted with higher accuracy. Manuscript profile -
Open Access Article
155 - ارائه مدلی برای پیش بینی سطح معنویت در سازمان های ورزشی با رویکرد شبکه عصبی مصنوعی
سید احسان امیرحسینی ابوذر زارع -
Open Access Article
156 - Prediction of Adhesion Parameters of Hook-shaped Steel Fibers and Concrete Using Artificial Neural Networks
Amir ebrahim akbari bagalAs 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 MoreAs 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
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