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Open Access Article
1 - Short-term prediction of carbon monoxide gas concentration in the air of Ahvaz city using artificial neural network analysis
Maryam Kavosi سیما سبزعلی پور hossein fathianIntroduction: Air pollution in cities is one of the most critical environmental problems, representing a constant and severe threat to both the health and hygiene of society and the environment. The primary air pollutants include nitrogen oxides, with a particular empha MoreIntroduction: Air pollution in cities is one of the most critical environmental problems, representing a constant and severe threat to both the health and hygiene of society and the environment. The primary air pollutants include nitrogen oxides, with a particular emphasis on nitrogen dioxide, sulfur oxides, especially sulfur dioxide, hydrocarbons, carbon monoxide (CO), carbon dioxide, and suspended particles. Ahvaz, a metropolis in Iran, stands out as one of the most polluted cities. Effective environmental management, particularly in addressing air pollution, is of paramount importance. This research aims to predict the concentration of CO pollutants in Ahvaz city for the first seven days of 2015. Materials and Methods: Based on previous studies, meteorological variables including weather, air temperature and wind speed were selected as gas input titles in the network for gas prediction. CO gas was procured in 2014 through the Environmental Protection Organization of Ahvaz city. In order to develop the Multilayer Perceptron (MLP) neural network, Neuro Solution5 software was used to create the neural network, 70% of the data was used for training (validation), 15% for testing, and the remaining 15% for validating the results of the network. is used. was used. Results and Discussion: In order to determine the best MLP network structure for short-term prediction of CO gas concentration, different structures were considered in terms of the number of intermediate layers, the type of network training algorithm, the type of transfer function, the number of intermediate layer neurons and the number of repetitions (Epoch) of training. The results showed that the MLP network with a structure of 1-5-3 (that is, 3 input neurons, 5 neurons in the middle layer and one neuron for the output layer) with 1500 repetitions of training per Tansig transfer function (Tansant Sigmoid) and Traingdm training algorithm (reduction gradient with momentum), is the best MLP network. In addition, the values of NSE, RMSE and MAE statistical indices for the network training stage are equal to 0.72, 0.22 and 0.15 respectively. Conclusion: Air pollution, the primary environmental challenge in Ahvaz, arises from the intersection of traffic and the oil industry. Its impacts on health and the environment necessitate comprehensive investigation. In this study, an MLP network was employed to predict CO gas concentration values in the air of Ahvaz city. The findings demonstrate that the network's accuracy and performance in forecasting CO gas concentration are at an optimal level. As this research progresses, it is recommended to extend the prediction to other gaseous pollutants and to employ optimization algorithms for determining the optimal structure of the artificial neural network Manuscript profile -
Open Access Article
2 - Investigating the possibility of biasing recommendation algorithms from users' rating behavior in online social networks
Mehdi Safarpour Seyed Hadi Yaghobian Karamollah BagheriFard razieh malekhoseini Samad NejatianAs online social networks become more widely used, there is a growing focus on the role of recommender algorithms within these platforms. It is important to assess the accuracy of these algorithms in providing suitable recommendations. Our research demonstrates that the MoreAs online social networks become more widely used, there is a growing focus on the role of recommender algorithms within these platforms. It is important to assess the accuracy of these algorithms in providing suitable recommendations. Our research demonstrates that the presence of individuals and acquaintances within social networks influences user behavior in ways that are largely psychological. Many user actions on a post are influenced by their respect or closeness to the post's owner. This article explores how the predictability of user behavior towards posts from friends and acquaintances highlights the impact of emotional connections stemming from stable social relationships on post acceptance. It also raises concerns about the potential for incorrect recommendations in algorithms based on collaborative filtering due to data bias caused by these factors. Manuscript profile -
Open Access Article
3 - Improving Web Recommendation Systems via Feature Engineering for Anticipating User's Subsequent Links
Vahid Saffari Karamolah BagheriFard Hamid Parvin Samad Nejatian Vahide RezaieGiven the remarkable growth in online content and extensive user engagement, understanding user behavior and providing accurate content recommendations stands as a significant challenge in data mining and recommendation systems. This article introduces a comprehensive a MoreGiven the remarkable growth in online content and extensive user engagement, understanding user behavior and providing accurate content recommendations stands as a significant challenge in data mining and recommendation systems. This article introduces a comprehensive approach to enhance user profiling accuracy and increase precision in web page recommendations. It initiates this process by introducing an innovative feature called "user engagement duration with web pages," significantly aiding in improving user profiles. Leveraging these enriched profiles facilitates predicting a user's next web page visit. Evaluating this model, comparison with a scenario lacking this new feature demonstrates a substantial increase in prediction accuracy upon its inclusion. Additionally, we delve into cluster analysis, employing k-means and k-medoids algorithms, where k-medoids demonstrate greater diversity in sample clustering. The paper establishes the superiority of using k-medoids in this domain and emphasizes the importance of determining optimal cluster sizes. Ultimately, this research culminates in developing a web recommendation system capable of highly accurate predictions regarding the user's next web destination. Hence, the proposed approach enhances the model's precision in recommending links to users and promises further advancements in this field. Manuscript profile -
Open Access Article
4 - The prediction of Bankruptcy Risk Investigation Using Artificial Neural Networks Based on Multilayer Perceptron Approach (Empirical Evidence: Tehran Stock Exchange)
Somayeh Saroei Hamid Reza Vkili Fard Ghodratolah Taleb NiaThe aim of this research is Identification of the effective factors on bankruptcy prediction of Iranian companies by findings of artificial neural network (ANN) system based on Multilayer Perceptron Approach (PS) , and providing an appropriate statistical model for esti MoreThe aim of this research is Identification of the effective factors on bankruptcy prediction of Iranian companies by findings of artificial neural network (ANN) system based on Multilayer Perceptron Approach (PS) , and providing an appropriate statistical model for estimating the bankruptcy of Iranian companies by using the findings of The ANN implementation. we seek to answer the following question: Are we able to design a valid statistical model by using findings of artificial neural network (ANN) system to predict the bankruptcy of Iranian companies? The statistical population in this study is all of listed companies in Tehran Stock Exchange. By considering the criteria and method of systematic deletion, 172 companies from this statistical society have been selected as the sample in this research from 2007 to 2016. In order to make statistical analyzes in this study, we used from methods such as artificial neural network system based on multilevel perceptron approach, binary logistic regression, and tests such as Akaic, Schwarz, Hanan Quinn and Z wang test. The results of the analysis of the research data show that the ANN system can identify of the factors affecting on bankruptcy of Iranian companies in the year before bankruptcy by Precision equal 98%. Manuscript profile -
Open Access Article
5 - An Analysis of the Energy Consumption Status in the Framework of Future Study Case Study: Tabriz City
Abolfazl Ghanbari Musa Vaezi Zahra AmjadiBackground: The increasing importance of energy resources in the formation and growth of economic processes, as well as the need to exploit these resources, based on environmental considerations and sustainable economic and social development, highlights the issue of id MoreBackground: The increasing importance of energy resources in the formation and growth of economic processes, as well as the need to exploit these resources, based on environmental considerations and sustainable economic and social development, highlights the issue of identifying and Future Study of the factors affecting energy consumption. Objective: The present research studies the status of energy consumption in Tabriz city in the framework of Foresight. Methods: This research is applied and descriptive-analytic. The Delphi method and the group of experts have been used to identify the factors affecting energy consumption. After analyzing the factors, 40 factors were identified and selected as influential factors. Using MICMAC software, the interactive effects analysis method has been identified. Finally, out of 40 factors, 16 main factors were selected as effective key proponents. Findings: Based on the data included in the questionnaire and the Wizard scenario software analysis, there are five strong scenarios, of which two scenarios are desirable conditions, a scenario of critical conditions, and two other scenarios of intermediate conditions. 13 scenarios with high adaptability and 292 poor scenarios. Initial studies of 13 scenarios show that the relative relativity of undesirable numbers is favorable on desired conditions. Apart from a few limited scenarios that have the characteristics that are desirable and progressing, the rest of the scenarios do not have a good future. Conclusion: The main result of this research is that the future energy consumption situation in Tabriz will continue to be a continuation of the current situation with an unfavorable and unfavorable situation. Manuscript profile -
Open Access Article
6 - Cluster Analysis of Iran's Position in the World and Future Trends Based on Good Governance Components
Mona Ahani Morteza Mosakhani Reza Najafbeigi Mohammad Ali AfsharkazemiThe Study of good governance and the quality of government institutions is a debate that began in the 90's. Good governance, which consists of six components: control of corruption, government effectiveness, political stability and absence of violence, regulatory qualit MoreThe Study of good governance and the quality of government institutions is a debate that began in the 90's. Good governance, which consists of six components: control of corruption, government effectiveness, political stability and absence of violence, regulatory quality, rule of law, voice and accountability, is a model for development. In this study, the World Bank's assessments and statistics on the six-fold good governance indicators published each year, were used to survey 186 countries worldwide. The aims of this research were studying the status of countries based on good governance and determining the status of Iran among other countries, using clustering technique; And analyzing the trend of Iran's position in the 2021 horizons using time series analysis. Using the clustering method of the countries of the world, based on the good governance and the frequent clustering of Iran with other countries, they were separated. Then, from the time series method using of the exponential smoothing based on the ARIMA's method was investigate to predict the six's good governance indicators and the situation of the country in the next five years. Findings of the research show that in the 2021 horizons, the accountability index will be problematic in the country and the rule of law and control of corruption ratios will remain almost unchanged, and, on the other hand, the rest of the indicators show a slight improvement. Manuscript profile -
Open Access Article
7 - Forecasting of OPEC's Global Crude Oil Demand using Vector Self-Engagement Models, Collective Exploration and Gravitational Search
heshmatolah asgari mohammadreza omidi zahra malekinia ALIAKBAR OMIDIKnowledge about future oil demand is essential for OPEC member countries to set priorities and select policies in order to achieve economic growth and development. So in this study, the OPEC oil demand has been predicted using time series models Including Structural Vec MoreKnowledge about future oil demand is essential for OPEC member countries to set priorities and select policies in order to achieve economic growth and development. So in this study, the OPEC oil demand has been predicted using time series models Including Structural Vector Autoregressive model (SVAR), Autoregressive Integrated Moving Average model (ARIMA) and Gravitational Search Algorithm (That is one of the Innovative Search Algorithms) applying demand data from 1970 to 2014. In this regard, three criteria including Mean Sum of Squared Errors (MSSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) have been used to measure the predictive power of triple models. Results indicate that the SVAR model has the most appropriate prediction of OPEC global demand. According to results of this model, net export variable has a positive and significant impact on oil demand and OPEC petroleum price and non- OPEC production variables have a negative and significant impact on oil demand. Manuscript profile -
Open Access Article
8 - Prediction of Customer Satisfaction level in after-sales service in automotive industry- Dealers in Saipayadak Co.
Reyhaneh Varasteh Ahmad EbrahimiBackground: Based on the competition and product variety in the automotive industry, auto makers require to achieve capability to respond properly to customers and their competitors. The special position of after-sales service in automotive industry and also maintain th MoreBackground: Based on the competition and product variety in the automotive industry, auto makers require to achieve capability to respond properly to customers and their competitors. The special position of after-sales service in automotive industry and also maintain the existing customers and attract the new ones, makes the prediction and measurement of customer satisfaction as a must in this industry.Purpose: In this paper, using prediction approach in futures studies, has made us firstly to identify the influential factors on customer satisfaction. Then the customer satisfaction level has been predicted and analyzed in after-sales service of dealers in an automotive manufacturer.Methods: The statistical population includes the dealers of Saipayadak Co. The statistical sample includes 14486 of after-sales service dealers in the period of April 2017 to June 2017. Independent variables, after extracting through literature review, were finalized using brainstorming and fishbone diagrams. Statistical analysis and prediction was performed using stepwise regression method with coding in RStudio software.Findings: By using data mining method, the customer satisfaction score in after-sales service of dealers in Saipayadak Co. has been predicted with 80% accuracy.Conclusion: By knowing the customer satisfaction level, auto makers can define quality improvement projects and move toward to competitiveness desirably. Manuscript profile -
Open Access Article
9 - An Impirical Investigation of the Effects of Financial Statement Analysis in Predicting Future Dividend of the Firm Member in Tehran Stick Exchange.
R. Shabahang F. HeydarpourPrediction of dividend is an important factor for decision making. Financial statement analysis can used for divided prediction . Decision makers usually co MorePrediction of dividend is an important factor for decision making. Financial statement analysis can used for divided prediction . Decision makers usually consider earnings as a signal for dividend figure . In this research two hypothesis are investigated : 1) There is a relation between financial variables / ratios ( other than earning figure alone ) and dividend and 2) financial variables / ratios are useful for prediction of dividend by using model . For that ,194 firms member in Tehran Stock Exchange were investigated .Methodology of research is correlation . Dependent variable is dividend and independent variables are 24 financial variables / rations . At first , the model was derived by using 1375-1380 data and the it’s fitness was evaluated . 8 variables was remained in model . For more confidence those process were done with 1375-1379 data and dividends of 1380 were predicted . It showed about 75 percent of real dividends in 1380 were between upper / lower limit at 95 percent level . Then the research hypothesis were confirmed . The non – earning / non-dividend variables are debit to total assets , equity . inventory to assets , debit and share price before stockholder’s meeting . Other variables are EPS , ROI , net sales to equity. Manuscript profile -
Open Access Article
10 - A study on the financial performance of companies using data envelopment analysis model and Zemijsky's model, and a comparison of their results . (the case of accepted pharmaceutical companies in stock market)
Akbar Rahimipoor Masomeh Tadress HasaniRecent bankruptcy of big companies all over the world and fluctuations in Iran's stock market require that some methods be developed for the evaluation of companies' financial potential. Different models are used for the prediction of bankruptcy and the evaluation of MoreRecent bankruptcy of big companies all over the world and fluctuations in Iran's stock market require that some methods be developed for the evaluation of companies' financial potential. Different models are used for the prediction of bankruptcy and the evaluation of organizational financial situation. Environmental changes and increasing competition among agencies led to companies' and organizations' limited access to expected profit. Thus, financial decision making is, nowadays, more and more important, forcing managers to apply modern control methods through sophisticated techniques. The present study aims to evaluate the performance of companies' situation. For this purpose, we use the two models of data envelopment analysis and Zemijsky and compare results derived from them. The research data were gathered from 10 accepted in stock market. Results from data envelopment analysis model indicated that only one company was in a proper financial situation while results from Zemijsky's model showed that there were two companies in good financial condition. We also managed to develop strategies for the improvement of financial situation in other companies using data envelopment analysis model. Manuscript profile -
Open Access Article
11 - Bankruptcy Prediction Using Artifical Neural Networks with Camparsion to the Altman Model
M.R. Setayesh D. Ahadianpoor ParvinThis research has been done under title: Bankruptcy Prediction using Artificsl Neural Networks withcamparsion to the Altman Model.The goal of this study is to provide exact explanation and presentation of theoretical basis of research andmeasurement of usefulness bankru MoreThis research has been done under title: Bankruptcy Prediction using Artificsl Neural Networks withcamparsion to the Altman Model.The goal of this study is to provide exact explanation and presentation of theoretical basis of research andmeasurement of usefulness bankruptcy financial models. We presented the research hypotheses in order toprovide suitable scientific context for the study.Hypothese 1: Artificsl Neural Networks and Altman models are suitable instrumental for prediction ofbankruptcy.Hypothese 2: In prediction of bankruptcy one firm, have significant difference the resultsof this two models.The means of the research statements (Balance sheets, Income statement, cash flow statement) of thecompanies which were accepted in Tehran Stock Exchange. The library method was employed in datagathering. Statistical population of research includes active companies whose financial statements areaccessable in Tehran Stock Exchange. The statistical sample of the research includes active companies inproductive industries, from 1379 to 1384.In order to analysis data, We used statistical metods of nonparametric binomial, and for cointegrationsignificant difference two models employed wilcoxon signed- rank test and sign test for hypothese 2. Afteranalyzing the data the results gained id confirmed and supported by above tests Manuscript profile -
Open Access Article
12 - The Application of Altman & Springate Models In Bankruptcy Prediction of Accepted Companies In Tehran Stock Exchange
A. Mohammadzadeh M. NoferestiThe investors are always trying to prevent from losing their capital ad interest through predicting theprobability of the bankruptcy of a Company since in the event of the bankruptcy, value of thesecurities decreases intensely. So, the investors are looking for methods MoreThe investors are always trying to prevent from losing their capital ad interest through predicting theprobability of the bankruptcy of a Company since in the event of the bankruptcy, value of thesecurities decreases intensely. So, the investors are looking for methods by which they could predictthe bankruptcy of the Companies. Moreover, one of the issues discussed 'in financial management isthe investment and trust in the investment and one of the things that could help the investors to makeright decisions in their investments is the existence of some tools and models for the assessmenl offinancial status and the condition of the Organizations.The purpose behind this research is to determine the efficiency of Altman and Springate Models inpredicting the bankruptcy of a Company. The statistic Community on which this research was 'madeis~ the successful and bankrupted Companies in Tehran's Security Exchange and the required Data tomake this research is collected in a period of five years (2001-2006). After calculating the ratiosexistent in the models and determining z index, accuracy and error for each of the models arecalculated .Regarding the results obtained from the, research, both, of Altman and Springate Models have thecapability to predict 'the bankruptcy of the Companies in Tehran's security Exchange while AltmanModel enjoys from more accuracy in comparison with Springate Model. So, the prospective investors,shareholders and others are recommended to use Altman Model in predicting the bankruptcy of theCompanies accepted in Tehran 's Security Exchange. Manuscript profile -
Open Access Article
13 - A Review Of Cross Impact Analysis Methods And An Introduction To the Correlation Logic Method
Ebrahim Hajiani Alireza HemmatiMost of the futures study methods evaluate the concerning variables and drivers seperately to predict or examine the events. However, some times there is a need to analyze the event occurance probability in correlation with a series of predicted events. The Cross Impact MoreMost of the futures study methods evaluate the concerning variables and drivers seperately to predict or examine the events. However, some times there is a need to analyze the event occurance probability in correlation with a series of predicted events. The Cross Impact Analysis method is the key to this problem. Requiring more complex statistical processing to achieve the results, the Cross Impact Analysis method, like the Delphi method, is based on the experts opinions. The main approach in this method is to determine the event occurance probability or various driver forces seperately and ask the experts opinions for the event occurance probability in case of other event occurances and their cross impact. In advanced methods of this analysis, discussed in this study, event occurance probability is reviewed in the chain of reasons between events. Thus, a matrix of the primary probabilities and conditional probabilities and directed event cross impact relations and driving forces is designed. The common methods of this analysis defines rules based on the two logic of probabilities and structures relations for the events impacts on each other. However, both methods are not used for the cross impacts. In this study, authors presented a new method of correlation logic to cover both positive and negative impacts of events on each other using a review on available methods of cross impact analysis. Cross impact analysis method usually leads to a scenario Manuscript profile -
Open Access Article
14 - Using a Modified Trainable Neural Network Ensemble for Trend Prediction of Tehran Stock Exchange (Case Study: Kharg Petrochemical Company)
A. Shahrabadi R. Ebrahimpour H. NikooThis paper represents a comparison between modified trainable neural network ensemble with othertrainable and non-trainable ensembles. The historical data available in this case study are from khargpetrochemical company in Tehran stock exchange. This company is one of t MoreThis paper represents a comparison between modified trainable neural network ensemble with othertrainable and non-trainable ensembles. The historical data available in this case study are from khargpetrochemical company in Tehran stock exchange. This company is one of the biggest producers ofpetrochemicals, including methanol, in Iran and its stock price is very much dependent on worldmethanol price. Therefore Kharg stock price reflects its financial information more clearly than otherswith no products for global exportation. The reason of choosing Kharg is related to its large datahistory and high rate of stock free float1. The results show how a modified trainable neural networkensemble can overcome other trainable and non-trainable ensembles. This study also demonstrateshow we can beat the market through our proposed model without the use of extensive market data orknowledge Manuscript profile -
Open Access Article
15 - Sports Result Prediction Based on Machine Learning and Computational Intelligence Approaches: A Survey
Milad Keshtkar Langaroudi Mohammadreza Yamaghani -
Open Access Article
16 - Optimization of weighting-based approach to predict and deal with cold start of web recommender systems using cuckoo algorithm
reza molaee fard -
Open Access Article
17 - Designing a smart algorithm for determining stock exchange signals by data mining
pantea maleki-moghadam akbar alem-tabriz esmael najafiOne of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock exchange market. This research proposes a smart algorithm by means of valuable big data that is generated by stock exchange market and different kinds of MoreOne of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock exchange market. This research proposes a smart algorithm by means of valuable big data that is generated by stock exchange market and different kinds of methodology to present a smart model.In this paper, we investigate relationships between the data and access to their latent information with an enormous amount of data which has a significant impact on the investor’s decisions. First, extracting technical indicators from different point of the charts based on two groups of stock exchanges like petrochemical and automotive during 1387 to 1396, then analyzing clusters by means of k-means algorithm and data mining methodology. The contributions of this paper are: 1. To create a model with twenty technical indicators in different stock exchange companies and industries.2. To evaluate the proposed model and finally to predict the sales signals at the maximum points which has significant performance and can be predicted with acceptable accuracy. Manuscript profile -
Open Access Article
18 - A framework for measuring and predicting system risk with the conditional value at risk approach
Ja'far Baba Jani M. Taghi Taghavi Fard Amin GhazaliIn recent years with the increasing integration and innovation in financial markets, concerns about the overall stability of the financial system has increased and the concept of systemic risk has become more important. systemic risk is the risk imposed by interlinkages MoreIn recent years with the increasing integration and innovation in financial markets, concerns about the overall stability of the financial system has increased and the concept of systemic risk has become more important. systemic risk is the risk imposed by interlinkages and interdependencies in a system or market, where the failure of a single entity or cluster of entities can cause a crisis in the entire system or market. In this study, we presented a framework for measuring and predicting systemic risk in the capital market of Iran using conditional value at risk approach (CoVaR). On this basis, ΔCoVaR as a measure of systematic risk using quintile regression based on a set of state variables that indicates changes in the distribution of asset returns has been estimated. As well as to enhance the accuracy of the estimate, the research variables modeled after the conditional autoregressive value at risk model (CAViaR) has been developed and some Lagged firm specific characteristic has also been added. In order to test the validity of the model back testing methods have been used. On the other hand, The potential for systemic risk increases when volatility decreases (volatility paradox). In this study, we try to predict systemic risk by take advantage of the panel structure of the data and the relationship between ΔCoVaR and firm-specific variables that are available in certain sections. Manuscript profile -
Open Access Article
19 - The Relation between Characteristics of Predicted Earnings per Share by Management and Risk and Firm Value in Terms of Future Decision-Making
فرزانه حیدرپور زیبا خواجه محمودAbstractThe firms that have provided a clear picture of their future are more widely acceptedin the market. One of the ways to draw this picture is exposing the prediction of earningper share. This broadcast makes the capital market sure that firm provides informationim MoreAbstractThe firms that have provided a clear picture of their future are more widely acceptedin the market. One of the ways to draw this picture is exposing the prediction of earningper share. This broadcast makes the capital market sure that firm provides informationimpartially.This study tries to examine the relation between the predicted earning by managementand the firm value and risk. Sampling is done by systematic elimination method andregression analysis was used for testing hypothesis. Sample consists of 178 firms whichare listed in Tehran Stock Exchange and their data were statistically analyzed during theyears of 2007 to 2011; therefore, the sample size in this study is 1068. The results oflogistic regression has shown that the firm reported prediction of earning per share ispotentially considered by capital market , and the activists in this market would use thesefigures to decide in providence model for investing. Manuscript profile -
Open Access Article
20 - Using neural network approach to predict company’s profitability and comparison with decision tree c5 and support vector machine (svm)
Malihe Habibzade Mostafa EzadpourProfit as one of the most important indicators of measuring the performance of the economic unit is one of the important accounting issues that has a high status due to the competitive environment and the importance of quick and proper decision making by managers. There MoreProfit as one of the most important indicators of measuring the performance of the economic unit is one of the important accounting issues that has a high status due to the competitive environment and the importance of quick and proper decision making by managers. Therefore, it is important to analyze the index, factors affecting it and predict profitability. In this regard, the present study was conducted by selecting a sample of 124 observations for the period from 1387 to 1395, based on the basic information of the companies financial statements; the effect of 34 variables on the accuracy of predicting the profitability of the accepted companies by Tehran stock exchange, has been investigated. Tree C5 method was used to determine the significant variables in predicting profitability due to the high ease of understanding of the model. Finally, after determining the effective variables and identifying 8 variables, the accuracy of the predictions was measured using the neural network technique, the C5 decision tree and the backup vector machine (SVM), and the results from these three algorithms were compared. The results of the comparison show that using the c5 decision tree and the 8 variables have the best prediction with accuracy of 93.54%, and then the neural network model is 81.45% more accurate than the supported vector machine (69.35%) and has an error. Manuscript profile -
Open Access Article
21 - Predict the trend of stock prices using XCS based on genetic algorithms and reinforcement learning
Ahmad Reza PakraeiDevelopments for investigation in the area of artificial intelligence and machine learning, especially in the field of evolutionary computation not only enabled us for having more effective analysis of data, but also providing the ability to use it for under MoreDevelopments for investigation in the area of artificial intelligence and machine learning, especially in the field of evolutionary computation not only enabled us for having more effective analysis of data, but also providing the ability to use it for understanding any underlying model of financial markets. Economists, statisticians, and finance teachers were always interested in the development and experiment of stock price behavioral models. XCS is a compound system of genetic algorithm and reinforcement learning, which has on-line interaction with the environment and the ability of learning from its own experience. In this study we will provide a model which predicts the movements of next day‘s stock price on one of the corporations in Tehran stock exchange based on historical data and different technical indicators by using XCS. Then, efficiency of the proposed model was measured in comparison with the random walk model. Results showed that the proposed model has more predicting accuracy in comparison with that random walk model Manuscript profile -
Open Access Article
22 - Analyzing risky and no risky approach of investment opportunities levels on share return prediction ability
Leila Shirnejhad sina kheradyar ebrahim chiraniThe goal of this study is analyzing investors’ decision based on different levels of investment opportunities based on risky and no risky approach on share return prediction ability. In this study, concentration is on using three regression models with replacement MoreThe goal of this study is analyzing investors’ decision based on different levels of investment opportunities based on risky and no risky approach on share return prediction ability. In this study, concentration is on using three regression models with replacement ability on variable coefficient of investment opportunities levels that can analyze share return prediction ability from viewpoint of risky and no risky investors. Study sample includes 113 firms from accepted companies in Tehran stock exchange that includes a time period of ten years from the beginning of 2008 to the end of 2017. Results show that only when investment opportunities level is average, share return prediction ability based on risky approach exists, and against, when investment opportunities level is high and low, share return prediction ability is performed based on investment no risky return. Thus, investment opportunities level has important roles on basis of no risky approach on share return prediction ability. Manuscript profile -
Open Access Article
23 - Comparison of various static and dynamic artificial neural networks models in predicting stock prices
علی اکبر نیکواقبال نادیا گندلی علیخانی اسماعیل نادریAbstractIn this disquisition, has been paid to comparing the performance of static anddynamics neural network by purpose choosing appropriate model in predicting of TehranStock Exchange. The data used in this study consists of daily and interval of time1388/1/5 to 1390/ MoreAbstractIn this disquisition, has been paid to comparing the performance of static anddynamics neural network by purpose choosing appropriate model in predicting of TehranStock Exchange. The data used in this study consists of daily and interval of time1388/1/5 to 1390/8/30, that Including 616 observation for in sample and out of sampleforecasting. Approximately 90% of these observations (556 data) use to estimatecoefficients of the model and the rest of them (60 data) use to forecast out of sample.Models are also employed in this research; two stationary neural network models such asfuzzy neural network (ANFIS) and artificial neural network (ANN) and a dynamicregression neural network model (NNARX). The results of this survey indicate thatAccording to Criteria to calculate the forecast error, among Mean squared error (MSE)and root mean square error (RMSE), Fuzzy neural network model of static, dynamicregression models, neural networks, and finally static artificial neural network modelshave lowest prediction error, Respectively. Manuscript profile -
Open Access Article
24 - The estimation of exchange rate of (IRR-Dollars) based on Purchasing Power Parity and Monetary Approach
مهدی تقوی مهدیه مرادیThis research is an estimation of the exchange rate between the US Dollar and the Iranian Rial, for the period between 1352 and 1387. Using ARDL method for hypothesis testing, and selecting the optimal model, the Mean Square Error (MSE) and Root Mean Square Error (RMSE) MoreThis research is an estimation of the exchange rate between the US Dollar and the Iranian Rial, for the period between 1352 and 1387. Using ARDL method for hypothesis testing, and selecting the optimal model, the Mean Square Error (MSE) and Root Mean Square Error (RMSE) are used to test our hypothesis. The selection of the optimal model for prediction is based on the Purchasing Power Parity (PPP) theory and the Monetary Approach to Balance of Payment. The results of this study indicate that the PPP model is a more accurate indicator of the exchange rate, and is therefore preferred to the monetary approach. Manuscript profile -
Open Access Article
25 - Prediction of Message Diffusion: A Deep Learning Approach on Social Networks
husnyeh safearyan Mohammad Jafar Tarokh Mohammad Ali Afshar Kazemi -
Open Access Article
26 - The Prediction of Iran's Per Capita Health Expenditures up to 2041 Horizon Using the Genetic and Particle Swarm Optimization Algorithms
abolghasem golkhandan Somayeh SahraeiIntroduction: prediction the per capita health expenditures can be useful and effective in determining the best policies for financing and managing of health expenditures. Accordingly, the main objective of this study was to predict the per capita health expenditures tr MoreIntroduction: prediction the per capita health expenditures can be useful and effective in determining the best policies for financing and managing of health expenditures. Accordingly, the main objective of this study was to predict the per capita health expenditures trend in Iran. Methods: In this paper, we specified a health expenditure model relying on theoretical basics in order to obtain desirable forecasts. On the basis of three forms of linear, exponential and quadratic equations and using theoretical foundations in the field of per capita health expenditure function, we used genetic algorithm (GA) and particle swarm optimization (PSO) algorithm to simulate Iranians per capita health expenditure during 1979-2015. Then we selected the superior model in terms of prediction power criteria and forecast per capita health expenditure until 2041. Also, the statistical analyzes were performed using the MATLAB software version R2016b. Results: The predicted results indicate that per capita health expenditures in Iran will increase with a positive slope by 2041. The amount of this expenditure will be from $ 1081 (based on 2011 constant prices) in 2015 to $ 2628 in 2041 (about 2.5 times). Conclusion: With regard to the projected amount of per capita health expenditures up to 2041 horizon, policy makers in the health sector should take the necessary measures to finance the expenditures of this sector. Manuscript profile -
Open Access Article
27 - Customer Retention Based on the Number of Purchase: A Data Mining Approach
Sahar Mehregan Reza Samizadeh -
Open Access Article
28 - Artificial Neural Network Model for Predicting Insurance Insolvency
Ade Ibiwoye Olawale Ajibola Ashim Sogunro -
Open Access Article
29 - Investigation on the role of conservative accounting in the reduction of company bankruptcy risk (evidence from Iranian capital market, based on Zawgin bankruptcy Model)
زهره حاجیها مهدی قائم مقامیThis research examines the existing relations between conservative accounting andbankruptcy risk, that conservative accounting causes increasing properties and cashand bankruptcy is as a condition in which there is not enough money or cash, soaccounting theory indicates MoreThis research examines the existing relations between conservative accounting andbankruptcy risk, that conservative accounting causes increasing properties and cashand bankruptcy is as a condition in which there is not enough money or cash, soaccounting theory indicates the existence of this relation between these two issues. Inthis research we used from classifications of the conditional and non conditionalconservatism and Zavgin bankruptcy prediction Model. Which just shows a spectrumof probability of bankruptcy instead of just expressing two cases?To investigate the relationship we used regression model. Based on financial dataof companies listed in Tehran Stock Exchange during the period of 2000 to 2009 (772year/ firm of observation), the research results indicate there is significant relationshipbetween conservatism and bankruptcy risk. In other words, the operations of firmsinclude conservative accounting to reduce bankruptcy risk. The finding is consistentwith other research and it could help both insiders and outsiders to decision making. Manuscript profile -
Open Access Article
30 - 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
31 - Rehabilitation of Aquatic Ecosystems Based on environmental water rights upstream of Water Reservoirs with Inlet Flow Prediction Approach (Case Study: Taleghan Dam Basin)
Zahra Nafariyeh Mahdi Sarai Tabrizi Hossein Babazadeh Hamid Kardan MoghaddamLimited water resources and increased water demand in recent decades have caused irreparable damage to the country's water resources. One of the important components in surface water optimization and management is long-term and short-term river flow forecasts. The aim o MoreLimited water resources and increased water demand in recent decades have caused irreparable damage to the country's water resources. One of the important components in surface water optimization and management is long-term and short-term river flow forecasts. The aim of the present study is to compare the performance of two Bayesian BN network models with probabilistic approach and MLP neural network. Then selecting the best structural model for flow prediction is another goal of the present study. Monthly meteorological data including precipitation, monthly average temperature, evaporation and. Also, the volume of water transferred from five hydrometric stations entering the Taleghan Dam from 2006 to 2018 was introduced as input data to the models. and runoff to the dam was considered as predictable. Then, with the aim of estimating the best Prediction pattern structure, Input data with different layouts were introduced to the models. In the next step, using the hydrological method of Tennant, The environmental discharge was calculated And the probability of these discharges occurring in the registration data and seventeen patterns in the Easyfit software environment was calculated. Then comparing the selected pattern according to the probability of occurrence and the criteria of the index, Nash-Sutcliffe coefficient (NS), root mean square error (RMSE) and mean absolute prediction error (MAPE) was performed. The best model in BN model with 43.3% similarity and index criteria were estimated to be -3.98, 300, 17.3 and 0.06, respectively. MLP model with 80% similarity and index criteria were introduced as -10.3, 8266, 23.9 and 122.3 in the best model, respectively. As a result, both models performed well in runoff estimation, but comparing the environmental probabilities of the two models in the top five patterns, the BN model has an acceptable accuracy . The basin was also found to be at environmental risk. Manuscript profile -
Open Access Article
32 - Change prediction of Karoon river lengths by using historical and quantitative geomorphologic data (From Shoshtar to Arvandrod)
Jafar Morshedi Seyed Kazam Alavi panahThe study area is a part of Karoon river located in Khuzestan province in southwestof Iran. The length of this reach is about 364 km from the north of Shoshtar to theArvandrod. The changes and local difference on the river reaches consider togeological, tectonicaly, hyd MoreThe study area is a part of Karoon river located in Khuzestan province in southwestof Iran. The length of this reach is about 364 km from the north of Shoshtar to theArvandrod. The changes and local difference on the river reaches consider togeological, tectonicaly, hydrological and artificial parameter in the dry flood plain ofKhuzestan has caused some damages, risks and hazards during the time. By recognizeof fluvial environment of Karoon River and determining the changes of the river,control of these hazards is possible. Because of morphometric characteristics study ofKaroon River, for changes prediction, with use of satellite images of IRS and land satin the years of 1991 and 2007, channel length of the river has drawn, measured andanalyzed by GIS software. so total length of Karoon consider to the number of theircurves(100 curve) divided to smaller limits and crossing point selected as upper andlower limits of each curves. Then geometric parameter of channel like radius ofcurvature, mean central point of each curve, curve direction and annual rate of channelmigration measured. The results show that the most risks belong to meanderingreaches. Therefore the land use and sensitive area of river to erosion spatially oncurves if dose not controlled. There are a lot of area like farms, roads, settlement,national fields and other mankind Struthers that will be destroyed. Manuscript profile -
Open Access Article
33 - Analysis of quantitative and economic indicators of housing and forecasting of population structure, housing prices and houses required till 1410 in Tehran
javad mahdianpoor hamidreza saremiIntroduction and Objectiv: the quality of the urban housing indicators is one of the indicators of the socio - economic development in the countries of the world.in developing countries, including Iran housing supply is one of the Acute issues, are due to the presence o MoreIntroduction and Objectiv: the quality of the urban housing indicators is one of the indicators of the socio - economic development in the countries of the world.in developing countries, including Iran housing supply is one of the Acute issues, are due to the presence of the defects in the planning of Housing and also increase urbanization rapidly. Over the decades, the changes in the city of Tehran have made it important for housing planning. The purpose of this article is to analyze Indicators of the quantitative and economic housing. Method: The method used in this research is based on objective, applied and based on data collection, descriptive and analytical - quantitative. The library and documentation method were used to collect information. Finally, the prediction of population structure (exponential method), housing needs (aggregation and index methods) and housing prices (Arima method) have been addressed in three minimum, average and maximum scenarios. Findings:The research findings indicate that the population of Tehran will reach from 8537000 to 9734000 by 1410 And will require 2863,000 to 3534,000 residential units. It is also expected that the average price per square meter of housing will reach 6.4 to 6.8 million tomans. Conclusion:Housing Analysis in Tehran shows that quantitative indicators of housing have improved and economic indicators of housing have declined. And for the future of Tehran, a scenario with at least population growth and the scenario of maximum housing prices seems more likely. Manuscript profile -
Open Access Article
34 - A Study on the Financial Performance of Companies Using Data Envelopment Analysis Model and Zemijsky's Model and a Comparison of their Results
Akbar rahimipoor masomeh tadress hasani -
Open Access Article
35 - The performance of the maximum entropy algorithm and geographic information system in shallow landslide susceptibility assessment
Faezeh Rajabzadeh saeed ghiasi omid RahmatiShallow landslide is one of the natural hazards that damage life and property of people in mountainous watershed. Due to the fact that a lot of landslides events have been occurred in this watershed, assessment the risk of shallow landslides by using appropriate methods MoreShallow landslide is one of the natural hazards that damage life and property of people in mountainous watershed. Due to the fact that a lot of landslides events have been occurred in this watershed, assessment the risk of shallow landslides by using appropriate methods and determine of effective factors in reduce the hazards of it is so effective. The potential of using maximum entropy modeling for landslide susceptibility mapping is investigated. In the case study of west of Ardabil province, 74 landslide occurrences were identified, 52 landslides (70%) used for training and the 22 landslides (30%) applied for validation purpose. environmental factors including continuous (altitude, slope, aspect, plan curvature, drainage density, and rainfall) and categorical (lithology and landuse) data were used as inputs for modeling. From the optimal setting test based on cross-validation, a continuous data and its combination with categorical data showed the best predictive performance. The results of validation showed that the ROC and AUC for success and prediction rate of model was 96.1 and 97.6%, respectively. Factor contribution analysis indicated that altitude and rainfall layers were the most influential factors. From interpretations on a response curve, steeply sloping areas that consisted of excessively covered with old alluvial terrace soils were very susceptible to landslides. Predictive performance of maximum entropy modeling was slightly better than that other models like of a logistic regression which has been used widely to assess landslide susceptibility. Therefore, Maximum entropy modeling is shown to be an effective prediction model for landslide susceptibility mapping. Manuscript profile -
Open Access Article
36 - Estimation of Inflow to Urmia Lake Using Time Series and Basin's Future Simulation Modeling in Two Long and Short Term Scenarios
اردلان شریف نسب Mojtaba ShourianThe Urmia Lake is the largest and the most important internal lake in Iran and is one of the most valuable international hemispherical resources in the world. But the Lake has been gradually getting dried nowadays. If the Lake gets completely dried, irreparable environm MoreThe Urmia Lake is the largest and the most important internal lake in Iran and is one of the most valuable international hemispherical resources in the world. But the Lake has been gradually getting dried nowadays. If the Lake gets completely dried, irreparable environmental, economical and social damages would be appeared in the region. So, finding a practical solution for surviving the Urmia Lake is crucial. In the present research, it has been tried to predict the inflows of the rivers leading to the Urmia Lake, once based on the long term period’s recorded data and another time based on the recent dry period’s recorded data, by using autoregressive moving average (ARMA) time series models in order to exert the effects of the recent drought in the forecasted data. The ARMA models are developed in the MATLAB soft ware. After calibration of the created models, the predicted discharges of the basin’s rivers were entered into the simulation model of MODSIM in order to estimate the water consumptions in the basin's future condition and finally the entering flows to the Urmia Lake in each of two forecasting scenarios. Results show that in each of two forecasting scenarios of long and short periods, the environmental water right of the lake wouldn’t be supplied totally. Also, if the agricultural water consumptions would get reduced about 14% and 56% in long and short periods respectively, the lake’s water right would be supplied completely. Manuscript profile -
Open Access Article
37 - Prediction of SPI drought index using support vector and multiple linear regressions
Saeed Samadianfard اسماعیل اسدیDrought is a natural phenomenon, which has a complex mechanism as a result of interactions of meteorological parameters and usually occurs in all climates. So, predicting drought indices and their chronological evaluation is an effective way for the drought management a MoreDrought is a natural phenomenon, which has a complex mechanism as a result of interactions of meteorological parameters and usually occurs in all climates. So, predicting drought indices and their chronological evaluation is an effective way for the drought management and adaptation with its consequences. In the current research, prediction of drought indices are carried out for Tabriz synoptic station, using support vector regression, multiple linear regression and standard precipitation index (SPI) for the time period of 1979 to 2012. In this regard, for predicting SPI indices in the periods of 3, 6, 9, 12, 24 and 48 months, six different input combinations including the antecedent correspondent values of the mentioned index have been utilized. The results of statistical analysis showed that both methods had significant accuracy. Nonetheless, the support vector regressions for predicting SPI-6, SPI-9 and SPI-24 had better performances, regarding the root mean squared errors of 0.4985, 0.4340 and 0.2427, respectively. However, the multiple linear regressions showed lower relative errors, for predicting SPI-3, SPI-12 and SPI-48. Meanwhile, it can be concluded that both examined methods including support vector and multiple linear regressions had acceptable predictions of drought index and can be used with an admissible confidentiality for the management of drought consequences. Manuscript profile -
Open Access Article
38 - 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
39 - Assessing the Performance of WRF Model in Prediction of Evapotranspiration in Paddy Fields
Ebrahim Asadi Oskouei Mohammadreza Mohammadpour Penchah Leila Goodarzi Mojtaba ShokouhiBackground and Aim: Evapotranspiration as one of the main components of the hydrological cycle, has a significant role in proper irrigation planning and water resources management. In this case, estimating evapotranspiration is limited due to a lack of data and a defici MoreBackground and Aim: Evapotranspiration as one of the main components of the hydrological cycle, has a significant role in proper irrigation planning and water resources management. In this case, estimating evapotranspiration is limited due to a lack of data and a deficiency of meteorological stations. Therefore, today numerical models such as WRF are a powerful tool for generating and predicting meteorological quantities (wind speed, humidity, etc.) that are needed to estimate evapotranspiration. So far, no research has been conducted to investigate the effect of different schemes of the WRF model on the estimate of rice evapotranspiration. The purpose of this study is to evaluate the efficiency of the WRF model and obtain the result for estimating evaporation for rice plant in the central plain of Guilan.Method: Evapotranspiration rates vary from 2.7 to 8.5 mm per day. The average ET during three different periods of plant growth, including the initial, middle, and final periods, is estimated to be 4.63, 5.97, and 5.98 mm per day, respectively. The three configurations 1, 2, and 4 are mainly overestimated in predicting evapotranspiration of rice plants, and the computational values are estimated to be higher than the values measured by the lysimeter. The results show that the highest amount of RMSE occurred in configuration No. 4 at 8.47 and the lowest rate occurred in configuration No. 3 at 1.26. Summary of results shows that configuration No. 3 in all four criteria mentioned has performed better than other configurations to predict daily evapotranspiration of rice. The results showed that the non-local schema used in the model, simulates better than the local schemas for the daily evapotranspiration of the rice plant. Findings show that in the local YSU schema, the accuracy of predictions is significantly increased and is only 0.64 mm on average less than the estimated lysimetric data.Results: The results showed that using appropriate schemas in the surface layer and boundary layer of the WRF model, affects on accuracy of evapotranspiration predictions. The results of this study showed that, this model by using the YSU non-local boundary layer scheme can accurately predict the evapotranspiration rates of the rice plant for the next day and this is due to the higher ability of this schema in predicting the parameters affecting evapotranspiration (including temperature and wind). Therefore, the WRF model can be implemented by using GFS forecast data for the next few days and by applying the FAO-Penman-Monteith equations to the model outputs, the values of potential evapotranspiration for different regions of the country can be calculated. Since evapotranspiration is directly related to atmospheric thermodynamic processes, so using other different atmospheric physics schemas (not considered in this study) can produce different results. Manuscript profile -
Open Access Article
40 - Rainfall-Runoff modeling using Deep Learning model (Case Study: Galikesh Watershed)
Razieh Tatar Khalil Ghorbani mehdi Meftah halghi meysam 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
41 - Drought Prediction Using North American Multi-Model Ensemble (NMME) Over Western Regions of Iran
Mehdi Moghasemi Narges Zohrabi Hossein Fathian Alireza Nikbakht Shahbazi Mohammadreza YeganegiBackground and Aim: Drought as a natural hazard significantly impacts various sectors such as agriculture and water resources and causes considerable damage to these sectors worldwide. Therefore, adaptation strategies should be taken to reduce drought damage, and in the MoreBackground and Aim: Drought as a natural hazard significantly impacts various sectors such as agriculture and water resources and causes considerable damage to these sectors worldwide. Therefore, adaptation strategies should be taken to reduce drought damage, and in the meantime, planning and adaptation to drought conditions using drought forecasting is one of the most effective strategies. Due to the need for drought forecasting and the limited studies that evaluated drought indicators obtained from the rainfall forecast output from the North American Multi-Model Ensemble (NMME) in Iran. This study evaluated these models in four catchments of Karkheh, Karun, Heleh, and Hindijan-Jarahi for1982-2018.Method: In this study, the monthly output of different NMME ensembles were evaluated in the forecast leads of 0 to 9 months from 1982 to 2018, the SPI drought index was calculated. Comparison of these data with GPCC data was used for evaluation. Three quantitative criteria, including correlation coefficient, RMSE, and BIAS, were used for evaluation. Also, to integrate the existing models, two methods: a: Arithmetic mean between the existing models and B: Weighted average between the models have been evaluated by considering the correlation coefficient (CC) results. Also, two criteria (i.e., POD and FAR) and the quantitative statistical criterion (i.e., correlation coefficient) were used to evaluate the SPI drought index.Results: The results of the precipitation evaluation of the models showed that the integrated models have better performance than the individual models. In this integrated model, the weighted model also had better performance. Evaluation of spatial distribution of precipitation models also showed the excellent performance of NMME models in Karun and Hindijan-Jarahi catchments in the zero-month forecast lead and Hindijan-Jarahi catchments in the one-month forecast lead. The results of drought index evaluation showed that integrated models, although having better performance in precipitation forecasting, but in drought forecasting, the best performance belongs to NASA-GMAO-062012 and CFSv2 models. The results also showed that drought index forecasts in three and six-month periods have better performance than one month. Spatial distribution evaluation also showed that the models perform better in the southern basins. In general, it can be concluded that NMME models have good performance in predicting drought in some places and specific forecast leads, so they should be evaluated at each point before use.Conclusion: The results of precipitation evaluation showed that, in general, integrating the output of dynamic models increases its proficiency, and integration in weighted mode (WeightedNMME) performs better than the non-weighted model (NMME). According to the zero-month forecast among individual models, the NASA-GMAO-062012 model has the most skills in terms of the correlation coefficient. However, in the one-month forecast lead in terms of the correlation coefficient, RMSE and BIAS, the best performance belongs to the CFSv2 model. Evaluation of drought indices showed that the model's performance could be different from their performance in predicting rainfall. WeightedNMME, for example, performed well in NASA-GMAO-062012 and CFSv2 months, although they performed well in predicting drought. The spatial evaluation also showed that the southern catchments perform better than other basins. Manuscript profile -
Open Access Article
42 - Uncertainty Evaluation due to TIGGE Global System Precipitation Data for Flood Forecasting
Soudabeh Behiyan Motlagh Afshin Honarbakhsh Asghar AzizianBackground and Aim: The occurrence of frequent floods in Iran necessitates a flood forecasting and warning system with a suitable lead time. The use of numerical rainfall forecasting models in flood forecasting and warning is one of the important measures taken by resea MoreBackground and Aim: The occurrence of frequent floods in Iran necessitates a flood forecasting and warning system with a suitable lead time. The use of numerical rainfall forecasting models in flood forecasting and warning is one of the important measures taken by researchers in most parts of the world. The TIGGE database includes mid-term precipitation forecasts simulated by global forecast centers. The purpose of this research is to evaluate the efficiency and the degree of uncertainty caused by the rainfall forecasts of four numerical models of the TIGGE database (including CPTEC, ECCC, ECMWF, and KMA) for simulating floods with the HEC-HMS hydrological model.Methods: In this research, the precipitation data of seven meteorological stations were used to evaluate the uncertainty of discharge from TIGGE database precipitation prediction models in the Poldokhtar watershed. Also, three flood events on March 24, 2017, April 6, 2018, and April 15, 2018, were studied. At first, precipitation forecasts were extracted from four centers CPTEC, ECCC, ECMWF, and KMA. Due to the existence of systematic error in the forecasts, a bias correction was done on them, and to correct the bias, the Delta method was used. Processed and raw forecasts of four rainfall forecasting models were entered into the HEC-HMS model for flood forecasting, and in the next step, the flow uncertainty assessment of the HEC-HMS model was performed in all members of the four rainfall forecasting models. In this research, 5 factors P, R, S, T, and RD were used for uncertainty analysis.Results: The results indicate the significant superiority of the ECMWF model in predicting precipitation events. The use of all 4 rainfall sources led to an acceptable simulation of the flood peak flow in three different events. Also, the predicted peak discharge time had little difference from the observed data. According to the results of the uncertainty analysis, the ECMWF model was considered the best model based on P, R, S, T, and RD factors. The KMA model performed well in severe and very severe floods. The group prediction system of TIGGE models also had an acceptable performance in all events. Also, the hydrological-meteorological prediction model predicted the time of flood occurrence and the probability of occurrence well.Conclusion: The intended research investigates flood forecasting and warning in the Poldokhtar watershed using the meteorological-hydrological system, based on meteorological forecasts of the TIGGE database and flood simulation using the HEC-HMS hydrological model. The final product of this system is probable discharge and flood forecast. The results reveal the success of the TIGGE database in flood forecasting. The ECMWF model excelled in predicting peak discharge. The upper and lower band calculation method was used to determine the uncertainty, which showed the uncertainty well. This system displayed the time of peak discharge well and with a small time delay, which indicates its good performance. The predicted rainfall from the four centers used in this study (ECMWF, ECCC, CPTEC, and KMA) have significant differences. To reduce these differences, we used a multi-model group forecasting system that had encouraging results. Manuscript profile -
Open Access Article
43 - 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
44 - Prediction Impact of Climate Change on the Temperature & Precipitation by General Circulation Model, a Strategy for Sustainable Agriculture: (Case of Kermanshah Township)
Samireh Seymohammadi Mohsen Tavakoli Kiumars Zarafshani Hossien Mahdizadeh Farzad AmiriBackground and Objectives: Concern about climate change and its effects on various aspects of human life in general and agricultural production in particular is growing. Therefore, the main purpose of this study is to assess and predict of climate change induced tempera MoreBackground and Objectives: Concern about climate change and its effects on various aspects of human life in general and agricultural production in particular is growing. Therefore, the main purpose of this study is to assess and predict of climate change induced temperature and precipitation of Kermanshah township.Method: The calibration and validation of the HadCM3 model was performed 1961-2001 of daily temperature and precipitation. The data on temperature and precipitation from 1961 to 1990 were used for calibration whereas data from 1991 to 2001 were used for model validation. SDSM version 4.2 as a downscaling model used to downscale general circulation models to station scales. Findings: The least difference between observed data and simulation data during calibration and validation showed that the parameter was precisely modeled for most of the year. This study under A2 scenario, three time periods (2020, 2050, 2080) were simulated. Discussion and Conclusion: According to our simulated model, precipitation showed a decreasing trend whereas temperature showed an increasing trend. The result of this study can also be used as an optimal model for land allocation in agriculture. Manuscript profile -
Open Access Article
45 - Surveying and Predicting Surface Currents of Khuzestan Province Using Time Series Models
Alireza Entezari Rasoul SarvestanThe purpose of this study was to study the surface currents of Khuzestan province and its prediction for the period (2019Background and Objective: The present study is to evaluate the surface currents of Khuzestan province and its forecast for the period 2019 to 2021 us MoreThe purpose of this study was to study the surface currents of Khuzestan province and its prediction for the period (2019Background and Objective: The present study is to evaluate the surface currents of Khuzestan province and its forecast for the period 2019 to 2021 using time series models.Material & Methodlogy: The present study was conducted in 9 selected stations from Khuzestan province in order to compare the accuracy of the time series model and predict the amount of surface currents. For this purpose, the monthly flow data of the hydrometric station for 22 years (1391-2014) has been used. The multiplicative seasonal time series model of surface currents was investigated and the best model was fitted. Findings: The results of these studies show that the best models fitted in SARIMA (1,1,1) (1,0,1), SARIMA, SARIMA (0,1,1) (1,0,1), telephoto SARIMA, Primate (1,0,1) (1,1,1) SARIMA, Dezful (1,0,2) (1,1,1) SARIMA, Plain SARIMA, Dokehe (0,2,2) (1,1,1) SARIMA, Gotvand (1,1,2) (1,0,1) SARIMA (1,1,1) And SARAB (1.1.2) (2.1.1), which had good accuracy to predict surface currents.Discussion and Conclusion: Surveying the annual prediction of surface currents for 2019 to 2029 showed that surface currents in all selected stations decreased and this decrease in Ahwaz station to the highest and the two-hill station to the lowest values reaches to 9.78 and 0/58 respectively; also, the monthly forecast showed that in December, with 6/98 and 1/67, the highest and lowest decreases would occur. Manuscript profile -
Open Access Article
46 - 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
47 - Using dynamic recurrent neural network NAR for predicting monoxide carbon concentration
Mehrdad Rafiepour Ali Asghar Alesheikh Abbas Alimohammad Abolghasem Sadeghi NiarakiBackground and Objective: Air pollution is one of the most important problems in big cities. One of the goals of urban managers is their awareness on air pollution in the future. For prediction of air quality, air pollutant must be modeled first. Carbon monoxide is one MoreBackground and Objective: Air pollution is one of the most important problems in big cities. One of the goals of urban managers is their awareness on air pollution in the future. For prediction of air quality, air pollutant must be modeled first. Carbon monoxide is one of the most toxic air pollutants that has harmful effect on human health. Method: In this paper, modeling carbon monoxide concentration and 24-h prediction by ARMA and NAR neural network have been studied. Then, the results of the two methods are compared. For this purpose, data is collected on 29 November until 31 December 2009 in Azadi air quality monitoring station: belonged to Tehran department of environment. Findings: The results of the two methods showed that, NAR is more accurate than ARMA for modeling and prediction of carbon monoxide. NAR neural network had MSE=1.6 and a correlation coefficient of 0.84 while ARMA had MSE=5.46 and correlation coefficient=0.72 for 24 hours prediction. Discussion and Conclusion: Finally, the predicted values can be used and published in internet for public awareness. Also urban managers can use the results of modeling and prediction for a better management. Result of this paper showed NAR neural network has sufficient ability to model and predict time series of monoxide carbon Manuscript profile -
Open Access Article
48 - Structural modeling of the precondition of financial behavior of investors in Iran’s stock market
Fatemeh Ahmady Mehrdad Ghanbari babak Jamshidinavid Shahram MamiThis study followed the design of a model to predict the financial behavior of investors in the Iranian stock market. Qualitative content analysis of scientific texts related to the research topic was used to identify the criteria. Interactive matrix and based on expert MoreThis study followed the design of a model to predict the financial behavior of investors in the Iranian stock market. Qualitative content analysis of scientific texts related to the research topic was used to identify the criteria. Interactive matrix and based on expert opinion based on interpretive structural modeling method was developed and a five-level model was obtained. To determine the type of variables, the MicMac analysis was used. In the five-level model of this study the variables of informal news, investor financial conditions, subjective financial knowledge, and herd behavior at the fifth level were the most influential and believable, personal judgment, emotion control, and loss aversion were the most influential variables of this model at level one of this model, were affected. MicMac analysis also indicated that the variables of informal news, subjective financial knowledge, investor financial conditions, self-esteem, and herd behavior were independent, and the variables of avoidance, believability, financial technology, emotional control, personal judgment, and financial specialties are also type dependent and other variables are interface type. Manuscript profile -
Open Access Article
49 - Application of Genetic Algorithm, Particle Swarm and Artificial Neural Networks in Predicting Profit Manipulation
Morteza Hoseinalinezhad Seyed Mohamad Hassan Hashemi Kucheksarai Ali JafariProfit management has been one of the most controversial topics in recent research. Most research on earnings management has examined the linear relationship between independent variables and earnings management using statistical methods but they did not use these varia MoreProfit management has been one of the most controversial topics in recent research. Most research on earnings management has examined the linear relationship between independent variables and earnings management using statistical methods but they did not use these variables to predict earnings management. Today, with the growth of information technology and the introduction of artificial intelligence, including artificial neural networks into the field of scientific research, it has become possible to study nonlinear relationships between variables. In this study, an attempt was made to estimate optional accruals for predicting earnings management using artificial neural networks. Also in this research, the genetic algorithm optimizer model and Particle swarm has been used to optimize the weights of the artificial neural network model to enhance the predictive power. Then, the ability to predict profit management was evaluated using the modified Jones statistical model, artificial neural network and the combined technique of genetic algorithm, Particle swarm and neural network. The sample used in this study included 150 companies listed on the Tehran Stock Exchange between 2015 and 2020. Findings showed that the artificial neural network has a high ability to predict profit management, compared to the modified Jones linear model. The findings also indicate that the accuracy and ability of the combined model of genetic algorithm, particle swarm and neural network in predicting profit management is higher than the combined model of genetic algorithm-artificial neural network. Manuscript profile -
Open Access Article
50 - Designing a model for forecasting the return of the stock index (with emphasis on neural network combined models and long-term memory models)
Reza Najarzadeh Mehdi Zolfaghari Samad GolamiThis study presents the new hybrid network of GARCH family and an artificial neural network to predict the Tehran Stock Exchange index during the period of 2008-2017. The existence of long-term memory in the conditional variance of the Tehran stock returns causes use in MoreThis study presents the new hybrid network of GARCH family and an artificial neural network to predict the Tehran Stock Exchange index during the period of 2008-2017. The existence of long-term memory in the conditional variance of the Tehran stock returns causes use in addition GARCH and EGARCH models with short- memory, long-term memory models. In addition to long-term memory models, considering the better performance of hybrid models in predicting financial data of the Garch family models (short and long-term) are combined with the artificial neural network. Using hybrid models the return of stock index was forecast for the next 10 days and its accuracy was evaluated using the evaluation criteria. The results showed that the hybrid FIEGARCH with the student-t distribution model was more efficient in forecasting return of stock and had a lower forecast error than others models Manuscript profile -
Open Access Article
51 - Investigate the Effect of Prediction Profit Reporting Tone on Investors' Reactions and Performance Prediction
mahmoud toorchi Mahmoud Lari Dashtebayaz Mohammad Reza RazdarIn recent years, the analysis of various aspects of qualitative information on financial reports is one of the important tools for decision making by investors and other capital market users. The purpose of this study, was to investigate The effect of the earnings forec MoreIn recent years, the analysis of various aspects of qualitative information on financial reports is one of the important tools for decision making by investors and other capital market users. The purpose of this study, was to investigate The effect of the earnings forecast report tone on investor response and performance prediction. To investigate this issue, the present study uses 140 years- company listed companies at Tehran Stock Exchange between 2011 and 2017. In order to test the hypotheses of the research, multiple regression and logistic regression were used. The research findings suggest that earnings forecast report tone is not a suitable tool for investors and other users to predict future earnings and future cash flows (future performance). Also, no significant relationship was found between the earnings forecast report tone with some of the incremental (decreasing) criteria of managers' understanding to manage the report tone, such as renewing the presentation of profits and realizing the near real. In other words, the earnings forecast report tone does not have any effect on the perception and awareness of the investors. Manuscript profile -
Open Access Article
52 - A Model to Predict Bankruptcy using the Mechanisms of Corporate Governance and financial ratios
ghazaleh Alibabaee Hamed KhanmohammadiImproving the economic and business environment is the most important factor in preventing bankruptcy, therefor, Artificial intelligence uses to predict the bankruptcy of companies in the future. In this study, companies in the Tehran Stock Exchange over a period of 10 MoreImproving the economic and business environment is the most important factor in preventing bankruptcy, therefor, Artificial intelligence uses to predict the bankruptcy of companies in the future. In this study, companies in the Tehran Stock Exchange over a period of 10 years in terms of bankruptcy localized model of Kurdistani-Tatli based on the Altman model were examined and companies were identified as bankrupt and healthy. Research data were collected, categorized and refined using secondary data extracted from financial statements and through the database of the Exchange Organization and the Central Bank.The models used to evaluate the data and predict the bankruptcy of companies are artificial intelligence models . Artificial neural network, combination of neural network and genetic algorithm and the K-nearest neighbor method has been used. They were also compared in terms of prediction accuracy. The output of the models indicates that the addition of corporate governance indicators to the financial ratios indicators has not improved the results. Therefore, financial ratios alone are sufficient for predicting and determining bankruptcy. The proposed model of this research based on accuracy is a combined model of neural network and genetic algorithm that has the highest accuracy. Genetic algorithm improves the optimal results of the neural network and provides a more optimal answer. Manuscript profile -
Open Access Article
53 - Predicting Network linkages of banking system distress based on operational risks and behavioral finance components
ahmad bidi Fraydoon Rahnamay Roodposhti Gholam Reza Gholami Jamkarani HAMIDREZA KORDLOUIE Mortaza Baky HasueeThe present research is aimed at prediction of network linkages of banking system distress based on operational risks and behavioral finance approach. Methodology of the present research is of survey descriptive, practical from the purpose standpoint. Notably, in order MoreThe present research is aimed at prediction of network linkages of banking system distress based on operational risks and behavioral finance approach. Methodology of the present research is of survey descriptive, practical from the purpose standpoint. Notably, in order to reach this purpose, firstly, based on study and review of theoretical basics, research variables were introduced. Then, by making use of Krejcie and Morgan Table, 384 participants were selected, and upon distribution of questionnaire among the aforesaid, research data were collected. Furthermore, in order for analysis of data and estimation of research empirical models, the researcher used structural equation modeling (SEM) and Smart PLS software. Of note, findings of this research indicate that behavioral financial standpoints and operational risk have significant effects on prediction of banking network disorder. Furthermore, based on estimated beta coefficients, among behavioral financial elements, economic behavior, cognitive standpoint, judgment biases, heuristic behaviors, decision making biases and value and return of stocks have respectively the highest effect on banking disorder, and among operational risk elements, human resources risk, systemic risk, transaction risk, technology risk and fraudulent and deception risk have respectively the highest effect on banking disorder. Manuscript profile -
Open Access Article
54 - An Optimization of Moving Average Stock Price in Tehran Stock Exchange: Meta-heuristic approach Adaptive Improved Genetic Algorithm
Mahboobeh Asghartabar ledari Ahmad Jafari SamimiPredict the stock price is an important topic in financial markets. Is commonly use of technical tools in this area and one of them most functional, are moving averages. The use of two moving averages, the most common method to predict trends, which is in need of two pe MorePredict the stock price is an important topic in financial markets. Is commonly use of technical tools in this area and one of them most functional, are moving averages. The use of two moving averages, the most common method to predict trends, which is in need of two periods. The optimal lengths for both short-term and long-term period for each stock, according to a recent trend, they are different. Find the optimal lengths with traditional methods of costly and often do not reach the global optimal answer. The perfect solution are using of smart tools such as genetic algorithms. Genetic algorithm have been used in this study, is Adaptive Improved Genetic Algorithm that much faster finds a global optimal answer. In this study, data's of the selected companies in diverse industries in Tehran Stock Exchange from April 2011 to March 2016 have been evaluated. The results show, when the algorithm reaches the optimal time period, which its parameters are correctly set. Manuscript profile -
Open Access Article
55 - ARIMA and ARFIMA Prediction of Persian Gulf Gas-Oil F.O.B
H. Amadeh A. Amini F. EffatiGas-oil is one of the most important energy carriers and the changes in its prices could have significant effects in economic decisions. The price of this carrier should not be more than 90 percent of F.O.B price of Persian Gulf, legislated in subsidizes regulation law MoreGas-oil is one of the most important energy carriers and the changes in its prices could have significant effects in economic decisions. The price of this carrier should not be more than 90 percent of F.O.B price of Persian Gulf, legislated in subsidizes regulation law in Iran. Time series models have been used to forecast various phenomena in many fields. In this paper we fit time series models to forecast the weekly gas-oil prices using ARIMA and ARFIMA models and make predictions of each category. Data used in this paperstarted with the first week of the year 2009 until the first week of 2012 for fitting the model and the second week of 2012 until 13th week of 2012 for predicting the values, are extracted from the OPEC website. Our results indicate that the ARFIMA(0.0.-19,1) model appear to be the better model than ARIMA(1,1,0)and the error criterions RMSE, MSE and MAPE for the forecasted amounts is given after the predictions, respectively Manuscript profile -
Open Access Article
56 - Application of Geometric Brownian motion in prediction of gold price and currency rate
Hojjatollah Sadeqi Mohammadesmaeil Fadaeinejad Alireza VarzidehVariables such as exchange rates and gold prices has a great importance for economic actors therefore the aim of this study were determined as prediction of U.S Dollar exchange rate and gold coin price in Iran Market. Forecasting has been done by Geometric Brownian Moti MoreVariables such as exchange rates and gold prices has a great importance for economic actors therefore the aim of this study were determined as prediction of U.S Dollar exchange rate and gold coin price in Iran Market. Forecasting has been done by Geometric Brownian Motion model that is considered as one of the stochastic differential equations. Data were collected and analyzed in the period from the beginning of 1392 until the end of 1395. also forecasting prices for each under study time series has been done in various forecasting horizons involved 7, 14, 21, 30, 60, 90, 180 and 360 day time period. The results show that Geometric Brownian Motion model can simulate the prices of gold coin and exchange rate highly accurate in accordance with the criteria of mean absolute percentage error. Also The other results obtained from this study is that According to ten different prediction accuracy criteria, By increasing the forecast horizon, ability of the GBM model in simulation and forecasting exchange rates and the price of gold coin decreases. Manuscript profile -
Open Access Article
57 - 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
58 - پیش بینی خطر سکته مغزی بر اساس علائم کلینیکی با استفاده از روش رگرسیون لجستیک
مائده غلام آزاد جعفر پورمحمود علیرضا آتشی مهدی فرهودی رضا دلجوان انوریمدل سازی ریاضی یکی از روش های عملی است که می توان از آن برای حل مسائل واقعی استفاده کرد. مدلسازی را میتوان با استفاده از روشهای مختلفی از جمله روشهای آماری که میتوان از آنها برای پیشبینی رویدادهای مختلف استفاده کرد، انجام داد. سلامت یکی از مهمترین زمینه های تحقیقا Moreمدل سازی ریاضی یکی از روش های عملی است که می توان از آن برای حل مسائل واقعی استفاده کرد. مدلسازی را میتوان با استفاده از روشهای مختلفی از جمله روشهای آماری که میتوان از آنها برای پیشبینی رویدادهای مختلف استفاده کرد، انجام داد. سلامت یکی از مهمترین زمینه های تحقیقاتی در جهان امروز است. از بین بیماری های مختلف در بخش سلامت، این مطالعه مربوط به سکته مغزی است که دومین عامل مرگ و میر و ناتوانی طولانی مدت انسان است که منجر به انجام این تحقیق شده است. هدف اصلی این تحقیق طراحی و ساخت یک مدل پیشبینیکننده سکته مغزی بر اساس علائم و گزارشهای بالینی بیماران است که پیش بینی میکند که آیا در آینده نزدیک سکته مغزی در بیماران رخ میدهد یا خیر. با استفاده از روش رگرسیون لجستیک، عوامل خطر اصلی سکته مغزی شناسایی و میزان بروز آنها پیشبینی شده است. در این مطالعه اطلاعات بالینی از 5411 بیمار جمعآوری و پس از اعمال روش LR، مدل پیشبینیکننده طراحی شد. Manuscript profile -
Open Access Article
59 - A Review of Urban Growth Prediction Models
Fatemeh Hajizadeh Abdolrasoul Salman MahinyHuman population continues to aggregate in urban centers, who inevitably increases the urban footprint with significant consequences for biodiversity, climate, and environmental resources. Urban growth prediction models have been extensively studied with the overarching MoreHuman population continues to aggregate in urban centers, who inevitably increases the urban footprint with significant consequences for biodiversity, climate, and environmental resources. Urban growth prediction models have been extensively studied with the overarching goal to assist in sustainable management of urban centers. Despite the extensive research, these models are not frequently included in the decision making process. The survey found a strong recognition of the models’ potential in decision making, but limited agreement which these models actually reach enough potential in practice. This review aims are an overview of existing models, including advantages and limitations. Also, in general, it will be discussed to main reason for not applying these models in the decision making. Analysis of aggregated statistics indicates that cellular automata are the prevailing modeling technique, present in the majority of published works. Also, being unfamiliar decision-makers with models and thelack of popularity models to research are significant reasons for not using these models in the decision making. Manuscript profile -
Open Access Article
60 - Fire of Iranian forests, consequences, opposition methods and solutions
Saeedeh Eskandari سمانه اسکندریFire in the forests of Iran has destructed a large part of these valuable ecosystems in the recent years. Regarding to that Iran is one of the low-forest cover countries in the world, investigation of fire consequences in the forests of Iran and recognition of oppositio MoreFire in the forests of Iran has destructed a large part of these valuable ecosystems in the recent years. Regarding to that Iran is one of the low-forest cover countries in the world, investigation of fire consequences in the forests of Iran and recognition of opposition methods to fire in these forests is essential to present a solution to decrease these fires. Fire in the forests of Iran has had an important effect in destruction of unique flora and fauna, decrease of biodiversity and decrease of qualitative value of industrial plant species in addition to the economic damages and environmental pollutions. Furthermore, the recent fires in the forests of Iran have also increased the greenhouse gas emissions which it has an important role in the warming of these ecosystems and increasing of subsequent fires occurrence in these forests. This pre-background along with human-caused intentional and non-intentional fires in these ecosystems, has increased the continuous fires occurrence in the forests of Iran. Thus, development and designing of the effective opposition methods to these fires as preventing and operating methods is essential. Many different methods of fire occurrence and spread modeling have been developed using RS and GIS technologies which it seems that these methods have the effective role in predicting and preventing of these forests. Manuscript profile -
Open Access Article
61 - Evaluating the efficiency of artificial neural network in prediction of Electrical conductivity of Zarrinehroud River
Ali Khoshnazar Touraj Nasrabadi Pouyan Abbasi MaedehSixteen stations on Zarrinehroud River were sampled and parameters like temperature, alkalinity, Ph, electrical conductivity, dissolved oxygen and major anions and cations were measured on water samples. Afterwards, Pearson correlation coefficient between EC and other p MoreSixteen stations on Zarrinehroud River were sampled and parameters like temperature, alkalinity, Ph, electrical conductivity, dissolved oxygen and major anions and cations were measured on water samples. Afterwards, Pearson correlation coefficient between EC and other parameters were determined and the ones with lower cost of measurement were considered as the inputs of neural network models. Finally, the model number 5 with tangent Simulating algorithm and Levenberg-Marquet training Algorithm with minimum prediction error was accepted. The maximum determination coefficient, RMSE and NRMSE Were estimated to be 0.98, 168.33 and 0.28 respectively. Furthermore, it is observed that pH has a remarkable sensitivity more over 60 percent on the artificial neural network prediction. Manuscript profile -
Open Access Article
62 - Application of Artificial Neural Network and Regression Model to Predict the Phenomenon of Dust in the City of Ahvaz
Nabiollah Hosseini Shahpariyan Mohammad Ali Firozi Seyyed Reza Hosseini KahnojDust is one of the phenomena of destructive climate in the western provinces that causes great damage to the environment and many factors are involved in creating this problem. The aim of this study is to predict the phenomenon of dust in Ahvaz city. In this study, Ahv MoreDust is one of the phenomena of destructive climate in the western provinces that causes great damage to the environment and many factors are involved in creating this problem. The aim of this study is to predict the phenomenon of dust in Ahvaz city. In this study, Ahvaz synoptic data during the years (2000-2010) have been used. These data include mean dew point (in degrees Celsius), mean wind speed in knots, relative humidity in terms of average percentage and average monthly rainfall as input, and data on dusty days as target. Networks were introduced. Then, using causal modeling, the relationships between the variables are extracted and finally, the model is tested by neural network and stepwise regression model. The results confirm the ability of more than 74% of the model used to predict the dust phenomenon in Ahvaz. The regression rate of dust data in a linear combination with the variables entered in the equation is equal to 0.651. Also, the resulting coefficient of determination is equal to 0.424 and the modified coefficient of determination is equal to 0.410; That is, in fact, about 41% of the variance of the dust variable is explained and justified through independent variables. Manuscript profile -
Open Access Article
63 - Methods of modeling and evaluation of fire occurrence risk in the forests of world and Iran
Saeedeh EskandariThe growing trend of forest fires necessitates presentation of a solution to predict and control them. Fire occurrence modeling with attention to all effective factors, is a proper solution to predict the fire occurrence in the forests because many factors affect on for MoreThe growing trend of forest fires necessitates presentation of a solution to predict and control them. Fire occurrence modeling with attention to all effective factors, is a proper solution to predict the fire occurrence in the forests because many factors affect on forest fire occurrence. This study has been done to investigate the different methods of fire modeling and fire risk assessment in forests of the world and Iran. Investigation of the researches implemented in Iran shows that the studies about fire risk potential evaluation in Iran have been limited and AHP has been used to weigh the effective factors in forest fire in most of these studies. Conclusion of researches implemented in the world shows that vegetation type, slope, aspect, distance from roads, topography and land use, have been the most effective factors in modeling of fire occurrence and integration of digital layers has often been based on a hierarchy and a risk coefficient in fire occurrence. The past actual fires map has been compared with the fire risk map to assess the accuracy of models used in provision of fire risk potential map. Logistic regression and decision-making tree algorithm have been used to select the effective variables in fire and to model the fire risk in some new studies. Integration of fuzzy inference system and neural network, neural intelligence and support vector machine has been used to predict the future fires in some advanced methods. Multi-criteria analysis is a subject used in the new studies and organization of the criteria in a spatial model using GIS has had the good results. Manuscript profile -
Open Access Article
64 - Prediction Micro-Hardness of Al-based Composites by Using Artificial Neural Network in Mechanical Alloying
R, M Babaheydari S, O Mirabootalebi -
Open Access Article
65 - Designing an Artificial Neural Network Based Model for Online Prediction of Tool Life in Turning
A. Salimiasl A. Özdemir I. Safarian -
Open Access Article
66 - Investigation of Magnitude and Position of Maximum von Mises Stress in The Cylindrical Contact Problems
Hasan Heirani Reza Naseri -
Open Access Article
67 - The Role of Cognitive Emotion Regulation Strategies in the Prediction of Depression
azam salehiThe goal of the present study was to determine the role of cognitive emotion regulation strategies in predicting depression. In this correlation study, 262 Isfahan Payame noor University students from different fields were selected by multi-stage random sampling method. MoreThe goal of the present study was to determine the role of cognitive emotion regulation strategies in predicting depression. In this correlation study, 262 Isfahan Payame noor University students from different fields were selected by multi-stage random sampling method. Cognitive Emotion Regulation Strategies Questionaire (Garnefski et al., 2002) and SCL-90 (Derogatis et al., 1973), Were administered. Research data were analyzed by Pearson correlation coefficient and stepwise regression. The results of stepwise regression analysis showed that from among of the nine scales of cognitive emotion regulation strategies scale, catastrophizing, acceptance, refocusing on planning, rumination, and recent stress, respectively, influence and significantly predict depression (P < 0.001). But, self-blame, blaming others, positive reappraisal, positive refocusing and putting into perspective strategies had no effect on depression and were removed from the equation. These results provide guidelines for the prevention of depression through modification of cognitive emotion regulation strategies. Manuscript profile -
Open Access Article
68 - Prediction of Internet Addiction,Based on Emotional Intelligence Among Isfahan University Students
nasim jafari maryam fatehizadeThe aim of this research was to investigate the predictive role of emotional intelligence in internet addiction among Isfahan university students. This is a multiple correlation research. The statistical population included students of Isfahan university of Iran. The MoreThe aim of this research was to investigate the predictive role of emotional intelligence in internet addiction among Isfahan university students. This is a multiple correlation research. The statistical population included students of Isfahan university of Iran. The randomly selected training samples included 71 students (36 girls and 35 boys). Assessment instrument consisted of Internet Addiction Test Young (1998) and Trait Emotional Intelligence Questionnaire Petrides and Furnham (2001). The gathered data was analyzed by descriptive and inferential statistics and regression methods. The results showed that there were correlations (r=-0.54) between internet addiction and emotional intelligence (P < 0.001) and emotional intelligence can predict 29% of internet addiction (P < 0.001) . Manuscript profile -
Open Access Article
69 - A Comparative Overview of Electronic Devices Reliability Prediction Methods-Applications and Trends
Frederick Ehiagwina Titus Adewunmi Emmanuel Seluwa Olufemi Kehinde Nafiu Abubakar -
Open Access Article
70 - An Improved Decision Tree Classification Method based on Wild Horse Optimization Algorithm
raheleh sharifi Mohammadreza RamezanpourIn this paper, an improved decision tree classification method based on wild horse optimization algorithm is proposed and then the application in customer behavior analysis is evaluated. Customer behavior is modeled in the form of time series. The proposed method includ MoreIn this paper, an improved decision tree classification method based on wild horse optimization algorithm is proposed and then the application in customer behavior analysis is evaluated. Customer behavior is modeled in the form of time series. The proposed method includes two general steps. First, the customers are classified into clusters based on the features extracted from the time series, and then the customers’ behavior is estimated based on an efficient predictive algorithm in the second step. In this paper, an improved decision tree classification based on wild horse optimization algorithm is used to predict customer behavior. The proposed method is implemented in the MATLAB software environment and its efficiency is evaluated in the Symmetric Mean Absolute Percentage Error (SMAPE) index. The experimental results show that variance, spikiness, lumpiness and entropy have a high impact intensity among the extracted features. The overall evaluation indicate that this proposed method obtains the lowest prediction error in compared to other evaluated methods. Manuscript profile -
Open Access Article
71 - Predicting locational trend of land use changes using CA-Markov Model (Case study: Kohmare Sorkhi, Fars province)
Sara Azizi Ghalati Kazem Rangzan Javad Sadidy Peyman Heydarian Ayoub TaghizadehLand use changes act as a significant factor in the environmental changes and have become a global threat. Monitoring and prediction these changes by satellite images and models can help the planners and managers to make more conscious planning decisions. In this regard MoreLand use changes act as a significant factor in the environmental changes and have become a global threat. Monitoring and prediction these changes by satellite images and models can help the planners and managers to make more conscious planning decisions. In this regard, the current research aimed to monitor, model and predict land use changes using CA-Markov model in Kohmare Sorkhi region, Fars province in 2024 for a period of 25 years (1987-2012). To implement the mentioned model, the land use map was first prepared by ETM+ and TM sensors during three years (1987, 2000, 2012). Then, validation of maps and change detection process were performed. The results of change detection for the first period (1987-2000) and second period (2000-2012) with an accuracy of 83% and Kappa index of 88% have shown the greatest increase in the rangeland area (4224.24 ha) and the greatest decrease in the forest area (3953.75 ha). In the next stage, in order to calibrate the CA-Markov model, land use map for 2012 was predicted; on the other hand, regarding Error Matrix between the modeling land use map and the reference land use map, the Kappa index wad given as 75%. Finally, considering the previous stage, the land use map for the outlook of 2024 was predicted. The final results for 2024 indicated that the forest area would endure the great amount of changes in comparison with 2012. The forests would change into the irrigated agricultural area and rangelands which can be considered in sustainable development planning by decision makers. Manuscript profile -
Open Access Article
72 - Forecasting of forest land changes in the Chaloosrood watershed
Vajiheh Ghorbannia Kheybari Mir Mehrdad Mirsanjari Mohsen ArminDeforestation affects watershed processes and biochemical cycles and lead to soil erosion and lack of water in the catchment areas. This study is aimed to investigate the changes in forest land in the Chaloorood watershed on the west of Mazandaran province using Geomod. MoreDeforestation affects watershed processes and biochemical cycles and lead to soil erosion and lack of water in the catchment areas. This study is aimed to investigate the changes in forest land in the Chaloorood watershed on the west of Mazandaran province using Geomod. In this study, maps of forest in the years of 1987 and 2015 were prepared using satellite images. Then the suitability forest map was produced by making a regression equation between suitability criteria maps and forest changes map in the period of 1987-2015. Finally, by using forest map in 1987, forest suitability map and the number of modified pixels in forest land between 1987 and 2015, Forecast of the forest map for 2043 was done using Geomod. Also, by using the Validate function and classified forest map 2015, as a reference map, and the forecasting forest map 2015, as a comparative map, the validity of the production map was evaluated. The results showed that the area of forest land in 1987, 2015, and 2043 was 38683.65, 2464.354 and 15227.25 hectares, respectively. The extent of forest changes in the last 28 years and the next 28 years is 35.72% and 38.76% respectively. Forest changes in the period between 1987 and 2015 under the influence of factors such as distance from the road, forest cover density, distance from the village, slope and elevation above sea level, respectively. The Pseudo R2 and ROC coefficients are 0.29 and 0.85 respectively, which indicates the proper ability of the model to estimate forest changes over the past 28 years and the relative agreement of the model with the real changes. In this study the accuracy of resulting land use maps was 96%, which represent the appropriate capability of Geomod in land use changes modeling in Chaloosrood watershed. Manuscript profile -
Open Access Article
73 - Monitoring and prediction land use/ land cover changes and its relation to drought (Case study: sub-basin Parsel B2, Zayandeh Rood watershed)
Shahin Mohammadi Khalil Habashi Saeed PormanafiLand use and land cover (LULC) change because of its impact on natural ecosystems has become a concern for natural resources protectors and managers. The present study aimed to predict LULC changes and also to study the relation of drought with these changes in the sub- MoreLand use and land cover (LULC) change because of its impact on natural ecosystems has become a concern for natural resources protectors and managers. The present study aimed to predict LULC changes and also to study the relation of drought with these changes in the sub-basin Parsel B2 with an area of 21100 hectares using CA-Markov model and Standard Precipitation Index (SPI). For this purpose, using the preprocessed images of the sensors TM, ETM+, and OLI for the years 1986, 2001 and 2016, respectively, the LULC map was provided with supervised classification and maximum likelihood method. To validate the CA-Markov model, the LULC maps have been predicting for 2016 and they were compared to the reference land use map of 2016. After ensuring the accuracy of the predicted results for the year 2016, the related land use and land cover maps were predicted for the year 2030. The result showed a relation between LULC changes and drought condition. Based on result predicted for the year 2030, rain-fed agriculture 6.95% increase and range land 6.66% decrease in area. Thus In the event of drought and abandonment rain-fed agriculture land, soil erosion, increasing and also grazing pressure on the remaining range land causing range land degradation. Therefore, if the current land use strategy with current management remain, land degradation in the region will be inevitable. Manuscript profile -
Open Access Article
74 - Study and prediction of land surface temperature changes of Yazd city: assessing the proximity and changes of land cover
Mohammad Mansourmoghaddam Iman Rousta Mohammadsadegh Zamani Mohammad Hossein Mokhtari Mohammad Karimi Firozjaei Seyed Kazem AlavipanahBackground and Objective The expansion of urbanization has increased the scale and intensity of thermal islands in cities. Investigating how cities are affected by these thermal islands plays an important role in the future planning of cities. For this purpose, this stu MoreBackground and Objective The expansion of urbanization has increased the scale and intensity of thermal islands in cities. Investigating how cities are affected by these thermal islands plays an important role in the future planning of cities. For this purpose, this study examines and predicts the effect of land cover (LC) changes in the three classes of LC including urban areas, barren lands, and vegetation on land surface temperature (LST) in the city of Yazd during the last 30 years using Landsat 5 and 8 images. This study also examines the effect of the ratio of proximity to the barren land and vegetation classes during this period to examine how the recorded LST is affected by the mentioned ratio.Materials and Methods The LC maps of Yazd city were extracted using a supervised Artificial Neural Network classifier for 1990, 2000, 2010, and 2020. Terrestrial data, google earth, and ground truth maps were used to derive training data. The LST of Yazd was obtained from the thermal band of Landsat 5 and Landsat 8. After that, the LST was classified into six available classes, including 16-20, 21-25, 26-30, 31-35, 36-40, and 41-46°C which has shown that the four last classes play an important role in LST changes in Yazd city during last 30 years. To evaluate the effects of the proximity of barren land and vegetation LC classes on the LST recorded by the sensor, firstly the proximity ratio was calculated in 5×5 kernels for all image pixels. Then the mean of LST was derived based on this ratio of barren and vegetation lands.Results and Discussion The results of this study showed that in Yazd city, from 1990 to 2020, the area of the urban area has grown 91.5 % (33.6 km2) over the last 30 years. Barren and vegetation land, have negative growth in the area over the same period. From 1990 to 2020, barren lands in Yazd experienced a growth -79.4% (21.3 km2), which the sharp growth of urban areas justifies this negative growth in barren lands. Vegetation classes in Yazd from 1990 to 2020, have experienced a growth -68.5% (12.2 km2). The average ground temperature of this city has been constantly increasing during these 30 years. By 2020, the city of Yazd, reaching an average of 38.1°C compared to 29.2°C in 1990, has experienced a 30.4% increase in its average LST. The temperature classes of this city have also moved towards warmer temperature classes in these 30 years. As the main part of the LST area of Yazd, in 1990, in the first place, the class of 26-30 °C with 47 km2 and at the second place the class of 31-35 °C with 26.4 km2 are classified. In 2000, in a reverse trend, the main LST class was 31-35°C with 52.8 km2 as the first place and the 26-30°C class with 20 km2 as the second place. With an increased class, the LST class of 36-40 °C for both 2010 and 2020 with 40.2 and 63 km2 respectively has been recorded as the largest LST class. The LST class of 31-35 °C has been recorded as the second LST class of both years with 33.2 and 9.7 km2, respectively. The difference between these two years is in the growth -70.7% (23.5 km2) of the class area of 31-35°C and the increase of 10.3% (0.8 km2) of the hottest class of the statistical period, 41-46°C, in 2020, compared to 2010. The results of this study also showed that the highest average temperature in all year was recorded for barren lands at 37.3°C. Also, a positive correlation (mean correlation 0.95) was shown between the proximity to barren land cover and the mean LST. However, the sharp upward trend of urban areas in the whole statistical period (91.5% with 33.6 km2) as the second class with the highest average LST after the barren lands with an average of 34.1 °C versus a downward trend of 79.4% (21.3 km2) of barren lands has increased the average LST over a statistical period of 30 years. It is because the decrease of 68.5% (12.2 km2) of vegetation areas as an LC class with the lowest average LST (32.2°C) in the same period, neutralized the effect of decreasing barren lands and intensified the trend of increasing the LST. Meanwhile, a negative correlation (mean correlation -0.97) was established between the ratio of proximity to vegetation and the average LST. The results of forecasting land cover changes in 2030 in the city of Yazd indicate that in a process similar to previous periods, the class of urban areas will increase. This growth will not be significant compared to 2020, with 1.6% (1.1 km2). However, a significant decrease in green areas (vegetation) by -19.6% (1.1 km2) in the same period, along with a slight decrease in barren lands -1.8% (0.1 km2) will cause the earth’s surface to become warmer, and the area of LST classes will be increased by the year. Accordingly, the main area of the LST class in 2030 for the city of Yazd, as in 2020, is forecasted 36-40°C with 58.2 km2 (-7.6% growth compared to 2020). But the dramatic growth of the hottest class of LST over the statistical period (41-46°C) with 166.3% (14.3 km2) growth as the second major class of LST in this year (2030), as well as the negative and dramatic growth of the relatively cooler class 31-35°C with -97.9 % (9.5 km2) in this year indicates the warmer ground surface temperature in 2030.Conclusion The results of this study indicate that in 30 years in Yazd city, the decrease in vegetation in the first place, along with the increase in urban areas in the second place, has caused an increase in LST. Thus, the vegetation class reduces the LST due to its cooling effect considering its water content. In this study, it was shown that by taking all factors into account, the reduction of barren lands will lead to a decrease in LST, and also increasing urban areas with a lower impact factor than barren lands will increase the LST. However, the decrease in the area of green lands (vegetation) in recent years, along with the sharp increase in the area of urban areas has caused an increase in LST. Increasing the proximity to vegetation by creating green areas by increasing the ratio of vegetation in the vicinity of different LC and also reducing the area of barren lands, can be a good solution to deal with the impact of urbanization in recent years on ground surface temperature. Manuscript profile -
Open Access Article
75 - Monitoring Bakhtegan wetland using a time series of satellite data on the Google Earth Engine platform and predicting parameters with Facebook’s Prophet model
Mohsen Dastaran Shahin Jafari Hossein Moslemi Sara Attarchi Seyed Kazem AlavipanahBackground and Objective Wetlands are habitats for vegetation and wildlife and because of this, they have a high environmental value. Also, wetlands reduce soil erosion, restore aquifers, store rainwater in a flood event, and provide water for agriculture or livestock. MoreBackground and Objective Wetlands are habitats for vegetation and wildlife and because of this, they have a high environmental value. Also, wetlands reduce soil erosion, restore aquifers, store rainwater in a flood event, and provide water for agriculture or livestock. Wetlands are vulnerable to human interventions and changes such as drainage, urban sprawl, infrastructure development, and over-exploitation of groundwater resources. Prediction of the condition of wetlands in the future requires a correct understanding of the evolution of wetlands and identifying their trend of change. Nowadays, Remote Sensing technology is widely used for mapping wetlands, and its ability to monitor the changes in wetlands regardless of the diversity of wetlands has significantly increased the value of this science in this field. Remote Sensing can be an effective means of simulating and predicting wetland degradation processes by providing images at different times and through dynamic spatial modeling. In this study, the changes in the Bakhtegan wetland have been monitored. This wetland has high environmental and tourism importance and its drying affects negatively the living conditions and health of local people as well as tourism in the region. In addition, predictions of precipitation parameters, groundwater level, and temperature have been conducted. For this purpose, the Google Earth Engine platform was used to capture and process images. Google Earth Engine is a platform that can capture and process images in the shortest time and at high speed. In this regard, using Google Earth Engine, changes in the lake water area along with changes in temperature, groundwater level, and precipitation were extracted and monitored. Moreover, a comparison took place between these parameters to determine the changes that have taken place in the lake over the past two decades. To predict the parameters, the changing pattern was predicted and analyzed using the Prophet model. The most important advantage of the Prophet model is its ability to convert discrete data to continuous data to make the best predictions. This method automatically detects the trend of seasonal data and displays the trend of seasonal changes.Materials and Methods Satellite images were acquired from the Google Earth Engine platform to monitor the wetland. Landsat 7 and 8 images were used for water body extraction, GRACE Data were used for extraction of groundwater level changes, MODIS product was used for extraction of vegetation and wetland surface temperature, and TRMM image product was used to extract precipitation values. An automated water extraction index was used to extract the wetland body water. The groundwater level was extracted from the GRACE sensor. MODIS sensor product was used to obtain the surface temperature time series for the study area. For the extraction of precipitation time series, the monthly cumulative data of the TRMM (3B43V7) satellite with a spatial resolution of 0.25°C was extracted using Google Earth Engine and the trend of changes was evaluated and analyzed. The Mann-Kendall test is one of the most widely used non-parametric tests for detecting meteorological and environmental data trends, which is used to detect a monotonic trend line since this test is a non-parametric method, it does not need that the data follow a normal distribution. The Prophet predictive model is a predictive library developed by Facebook and is available in R and Python programming languages. This library supports additive modeling methods and can properly predict discrete values continuously. This feature is called "Holiday". Another feature of this library is the automatic detection of daily, weekly, seasonal and annual trends. The mean absolute error (MAE), by default, exists in the Prophet library. This error represents a more natural standard than the mean error and unlike the RMSE error, it is unambiguous.Results and Discussion In the present study, we monitored the Bakhtegan wetland using the Google Earth Engine platform to observe the trend of water level changes in this wetland from 2000 to 2020. In addition, Parameters were also predicted using the Prophet Prediction method which is developed and published by Facebook. By examining this trend, it can be observed that the water level of the wetland has been significantly reduced during two decades. In this regard, the trend of groundwater level, temperature, and precipitation in the area was investigated. Examining these factors, it was found that along with a 58.3% decrease in the water level of the wetland, there was a 260% decrease in the groundwater level of the region, although the amount of rainfall in the region has been less compared to other factors and has been decreased about 29%. Using Mann-Kendall statistical test, the trend of this decline was proved. To predict the parameters, the Prophet model has been able to make predictions for 1500 days as continuous data using discrete data. The output of the model has shown that for rainfall parameters and groundwater level a downward trend is predictable over the next 1500 days which is low intensity for precipitation but with high intensity for groundwater level. Temperature prediction indicated that it has a seasonal trend, and has a high amount of fluctuation within a year, but its annual trend indicates stability in the coming years. The results of the model for the water level of the wetland also show a relatively low upward trend that has a probability of change of ±12.5 Square kilometers. Also, the error of the parameters at the 95% significant level has acceptable accuracy, which indicates the validity of the prediction. An automated water extraction index was used in this study to extract the time series of the water body of the wetland. Using the mean time series extracted, the maximum and minimum wetland’s water body area belongs to 2006 with 629.23 square kilometers and 2014 with 156.82 square kilometers, respectively. The time series of changes in this wetland indicates that the water volume of the wetland has been declining in the last two decades. According to this study, it can be concluded that the trend of changes in the water level of the wetland has been decreasing. The descending changes in the lake based on the trend of changes in groundwater levels indicates a decrease in water volume in the area. Considering that the trend of precipitation changes has been stable, it can have assumed that improper management and excessive use of groundwater may be a reason for lowering the water level of the wetland. Due to the same decrease in the water level of the lake, the temperature has also decreased by about 3°C.Conclusion According to this study, it can be concluded that groundwater levels and precipitation will have a downward trend in the future, which will lead to a decrease in the water level of the wetland, which itself has the potential to fluctuate in the future, and the downward trend continues. With the current trend, the only solution is to plan properly to preserve the wetland. If this trend continues, we will face the destruction of the wetland. Given the monthly trend of the wetland surface, it is suggested not to over-exploit groundwater resources, especially in the summer. For further research, the Google Earth Engine platform can be used without the need to download the images and spend a lot of time and money, to obtain the time series of images. Regarding the prediction, in future studies, the Prophet model can be applied, since it uses discrete data and at the same time provides the desired accuracy. Manuscript profile -
Open Access Article
76 - Application of spatial statistics in zoning and spatial analysis of the sound speed in the Persian Gulf
Mahyar Majidy Nik Hamed DeldarThe aims of this study were to find the distribution of sound speed under the influence of water's physical parameters; to predict spatial analysis in oceanography using geostatistical methods; to forecast value parameters for the Persian Gulf and zoning the sound speed MoreThe aims of this study were to find the distribution of sound speed under the influence of water's physical parameters; to predict spatial analysis in oceanography using geostatistical methods; to forecast value parameters for the Persian Gulf and zoning the sound speed. Sound Speed was calculated using Chen-Millero formula and pressure, salinity, and temperature data. The data extracted from World Ocean Atlas 2013 with regular mesh grid 0.25 degree. Sound speed was calculated using the Chen-Millero formula. Spatial analysis of the sound speed comparison based on three methods Kriging, Co-Kriging and Inverse Distance Weighted. These methods were performed using GS+ software in both warm and cold season. The best method finally used to forcast and prepare the plans of zoning sound speed. The Pearson’s correlation test was performed between independent variables and sound speed showed that the maximum correlation occurs between temperature and sound speed. Therefore, the temperature was considered as the auxiliary variable in Co-Kriging method for spatial analysis of sound speed. Cross-validation results showed that model's forecasting in cold season was better compared to warm season in this region. Results of spatial analysis showed that the sound speed decreased about 20m/s in all layers from the Hormuz Strait toward the northwestern part of the Persian Gulf. Because of the increased salinity the maximum of sound speed was always in the south shallow area. In all investigated stations, sound speed reduced with increasing depth, due to temperature reduction and the sound channel is not also observed. Manuscript profile -
Open Access Article
77 - 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
78 - Comparison of Linear and Non-linear Support Vector Machine Method with Linear Regression for Short-term Prediction of Queue Length Parameter and Arrival Volume of Intersection Approach for Adaptive Control of Individual Traffic Lights
mohammad ali kooshan moghadam Mehdi Fallah TaftiIntroductionThis study was carried out in line with the development of adaptive traffic signal control systems to provide a better traffic control at intersections. In this approach, if the predicted data related to the future cycles are used to optimize the upcoming sc MoreIntroductionThis study was carried out in line with the development of adaptive traffic signal control systems to provide a better traffic control at intersections. In this approach, if the predicted data related to the future cycles are used to optimize the upcoming schedule, it will control the traffic in unforeseen cases and manage it before reaching the forthcoming cycles. In order to have enough data to create such a model, the required data from two intersections in Yazd city were collected and these intersections were simulated using AIMSUN software. Then these intersections were calibrated and validated for existing conditions. The prediction accuracy results were extracted by the proposed methods and compared with the linear regression method. RMSE, MAE and GEH errors were used to compare the methods.Method: The predicted queue length and arrival volume parameters for any entry approach of itersections are major variables required during the adaptive signal control process, Hence, Linear and Non-linear Support Vector Regression Methods combined with the time series method were used to predict these parameters. For comparison of the performance of these models with a conventional model, Linear Regression models were also developed for the prediction of these parameters.ResultsFor the developed model based on combined Linear Support Vector Regression and the time series methods, the number of optimal previous cycle data used in the model was measured as 6 and 2 previous data cycles for predicting the arrival volume at Pajuhesh and Seyed Hassan Nasrollah intersections, respectively. The optimal number of previous data used in the model was measured as 9 and 11 previous data cycles for predicting the queue length at Pajuhesh and Seyed Hassan Nasrollah intersections, respectively. Also, using the combined Non-Linear Support Vector Regression and the time series methods, the number of optimal previous data cycles was obtained as 8 and 2 cycles in predicting the arrival volume at Pajuhesh and Seyed Hassan Nasrollah intersections, and the number of optimal previous data cycles was obtained as 7 and 7 cycles in predicting the queue length at Pajuhesh and Seyed Hassan Nasrollah intersections.Discussion: The results of RMSE, MAE and GEH measures were used to compare the performance of the developed models with the real data. This comparison indicated that the model based on the combined Non-Linear Support Vector Regression and time series methods, has produced the best performance in predicting traffic arrival volume than the other aforementioned models. However, in terms of predicting the queue length, this model produced a better performance than the combined Linear Support Vector Regression at only one of the intersections. The Linear Regression model produced the weakest performance in all comparisons. Thus, it can be concluded that the combined Support Vector Regression and time series methods are appropriate tools in predicting traffic parameters in these situations. Manuscript profile -
Open Access Article
79 - A Similarity Measure for Link Prediction in Social Network
Ali Sarabadani Kheirollah RahseparFard Seyed Morteza PournaghiIntroduction: A social network is a social structure made up of individuals or organizations. Social network analysis is an approach in which the network is considered as a set of nodes and relationships between them. Nodes are individuals and actually actors in the net MoreIntroduction: A social network is a social structure made up of individuals or organizations. Social network analysis is an approach in which the network is considered as a set of nodes and relationships between them. Nodes are individuals and actually actors in the network and the relationships between them are displayed as connections between nodes.Method: Among many social network analysis issues, link prediction has attracted much attention due to the growing number of social network users. Link prediction means predicting which new interaction is going to happen in the future. Traditional link prediction methods considered pairs of nodes as a unit and made decisions based on commonalities between them. In addition, we proposed a new similarity measure for link prediction in social networks.Results: We compared this criterion with four prediction methods of Jaccard link, Salton Index, Salton Cosine, and resource allocation). Experimental runs in this article were carried out on five social network datasets. Our results showed that this criterion performed better than other link prediction techniques on all datasets.Discussion: Social network analysis has recently attracted lots of attention among researchers due to its wide applicability in capturing social interactions. Link prediction, related to the likelihood of having a link between two nodes of the network that are not connected, is a key problem in social network analysis. Many methods have been proposed to solve the problem. Among these methods, similarity-based methods exhibit good efficiency by considering the network structure and using as a fundamental criterion the number of common neighbors between two nodes to establish structural similarity. Manuscript profile -
Open Access Article
80 - Designing a conscious prediction system based on future literacy: social constraints in sport for all
Javad shahvali kohshouri Hadiseh BahramiObjective: Objective: After explaining the relationship between constraints and future research, one of the things that are effective in predicting or even making future in all areas is future literacy. It can be acknowledged that future literacy is a scientific skill t MoreObjective: Objective: After explaining the relationship between constraints and future research, one of the things that are effective in predicting or even making future in all areas is future literacy. It can be acknowledged that future literacy is a scientific skill that develops a better understanding of the predicted assumptions. Future literacy or future is an educational framework that makes it possible to visualize the approaches for the future.Health-centric exercise or sports for all are sports activities that are followed by the vitality of joy and health at the level of different societies. Future or lost literacy is a valuable futures study in sports. Therefore, the aim of the present study is to design a general informed forecasting system based on future literacy, which in this study was specifically tested on public sports.Methodology: This qualitative research, which is exploratory-fundamental in nature, with a focus on social constructivism and using the constructivist approach of the basic theory, presents a short-range theory in conscious forecasting based on future literacy. Research data were collected through purposive sampling using snowball technique and based on in-depth semi-structured interviews with experts in the field of futures, research and sports management. The number of participants in the study using the theoretical saturation index reached 14 people.In this study, data were coded and decomposed according to the field theory and with the construction approach. Instead of emphasizing the collection of facts and describing the actions, this approach emphasizes views, values, beliefs, feelings, defaults and ideologies of individuals. At the same time, interviews and receive feedback from interviewees during return to existing texts and documents, initial implications and codes were modified and modified. In this research, in all sampling process, three stages of free, communication and theoretical sampling stages were observed in the basis of the use of the construction approach of data theorem.Results: In this study, a multi-part model consisting of the main components of support processes, main processes, future images, future literacy and approaches was obtained. In this model, it was found that future literacy, which means encoding and decoding future images, can make conscious predictions about the future of public sports by using present-day potentials and existing processes.Conclusion: The arbitrary theory acknowledges that future literacy, which is an accessible skill based on human intrinsic capacity, can be based on current time potentials such as the main processes, such as identifying weak signals, surprises, macro trends, propulsion forces, uncertainties, design Strategic radar, based on environmental scanning and backup processes, such as inter-surface participation, key actor analysis, perspective, planning, entirely, cramps, and reengineering, make upcoming images; The upcoming literacy by decoding or coding these images based on the empowerment loop, which consists of four elements of awareness of the past and present, choosing and participating in the present and discovering in the coming time, provide information for different future.The existing system, beyond the conventional views in futures studies, can be a starting point for modeling informed predictions in sports. Manuscript profile -
Open Access Article
81 - Fatigue Life Prediction of Rivet Joints
M. M Amiri -
Open Access Article
82 - Creep Life Forecasting of Weldment
J Jelwan M Chowdhry G Pearce -
Open Access Article
83 - A Review of Weed Interference Models
rahman khakzad Mostafa Oviesi Reza DeihimfardWeeds represent a continuous problem in agricultural production due to their dynamic and resilient nature. Mathematical models offer a significant tool for understanding and predicting the crop yield losses incurred due to weed-crop interference. Weed-crop competition m MoreWeeds represent a continuous problem in agricultural production due to their dynamic and resilient nature. Mathematical models offer a significant tool for understanding and predicting the crop yield losses incurred due to weed-crop interference. Weed-crop competition models help to inform weed management decisions, both on a short-term basis to tackle the present weed population and in the long term to plan sustainable weed management strategies. Most competition studies are based on empirical models. Empirical functions are the most commonly used models, which provide information for weed threshold values. The limitations of such models are that they are based on statistical functions and usually do not consider biological insights for crop-weed interference. Crop-weed competition is a complex phenomenon, and to understand this, a detailed mechanistic model offers better insights than an empirical model. Mechanistic or explanatory models take into account all underlying processes or mechanisms and their dependence on each other with respect to time and external drivers. Competition models can be integrated within the framework of a decision support system (DSS). In this review, we present empirical and mechanistic models that are currently in use for studying crop-weed interference. Manuscript profile -
Open Access Article
84 - Improved NARX-ANFIS Network structure with Genetic Algorithm to optimizing Cash Flow of ATM Model
Neda kiani Ghasem Tohidi Shabnam Razavyan Nosratallah Shadnoosh Masood Sanei -
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85 - Prediction the Return Fluctuations with Artificial Neural Networks' Approach
Masoud Taherinia Mohsen Rashidi Baghi -
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86 - The Role of Earnings Management in Theoretical Development and Improving the Efficiency of Accounting-Based Financial Distress Prediction Models
Abbas Ramezanzadeh Zeidi Khosro Faghani Makarani Ali Jafari -
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87 - Modelling Crowdfunding Ensemble Learning Prediction
Mehran Saeidi Aghdam Akbar Alam Tabriz Alireza Bahiraie Ahmad Sadeghi -
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88 - Experimental Comparison of Financial Distress Prediction Models Using Imbalanced data sets
Seyed Behrooz Razavi Ghomi Alireza Mehrazin Mohammad Reza Shoorvarzi Abolghasem Masih Abadi -
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89 - Support Vector Regression Parameters Optimization using Golden Sine Algorithm and Its Application in Stock Market
Mohammadreza Ghanbari Mahdi Goldani -
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90 - Comparison of the Ability of Modern and Conventional Metaheuristic and Regression Models to Predict Stock Returns by Accounting Variables and Presenting an Effective Model
Mahmood Kohansal Kafshgari Alireza Zarei Sodani Reza Behmanesh -
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91 - Developing a Prediction-Based Stock Returns and Portfolio Optimization Model
Farzad Eivani Davood Jafari Seresht Abbas Aflatooni -
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92 - Capsule Network Regression Using Information Measures: An Application in Bitcoin Market
Mahsa Tavakoli Hassan Doosti Christophe Chesneau -
Open Access Article
93 - Developing Financial Distress Prediction Models Based on Imbalanced Dataset: Random Undersampling and Clustering Based Undersampling Approaches
Seyed behrooz Razavi ghomi Alireza Mehrazin Mohammad reza shoorvarzi Abolghasem Masih AbadiSo far, distress prediction models have been based on balanced, such sampling is not consistent with the reality of the statistical community of companies. If the data are balanced, the bias in sample selection may lead to an underestimation of typeI error and an overes MoreSo far, distress prediction models have been based on balanced, such sampling is not consistent with the reality of the statistical community of companies. If the data are balanced, the bias in sample selection may lead to an underestimation of typeI error and an overestimation of the typeII error of models. Although imbalanced data-based models are compatible with reality, they have a higher typeI error compared to balanced data-based models. The cost of typeI error is more important to Beneficiaries than the cost of typeII error. In this study, for reducing typeI error of imbalanced data-based models, random and clustering-based undersampling were used. Tested data included 760 companies since 2007-2007 with 4 different degrees and the results of the H1 to H3 test represented them. In all cases of the typeI error, typeII error of balanced data-based models were lower and more, respectively, compared to imbalanced data-based models; also, in most cases, the geometric mean of balanced data-based models was higher compared to imbalanced data-based models, respectively. The results of testing H4 to H6 show that in most cases, typeI error, typeII error and the geometric mean criterion of models based on modified imbalanced data were less, more, and more, respectiively compared to the models based on imbalanced data, in other words, applying Undersampling methods on imbalanced training data led to a decrease in typeI error and an increase in typeII error and geometric mean criteria. As a result using models based on modified imbalanced data is suggested to Beneficiaries Manuscript profile -
Open Access Article
94 - Predicting the Top and Bottom Prices of Bitcoin Using Ensemble Machine Learning
Emad Koosha Mohsen Seighaly Ebrahim Abbasi -
Open Access Article
95 - Analyzing the performance of DEA models for bankruptcy prediction in the energy sector: with emphasis on Dynamic DEA approach
Mohammad Ali Khorami Seyed Babak Ebrahimi Majid Mirzaee Ghazani -
Open Access Article
96 - Designing Prediction Model of Financial Restatements Using Neural-Genetic Simulation Algorithm
Sasan Mehrani Akbar Rahimi poorThe increased number of restatements in recent years has increased the wor-ries about the quality of financial reporting among the beneficiary groups. The pres-ence of prior period adjustments and, subsequently, the financial restatements have a negative impact on the r MoreThe increased number of restatements in recent years has increased the wor-ries about the quality of financial reporting among the beneficiary groups. The pres-ence of prior period adjustments and, subsequently, the financial restatements have a negative impact on the relatedness and reliability of the financial state-ments. The present study is aimed to present an appropriate criterion for predict-ing the financial restatements based on the Beneish model and its indices in companies admitted to the Tehran Stock & Exchange between 2009 and 2020. For this purpose, a total of 265 companies were selected considering the limitations. Also, the model estimation was per-formed using Beneish's primary model, a meta-heuristic neural network model, and optimization through genetic programming. As indicated by the obtained results based on the confusion matrix, the efficiency of the pro-posed model derived from the enhanced Beneish model with a genetic algo-rithm(S – 𝑆𝑐𝑜𝑟𝑒) had a total prediction accuracy of 73.21%, which was the highest prediction power compared to the Beneish Model . Manuscript profile -
Open Access Article
97 - Presenting an Explanatory Model of Stock Price Using Deep Learning Algorithm
Mojtaba Bavaghar Zaeimi Gholamreza Zomorodian Mehrzad Minooee Amirreza KeyghobadiThis study aimed to present an explanatory model of stock price using deep learning algorithm for companies listed in the Tehran Stock Exchange. In this study, a deep learning network was used to predict stock prices. The study was applied-developmental research in term MoreThis study aimed to present an explanatory model of stock price using deep learning algorithm for companies listed in the Tehran Stock Exchange. In this study, a deep learning network was used to predict stock prices. The study was applied-developmental research in terms of purpose. To test the research questions, accounting data were prepared from 2011 to 2020 and input variables were calculated based on it for the model. The method of systematic elimination sampling has been used in this study. The results indicated that the precisions of prediction has a high precisions in the deep learning model. The proposed algorithm was reviewed according to its prediction accuracy and modeling cost. According to the data volume, it was found that the prediction accuracy in the deep learning model has a relative superiority and the diagram of performance characteristic and AUC criteria also showed this superiority in detection power. Manuscript profile -
Open Access Article
98 - Designing a Model for Predicting Corporate Bankruptcy Using Ensemble Learning Techniques
Hossein Eghbali Alimohamad AhmadvandThe bankruptcy of corporations causes huge losses for investors, managers, creditors, employees, suppliers, and customers. If someone understands the reason for the corporate's bankruptcy, then he can save the corporate from certain death with the necessary planning. Th MoreThe bankruptcy of corporations causes huge losses for investors, managers, creditors, employees, suppliers, and customers. If someone understands the reason for the corporate's bankruptcy, then he can save the corporate from certain death with the necessary planning. Therefore, bankruptcy forecasting is the most important prerequisite for bankruptcy prevention. Due to this issue, the main aim of this article is the prediction of the economic bankrupt-cy of corporations in the Tehran Stock Exchange using group machine learn-ing algorithms. Financial ratios have been used as independent variables and healthy and bankrupt corporations as research dependent variables. The statistical population of the study is the information of financial statements of corporations on the Tehran Stock Exchange from the years 2004 to 2021. In this study, sampling is not used and corporations include two groups healthy and bankrupt. The bankrupt and non-bankrupt groups are selected based on the threshold of the Springate model. The research findings indicate that the accuracy of predicting the bankruptcy of corporations in the group learning model by stacking method is higher than other used models where the AUC and Accuracy Ratio were 0.9276 and 0.8247, respectively. Manuscript profile -
Open Access Article
99 - Predicting Social Responsibility Reporting using Financial Ratios
Mohammad Javad Zare Bahnamiri mahsa golkar niloofar BeikyThe purpose of this research is to investigate the prediction of corporate social responsibility reporting using financial ratios. To answer the research question, four prediction models of linear regression, K Nearest Neighbor, decision tree, and deep learning were inv MoreThe purpose of this research is to investigate the prediction of corporate social responsibility reporting using financial ratios. To answer the research question, four prediction models of linear regression, K Nearest Neighbor, decision tree, and deep learning were investigated. Also, 61 financial ratios were used according to previous research using data related to listed and non-listed companies of Iran from the years 2012 to 2018. According to the re-sults obtained from the estimation of each of the proposed prediction mod-els, it can be stated that the k-nearest neighbor model has the lowest RMSE value, and in fact, this model predicts the amount of social responsibility with less error than other models. The linear regression model with the high-est RMSE value has a weaker performance than other models. LSTM model and decision tree respectively had the lowest RMSE value after the k-nearest neighbor model. As a result, since the LSTM model requires a large number of test sam-ples for deeper learning, it could not achieve high performance in the evaluated data set. Based on the investigations, it can be stated that the current research does not have a similar example inside or outside of Iran. Manuscript profile -
Open Access Article
100 - Thermal anomalies detection before earthquake using three filters (Fourier, Wavelet and Logarithmic Differential Filter), A Case study of two earthquakes in Iran
Sina Saber Mahani Marzieh Khalili -
Open Access Article
101 - A Robust Methodology for Prediction of DT Wireline Log
Sh. Maleki A. Moradzadeh R. Ghavami F. SadeghzadehDT log is one of the most frequently used wireline logs to determine compression wave velocity. This log is commonly used to gain insight into the elastic and petrophysical parameters of reservoir rocks. Acquisition of DT log is, however, a very expensive and time consu MoreDT log is one of the most frequently used wireline logs to determine compression wave velocity. This log is commonly used to gain insight into the elastic and petrophysical parameters of reservoir rocks. Acquisition of DT log is, however, a very expensive and time consuming task. Thus prediction of this log by any means can be a great help by decreasing the amount of money that needs to be allocated for acquisition. Support vector machine (SVM) is one of the best artificial intelligence techniques proven to be a reliable method in the prediction of various real world problems. The aim of this paper is to use SVM to predict the DT log data of a well located in the southern oilfields of Iran. By comparing the results of SVM with those obtained by a Back Propagation Neural Network (BPNN) we were able to verify the accuracy of SVM in the prediction of P-wave velocity. Hence, this method is recommended as a cost effective tool in the prediction of P- wave velocity Manuscript profile -
Open Access Article
102 - 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
103 - Predicting Banks' Financial Distress by Data Envelopment Analysis Model and CAMELS Indicators
Abass Paidar Morteza Shafiee Fariborz Avazzadeh Hashem Valipour -
Open Access Article
104 - A Stock Market Prediction Model Based on Deep Learning Networks
seyyedeh mozhgan Beheshti Masalegou Mohammad-Ali Afshar-Kazemie jalal haghighat monfared Ali Rezaeian -
Open Access Article
105 - Application of Genetic Algorithm in Development of Bankruptcy Predication Theory Case Study: Companies Listed on Tehran Stock Exchange
Mohsen Hajiamiri Mohammad Reza Shahraki Seyyed Masoud Barakati -
Open Access Article
106 - استفاده از روشهای شبکه عصبی موجکی و سیستم استنتاج فازی عصبی تطبیقی در پیشبینی بارش ماهانه
اباذر سلگی حیدر زارعی بهداد فلامرزیپیش بینی بارش به دلیل ماهیت تصادفی آن در مکان و زمان همواره با مشکلات بسیاری مواجه بوده است و این عدم قطعیت از اعتبار بسیاری از مدل های پیش بینی می کاهد. امروزه شبکه های غیرخطی به عنوان یکی از سیستم های هوشمند در پیش بینی یک چنین پدیده های پیچیده ای بسیار مورد استفاده ق Moreپیش بینی بارش به دلیل ماهیت تصادفی آن در مکان و زمان همواره با مشکلات بسیاری مواجه بوده است و این عدم قطعیت از اعتبار بسیاری از مدل های پیش بینی می کاهد. امروزه شبکه های غیرخطی به عنوان یکی از سیستم های هوشمند در پیش بینی یک چنین پدیده های پیچیده ای بسیار مورد استفاده قرار می گیرند. یکی از روش هایی که در سال های اخیر در زمینه هیدرولوژی مورد توجه قرار گرفته است، استفاده از تبدیل موجک به عنوان روشی نوین و مؤثر در زمینه آنالیز سیگنال ها و سری های زمانی است. در پژوهش حاضر، تجزیه و تحلیل موجک به صورت ترکیب با شبکه عصبی مصنوعی و مقایسه با سیستم استنتاج فازی- عصبی تطبیقی برای پیش بینی بارش ایستگاه وراینه در شهرستان نهاوند انجام شد. برای این منظور، سری زمانی اصلی با استفاده از تئوری موجک به چندین زیرسیگنال زمانی تجزیه شد، پس از آن این زیرسیگنال ها به عنوان داده های ورودی به شبکه عصبی مصنوعی برای پیش بینی بارش ماهانه استفاده شد. نتایج به دست آمده نشان داد که مدل ترکیبی موجک- شبکه عصبی عملکرد بهتری نسبت به مدل سیستم استنتاج فازی- عصبی تطبیقی دارد و می تواند برای پیش بینی بارش کوتاه مدت و بلند مدت استفاده شود. همچنین نتایج نشان داد که مدل ترکیبی موجک- شبکه عصبی در برآورد نقاط حدی به خوبی عمل می کند. Manuscript profile -
Open Access Article
107 - تغییرات شوری اعماق خاک در اثر آبیاری با آب شور
وحید یزدانی سپیده یکه باش محمد سلطانیاین تحقیق به منظور بررسی تاثیر شوری آب آبیاری بر کیفیت خاک در سطح و اعماق انجام گرفت. در این تحقیق از نسبت­های مختلف سنگ نمک طبیعی و آب چاه با دبی 35 لیتر در ثانیه (در مختصات ً5/39 َ27 º59 و ً2/39 َ27 º36) به منظور ایجاد شوری­های متفاوت در Moreاین تحقیق به منظور بررسی تاثیر شوری آب آبیاری بر کیفیت خاک در سطح و اعماق انجام گرفت. در این تحقیق از نسبت­های مختلف سنگ نمک طبیعی و آب چاه با دبی 35 لیتر در ثانیه (در مختصات ً5/39 َ27 º59 و ً2/39 َ27 º36) به منظور ایجاد شوری­های متفاوت در شرایط کشت کلزا استفاده شد. در شرایط ماندگار، در یک غلظت مشخص از آب آبیاری، توزیع متفاوت در جذب آب سبب توزیع متفاوت شوری در خاک می­شود. بر این اساس، از معادلات تابع جذب آب نمایی، ذوزنقه­ای و الگوی جذب جهت بررسی تغییرات شوری استفاده شد. نتایج نشان داد با افزایش زمان بعد از کاشت و اعمال تیمار­های مختلف آبیاری، مقدار شوری عصاره اشباع خاک در اعماق مختلف خاک افزایش می­یابد. در 3 تاریخ اولیه مقدار تفاوت در EC عصاره اشباع خاک خیلی زیاد نیست و در تاریخ 4 و 5 نمونه‎برداری (یعنی 102 و 118 روز بعد از کشت کلزا) مقدار تفاوت­ها بیشتر می­شود. دلیل تفاوت کم در تاریخ‎های 56، 71 و 87 روز بعد از کشت کلزا، وجود بارش در این مدت می­باشد. در 71 روز بعد از کشت کلزا مقدار EC عصاره اشباع در اغلب تیمار­ها کاهش داشت و از روند افزایشی پیروی نمی­کرد که دلیل آن وقوع بارش در بازه اول الی 15 خرداد بود؛ که باعث آب‎شویی املاح شده و EC عصاره اشباع خاک کاهش یافته است. البته باید اشاره داشت که در تیمار I4 چنین روندی مشاهده نمی­گردد. زیرا کم‎آبیاری شدید در این تیمار باعث تجمع املاح در سطح خاک شده است که بارش­ها تنها سطح خاک را آب‎شویی نموده و املاح را به اعماق پایین‎تر منتقل کرده است. نتایج نشان داد که مدل ذوزنقه­ای قادر به پیش‎بینی شوری عصاره اشباع خاک نمی­باشد. این روش شوری عصاره اشباع خاک را بسیار بیشتر از واقعیت برآورد می­کند و نتایج آن تنها در شوری 5/0 دسی زیمنس بر متر تاحدودی قابل قبول است. در مقابل، نتایج دو مدل دیگر یعنی مدل نمایی و مدل تابع جذب، نتایج مناسب‎تری را ارائه دادند. مدل نمایی در این سه سطح آبیاری دارای دقت قابل قبول­تری نسبت به مدل تابع جذب بود. Manuscript profile -
Open Access Article
108 - ارزیابی مدلهای سری زمانی SARIMAدر برآورد جریان ماهانه در ایستگاه هیدرومتری ایدنک
عباس احمدپور حسین فتحیان جبرائیل قربانیاندبی جریان آبراههها ازجمله مهمترین داده هیدرولوژیکی هستند و به عنوان اطلاعات پایه در بسیاری از فعالیتهای مرتبط با منابع آب در نقاط مختلف جهان استفاده میشوند. یکی از ابزارهای مهم در مدلسازی فرآیندهای هیدرولوژیکی استفاده از مدلهای سری زمانی است. جریان آبراهه برآوردشده Moreدبی جریان آبراههها ازجمله مهمترین داده هیدرولوژیکی هستند و به عنوان اطلاعات پایه در بسیاری از فعالیتهای مرتبط با منابع آب در نقاط مختلف جهان استفاده میشوند. یکی از ابزارهای مهم در مدلسازی فرآیندهای هیدرولوژیکی استفاده از مدلهای سری زمانی است. جریان آبراهه برآوردشده با استفاده از مدلهای سری زمانی در مطالعات مختلفی نظیر خشکسالی، سیلاب، طراحی سیستم های مخازن و مدیریت منابع آب قابل استفاده میباشد. این امر بخصوص در مناطق خشک اهمیت بیشتری دارد. در این مقاله به ارزیابی دقت مدلهای سری زمانی SARIMA در برآورد جریان ماهانه در ایستگاه هیدرومتری ایدنک پرداخته میشود. برای این منظور از دادههای دبی جریان ماهانه این ایستگاه به مدت 30 سال، طی سالهای (1390-1361) استفاده شده است. برای صحتسنجی مدلهای سری زمانی SARIMA برازش یافته، از مقادیر آماره آزمون پورت مانتو، و باقیماندهای توابع خودهمبستگی و خودهمبستگی جزیی استفاده شد و برای انتخاب بهترین مدل SARIMA، از معیار اکائیکه اصلاحشده (AIK) و معیار بیزین شوارتز ((SBC بهره گرفتهشد. نتایج این تحقیق نشان میدهد که از بین مدلهای مناسب برازش یافته بر دبی جریان ماهانه در ایستگاه هیدرومتری ایدنک،مدلهای SARIMA(1,0,1)*(2,0,2)12،SARIMA(2,0,2)*(2,0,2)12 و SARIMA(1,0,2)*(2,0,2)12 به ترتیب در اولویت اول، دوم و سوم از لحاظ دقت در برآورد دبی جریان برخوردار می باشند. Manuscript profile -
Open Access Article
109 - Identifying the influencing factors in customer churn of Kurdistan Telecommunications Company and presenting models for predicting churn using machine learning algorithms
vida sadeghi Anvar Bahrampour Seyed Ali HosseiniThe main sources of income and assets are important for any organization. With this view, companies have started to do more to maintain health. Since in many companies the cost of acquiring a new customer is much higher than actual customer satisfaction, customer churn MoreThe main sources of income and assets are important for any organization. With this view, companies have started to do more to maintain health. Since in many companies the cost of acquiring a new customer is much higher than actual customer satisfaction, customer churn has become the main area of evaluation for these companies. Client-facing companies, including those active in the technology industry, are facing a major challenge due to customer attrition. With the rapid development of the telecommunications industry, dropout prediction becomes one of the main activities in gaining a competitive advantage in the market. Predicting customer churn allows operators a period of time to remediate and implement a series of preventative measures before customers migrate to other operators. In this research, a decision support system for predicting and estimating the churn of customers of Kurdistan Telecommunication Company (with 52,900 subscribers) with different data-mining and machine methods (including simple linear regression (SLR), multiple linear regression (MLR). Polynomial regression. (PR), logistic regression, artificial neural networks, Adabust and random forest) are presented. The results of the evaluations carried out on the data set of the Kurdistan Province Telecommunication Company, the high performance of artificial neural network methods with 99.9% accuracy, Adabust with 99.9% accuracy, 100% accuracy and random forest It shows 100% with accuracy. Manuscript profile -
Open Access Article
110 - A Learning Automaton Based Algorithm for Bankruptcy Prediction of Acceptable Firms within Power and Energy Exchange
Seyed Mahdi Mazhari Hassan Monsef Hooman MirzaeiIn today's world, insurance of productive capital investment and reducing economic risk causes more fundraising and therefore the greatest economic boom cycle. One way to arrive capital investment security is to predict bankruptcy of a business unit. As the Iranian powe MoreIn today's world, insurance of productive capital investment and reducing economic risk causes more fundraising and therefore the greatest economic boom cycle. One way to arrive capital investment security is to predict bankruptcy of a business unit. As the Iranian power and energy stock is going to start working by 2012, providing suitable bits of advice to investors would be a priority. This paper proposes a new solution approach for bankruptcy prediction of the Iranian power and energy industries. To do so, an evolutionary algorithm premised on Learning Automata is employed and adapted to the problem. Two sets of firms related to power and energy industries that are listed on the Tehran Stock Exchange (TSE) are selected as the training and test data, respectively. The developed algorithm is conducted on both train and test data, and the efficiency of the proposed method is evaluated via several scenarios. It was practically seen in simulations that the learning automata-based algorithm could achieve an accuracy of 91% and 88% over the train and test data, respectively. Besides these, the same data sets are also conducted by other methods such as MDA and Logit, and the obtained results are compared with reality. The yielded results prove the accuracy as well as the efficiency of the proposed solution technique Manuscript profile -
Open Access Article
111 - Investigating the element of prophecy in the heroic system of shahriarnameh
Morteza Maghsudinejad Majid Hajizadeh Maryam gholamrezabeigiAbstractConsidering the necessity of research in epic poems after Shahnameh and with the aim of knowing more about their various aspects, in this article the used method is library research based on description and content analysis the "element of prophecy" in the poem MoreAbstractConsidering the necessity of research in epic poems after Shahnameh and with the aim of knowing more about their various aspects, in this article the used method is library research based on description and content analysis the "element of prophecy" in the poem "Shahriarnameh" of Mokhtari's epopee. There are about twenty-seven different predictions in the Shahriarnameh system. The most important of these prophecies is the prophecy about the grudge and revenge of Bahman, Esfandiar's son from the Zal family, which has been mentioned eight times by different people in different sections. The predictions of Shahriarnameh are often based on dreams, astronomical rulings and with the help of Soroush or with miraculous tools and objects such as the Cup of Wisdom, the Mirror of Wisdom and the Golden Tablet, by wise men and ministers, especially Jamasp, astrologers, priests . The subject of dreaming and dreaming of characters, especially the protagonist, is one of the main axes in epic poems and is also reflected in Shahriarnameh. Some of the dreams of Shahriarnameh have a prophetic aspect and others are based on commands, prohibitions and advices. Dreams that have a predictive aspect in many cases untie the knot of events and hardships and free the heroes and heroines of the story from the clutches of troubles .Keywords: Shahriarnameh, Astronomical Rulings , Prediction , Tool Prediction , Sleep Manuscript profile -
Open Access Article
112 - Counsel in The National Epics of Iran
Hadi YousefiIn this paper, counsel and consultation among the negative characters, or antagonists, in the national epic of Iran, are reviewed in analytic-descriptive approach. Antagonists and, in some occasions, generals consult with officials when important issues determini MoreIn this paper, counsel and consultation among the negative characters, or antagonists, in the national epic of Iran, are reviewed in analytic-descriptive approach. Antagonists and, in some occasions, generals consult with officials when important issues determining the future of wars and the destiny of kings’ realms, are concerned. Based on mentioned above, the function of antagonist’s counselors is analyzed and divided into two categories: first , offering helpful resolutions when antagonists encounter and fight against protagonists and their army ; second ,sleep interpretation and prediction. In accordance with carried out studies, antagonists’ counselors include ministers,zorasterian clergymen ,astrologers, fortunetellers, generals and antagonists’ close relatives , alliances and in some cases the armies that each group assist the antagonists with their opinions to make decisions especially about their territory and the army. Manuscript profile -
Open Access Article
113 - Prediction of Residual Gas Consumption using Temperature and Population of ConsumersUse case : Residual Consumers of Karaj
Masoud Akbari Mahdi Asghari Aliakbar Imami Satlou Parham Davari Shahnaz Salamat Thani Nahid Taherian Mansour GholinejdaNatural gas has a vital role as energy supplier in Iranian residual regions. According to reports of manager of dispatching department of National Iranian Gas Company, residual consumers had a share of 70 percentage of all manufactured natural gas, on cold days of 2021. MoreNatural gas has a vital role as energy supplier in Iranian residual regions. According to reports of manager of dispatching department of National Iranian Gas Company, residual consumers had a share of 70 percentage of all manufactured natural gas, on cold days of 2021. Also about 88 percentage of the country's electricity is supplied by fossil fuels, based on the report of Water and Electricity Industry. All of these statistics warn us about the importance of residential gas management. In this article, a nonlinear regression model was produced based on temperature and population of residential consumers in different periods of year. Also consumptions of residential consumers of Karaj city used to evaluate performance of the model. Results show that there is a meaningful correlation between selected features and consumed amount of natural gas that can help us to predict consumption more accurate in future. Manuscript profile -
Open Access Article
114 - Investigation and Prediction of Spatial and Temporal Land Use Changes in New Hashtgerd City by Integrating Remote Sensing Data and Cellular Automata Markov model
Sara SoukhtezariLand use changes due to the physical expansion of the city in most cities in Iran are so rapid, that urban planners and managers are facing a dynamic and complex development as they integrate the planning process in these areas. The purpose of this study is to investiga MoreLand use changes due to the physical expansion of the city in most cities in Iran are so rapid, that urban planners and managers are facing a dynamic and complex development as they integrate the planning process in these areas. The purpose of this study is to investigate land use changes and physical development of Hashtgerd city during the past 19 years and to predict land use change trends for the future. In this study, Landsat multi-time images were used. Using the support vector classification machine algorithm and the algorithm for Cross-Tab change, land use change trends over the past 19 years was evaluated. Also, using the Cellular Automata Markov prediction model, the process of land use change and physical expansion of the city is predicted for the future. The results of this study indicate the unnecessary expansion of the city over the past 19 years. So that the built-up with 736.56% growth have caused excessive destruction of agricultural and bare lands on the outskirts of the city. Investigations show that with increasing distance from land use changes have significantly reduced the amount of land use. Investigation of changes in land uses showed that 564/166 hectares of waste land has become residential land use. Predicting land use changes for 2028 and 2038 showed that residential land use will continue to increase. This highlights the need for special attention of urban planners and managers to the issue of urban development and its consequences in the region. Finally, the evaluation of the accuracy of the automated cell model showed that the percentage of classes area difference was less than 8%. Manuscript profile -
Open Access Article
115 - Prediction of Urban Construction Changes Using Satellite Images Based on CA-MARKOV Models (case study: Sari)
Sahab Bidgoli Kashani Mehran Fadavi Valiollah AzizifarAlong with the ever-increasing urban population, the amount of construction in the city space has been developed. The development of construction in the horizontal space and regardless of the existing restrictions has led to environmental, economic and legal problems fo MoreAlong with the ever-increasing urban population, the amount of construction in the city space has been developed. The development of construction in the horizontal space and regardless of the existing restrictions has led to environmental, economic and legal problems for the citizens. Achieving the amount, intensity and direction of construction development from the past to the present and predicting the construction situation in the future is the first step towards the scientific and practical management of the physical development of urban construction, planning and providing suitable solutions in order to create a balance between allocation Spatial-spatial construction and all kinds of legal, economic and environmental considerations. Data and information extracted from satellite images, while showing the historical changes of urban construction, are used as the main, necessary and necessary input data for models to predict its future state. In this research, satellite images of TM, ETM+ and OLI sensors of Landsat satellite were used in the time periods of 1997-2007 and 2007-2017 related to the city of Sari. After performing geometrical corrections, city area maps were prepared. Then, by using the effective parameters in urban construction changes, using the Cellular Automata(CA) Markov Model, the accuracy of the simulations was checked. Finally, for validation, the simulated maps and the ground reality map were matched with each other. The simulation of the construction development process in 2027 using the CA-Markov model showed that if the existing management regulations continue, this area will decrease from 4617.90 hectares in 2017 to 4357.44 hectares in 2027. But the examination of change maps and stability maps showed that new areas will be under construction between 2017 and 2027, which were mainly used for agriculture and barren land. Manuscript profile -
Open Access Article
116 - A Non-deterministic CNN-LSTM Hybrid Model for Bitcoin Cryptocurrency Price Prediction
علی علی جماعت سید محسن میرحسینیAbstractIn today's society, investment diversity has become very important. People reduceinvestment risk by diversifying their portfolios. Bitcoin has gained muchpopularity as one of the digital capitals and has been included in the investmentportfolio of individuals an MoreAbstractIn today's society, investment diversity has become very important. People reduceinvestment risk by diversifying their portfolios. Bitcoin has gained muchpopularity as one of the digital capitals and has been included in the investmentportfolio of individuals and institutions. Bitcoin price prediction is essential fordetermining price trends and transactions. For this purpose, various traditionalmethods as well as methods based on machine learning have been presented, eachof which has its own advantages and disadvantages. Recently, the use of hybridmodels has received attention. Combined methods have good efficiency and usethe advantages of combined techniques. This paper presents a hybrid methodbased on a deep convolutional neural network and recurrent neural network withprobabilistic dropout. Eliminating possible randomness leads to the regularizationof learning, avoids overfitting, and reduces model error. The results of theexperiments show that the proposed method has a higher accuracy than thecompared methods in predicting the price of Bitcoin. Manuscript profile -
Open Access Article
117 - Molecular docking and ADMET prediction of active compounds in Tualang honey against Sex hormone-binding globulin (SHBG) for the treatment of male infertility
Hamed Shahriarpour Mostafa Ghaderi-ZefreheiIntroduction: Sex hormone-binding globulin (SHBG) is a protein that is synthesized by liver cells and binds to sex hormones to regulate their levels and bioavailability. Its binding to testosterone reduces bioavailable testosterone and causes diseases of the male reprod MoreIntroduction: Sex hormone-binding globulin (SHBG) is a protein that is synthesized by liver cells and binds to sex hormones to regulate their levels and bioavailability. Its binding to testosterone reduces bioavailable testosterone and causes diseases of the male reproductive tract such as infertility, erectile dysfunction and prostate cancer. Objective: In this in Silico study, the potential of several compounds present in Tulang honey against SHBG protein for the treatment of infertility has been investigated. Materials and methods: The six compounds in Tualang honey, Catechin, Ethyl oleate, Fisetin, Hesperetin, Kaempferol and Luteolin were obtained from previous studies and the PubChem pharmaceutical database. The binding energy and type of protein-ligand interactions were investigated by molecular docking of these compounds to SHBG protein. AutoDock Vina version 1.1.2 software was used to perform molecular docking and Discovery Studio v21.1.0.289 software was used to analyze molecular docking results. Then SwissADME and admetSAR 2.0 web servers were used to evaluate the pharmacokinetic properties of these compounds through ADMET predictions. Results: The binding energy obtained from molecular docking showed that Luteolin with a score of -10 kcal/mol binds to SHBG protein, and has more hydrogen-hydrophobic interactions than other studied compounds as well as compounds that have been worked on in recent papers. Catechin and Fisetin also showed an acceptable result. The study of ADMET and bioavailability radar showed that although these compounds have physicochemical properties for use as drugs, they have the potential to inhibit some cytochromes and toxicity for certain organs and DNA or other genetic material in the body that should be considered in the use of these compounds as drugs. Discussion and conclusion: Using this in silico study, several suitable molecules of natural origin against the SHBG protein were identified, which showed potential for the treatment of male infertility. Manuscript profile -
Open Access Article
118 - A Hybrid Type-2 Fuzzy-LSTM Model for Prediction of Environmental Temporal Patterns
Aref Safari Rahil Hosseini -
Open Access Article
119 - Portfolio optimization based on return prediction using multiple parallel input CNN-LSTM
Hatef Kiabakht Mahdi Ashrafzadeh -
Open Access Article
120 - An Adaptive Data Hiding Method for Compressed Videos in HEVC Standard
Mozhgan Zamani Mohammadreza Ramezanpour -
Open Access Article
121 - A MAPE-K Loop Based Model for Virtual Machine Consolidation in Cloud Data Centers
Negin Najafizadegan Eslam Nazemi Vahid Khajehvand -
Open Access Article
122 - A New Multi-Wave Cellular Learning Automata and Its Application for Link Prediction Problem in Social Networks
Mozhdeh Khaksar Manshad Mohammad Reza Meybodi Afshin Salajegheh -
Open Access Article
123 - An Interval Type-2 Fuzzy-Markov Model for Prediction of Urban Air Pollution
Aref Safari Rahil Hosseini Mahdi Mazinani -
Open Access Article
124 - Epileptic Seizure Prediction using Multi-Channel Raw EEGs with Convolutional Neural Network
Jamal Nazari Ali Motie Nasrabadi Mohamad Bager Menhaj Somayeh Raiesdana -
Open Access Article
125 - Protein Secondary Structure Prediction: a Literature Review with Focus on Machine Learning Approaches
Leila Khalatbari Mohammad Reza Kangavari -
Open Access Article
126 - Designing and Optimizing the Fetch Unit for a RISC Core
Bahman Javadi Mojtaba Shojaei Mohammad Kazem Akbari Farnaz Irannejad -
Open Access Article
127 - A Link Prediction Method Based on Learning Automata in Social Networks
Sara YounessZadeh Mohammad Reza Meybodi -
Open Access Article
128 - Multi-Step-Ahead Prediction of Stock Price Using a New Architecture of Neural Networks
Mohammad Talebi Motlagh Hamid Khaloozadeh -
Open Access Article
129 - Providing a Model for Predicting the Success of Investment Projects in Free and Special Economic Zones, Using the Multi-Layer Neural Network Technique
morteza shokrzadeh kamaleddin rahmani farzin modares khiyabani majid bagherzadehTo analyze the data of this research, descriptive statistics and inferential statistics were used and experts selection software, MATLAB, SPSS and PLS software were employed Using theoretical foundations and libraries, six effective factors and variables predicting the MoreTo analyze the data of this research, descriptive statistics and inferential statistics were used and experts selection software, MATLAB, SPSS and PLS software were employed Using theoretical foundations and libraries, six effective factors and variables predicting the success or failure of Investment projects in the free and special economic zones of the country were identified.After describing the variables and testing the normality,using the PLS software, a confirmatory factor analysis of the variables was carried out, in which all of the factors had a good confirmatory factor analysis and all the questions were approved Then, using linear regression and ANOVA, the effect of each of the factors on the success or failure of investment projects was investigated, and the results of this test showed confirmation of the impact of each of the factors, and then the results of the hierarchical analysis indicated this was the first rank of product and service, followed by the second-rank ,that is geographical considerations, and the characteristics of the investor's psychology, the third rank, the product market characteristics, the fourth rank, the investor's ability to rank fifth, and financial considerations ,also, earned the last rank.Considering this prioritization, the neural network used in this research contained data from 6variables as an input variable, with two intermediate layers with 30 nodes in the first layer, and three nodes in the second layer, which had one outlet.The results indicated that the neural network model had the power to predict the success of the investment projects. Manuscript profile -
Open Access Article
130 - Presenting a model for predicting tax evasion guilds based on Data mining techniques
Mohammad Ghasemi Sadegh Abedi Ali MohtashamiIn this research, considering the importance of the topic and deficiency in previous researches, amodel for predicting tax evasion of guilds based on data mining technique is presented. Theanalyzed data includes the review of 5600 tax files of all guilds holding tax cod MoreIn this research, considering the importance of the topic and deficiency in previous researches, amodel for predicting tax evasion of guilds based on data mining technique is presented. Theanalyzed data includes the review of 5600 tax files of all guilds holding tax codes in Qazvinprovince during the years 2014-2019. The tax file related to guilds is in five tax groups includethe guild group of owners of notary public offices, the guild group of real estate agencies, theguild group of catering halls, restaurants and related businesses, the guild group ofcommunication services, and the guild group of exhibitions and auto accessories stores andrelated businesses. For modeling, the classification model including the decision tree algorithmwas used. The results indicate that the coverage criterion is 68%, the Kappa criterion is 0.612,which indicates the good performance of the modeler. Also, using the Cross Validationtechnique, the validity of the prediction model was tested in order to more reliably estimate thepercentage of modeling performance. The accuracy criterion equal to 67.79% shows theappropriate reliability for the prediction model. The results of this research can be utilized informulating operational strategies based on data mining to predict the tax evasion of guilds in theprovinces. Manuscript profile -
Open Access Article
131 - Optimization ELM neural network in prediction problem: case study forecasting demand steel in Iran
Jalal Rezaeenour Mansoureh Yari eili Esmaiel roozbahani Mohammad hossein RoozbahaniPrediction of supply and demand, is a crucial issue for supply chain partners. With the accurate forecasted supply and patterns that indicate the sizes and directions of future production flow, the government and suppliers can have a well-organized strategy and provide MorePrediction of supply and demand, is a crucial issue for supply chain partners. With the accurate forecasted supply and patterns that indicate the sizes and directions of future production flow, the government and suppliers can have a well-organized strategy and provide a better infrastructure for improving industrial sector.With the aim of developing accurate forecasting tool in steel industry, this study present a new optimized neural network, by combination of Extreme Learning Machine and genetic algorithm. We employed our model on a dataset for steel supply - demand in Iran from jul-2009 to jan2013 to estimating the performance. The results show that prediction accuracy and performance relatively better than other classical approaches, according to RMSE and MAPE evaluations. In our model. Based on statistical tests, our new model is better than other model in accuracy, so can be used in as a suitable forecasting tool in steel supply forecasting problems. Manuscript profile -
Open Access Article
132 - Dynamic Algorithm Design for Data Mining and Accurate Prediction of Customer Response
Mehdi Zakipour Sina Nematizadeh MohamdAli AfsharkezemiThe problem of identifying and anticipating potential customers to be addressed at direct marketing programs has been considered as one of the popular and most important marketers' issue. Marketers who use these approaches are threatened by the severe reaction of those MoreThe problem of identifying and anticipating potential customers to be addressed at direct marketing programs has been considered as one of the popular and most important marketers' issue. Marketers who use these approaches are threatened by the severe reaction of those consumers who consider the direct marketing as an attack to their private lives, so it may even be possible to boycott companies that use these methods. Neural networks are known as a powerful tool for prediction, but as previously mentioned, as with other prediction algorithms, they tend to deviate toward imbalanced data. In this research, in order to enhance the ability to identify and predict potential customers by multilayer perceptron networks, using Random under-over sampling methods, which has been used frequently in other articles, we attempted cluster customers and create different combinations of them, and then from the observed results, we finally introduced an innovative and highly efficient method for identifying and rating potential customers. The results indicate that, in addition to the undeniable power of multilayer neural networks in the field of identification and prediction, imbalanced data has greatly damaged the results. In this regard, creating an optimal combination of customer data and implementing the innovative algorithm of the present study significantly improves the results and the performance of artificial neural networks has yielded a reliable consequences. Manuscript profile -
Open Access Article
133 - Forecasting the Cost of Water Using a Neural Network Method in the Municipality of Isfahan
Amir Mohammadzadeh Nasrin Mahdipour Arash Mohammadzadeh -
Open Access Article
134 - Time Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry
Behnam Vahdani Seyed Meysam Mousavi Morteza Mousakhani Hassan Hashemi -
Open Access Article
135 - Predicting Tehran Securities Stock Index By Using Neural Networks
Bita Delnavaz Asghari Mir Feiz FallahshamsThe size and process of the stock price indices are among the most important factors affecting the decisions of the investors in the financial markets. In order to predict the market, different techniques have been used, the most common of which are regression methods a MoreThe size and process of the stock price indices are among the most important factors affecting the decisions of the investors in the financial markets. In order to predict the market, different techniques have been used, the most common of which are regression methods and ARIMA models. However, these models have been unsuccessful in the practical prediction of some series. In the present research, in order to predict the total index of the stock, the Feed Forward Neural Network model with the law of back propagation was used in three networks with different input models, and the results of the model were compared to the result of multi – variable regression models and ARIMA models. The results indicated that the neural network method showed considerably fewer RMSE errors than RMSE errors in other methods, and that in Tehran stock market short – term prediction within a shorter interval is more suitable than long – term prediction within a longer interval. Manuscript profile -
Open Access Article
136 - Determination of the effective factors in prediction of traffic created from urban applications. (Sample item, Neyshabur)
Rostam Saberifar Ahmad KhaderianThe purpose of this paper was to determination of the effective factors in prediction of traffic created from urban applications in Neyshabour. This study has been performed by descriptive in the form of correlation of prediction. Field data has been gathered, in case o MoreThe purpose of this paper was to determination of the effective factors in prediction of traffic created from urban applications in Neyshabour. This study has been performed by descriptive in the form of correlation of prediction. Field data has been gathered, in case of a questionnaire. The case study in poll section determine 60 person in basis of Morgan chart that were separated into two groups in each 30 persons between employees in urban administration and other people. Data that gathered in this way analyzed by use of spss. The results showed that number of employees in each land use or its functions, has undeniable role in increasing the scale of traffic production (P= 0/000), but other noted factors can’t predict it however urban lists are related to the scale of traffic production. The number of employees in each land use is also clarified in this research. We can say that urban administration have to estimate any amount of traffic before getting any license for land use change or localization and specific performance and also estimate its effect over the entire transport ion net . Manuscript profile -
Open Access Article
137 - Analysis and prediction of water level fluctuations in Urmia Lake using ARIMA model
khadijeh javan Farhad NasiriThis study has been done to evaluate the fluctuations of water level in Urmia Lake and to provide a best model for prediction the water level fluctuations. Monthly water level data for the period (1345 - 1392) was used and homogeneity was assessed by Run Test. Then the MoreThis study has been done to evaluate the fluctuations of water level in Urmia Lake and to provide a best model for prediction the water level fluctuations. Monthly water level data for the period (1345 - 1392) was used and homogeneity was assessed by Run Test. Then the stability of mean and variance of the data was tested in order to put down the non-stability by creation a rank in series. Trend of the monthly series was eliminated by making a difference and the time series of water level was evaluated by using Box- Jenkins model and the best model was fitted. Accuracy of the model was verified based on AIC, BIC and chart analysis of autocorrelation and partial autocorrelation functions and ARIMA = (0, 1, 4) (1, 1, 1)12 was selected as a suitable model. The selected model was fitted then the model was tested by Analysis of residuals and confirmed its authenticity. Finally, the monthly behavior of the series was predicted for 9 years later by using this model. Manuscript profile -
Open Access Article
138 - Study and Evaluation of Temperature in Aleshtar City based on Artificial Neural Network Model
Mahnaz Hassanvand Reza Borna Manijeh Zohoorian Pordel Alireza ShakibaTemperature assessment and forecasting is one of the most practical estimates of climatic elements. Today, the agricultural and industrial sectors are highly dependent on the temperature conditions. Temperature is one of the most important climatic meters that is one of MoreTemperature assessment and forecasting is one of the most practical estimates of climatic elements. Today, the agricultural and industrial sectors are highly dependent on the temperature conditions. Temperature is one of the most important climatic meters that is one of the main factors in the climate identity of each region. The purpose of this study is to make a model for predicting the average monthly seasonal temperature of selected stations in Lorestan province, including Al-Shatrami region. Identification and detection of vulnerabilities in the infrastructure of Aleshtar districts in the conditions of climate change. And due to the inadequacy of the 30-year time series of Al-Ashtarl, neighboring cities such as Khorramabad-Aleshtar-Borujerd synoptic stations have been used, because the artificial neural network method has a great ability to simulate and predict atmospheric elements. And the weather, especially the temperature. To model and predict the seasonal monthly temperature, the r programming tool software of the fOre gast package has been used. Two tests of estimator trend analysis have been used. The 30-year time series trend of these elements was examined during the basic statistical period (1989-2019). The climate cycle was reported and extracted under two scenarios: NNAR and forEgast. The artificial neural network is one of the most powerful models capable of receiving and displaying complex Data input and output is one of the most widely used neural network (NNA) models to determine the best network inputs. Manuscript profile -
Open Access Article
139 - Enabling Link Prediction Optimization on Social Networks
banafshe poorsoltani fariba salahi Amir Daneshvar -
Open Access Article
140 - A Comparative Study on Existing Techniques for Variable Reduction Including Factor Analysis, Principal Component Analysis, Correlation-Based Techniques, and Relief in Predicting the Risk of Stock Price Crash in Tehran Stock Exchange
Hassan Mohammadi Alireza Zarei Soudani -
Open Access Article
141 - PSPGA: A New Method for Protein Structure Prediction based on Genetic Algorithm
Arash Mazidi Fahimeh Roshanfar -
Open Access Article
142 - Sport Result Prediction Using Classification Methods
Arash Mazidi Mehdi Golsorkhtabaramiri Naznoosh Etminan -
Open Access Article
143 - A Hybrid Model Using Deep Learning to Predict Stock Price Index
Mohammad Reza Shahraki -
Open Access Article
144 - Identification and Prediction of Banking Crisis in Iran
Z. Zarei A. KomijaniAbstract This study uses the early warning system approach to predict banking crisis in Iran during 1990Q1– 2013Q4. To achieve the goal, Money Market Pressure index approach will be used by Markov Switching Model. The results indicate that, although, based on gov MoreAbstract This study uses the early warning system approach to predict banking crisis in Iran during 1990Q1– 2013Q4. To achieve the goal, Money Market Pressure index approach will be used by Markov Switching Model. The results indicate that, although, based on governmental supporting, the banking section in Iran has never encountered the phenomena such as bank run and bankruptcy, but it has also experienced banking crisis. Likewise, the assessment of probit model suggests that some indexes are leading banking crisis probability. These indicators include the variables of real exchange rate growth, the growth rate of credit endowed to private sector, real GDP, housing price, and real interest rate. Furthermore, the measures of expectation-prediction represent that the model developed has considerable potential to predict in sample banking crisis. Also, this model is unsuccessful in the prediction of the crisis in only 12 percent, but capable of predicting crisis in 77 percent of cases, where the crisis has occurred with probability of more than 40 percent. Manuscript profile -
Open Access Article
145 - Comparing the Exchange Rates Predicted by STAR Non-linear Models and Alternative Models
Hasan Khodavaisi Ali VafamandExchange rate known as a strategic variable plays an important role in the economy, because of affecting on different sectors in economy all over the world. So, exchange-rate predictions have always been an important subject for the researchers in Economics. This paper MoreExchange rate known as a strategic variable plays an important role in the economy, because of affecting on different sectors in economy all over the world. So, exchange-rate predictions have always been an important subject for the researchers in Economics. This paper tries to study the attributes of exchange rate developed by monthly official data of Iran Stock Exchange based on Smooth Transition Autoregressive (STAR) models. The result of simulation based on STAR models and estimated by Genetic Algorithm method, outperforms linear time series models, such as ARIMA out of sample predictions based on RMSE, MAE and DA criteria. Manuscript profile -
Open Access Article
146 - Expansion of Financial Distress Modeling Using Corporate Earnings Management in the Iran's Economic Environment
Abbas Ramezanzadeh Zeidi Khosro Faghani Makrani Ali jafariThe purpose of this study is to provide a model for financial distress predicting with real earnings management.So the redesignthe financial distress prediction model of Altman (1983) with the real earnings management variable as a predictor variable, the performance of MoreThe purpose of this study is to provide a model for financial distress predicting with real earnings management.So the redesignthe financial distress prediction model of Altman (1983) with the real earnings management variable as a predictor variable, the performance of the unadjusted model and the adjusted model in predicting of financial distress among companies accepted in the Tehran Stock Exchange was compared.The statistical sample consists of 179 Companies during the years 2008- 2017.Data analysis and hypothesis testing were performed using multiple logistic regression.The results show that the overall accuracy of the adjusted model is higher than the unadjusted model. Manuscript profile -
Open Access Article
147 - Investing Neural Network Trianing with Metaheuristic Algorithms in order to Prediction of Iran Stock Index
Seyed Ahmad Mirzaei Zakiyeh Nikdel Zahra NikdelPrediction and analysis of stock market movements are an important topic for researchers, traders and have got an important role in today’s economy. Variety in policies, such as government policies and economic policies affect the stock market and cause stock pric MorePrediction and analysis of stock market movements are an important topic for researchers, traders and have got an important role in today’s economy. Variety in policies, such as government policies and economic policies affect the stock market and cause stock price changes. The predicting stock price movement on a daily basis due to the non-linear and chaotic stock price movements is a difficult task. There are several ways for predicting in stock market. Artificial intelligence techniques have been widely used to predict data with nonlinear and chaotic structure. One of these techniques is neural network. If neural network is trained correctly, then it has minimum error in predicting. In this research, we will train the multi layer perceptron neural network with 8 meta heuristics algorithms and we predict Tehran Exchange Dividend Price Index (TEDPIX). The Results show that grey wolf optimization has the minimum error in training of neural network. Manuscript profile -
Open Access Article
148 - An Intelligent Method for Death Prediction Using Patient Age and Bleeding Volume on CT scan
Yosra Azizi Nasrabadi Ali Jamali Nazari Hamid Ghadiri Farshid Babapour MofradThe purpose of this paper's prediction of survival or death within 30 days is based on a cerebral hemorrhage. Timely and correct diagnosis and treatment of cerebral hemorrhage are essential. If the patient's death is predicted during these thirty days, the treating phys MoreThe purpose of this paper's prediction of survival or death within 30 days is based on a cerebral hemorrhage. Timely and correct diagnosis and treatment of cerebral hemorrhage are essential. If the patient's death is predicted during these thirty days, the treating physician should use intensive care and more treatment for the patient. Cerebral hemorrhages require immediate treatment and rapid and accurate diagnosis. In this article, using the volume of cerebral hemorrhage and the patient's age and using the neural network of support vector machine (SVM), it is predicted what percentage of people with cerebral hemorrhage survive and what percentage die. Parameters of cerebral hemorrhage volume and, age of patients, neural network input are considered. The network's output is the survival or death of patients with cerebral hemorrhage over the next thirty days. The data we used included the bleeding volume and age of 66 patients with lobar hemorrhage, 76 patients with deep bleeding, nine patients with Pontine hemorrhage and 11 patients with cerebellar hemorrhage. All bleeding models are considered as input to the support vector machine neural network. The overall accuracy of the designed support vector machine neural network is 93%. Regardless of the type of cerebral hemorrhage, the survival or death of people with cerebral hemorrhage within 30 days is predicted. Manuscript profile -
Open Access Article
149 - The Electricity Consumption Prediction using Hybrid Red Kite Optimization Algorithm with Multi-Layer Perceptron Neural Network
Jalal Raeisi-Gahruei Zahra BeheshtiSince the electricity consumption’s prediction is one of the most important aspects of energy manage­ment in each country, various methods based on artificial intelligence have been proposed to manage it. One of these methods is Artificial Neural Networks (ANN MoreSince the electricity consumption’s prediction is one of the most important aspects of energy manage­ment in each country, various methods based on artificial intelligence have been proposed to manage it. One of these methods is Artificial Neural Networks (ANN). To improve the performance of ANNs, an efficient algorithm is necessary to train it. Back Propagation (BP) algorithm is the most common algorithm employed in training ANNs, which is based on gradient descent. Since BP may fall in local optima, it cannot provide a good solution in some problems. To overcome this shortcoming, optimiz­ation algorithms like meta-heuristic algorithms can be applied to train ANNs. In this study, a new meta-heuristic algorithm called Red Kite Optimization Algorithm (ROA) is introduced, which is inspired by the social life of red kites in nature. The ROA has several advantages such as simplicity in structure and implementation, having few parameters and good convergence rate. The perfprmance of ROA is compared with some recent metaheuristic algorithms on benchmark functions of CEC2018. Also, it is employed to train Multi-Layer Perceptron (MLP) for the electricity consumption prediction at peak load times in Iran. The results show the good performance of proposed algorithm compared with competitor algorithms in terms of solution accuracy and convergence speed. Manuscript profile -
Open Access Article
150 - Coping With the Loss of Quality of Job Future Predictors in Grid Computing Environments
Reza Ghaemi hosein salami Mehrdad JalaliDistributed processing environments, such as grids, are one of the most important platforms for meeting the user's processing needs. These environments have the potential to meet the needs of users, but they also have their own problems, including the failure of the job MoreDistributed processing environments, such as grids, are one of the most important platforms for meeting the user's processing needs. These environments have the potential to meet the needs of users, but they also have their own problems, including the failure of the jobs. Several attempts have been made to overcome this problem, which in general can be divided into two categories of resource side methods and job side methods. All these methods need some kind of prediction of the resources or jobs status in order to pursue a proactive approach to failures. However, due to the dynamics of these environments, the developed models quickly lose their quality and thus can not effectively help with the methods mentioned. In this paper, first, by identifying the reasons for reducing the quality of predictors in the grid environment, a solution has been proposed to deal with it, and then the proposed solution has been applied in the context of job failures. The results of experiments on the two experimental environments of AuverGrid and Grid5000 showed that the proposed method would increase the quality by 0.02 and 0.06 respectively in these two environments. Manuscript profile -
Open Access Article
151 - Prediction of Success in Neurofeedback Treatment for Attention-Deficit Hyperactivity Disorder before Starting Treatment
Nikoo Khanahmadi MR YousefiIn this paper, the method of predicting the treatability of patients suffering from hyperactivity with neurofeedback training with the help of extracting the frequency band of the electroencephalogram (EEG) signal and using the brain-functional communication evaluation MoreIn this paper, the method of predicting the treatability of patients suffering from hyperactivity with neurofeedback training with the help of extracting the frequency band of the electroencephalogram (EEG) signal and using the brain-functional communication evaluation criterion is done to determine the person's treatability before starting the neurofeedback treatment. This algorithm consists of six steps: In the first step, a data set of EEG signal recording during neurofeedback stimulation from 60 students in the age group of 7 to 14 years (regardless of gender) with hyperactivity in two treatable and non-treatable classes was obtained from the Mendelian database. In the second step, primary filtering has been done to reduce the noise of the data set using a pre-processing block. In the third step, the frequency distribution of the alpha and beta bands is extracted from the noise reduction signals. In this type of data, the difference in the EEG components of each group can be expressed by measuring brain-functional communication and using the phase lock index (PLI), which is used to detect the existence of a connection between the brain lobes involved once using the probability value index. In the t-test statistical test and to increase the accu­ra­c­y, the genetic algorithm was used to identify the effective electrodes in the treatment. So, the fourth step is to extract the feature, which is to measure the amount of brain communication in the brain signal recording electrodes. In the fifth step, it is to reduce the feature space, the results show show that the lobes involved during neurofeedback stimulation are the frontal and central lobes, and among the 32 EEG recording channels, only the data of 6 channels C3, FZ, F4, CZ, C4, and F3 show a significant difference in the amount of brain communication during stimulation. and finally, in the sixth step, by using different classifications, the output of the combination of classifications was the label of one of two classes, treatable or non-treatable. In this proposed method, the correctness cri­te­rion is used to express the research result, and finally the percentage of correctness obtained is 90.6%. Manuscript profile -
Open Access Article
152 - Static Voltage Stability Analysis by Using SVM and Neural Network
Mehdi Hajian Asghar Akbari Foroud Hossein NorouzianVoltage stability is an important problem in power system networks. In this paper, in terms of static voltage stability, and application of Neural Networks (NN) and Supported Vector Machine (SVM) for estimating of voltage stability margin (VSM) and predicting of voltage MoreVoltage stability is an important problem in power system networks. In this paper, in terms of static voltage stability, and application of Neural Networks (NN) and Supported Vector Machine (SVM) for estimating of voltage stability margin (VSM) and predicting of voltage collapse has been investigated. This paper considers voltage stability in power system in two parts. The first part calculates static voltage stability margin by Radial Basis Function Neural Network (RBFNN). The advantage of the used method is high accuracy in online detecting the VSM. Whereas the second one, voltage collapse analysis of power system is performed by Probabilistic Neural Network (PNN) and SVM. The obtained results in this paper indicate, that time and number of training samples of SVM, are less than NN. In this paper, a new model of training samples for detection system, using the normal distribution load curve at each load feeder, has been used. Voltage stability analysis is estimated by well-know L and VSM indexes. To demonstrate the validity of the proposed methods, IEEE 14 bus grid and the actual network of Yazd Province are used. Manuscript profile -
Open Access Article
153 - Fast Intra and Inter Prediction Mode Decision of H.264/AVC for Medical Image Compression Based on Region of Interest
Mehdi Jafari Homayoun Mahdavi-Nasab Shohreh KasaeiThis paper aims at applying H.264 in medical video compression applications and improving the H.264 Compression performance with better perceptual quality and low coding complexity. In order to achieve higher compression of medical video, while maintaining high image qu MoreThis paper aims at applying H.264 in medical video compression applications and improving the H.264 Compression performance with better perceptual quality and low coding complexity. In order to achieve higher compression of medical video, while maintaining high image quality in the region of interest, with low coding complexity, here we propose a new model using H.264/AVC that uses lossless compression in the region of interest, and very high rate, lossy compression in other regions. This paper proposes a new method to achieve fast intra and inter prediction mode decision that is based on coarse macroblocks for intra and inter prediction mode decision of the background region and finer macroblocks for region of interest. Also the macroblocks of the background region are encoded with the maximum quantization parameter allowed by H.264/AVC in order to maximize the number of null coefficients. Experimental results show that the proposed algorithm achieves a higher compression rate on medical videos with a higher quality of region of interest with low coding complexity when compared to our previous algorithm and other standard algorithms reported in the literature. Manuscript profile -
Open Access Article
154 - A Compromise Solution Approach for Fuzzy Data Envelopment Analysis: A Case of the Efficiency Prediction
Nam Hyok Kim Feng He Kwang-Chol Ri Son-Il KwakThe data envelopment analysis (DEA) a data-oriented approach for evaluating the relative performance of decision-making units (DMUs). The traditional DEA applies only to crisp data, whereas the data collected in the real world may be ambiguous and imprecise. The fuzzy D MoreThe data envelopment analysis (DEA) a data-oriented approach for evaluating the relative performance of decision-making units (DMUs). The traditional DEA applies only to crisp data, whereas the data collected in the real world may be ambiguous and imprecise. The fuzzy DEA is an extension of the DEA using the fuzzy variable to deal with uncertain or imprecise data. This paper proposes two new fuzzy arithmetic-based DEA models with dynamic weights and common weights, formulated as multiple objective decision-making (MODM), and proposed models are represented as the linear programs providing the compromise solutions. The numerical experiment is illustrated to examine the validity of the proposed models, and the experiment shows that the proposed models give better results than other models. The proposed fuzzy DEA models are applied to predict the energy efficiency of 40 iron and steel enterprises in China. Manuscript profile -
Open Access Article
155 - Developing a model for predicting student performance on centralized test Based on Data Mining
mostafa yousefi Tezerjan Esrafil Ala Maryam MollabagherThe aim of this study is to provide a model for predicting University of Applied Science & Technology students' scores in centralized exams in the coming semesters of the university. For this purpose, the status of the 19/207 student/ course grades has been studied MoreThe aim of this study is to provide a model for predicting University of Applied Science & Technology students' scores in centralized exams in the coming semesters of the university. For this purpose, the status of the 19/207 student/ course grades has been studied in 8 courses in 6 provinces and 28 educational centers, that have been held in an associate's and bachelor's degree level and concurrently across the country in the second semester of 1397-98 And by using the feature selection method, the most effective ones were selected. To clarify the relationships between the selected features and the decision tree model with C5.0 algorithm using SPSS Modeler software, with 10 effective indicators, a model for predicting students' scores in the next semester is presented in the courses approved for the centralized exam. This predictive model can be effective in making the learning process more efficient in the academic system. The results of these models include suggestions for modifying the test process, finding students and centers, and out-of-pattern conditions for further monitoring and identifying centers whose students' average GPAs were high but poor on the centralized test. Manuscript profile -
Open Access Article
156 - شناسایی دسته های پیش بینی همپوشان : تمرکز بر کتب درسی زبان انگلیسی با اهداف ویژه
فرحناز بهلولی اسگوییمفهوم "پیش بینی" به عنوان یک ابزار بلاغی آینده نگر در بهبود توانایی های خواندن زبان آموزان در حوزه زبان انگلیسی با اهداف ویژه بر پایه این فرض اهمیت ویژه میابد که وقتی ساختار بلاغی متن توسط زبان آموز درک شود، وی می تواند از آن برای پیش بینی نوع اطلاعات پیش رو بهره ببرد. Moreمفهوم "پیش بینی" به عنوان یک ابزار بلاغی آینده نگر در بهبود توانایی های خواندن زبان آموزان در حوزه زبان انگلیسی با اهداف ویژه بر پایه این فرض اهمیت ویژه میابد که وقتی ساختار بلاغی متن توسط زبان آموز درک شود، وی می تواند از آن برای پیش بینی نوع اطلاعات پیش رو بهره ببرد. هدف این مطالعه بررسی امکان مرتبط کردن سیگنالهای متنی " مقوله های پیش بینی" با دانش محتوای متن بود. برای این منظور، یک پژوهش توصیفی کیفی بر روی داده ها مشتمل بر 10 فصل از 5 کتاب درسی در رشته مهندسی عمران با استفاده ازمدل تحلیلی طراحی شده توسط "تادرس" بر پایه مفهوم پیش بینی اتخاذ شد. شش دسته"مقوله پیشبینی" معرفیشده توسط تادروس که زیربنای این مدل هستند، شامل شمارش، برچسبگذاری پیشرفته، گزارش، بازپیدایی، فرضیه سازی و سؤال ، بهطور مداوم در متون انتخاب شده بررسی شدند. یافتههای این مطالعه یک دسته پیشبینیکننده جدیدی به نام «مقولههای پیشبینیهمپوشان» را نشان داد که در آن دو یا سه دسته مقوله های پیشبینی، دقیقاً روی یکدیگر همپوشانی دارند.بر مبنای نتایج بدست آمده از این مطالعه ، آگاه سازی زبان آموزان ، معلمان و طراحان کتب آموزشی در مورد وجود چنین مقوله های پیش بینی میتواند مزایای قابل توجهی برای همه اعضای جامعه زبان انگلیسی با اهداف ویژه به همراه داشته باشد. Manuscript profile -
Open Access Article
157 - Estimating Heritabilities and Breeding Values for Real and Predicted Milk Production in Holstein Dairy Cows with Artificial Neural Network and Multiple Linear Regression Models
M. Nosrati S.H. Hafezian M. Gholizadeh -
Open Access Article
158 - Assessment of Alternative Single Nucleotide Polymorphism (SNP) Weighting Methods for Single-Step Genomic Prediction of Traits with Different Genetic Architecture
S. Moghaddaszadeh-Ahrabi M. Bazrafshan -
Open Access Article
159 - The Estimation of Body Weight from Body Measurements in Kilakarsal Sheep of Tamil Nadu, India
T. Ravimurugan A.K. Thiruvenkadan K. Sudhakar S. Panneerselvam A. Elango -
Open Access Article
160 - پیشبینی نواحی اپیتوپی سلولهایB و T آنتیژن حفاظتی در باکتری Bacillus anthracis
م. طهمورثپور ن. نظیفی ز. پیرخضرانیانآنتیژن حفاظتی (PA) زیر واحدی از توکسین سیاه زخم در باکتری Anthracis میباشد که به عنوان یک عامل مهم در واکسنهای حفاظت در برابر بیماری سیاه زخم شناخته شده است. یکی از اهداف طراحی واکسنهای نوترکیب اجتناب از عوارض جانبی ارگانیسمهای کشته شده یا ضعیف شده با استفاده از اپ Moreآنتیژن حفاظتی (PA) زیر واحدی از توکسین سیاه زخم در باکتری Anthracis میباشد که به عنوان یک عامل مهم در واکسنهای حفاظت در برابر بیماری سیاه زخم شناخته شده است. یکی از اهداف طراحی واکسنهای نوترکیب اجتناب از عوارض جانبی ارگانیسمهای کشته شده یا ضعیف شده با استفاده از اپیتوپهای خطی خنثیساز آنتیژنهای حفاظتی میباشد. مطالعه حاضر با هدف تعیین اپیتوپهای غالب بر اساس آنالیزهای چند پارامتری انجام شد. از سرورهای بیوانفورماتیکی آنلاین شناخته شده به منظور پیشبینی اپیتوپها استفاده شد و بر اساس بالاترین امتیاز و بیشترین تکرار در نرم افزارهای مورد استفاده، بهترین اپیتوپها انتخاب شدند. تجزیه و تحلیلهای بیشتر در مورد اپیتوپهای پیشبینی شده با استفاده از نرم افزار آنلاین VaxiJen 2.0 و سرورهای هضم پروتئینی (Protein Digest) انجام پذیرفت. در میان اپیتوپهای انتخاب شده در مراحل قبل، آنهایی که دارای بالاترین آنتیژنسیته با حد آستانه 5/0 و کمترین جایگاه محدودکننده پروتئازهای دستگاه گوارش بودند به عنوان اپیتوپهای نهایی انتخاب شدند. اپیتوپهای نهایی برای سلولهای B شامل اسیدآمینههای 308-292، 521-507 و 719-706 بودند. همچنین اسیدآمینههای 31-17، 329-315 و 400-385 به عنوان بهترین اپیتوپهای کلاس MCHI سلولهای T و اسیدآمینههای شماره 464-455 و 669-661 به عنوان بهترین اپیتوپهای انتخابی برای کلاس MCHII در سلولهای T پیشبینی شدند. از آنجایی که وجود ساختار پیچههای تصادفی موجب بالا رفتن احتمال شکلگیری اپیتوپ آنتیژنتیک در ساختار پروتئین میشود، آنالیز نهایی ساختار دوم برای اپیتوپهای نهایی PA نشان داد که تمام این اپیتوپها دارای ۱۰۰ درصد ساختار مارپیچ تصادفی (نامنظم) هستند. Manuscript profile -
Open Access Article
161 - Genetic Trends for Milk Yield, Persistency of Milk Yield,Somatic Cell Count and Calving Interval in Holstein Dairy Cows of Iran
A. Chegini A.A. Shadparvar N. Ghavi Hossein-Zadeh -
Open Access Article
162 - پیشبینی اپی توپهای سلولهایB و Tآنتیژن Omp25 از باکتری بروسلا ملی تنسیس به منظور طراحی واکسن نوترکیب گوسفندی
س. یوسفی م. طهمورثپور م.ه. سخاوتیبروسلوز یکی از رایج ترین بیماریهای دامی است که توسط باکتری گرم منفی بروسلا ایجاد می­شود. با توجه به ضرر­های جدی اقتصادی و درمانی این بیماری که برای دام و انسان همواره به ارمغان دارد تلاش­های بسیاری جهت جلوگیری و درمان این بیماری توسط واکسن­های نوترکیب ب Moreبروسلوز یکی از رایج ترین بیماریهای دامی است که توسط باکتری گرم منفی بروسلا ایجاد می­شود. با توجه به ضرر­های جدی اقتصادی و درمانی این بیماری که برای دام و انسان همواره به ارمغان دارد تلاش­های بسیاری جهت جلوگیری و درمان این بیماری توسط واکسن­های نوترکیب بر پایه آنتیژن­های غشای پروتئینی خارجی صورت می­گیرد. بدین منظور هدف از مطالعه حاضر بررسی خصوصیات بیوانفورماتیکی آنتی ژن Omp25 به عنوان یکی از آنتیژنهای غالب غشای پروتئینی باکتری بروسلا بوده است. در این پژوهش از نرم افزار­های بیوانفورماتیکی مختلفی برای پیشبینی اپی توپ­های B وT، ساختار دوم و سوم پروتئین، خصوصیات ایمنیزایی و ویژگی­های هضم پروتئین استفاده گردید. پیش از استفاده از نرم افزارها میزان دقت آنها توسط دادههای تجربی اعتبار سنجی گردید. نتایج آنالیز بیوانفورماتیکی نشان داد که پنج اپی توپ برای سلولهای B در موقعیتهای 44-26، 79-59، 112-88، 166-146، 202-175 و پنج اپی توپ برای سلولهای T در مکانهای 10-1، 22-14، 132-122، 162-154 و 213-206 وجود دارد. تمامی اپی توپهای شناسایی شده به جز اپی توپهای 10-1 و 22-14 دارای توانایی ایمنیزایی بودند. نهایتاً اپی توپ ناحیه 162-154 به عنوان یک اپی توپ مشترک بین سلولهای B و T جهت طراحی واکسن نوترکیب پیشبینی گردید. Manuscript profile -
Open Access Article
163 - برآورد وزن بدن بر مبنای ابعاد قلب گوسفندان ساردی و تیماهدیت با استفاده از مدلهای مختلف
آی. بوجنانه اس. هلهالیهدف از این مطالعه تعیین رابطه بین وزن بدن (BW) و ابعاد قلب (HG) در گوسفندان ساردی و تیماهدیت و برازش یک معادله پیش­بینی BW بر مبنای HG بوده است. داده­های مورد استفاده در این مطالعه شامل 476 رکورد BW و HG (227 رکورد در ساردی و 249 رکورد در تیماهدیت) بود که از نره Moreهدف از این مطالعه تعیین رابطه بین وزن بدن (BW) و ابعاد قلب (HG) در گوسفندان ساردی و تیماهدیت و برازش یک معادله پیش­بینی BW بر مبنای HG بوده است. داده­های مورد استفاده در این مطالعه شامل 476 رکورد BW و HG (227 رکورد در ساردی و 249 رکورد در تیماهدیت) بود که از نرها و ماده­ها در سنین مختلف و از 33 مزرعه خصوصی جمعآوری گردیدند. میانگین BW و HG در ساردی به ترتیب 2/12 ± 8/34 کیلوگرم و 3/16 ± 0/74 سانتی­متر و در تیماهدیت به ترتیب 7/22 ± 2/39 کیلوگرم و 4/16 ± 4/78 سانتی­متر در تیماهدیت بود. ضرایب همبستگی بین BW و HG که به ترتیب در ساردی و تیماهدیت برابر با 958/0 و 944/0 بوده، نشان ­دهنده وجود همبستگی بالا بین این دو متغیر است. شش مدل پیش­بینی شامل رگرسیون خطی ساده، رگرسیون­های درجه سه و چهار چند جمله­ای و سه رگرسیون غیر خطی (گومپتز، آلومتریک و میشرلیک) برای این داده­ها برازش یافتند. این مدل­ها برای کل داده­ها (صرفنظر از نژاد و جنس)، به طور جداگانه برای همه حیوانات یک نژاد صرفنظر از جنس (مختص نژاد) و به طور جداگانه برای نرها و ماده­ها صرفنظر از نژاد (مختص جنس) به کار گرفته شدند. برای تعیین بهترین مدل رگرسیونی برازش یافته، ضریب تعین (2R یا شبه 2R) میانگین مربعات باقیمانده (MSE) و معیار اطلاعات آکایک (AIC) مورد استفاده قرار گرفتند. هر شش مدل به خوبی با مجموعه داده­ها انطباق داشتند. زیرا 2R یا شبه 2R آنها از 892/0 تا 969/0 متغیر بوده است. با این حال براساس سایر معیارهای انتخاب، چنین به نظر می­رسد که مدل درجه سوم چند جمله­ای بهترین مدل بوده و مدل آلومتریک بایستی نادیده گرفته شود. مشاهدات دور در هر سه مدل برتر با کمک باقیمانده­های استودنت و مقادیر مطلق بزرگتر از دو انحراف معیار که نشان دهنده انحراف معنی ­دار بوده است، کنترل گردیدند. سپس مشاهدات پرت حذف گردیده و بهترین مدل­ها بر روی مجموعه­ داده­های پاکسازی شده پیاده گردیده و مقایسه شدند. بدین ترتیب، برای کل داده­ها، نژاد ساردی و ماده­ها، مدل میشرلیک مناسب بوده و برای نژاد تیماهدیت و نرها، به ترتیب مدل­های درجه سوم و گومپتز مناسب بوده­اند. بنابراین یک معیار جهت کمک به پرورش دهندگان در مدیریت بهتر گوسفندان در هر دسته ارائه گردید. Manuscript profile -
Open Access Article
164 - Performance of Artificial Neural Networks Model under Various Structures and Algorithms to Prediction of Fat Tail Weight in Fat Tailed Breeds and Their Thin Tailed Crosses
ک. نوبری S.D. Sharifi N. Emam Jomea Kashan M. Momen A. Kavian -
Open Access Article
165 - Feasibility Study of Using Photovoltaic Systems in Water and Wastewater Industry (Case Study: Tehran Water and Wastewater Company)
Ensieh Ozgoli Younes Noorollahi Reza Arjmandi Ali MohammadiSince water and wastewater companies are one of the significant energy consumers in urban industries, there is a substantial share to increase electricity demand and as a result, increasing the power plants load. The purpose of this study is to present a new evaluation MoreSince water and wastewater companies are one of the significant energy consumers in urban industries, there is a substantial share to increase electricity demand and as a result, increasing the power plants load. The purpose of this study is to present a new evaluation approach for using solar energy in the water and wastewater industry. Therefore, while consideration of the energy consumption in the six regions of Tehran Water and Wastewater Company, requirements for the installation and operation of photovoltaic systems in this company has been investigated. In the present study, the objective functions are energy consumption costs and greenhouse gas emissions; Also, solar energy potential, increasing population rate and water consumption are the most important independent variables and forecasted electricity consumption, carbon tax, and electricity sales price are also dependent variables. The results of this study, which can be based on using the regression model, show that the increase in electricity consumption and costs are 1.5 and 3 times in this period, respectively. To calculate the amount of greenhouse gas emissions, the three scenarios are implemented and compared with the replacement of 5, 20, and 30% of the company required electricity by photovoltaic systems. The reduction in CO2 emissions due to the production of 30% of electricity consumption with solar energy, amounted to 26,712 thousand tons. On the other hand, taxing more than $ 10 per ton of CO2 emissions changes the consumption pattern and reduces the cost of electricity consumption in this industry by $ 5,987,086. Manuscript profile -
Open Access Article
166 - Forecasting the discharge of the Zayandeh Rood River at the Ghleeh Shahrokh station using deep learning techniques
Mohammad MehraniAbstract- Water discharge is a term in the water industry that refers to the amount of water that passes through a certain point per unit of time. Discharge rate is the amount of water that passes through a specific point such as a river,, water channel, dam valve, pipe MoreAbstract- Water discharge is a term in the water industry that refers to the amount of water that passes through a certain point per unit of time. Discharge rate is the amount of water that passes through a specific point such as a river,, water channel, dam valve, pipe or any other structure such as a faucet cartridge in a unit of time. In the metric system, water discharge rate is expressed in terms of cubic meters per second, cubic meters per hour, or liters per second. The unit of cubic meters per second is used for large flows such as rivers and large canals, and the unit of liters per second is used for the flow of water in wells and water that enters leaks. Measuring the discharge of the river has many effects on people's lives. Knowing the amount of water entering the areas of a river's catchment area is very important in agriculture, potential risks to human and animal life, industries, etc. Therefore, predicting river discharge can lead to effective management and prevent serious damage in the mentioned areas. According to the mentioned cases, the purpose of the presented paper is to predict the river discharge using deep learning techniques. In order to do this, the discharge of the Zayandeh Rood River at Qala Shahrokh station has been investigated and predicted using two techniques - ANFIS and LSTM. The simulation results show 93% to 94% accuracy in predicting the discharge of the studied river. Manuscript profile -
Open Access Article
167 - The effects of stars in Shahname and the Predictions made upon them.
hossein Mansoorian Sorkhgaryeh layla Tavakol RadMaking predications and telling about mysterious event is a common practice in mythology and epic literature of the world. Shahname as on epic contains infinite variety of secret believes, feelings, wishes, and thoughts of human beings. Among positive and negative pred MoreMaking predications and telling about mysterious event is a common practice in mythology and epic literature of the world. Shahname as on epic contains infinite variety of secret believes, feelings, wishes, and thoughts of human beings. Among positive and negative predictions in Shahname, Astronomy plays an important role in forming the events. The predictions based on stars in Shahnamen are described in full details and the heros of this book always try to predict the behavours of others. Ferdousi as an epic poet in his masterpiece, Shahname, makes use of prediction in some sections of his book to develop the stories. The present article investigates the effect of stars on predictions of stories in Shahname. It also anlase the deep stracture of these predictions. Finally, this article in written in descriptive analysis form by referring to various sources. Manuscript profile -
Open Access Article
168 - A Fast Block Size Decision For Intra Coding in HEVC Standard
Mohammadreza Ramezanpour Reihaneh Khorsand -
Open Access Article
169 - Fast Intra Mode Decision for Depth Map coding in 3D-HEVC Standard
Behrouz Rafi Mohammadreza Ramezanpour -
Open Access Article
170 - Multivariate Time Series Prediction Considering Intra-Time-Series and Inter-Time-Series Dependencies
Parinaz Eskandarian Jamshid Bagherzadeh Habibollah Pirnejad Zahra Niazkhani -
Open Access Article
171 - Improving Short-Term Wind Power Prediction with Neural Network and ICA Algorithm and Input Feature Selection
Elham Imaie Abdolreza Sheikholeslami Roya Ahmadi Ahangar -
Open Access Article
172 - The Impact of Mobility Prediction on the Performance of P2P Content Discovery Protocols over Mobile Ad-Hoc Networks
Mona Mojtabaee Morteza Romoozi Hamideh Babaei -
Open Access Article
173 - Chaotic Time Series Prediction by Auto Fuzzy Regression Model
Haleh Nazari Homayun Motameni Babak Shirazi -
Open Access Article
174 - The Role Of Flood Anticipation And Warming Systems In Reducing Flood Adverse Impacts
Hossain Mohammadi Mehran Maghsoudi Gholam Reza RowshanIn this field, flood one of the most dangers disasters for people of many countries face it and is one of the most destroying disasters among 15 known natural climatology all over the world. For example, about 196 million people in over 90 countries are exposed to dange MoreIn this field, flood one of the most dangers disasters for people of many countries face it and is one of the most destroying disasters among 15 known natural climatology all over the world. For example, about 196 million people in over 90 countries are exposed to danger of flood water.The increase in the population and the shortage of agricultural lands led to human population movement to the flood water plains and this intensifies the danger of flood water and its effects. But nowadays, considering the destroying effects of flood water on the human societies, structural methods of protection against flood water such as flood water bands and other methods of controlling and directing flood water, can be efficient only when the design capacity of these structures is high. But when these structures break, always a remaining risk exists. In most cases, such structures may be improper or their execution may be impossible because of environmental reasons and therefore non-structural methods are needed. The flood water warning for directing of the remaining risk is necessary and it is one of the most efficient methods of non-structural methods for flood water management. Manuscript profile -
Open Access Article
175 - Predicting rainfed barley crop yield using Artificial neural network and fuzzy neural systems in Khorasan provinces-Iran
Ahad Madani Abbas Khasheyi َAlireza khakzad sivakiIn this research, we try to predict the yield of rainfed barley in Khorasan provinces using climatic parameters and two methods of artificial nervous netwework (Ann) and fuzzy neural system (Anfis). Calculations were performed with MATLAB software and then the statistic MoreIn this research, we try to predict the yield of rainfed barley in Khorasan provinces using climatic parameters and two methods of artificial nervous netwework (Ann) and fuzzy neural system (Anfis). Calculations were performed with MATLAB software and then the statistical indices of correlation coefficient (R2), root mean square error (RMSE) and full mean error (MAE) were used to evaluate the performance of the models. Last year's yield and rainfall had an effective role in reducing prediction error and increasing correlation coefficient in both Ann and Anfis methods. Last year's yield and evapotranspiration made the Anfis method more accurate than the Ann method. The results of both Anfis and Ann methods for model L inputs, which included rainfall, relative humidity and last year's yield, showed that this model achieved the highest accuracy among the input models. However, in the Anfis method for model E inputs, which included evapotranspiration, rainfall, relative humidity and minimum temperature, the results showed that it was more accurate than the Ann method. The greatest difference in accuracy in estimating yield between the two Anfis and Ann methods was observed with R inputs model, which includes moisture inputs, Dew point temperatures and maximum temperatures. The presence of radiation parameters at the inputs reduced the accuracy of yield estimation in both methods. Overall, the Anfis method was more accurate in estimating yield than Ann. Manuscript profile -
Open Access Article
176 - الگوریتم رقابت استعماری (ICA) مبتنی بر روش بهینهسازی مخزن در سد کهیر
علی سردار شهرکی سمیه امایکمبود آب، به ویژه در ایران و در دوره خشکسالی­های اخیر، بر اهمیت دستیابی به یک سیاست عملیاتی بهینه برای مخازن بزرگ اهمیت پیدا کرده است. در دو دهه گذشته، بهینه­سازی سالانه مخازن در شرایط کنترل شده و همچنین شرایط آب و هوایی توجه بسیاری از محققان و کارشناسان را به خ Moreکمبود آب، به ویژه در ایران و در دوره خشکسالی­های اخیر، بر اهمیت دستیابی به یک سیاست عملیاتی بهینه برای مخازن بزرگ اهمیت پیدا کرده است. در دو دهه گذشته، بهینه­سازی سالانه مخازن در شرایط کنترل شده و همچنین شرایط آب و هوایی توجه بسیاری از محققان و کارشناسان را به خود جلب کرده است. در این مطالعه، رویکرد جدیدی برای پیش­بینی ذخیره مخزن ارائه شده است. الگوریتم رقابت استعماری (ICA) یک رویکرد جدید در زمینه محاسبات تکاملی است که راه حل بهینه را در مشکلات مختلف بهینه­سازی محاسبه می­کند. این الگوریتم با مدل­سازی ریاضی فرآیند تکامل اجتماعی روانشناختی، رویکرد جدیدی را برای حل مشکلات بهینه­سازی ریاضی ارائه می­دهد و در مقایسه با سایر الگوریتم­ها، سرعت مناسب و سرعت همگرایی بالایی را در یافتن پاسخ بهینه دارد. در این تحقیق از الگوریتم رقابتی امپریالیست برای بهینه­سازی سالانه مخزن کهیر برای به دست آوردن سیاست­های بهینه استفاده شده است. عملکرد هدف از جهت دستیابی به آب در پایین دست نیاز به ایجاد روابط براساس استمرار وجود دارد. عملکرد هدف از جهت دستیابی به آب در پایین دست نیاز به ایجاد روابط براساس استمرار دارد. مقایسه مدل ICA در جمع 100 نشان داد که الگوریتم ICA با میانگین بهترین ارزش تابع هدف 125، 6/114 و 60/85 با تعدادی از ارزیابی­های بیشتر تابع هدف برای دستیابی به ظرفیت بالاتر، پاسخ بهینه است. نتایج حاکی از خطای 1/6 درصدی در اجرای الگوریتم ICA بین انبارهای مشاهده شده و پیش بینی شده است. نتایج استفاده از الگوریتم رقابتی امپریالیست برای مسئله بهینه­سازی سالانه بیانگر توانایی روش پیشنهادی است. Manuscript profile -
Open Access Article
177 - پیشبینی صادرات زعفران ایران با مقایسه الگوریتم های یادگیری ماشین
علیرضا امیرتیموری منصور صوفی مهدی همایونفر مهدی فداییواردات و صادرات در همه کشورها نقش مهمی در رشد اقتصادی ایفا میکنند. بنابراین، انتخاب محصولات مناسب، باعث افزایش رقابتپذیری کشور در تجارت جهانی میشود. زعفران یکی از مهمترین و متمایزترین محصولات غیرنفتی ایران برای صادرات است. هدف این مطالعه، پیشبینی صادرات زعفران از ط Moreواردات و صادرات در همه کشورها نقش مهمی در رشد اقتصادی ایفا میکنند. بنابراین، انتخاب محصولات مناسب، باعث افزایش رقابتپذیری کشور در تجارت جهانی میشود. زعفران یکی از مهمترین و متمایزترین محصولات غیرنفتی ایران برای صادرات است. هدف این مطالعه، پیشبینی صادرات زعفران از طریق سه الگوریتم دادهکاوی و انتخاب الگوریتم مناسب در پیشبینی است. دوره نمونه مدلهای پیشبینی شامل دادههای صادرات زعفران ایران از سال ۲۰۱۲ تا ۲۰۱۹ است که از انجمن زعفران ایران جمعآوری شدهاند. پس از انجام مراحل آمادهسازی داده، پیشبینی صادرات زعفران با استفاده از سه الگوریتم دادهکاوی شامل شبکه عصبی مصنوعی، یادگیری عمیق و درخت تقویت گرادیانی انجام شد. برای انتخاب یک مدل پیشبینی بهتر، اعتبار مدل نقش مهمی ایفا میکند. صحت پیشبینی سه مدل طراحی شده به کمک خطای مطلق ( 036/0 = شبکهی عصبی مصنوعی، 031/0 = یادگیری عمیق شبکه، 047/0 = درخت تقویت گرادیانی)، معیار R2 (045/0 = شبکهی عصبی مصنوعی، 044/0 = یادگیری عمیق شبکه، 073/0 = درخت تقویت گرادیانی) و همبستگی (95/0 = شبکهی عصبی مصنوعی، 98/0 = یادگیری عمیق شبکه، 97/0 = درخت تقویت گرادیانی) اندازهگیری شدند. براساس یافتهها، تمامی این سه مدل طراحی شده دقیق هستند و خطای پیشبینی آنها بسیار کم و نزدیک به هم است. اما با تفاوت ناچیز، شبکه یادگیری عمیق کمترین خطا را دارد. نتایج میتوانند برای برنامهریزی دقیقتر صادرات زعفران مفید باشند. Manuscript profile -
Open Access Article
178 - Temporal and Spatial Prediction of Rainfall-Induced Landslides using the Specialized TRIGPS Model
Sahebeh Sadeghi Golam Reza Shoaei Mohammad Reza Nikudel -
Open Access Article
179 - A New Multi-Stage Feature Selection and Classification Approach: Bank Customer Credit Risk Scoring
Farshid Abdi -
Open Access Article
180 - Customer Behavior Mining Framework (CBMF) using clustering and classification techniques
Farshid Abdi Shaghayegh Abolmakarem -
Open Access Article
181 - A neuro-fuzzy approach to vehicular traffic flow prediction for a metropolis in a developing country
L Ogunwolu O Adedokun O Orimoloye S.A Oke -
Open Access Article
182 - Forecasting the price of electricity in the cash and advance markets and designing the optimal model for selling electricity in the mentioned markets with the Copola function approach.
Arash Jalebi mahmood khodam hossein mohammadnezhadThe purpose of this article was to predict the price of electricity in the cash and cash markets and to design the optimal model of electricity sales in the aforementioned markets with the Copula function approach. For this purpose, daily information was used in the per MoreThe purpose of this article was to predict the price of electricity in the cash and cash markets and to design the optimal model of electricity sales in the aforementioned markets with the Copula function approach. For this purpose, daily information was used in the period of 1396-1401. In order to forecast, time series models and OLS, GARCH and Copula approaches were used. The results showed that trigonometric functions can well explain the behavior of electricity prices, which is caused by the seasonal behavior of electricity prices during one-year periods. In the random part, the estimated values show that the random component has an average of almost zero and the speed of returning to the average in prices is high. The average of the shocks, their negativity and variance are very small. The small average values of the shocks actually show that the shocks that occurred in the price of the electricity market in Iran are very insignificant and more importantly, these shocks were more of the negative type. Regarding the optimal strategy when entering into futures transactions, our advice to players is to use the Copula-Garch method to calculate the optimal ratios for risk hedging, for two reasons. The need for risk hedging is less and as a result the transaction cost is lower, secondly, due to the existing restrictions and especially the low liquidity in energy exchange transactions, it is practically possible to cover more risk than the cash position Manuscript profile -
Open Access Article
183 - Presenting a model for predicting the price of digital currency in conditions of environmental uncertainty with a fuzzy artificial neural network
mohammad hasan darvish motevali shirin aminiAbstarctIn this research, using the method of fuzzy neural networks, the price of Bitcoin is predicted. In order to identify the appropriate criteria in this research in order to predict the price of Bitcoin, we have used previous studies and researches in this field in MoreAbstarctIn this research, using the method of fuzzy neural networks, the price of Bitcoin is predicted. In order to identify the appropriate criteria in this research in order to predict the price of Bitcoin, we have used previous studies and researches in this field in the first stage. In the following, using interviews with experts and experts in this field, the available information about Bitcoin became the final factors. Research information was collected using related sites and identified criteria. In this way, we first normalized the collected data. In the next step, by entering the normalized information into the MATLAB software and using the designed toolbox and using the fuzzy neural network method, Bitcoin price was predicted. In this way, 60% of the input data, which includes 1330 data, was considered as training data and 40% of the data, which is 887 data, was considered as testing. The research results show high accuracy prediction using the proposed method. As the error was considered in two cases, a small value was calculated for the error of the method. Keywords: prediction, bitcoin price, fuzzy neural network. Manuscript profile -
Open Access Article
184 - Prediction of aquifer reaction to different hydrological and management scenarios using visual MODFLOW model-Case study of Qazvin plain
N Faghihi F Kave H Babazadeh -
Open Access Article
185 - To Study the Effect of Characteristics of Corporate Governance on the Quality of Financial Reporting (Evidence from Tehran Stock Exchange)
Roya Darabi Mahnaz PiriThis research studies the effect of Corporate Governance on quality of financial reports of admitted companies in Tehran Stock Exchange in a 7-year period from 2006 to 2012. It is expected that establishment of appropriate Corporate Governance would prevent decrease in MoreThis research studies the effect of Corporate Governance on quality of financial reports of admitted companies in Tehran Stock Exchange in a 7-year period from 2006 to 2012. It is expected that establishment of appropriate Corporate Governance would prevent decrease in quality of financial reports along with making financial reporting process more controllable. In the present research we have used preciseness of prediction of future operational cash currents via operational profit elements as a measurement index from evaluation of financial reporting quality and also relation of off-duty members of board of directors, organizational investors owned share, power concentration, board of directors size, ownership structure, type of ownership, free floating share percentage, auditing quality, internal auditing, financial reporting quality and auditing period were used as Corporate Governance. A total number of 100 companies were selected as sample companies and using comparison test of average of the two societies we would analyze the results. The results achieved from the research indicated that ability index of Corporate Governance which consists of all structural specifications studied in this research is not effective on the quality of financial reporting of the companies admitted in Tehran Stock Exchange. Also in studying the effect of Corporate Governance individually it was observed that only quality of auditing reporting would affect the quality of financial reporting of the companies. Manuscript profile -
Open Access Article
186 - Reactive Power Management in Micro Grid with Considering Power Generation Uncertainty and State Estimation
Mohammad Reza Forozan Nasab Javad Olamaei -
Open Access Article
187 - Development of a Novel Method for Predicting Root Canals Working Length by Analyzing Dental Radiographs
Ahmad Moghadam Mohammad Adeli -
Open Access Article
188 - Evolutionary Interval Type-2 Fuzzy Rule Learning Approaches for Uncertain Time-Series Prediction
Aref Safari Rahil Hosseini -
Open Access Article
189 - Diagnostic Study for Neurodegenerative Disorders Based on Handwriting Analysis
Leila Soleimanidoust Abdalhossein Rezai Hamideh Barghamadi Iman AhanianOne of the most frequently acknowledged personal behavioral traits in the biometric system is the handwritten exam. Numerous fields, including e-health, psychological issues, medical diag-nosis, and many more, can benefit from handwriting analysis. In this study, a hand MoreOne of the most frequently acknowledged personal behavioral traits in the biometric system is the handwritten exam. Numerous fields, including e-health, psychological issues, medical diag-nosis, and many more, can benefit from handwriting analysis. In this study, a handwriting-based computer diagnostic method for identifying neurodegenerative disorders is established. The sug-gested computer diagnosis system uses the SFTA feature extraction approach, and the findings are classified using SVM, kNN, and D-Tree algorithms. MATLAB R2021b and the handwritten tests gathered at Botucatu Medical School, So Paulo State University—Brazil—are used to assess the performance of the suggested computer diagnosis method. The best results were related into two models of classifier, Optimizable model of SVM and kNN. The accuracy, sensitivity and specificity are 89.2%, 88.3% and 90.0% for SVM and 89.2%,90.0% and 88.3% for kNN over Meander handwritten exam. These results indicate that the use of SFTA feature extraction method, SVM classification algorithm and handwritten database in the proposed computer diagnosis system give acceptable results. Manuscript profile -
Open Access Article
190 - International Studies in Perspective of Positivism and Post- Positivism Traditions in International Relations
Hassan EyvazzadehAbstract: The study of international relations fall into two epistemological traditions: Positivism and post-positivism. The main question is what is the purpose of research and study of international relations? This article argue that the Purpose affected by research MoreAbstract: The study of international relations fall into two epistemological traditions: Positivism and post-positivism. The main question is what is the purpose of research and study of international relations? This article argue that the Purpose affected by researcher epistemological Position. In the study of international relations if researcher stay put in framework of positivism epistemology, thus, your purpose of the study will be containment and prediction of international relations phenomena. But, if she/ he stay put in the post positivism epistemology the Purpose will be understanding and emancipation of phenomena. The logic of this article is dialectic logic. Manuscript profile -
Open Access Article
191 - The Comparison of Financial Crisis Prediction Strength of Different Artificial Intelligence Techniques
Zahra Pourzamani Hassan kalantariRapid technological advances and vast environmental changes, leading to increasing competition and limit access to benefits and likely to suffer financial crisis has increased. Purpose of this study is investigating financial crisis prediction strength of different arti MoreRapid technological advances and vast environmental changes, leading to increasing competition and limit access to benefits and likely to suffer financial crisis has increased. Purpose of this study is investigating financial crisis prediction strength of different artificial intelligence techniques(linear and nonlinear genetic algorithm and neural network). Based on available information and statistics, of all companies listed in Tehran Stock Exchange, 72 companies have been subject to Article 141 trade law and 72 companies have not been subject to this Article was elected. Results of Mc-Nemar test for genetic algorithms techniques and neural network showed that there are not significant differences between linear and nonlinear genetic algorithms with neural network. Although the predictive accuracy of nonlinear genetic algorithm(90%) and linear genetic algorithms(80%) is more than of the neural network(70%) but this difference is not statistically significant. Manuscript profile -
Open Access Article
192 - Providing Three-Dimensional Composite Model (Financial, Economic, Sustainability) in predicting Companies' Financial Distress
احمد برگ بید علی جعفری hasan salehnejadFinancial distress is a serious issue for the economic life of countries and forecasting distress for various groups including managers, banks, investors, policymakers and auditors is of great importance. The purpose of this study is to provide a combined three-dimensio MoreFinancial distress is a serious issue for the economic life of countries and forecasting distress for various groups including managers, banks, investors, policymakers and auditors is of great importance. The purpose of this study is to provide a combined three-dimensional model (financial, economic, sustainability), two-dimensional model (financial and economic) and one-dimensional (financial) in predicting financial distress of companies and also comparing the predictive power of models with component analysis approach. It is the principle that using the post-event approach (through past information) is of the descriptive-correlation type and based on the objectives is also of the applied research type. Also, the statistical population and spatial scope of this research, listed companies and its time domain. Using the systematic removal method, 113 listed companies were selected as a sample. The results showed that the three-dimensional hybrid model (financial, economic, sustainability) has a high predictive power for helplessness. Manuscript profile -
Open Access Article
193 - Adaptive Online Traffic Flow Prediction Using Aggregated Neuro Fuzzy Approach
Shiva Rahimipour Mahnaz Mohaqeq S.Mehdi Hashemi -
Open Access Article
194 - Evaluation of Optimal Fuzzy Membership Function for Wind Speed Forecasting
Shahram Javadi Zeinab Hojjatinia -
Open Access Article
195 - Predicting the Next State of Traffic by Data Mining Classification Techniques
S.Mehdi Hashemi Mehrdad Almasi Roozbeh Ebrazi Mohsen Jahanshahi -
Open Access Article
196 - Neuro-Fuzzy Based Algorithm for Online Dynamic Voltage Stability Status Prediction Using Wide-Area Phasor Measurements
Ahmad Ahmadi Yousef Alinezhad Beromi -
Open Access Article
197 - Prediction of stock efficiency based on kernel distribution and mixture of normal distributions
Gholam reza Zeinali Narges yazdanianModeling and predicting stock returns has always been one of the challenges for researchers and investors. Hence, different methods and models have been proposed, most of which have been based on assumptions such as the distribution of returns. The kernel distribution a MoreModeling and predicting stock returns has always been one of the challenges for researchers and investors. Hence, different methods and models have been proposed, most of which have been based on assumptions such as the distribution of returns. The kernel distribution and mixture of normal distributions were examined to predict stock return in the present study. To this end, kernel functions and mixtures of normal distributions and related parameters have been estimated using maximization of likelihood function and quartiles 99%, 95% and 90% were computed for each of distributions and for 30 superior enterprises listed in Tehran Security and Exchange (TSE) at first quarter in 2019 as predictor values of stock return. In order to determine precision of prediction methods, MSE and PRED error criteria were employed and the findings showed that mixture of normal distributions and kernel approximation might propose favorable predictions for 5-day stock returns in quartiles 90% of return distribution. Comparison of precision between two methods indicated that kernel approximation, as a non parametric method for prediction of returns, leads to higher precision than mixture of normal distributions. Manuscript profile -
Open Access Article
198 - 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
199 - 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
200 - The Comparison of Cryptocurrency Returns Prediction Based on Geometric Brownian Motion and Wavelet Transform
Ahmad Shojaei Alireza Heidarzadeh HanzaeiIn the present study the accuracy of predicting cryptocurrencies return was compared through two approaches of Geometric Broanian Motion (GBM) and Wavelet Transforms (WT). In order to do that, 5 cryptocurrencies of BTC, ETH, XRP, BCH and EOS as representatives of risky MoreIn the present study the accuracy of predicting cryptocurrencies return was compared through two approaches of Geometric Broanian Motion (GBM) and Wavelet Transforms (WT). In order to do that, 5 cryptocurrencies of BTC, ETH, XRP, BCH and EOS as representatives of risky assets were studied with daily frequency during the one year period of 2018 to 2019. Two measures of RMSE and MAE were employed to compare the accuracy of approaches in prediction of returns. In geometric Brownian modeling, the Brownian process-based stochastic differential model for asset prices leads to the fact that the logarithmic return of an asset has a normal distribution with time-dependent parameters. The results of logarithmic returns prediction by both of methods showed that WTs have less error than GBM in returns prediction of BTC, ETH, XRP and BCH cryptocurrencies and for each of accuracy measures, an specific approach has desirable performance for prediction of EOS returns. citing these results it can be concluded that WT in prediction of risky assts returns has less error than GBM method. Manuscript profile -
Open Access Article
201 - Imaged financial Ratios and Bankruptcy Prediction using Convolutional Neural Networks
abbasali haghparast alireza momeni Aziz Gord fardin mansooriConvolutional neural networks are being applied to identification problems in a variety of fields, and in some areas are showing higher discrimination accuracies than conventional methods. Hence, in this research, an attempt is made to apply a convolutional neural network MoreConvolutional neural networks are being applied to identification problems in a variety of fields, and in some areas are showing higher discrimination accuracies than conventional methods. Hence, in this research, an attempt is made to apply a convolutional neural network to the prediction of corporate bankruptcy. The financial statements ratios has been choice 66 companies that have been delisted from the Iran Stock Market due to de facto bankruptcy as well as the financial statements of 66 listed companies over 2000 to 2019 financial periods. In this method, a set of financial ratios are derived from the financial statements and represented as a grayscale image. The image generated by this process is utilized for training and testing a convolutional neural network. The images for the bankrupt and continuing enterprises classes are used for training the convolutional neural network based on GoogLeNet. The findings shows, in prediction of going concern of firms, Convolutional neural network has predicted with 50 percent of precision. This means that 50 percent of continues firms and 50 percent of bankrupt firms has been predicted precisely. Manuscript profile -
Open Access Article
202 - Designing a model for predicting bitcoin returns (with emphasis on hybrid models of convolutional and recursive neural networks and models with long-term memory)
Mohammad Javad Bakhtiaran Mehdi ZolfaghariFinding the best way to optimize the portfolio is one of the concerns of activists in the investment management industry. In recent years, the introduction of economic and mathematical models in the prediction of Bitcoin has helped many investors to optimize portfolios. MoreFinding the best way to optimize the portfolio is one of the concerns of activists in the investment management industry. In recent years, the introduction of economic and mathematical models in the prediction of Bitcoin has helped many investors to optimize portfolios. Therefore, in this study, we introduce models of GARCH family composition and recurrent and convolutional neural network to predict the daily yield of Bitcoin will be paid during the period of 1398-1392. In this study, the Bitcoin is examined using GARCH and EGARCH short-term memory models. Of the two variables, the price of crude oil and the Gold as factors that their shocks and fluctuations have a major impact on Bitcoin are used as control variables. In addition to using long-term memory models, considering the better performance of combined models (compared to individual models) In anticipation In this study, all models of the GARCH family (both short and long run) with the recurrent and convolutional neural network were combined and using the combined models, the efficiency of the Bitcoin for the next 10 days were predicted step by step and its accuracy Based on the evaluation criteria. Manuscript profile -
Open Access Article
203 - Designing a Model for Forecasting the Gold Price Returns (Emphasizing on Combined convolutional neural network Models and GARCH Family Models)
Mohammad Javad Bakhtiaran mehdi ZolfaghariFinding the best way to optimize the portfolio is one of the concerns of activists in the investment management industry. In recent years, the introduction of economic and mathematical models in the prediction of Gold indice has helped many investors to optimize portfol MoreFinding the best way to optimize the portfolio is one of the concerns of activists in the investment management industry. In recent years, the introduction of economic and mathematical models in the prediction of Gold indice has helped many investors to optimize portfolios. Therefore, in this study, we introduce models of GARCH family composition and convoultional neural network to predict the daily yield of Gold index will be paid during the period of 1390-1398. In this study, the Gold index is examined using GARCH and EGARCH short-term memory models. Of the two variables, the price of crude oil and the dollar index as factors that their shocks and fluctuations have a major impact on Gold indices are used as control variables. In addition to using convolutional model, considering the better performance of combined models (compared to individual models ) In anticipation In this study, all models of the GARCH family (both short and long run) with the convoultional neural network were combined and using the combined models, the efficiency of the main stock index and the five selected indicators for the next 10 days were predicted step by step and its accuracy Based on the evaluation criteria. Manuscript profile -
Open Access Article
204 - Presentation Optimization portfolio model from market index prediction model despite of the long term memory with neural network
saeed moshtagh Farhad Hosseinzadeh Lotfi Esmail fadayi nezhadThe effect economic variables at investment markets is the important subject in financial theory. Tehran stock exchange to have special position in country financial system and efficiency development investment market is dependent being active this constitution in count MoreThe effect economic variables at investment markets is the important subject in financial theory. Tehran stock exchange to have special position in country financial system and efficiency development investment market is dependent being active this constitution in country. Two important function Tehran exchange market are gathering small savings and available liquidity in society and guide them to production process in country. In this way presentation optimization portfolio model from market index prediction model and exchange return rate is impact. One of the tools with high accuracy and applicable for predicting was neural network why so accuracy isnot decrease with increasing thesis data and its accuracy was very higher than regeression, linear and non linear for prediction. After some tests from artificial neural network and adaptive neuro fuzzy inference system and support vector regression with matlab software has been done. We design a model with high accurancy for predicting rate of liquidity index and total return index and then we design Ideal optimization portfolio. Manuscript profile -
Open Access Article
205 - Prediction of Tehran Stock Exchange Total Index Using Bacterial Foraging OptimizationAlgorithm
ahmad nateq golestanIt is impossible to advance the economic goals of any country without financial markets. Since the stock exchange is one of the most important financial markets in the country, and the stock index is one of the important parameters in determining it,s performance, So th MoreIt is impossible to advance the economic goals of any country without financial markets. Since the stock exchange is one of the most important financial markets in the country, and the stock index is one of the important parameters in determining it,s performance, So the stock index and economic development have an important interconnected relationship. Stock market forecasting has been considered as one of the most challenging financial issues and the accuracy of these forecasts is crucial for improving trading and investment strategies in the stock market. The total price of Tehran stock exchange price using intelligent methods. An optimization Bacterial Foraging Optimization Algorithm has been for modeling. In this research, the total index of Tehran stock exchange price data for the 23rd March 2006 to 21rd March 2018. Ten using the total price index data (consists of the highest price, lowest price, closing price and total volume of stocks traded for the day) and finally, by Matlab software, the forecast price index waz calculated. The results of the research show that the algorithm has the accuracy of ninety seven percent ability to predict the total index price of the stock exchange. Manuscript profile -
Open Access Article
206 - A model for predicting stock price reaction delays based on grounded theory
kyvan faramarzi jamal bahrisales Saeed Jabbarzadeh Kangarlouie ali ashtabThe aim of current study was to provide a model for predicting stock price reaction delay based on grounded theory. In the present study,semi-structured interviews have been used as data collection tools and snowball or chain sampling methods and purposeful sampling has MoreThe aim of current study was to provide a model for predicting stock price reaction delay based on grounded theory. In the present study,semi-structured interviews have been used as data collection tools and snowball or chain sampling methods and purposeful sampling has been used to select the sample which based on the principle of theoretical adequacy The research data were analyzed using open, axial and selective coding. Results: In this study, based on 42 conducted interviews, a total of 607 interview codes, 101 sub-categories (concepts) and 11 main categories were extracted. Then the qualitative model of the research is designed and based on the analysis of data (interviews) the link between the categories in the form of causal conditions, contextual conditions, intervening conditions, strategies and consequences has been conducted. The results indicated that macro factors and market shareholders are effective in predicting the stock price reaction delay.On the other hand, according to these affecting factors, strategies to improve the stock price reaction delay prediction, including the establishment of corporate information and financial statements, corporate information, market performance criteria, management and corporate control which are aroused in the context of affecting factors and interferers, are presented. Manuscript profile -
Open Access Article
207 - Investigating the Dynamic relations between the Trend of Tehran Stock Exchange’s index and the Cumulative Funds' Cash Flow".
mirfeiz fallah Amirhosseyn shamaeezadehThe purpose of this study is to investigate the relationship between the net cash flows of the Tehran Stock Exchange funds and the Tehran Stock Exchange index during the period of 2013 to December 2019, using the information of the 10 largest active mutual funds establi MoreThe purpose of this study is to investigate the relationship between the net cash flows of the Tehran Stock Exchange funds and the Tehran Stock Exchange index during the period of 2013 to December 2019, using the information of the 10 largest active mutual funds established and active in the Tehran Stock Exchange during this period. .In this study, an index of net cash flows into mutual funds daily and cumulatively is considered as a measure of cash flow compared to the TSE Index (TEDPIX). The results of this test indicate that the two indices are coherent in series and their relationships are significant in the long run. Also, the Granger causality test was used to examine the interrelationships between these two indices.The results of this test showed that there is an interaction between the two indices. This means that in the long run, both indices affect each other so net cash inflows to the funds can be a measure for predicting the overall indices trend but with Attention to the behavioral errors identified in similar articles.for predicting the index cannot be relied solely on net cash inflows into the funds. Manuscript profile -
Open Access Article
208 - The Assessment of the optimal Deep Learning Algorithm on Stock Price Prediction (Long Short-Term Memory Approach)
Amir Sharif far Maryam Khalili Araghi Iman Raeesi Vanani Mirfeiz FallahForecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. Deep Learning (DL) is a type of Artificial Neural Network (ANN) that consists of multiple processing layers and enables hi MoreForecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. Deep Learning (DL) is a type of Artificial Neural Network (ANN) that consists of multiple processing layers and enables high-level abstraction to model data. The key advantage of DL models is extracting the good features of input data automatically using a general-purpose learning procedure which is suitable for dynamic time series such as stock price.In this research the ability of Long Short-Term Memory (LSTM) to predict the stock price is studied; moreover, the factors that have significant effects on the stock price is classified and legal and natural person trading is introduced as an important factor which has influence on the stock price. Price data, technical indexes and legal and natural person trading is used as an input data for running the model. The results obtained from LSTM with Dropout layer are better and more stable than simple form of LSTM and RNN models. Manuscript profile -
Open Access Article
209 - Predicting the daily index of the Tehran Stock Exchange using the selection of appropriate features for the Long Short-Term Memory neural network (LSTM)
Somayeh Mohebi Mohammad Esmaeil Fadaeinejad mohammad osoolian Mohammad reza HamidizadehThe stock market index is one of the effective features in investment because it can well reflect the health status and macro change trend of a country’s economic development. Various features affect the stock index. The various combinations of these features crea MoreThe stock market index is one of the effective features in investment because it can well reflect the health status and macro change trend of a country’s economic development. Various features affect the stock index. The various combinations of these features create a wide state space. Hence, it is impractical to provide a data set containing all these combinations to train the stock index prediction model. in this research, an attempt has been made, after collecting a significant number of effective features on the index, to provide a method for selecting appropriate features for the stock index prediction model with aim of increasing prediction accuracy. For this purpose, the mRMR algorithm is used as the basic algorithm. Also, to select the appropriate model, a number of the most applicable artificial intelligence models for predicting the stock index were compared and according to the results, the LSTM network was selected to predict the stock index. The results of this study show that using the LSTM network and the proposed method in selecting features, with 8 selected features, high accuracy can be achieved in the daily prediction of the Tehran Stock Exchange Index. So that MPE is calculated to be about 2.66, Manuscript profile -
Open Access Article
210 - A prediction-based portfolio optimization model using support vector regression
Mohammad Amin Monadi Amirabbas NajafiThe purpose of portfolio optimization is to select an optimal combination of financial assets, which should be a guide for investors to achieve the highest returns against the lowest possible risk. On the other hand, one of the key factors in portfolio optimization deci MoreThe purpose of portfolio optimization is to select an optimal combination of financial assets, which should be a guide for investors to achieve the highest returns against the lowest possible risk. On the other hand, one of the key factors in portfolio optimization decisions is related to predict the stock prices. To do this, classical nonlinear mathematical and intelligent models such as regression are commonly used. In the present study, a nonlinear model of support vector regression with multiple outputs is applied to reduce the prediction errors. To show the effectiveness of the proposed model, the data of S & P500 index companies in the period 12/09/2016 to 02/08/2021 is used. The results show that the selection of a portfolio based on prediction using multiple vector backup regression due to considering the relationships between outputs simultaneously in terms of Sharp criteria has a better performance than the selection of portfolio based on prediction using regression method. Manuscript profile -
Open Access Article
211 - 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
212 - Deep learning for stock market forecasting using numerical and textual information (Long-Short Term Memory approach)
seyyedeh mozhgan beheshti masalegou Mohammad ali Afshar kazemi jalal Haghighat monfared Ali RezaeianStock prices are influenced by many factors, making forecasting challenging. This prediction is often ineffective if it only considers numerical data or textual information. This research aims to provide a method of forecasting the future price of stocks based on the st MoreStock prices are influenced by many factors, making forecasting challenging. This prediction is often ineffective if it only considers numerical data or textual information. This research aims to provide a method of forecasting the future price of stocks based on the structure of a deep neural network using price data, a set of technical indicators, and news headlines as input to the model. For this purpose, Dow Jones stock data and Reddit channel news data have been used. Technical features are extracted from the stock data, and the news data are converted into a feature vector by the Bag of Words method and fed into the Long-Short term memory network for prediction. Accuracy is used as a performance evaluation measure and experiments on two data sets. The only numerical and only text has been used to evaluate the simultaneous use of two information sources. Also, three networks, SVM, MLP, and RNN, have been used to evaluate the model. The results show that the LSTM model achieved the highest prediction accuracy of 69.19% using news and financial data. News data is 65.62% accurate, and numerical data is 51.89%. Also, the LSTM model performs better than SVM, MLP, and RNN neural networks. Manuscript profile -
Open Access Article
213 - Solving Imbalanced Data Distribution Problem in Bankruptcy Prediction by Cost-Sensitive Learning Method
seyed behrooz razavi ebrahim abbasiThis study aimed to add cost-sensitive learning technique to imbalanced data-based bankruptcy prediction models in order to reduce type I error and increase the geometric mean criterion of overall accuracy to reduce the misclassification costs of bankrupt companies for MoreThis study aimed to add cost-sensitive learning technique to imbalanced data-based bankruptcy prediction models in order to reduce type I error and increase the geometric mean criterion of overall accuracy to reduce the misclassification costs of bankrupt companies for stakeholders. For this purpose, type I error, type II error, and the geometric mean of overall accuracy of bankruptcy models based on cost-sensitive learning were compared with bankruptcy prediction models with highly imbalanced datasets. The statistical sample included 1200 year-companies since 2001- 2020, consisting of 90% healthy companies and 10% bankrupt companies. Hypotheses test results showed that adding a cost-sensitive learning technique to the bankruptcy prediction models led to a significant decrease in the type I error, a significant increase in the type II error, and a significant increase in geometric mean of accuracy of imbalanced data-based models at 95% confidence level. Also, with the increase in the misclassification cost of bankrupt companies, type I error had a downward trend and the II type error had an upward trend, and the geometric mean of accuracy had an upward trend. Manuscript profile -
Open Access Article
214 - Presenting a market direction prediction model for gold coin trades in Iran’s Commodity Exchange market using Long Short-Term Memory (LSTM) algorithm
Soheil Zoghi Reza Raei Saeed FalahporIn recent years, deep learning neural networks have been recognized as powerful tools for solving complex problems. Deep learning is a subfield of artificial intelligence in which complex problems with numerous parameters and inputs are modeled based on a set of algorit MoreIn recent years, deep learning neural networks have been recognized as powerful tools for solving complex problems. Deep learning is a subfield of artificial intelligence in which complex problems with numerous parameters and inputs are modeled based on a set of algorithms. In this research, a new framework of deep learning is presented. Using wavelet transform, stacked auto-encoders, and the Long Short-Term Memory or LSTM, we predict the market direction in the future contracts of gold coins of Iran's Commodity Exchange market. The input data is first denoised using the wavelet transformer in the proposed method. Then, using the stacked auto-encoder, the indicators influencing the market direction are identified. Ultimately, these indicators are given as input to the LSTM architecture to predict the market direction. Proposing several new technical indicators to increase the accuracy of the proposed model, adjusting the parameters of the utilized algorithms, including LSTM, for this problem, and suggesting a trading strategy to achieve appropriate profitability are among the contributions of the present study. Investigations reveal that the proposed method outperforms other approaches and achieves higher accuracy and efficiency. Manuscript profile -
Open Access Article
215 - Price predicting with LSTM artificial neural network and portfolio selection model of financial assets and digital currencies
Faranak Khonsarian Babak teimourpour Mohammad Ali RastegarFinding solutions for price prediction, forming an optimal portfolio and achieving more profit are the basic goals of financial market activists. The purpose of this research is to predict the price of financial assets such as several stocks, gold, coin and a number of MoreFinding solutions for price prediction, forming an optimal portfolio and achieving more profit are the basic goals of financial market activists. The purpose of this research is to predict the price of financial assets such as several stocks, gold, coin and a number of digital currencies using the LSTM neural network model and then form an optimal portfolio by calculating the rate of return, risk and the Sharpe ratio. The data used is from the archives of the Tehran Stock Exchange website, the website of the gold, coin and currency information network, as well as the website of buying and selling digital currencies. The time series of the prices of the investigated assets is between 2017 and 2020. Also, we used Python programming language and Gephi software to build the model and analyze the data. In the end, it was found that the LSTM neural network model is capable of predicting the price of financial assets with a very low error rate in each asset, and according to the Sharpe ratio obtained for each financial asset and the correlation matrix, Vebank stock, Khbahman 1 stock, and Digital currencies TRON, Tether and Bitcoin allocate more shares in the proposed portfolio. Manuscript profile -
Open Access Article
216 - Bankruptcy prediction using hybrid data mining models based on misclassification penalty
Atiye Torkaman AmirAbbas NajafiIn recent years, data mining, particularly the support vector machine, has gained considerable interest among investors, managers, and researchers as an effective means of bankruptcy prediction. However, studies indicate that it is highly sensitive to the selection of p MoreIn recent years, data mining, particularly the support vector machine, has gained considerable interest among investors, managers, and researchers as an effective means of bankruptcy prediction. However, studies indicate that it is highly sensitive to the selection of parameters and input variables. Hence, the aim of this research is to improve bankruptcy prediction accuracy by combining an advanced support vector machine model with the k-nearest neighbor approach to eliminate erroneous entries. To achieve this, first, by using five financial ratios: current ratio, net profit margin, debt ratio, return on assets, and return of investment from 150 companies listed on the Tehran Stock Exchange during the 10-year period (2010-2019), and k-nearest neighbor algorithm, the training data will be refined. Then, relying on a support vector machine based on classification penalty, a prediction model will be constructed. The parameters will be estimated, and its validity will be assessed using test data. Finally, a comparison will be made between the outcomes of the proposed model and traditional models.The findings demonstrate that the combination of the k-nearest neighbor models and support vector machine reduces the overall prediction error, and the penalty coefficients of the support vector machine exhibit a high level of statistical significance. Manuscript profile -
Open Access Article
217 - Corporates Manner and Comparing its Prediction Accuracy with Decision Tree and Bayes Models
zohre arefmanesh vahid zare mehrjardi Alireza Mohammadi nodooshanThe main objective of this study is to design corporate financial distress prediction models for the following three industries basic metals, non-metallic minerals and machinery and equipment, using the bagging model. Moreover, the prediction accuracies of the designed MoreThe main objective of this study is to design corporate financial distress prediction models for the following three industries basic metals, non-metallic minerals and machinery and equipment, using the bagging model. Moreover, the prediction accuracies of the designed models are compared to the bayes and decision tree models. Aimed Statistical population of this research includes all the corporations of each of the industries. The financial distress criterion employed in this research is the criteria of article 141 in commercial code and the timeline of the research is from 2001 to 2016. The results shows that, comparing to the base models (i.e. decision tree and bayes), the bagging model has a better prediction accuracy average. Moreover, based on the obtained results, it can be concluded that the bagging, decision tree and bayes models are qualified models for the corporate bankruptcy prediction Manuscript profile -
Open Access Article
218 - Providing a model for predicting stock prices using ultra-innovative neural networks
Seyyed Hosein Miralavi zahra pourzamaniDue to the complexity of the stock market and the high volume of processable information, often using a simple system to predict cannot release appropriate results. Therefore, researchers have been trying to provide a system with less complexity and more efficiency and MoreDue to the complexity of the stock market and the high volume of processable information, often using a simple system to predict cannot release appropriate results. Therefore, researchers have been trying to provide a system with less complexity and more efficiency and accuracy using hybrid models. nowadays various patters are used including statistical technique (discriminate analysis , logistic , analysis factors) and artificial intelligent techniques ( neural networks(NN) , decision trees , case based reasoning , genetic algorithm , rough sets , support vector machine , fuzzy logic ) and the combination of these two technique for predicating stock prices. For most predictive models, the system uses only one indicator to predict, but in the proposed model in this study, a two-level system of multilayered perceptron neural networks is presented which uses several indicators to predict. To do this, required information of Tehran Stock Exchange price indicators, for fiscal years 2012 - 2017 was collected. We also used the Grasshopper Optimization Algorithm to select the best samples for better nerve network training and thus to improve the results. The results show that the proposed model can operate with lower prediction error than other models. Manuscript profile -
Open Access Article
219 - Risk and Return Properties of Portfolios Based on Directional Forecasts
Vahid RooholelmIn this study, a formula is de rived for the period specific beta (market risk) for a portfolio of financial assets that has been formed on the basis of directional forecasts. This is an important contribution to the literature since measuring the risk of an actively ma MoreIn this study, a formula is de rived for the period specific beta (market risk) for a portfolio of financial assets that has been formed on the basis of directional forecasts. This is an important contribution to the literature since measuring the risk of an actively managed portfolio is problematic due to the fact that managers may change fund risk conditional on market expectations. The period- specific nature of the measure is a significant advantage since historical fund re turns are not required and the beta is not influenced by prior fund returns’ deviations from the bench mark. The methodology employed allows for the development of a time series of fund betas that permits investigation into a number of important Empirical issues. This study is also of practical interest from the perspective of risk management and for both portfolio performance and attribution. Finally, there are many active strategies based on directional forecasts and the approach used here en com passes a significant proportion of these. The author of this article used of consultation and guidance of Rahnama Roodposhti Fereidoun, Professor and A member of the science team Islamic Azad University, Science and Research Branch ,Tehran, and thankses a lot of him. Manuscript profile -
Open Access Article
220 - Smart Buying and Selling System Design Based on a Model Consisting of a Support Vector Machine Algorithm and Theory of Trend Channel
Shapoor Mohammadi Seyyed Ali Mousavi Sarhadi Mohammad NooribakhshPredicting future prices and consequently higher returns in financial markets has been one of the most important issues. In this study, the design of intelligent systems to buy and sell based on a complex model of support vector machine algorithm and theory of tre MorePredicting future prices and consequently higher returns in financial markets has been one of the most important issues. In this study, the design of intelligent systems to buy and sell based on a complex model of support vector machine algorithm and theory of trend channel been discussed. To achieve the aim of this object, this study was performed in four main steps. In the first phase, range or limits of trend channel at different time intervals were extracted and these limits in the second phase of the experiment was predicted by the algorithm and Support Vector Machine.In the second phase in the range of channel which been predicted in period of experiment, sales strategy was defined and implemented. And in the third stage, returns from system designed with efficiency resulting from the use of buy and hold strategies were compared. In all selection criteria as a sample, Intelligent system performance based on the model of combined sales and support vector machine algorithm and theory of trend channel was better than the performance of buy and hold strategy Manuscript profile -
Open Access Article
221 - Mathematical model design for predicting bankruptcy of companies accepted in the Tehran Stock Exchange
Reza Pirayesh Hassan Dadashi Arani Mohammadreza BarzegarIn this research, five major bankruptcy predictions model to study and among the components of the five models, redesigned bankruptcy prediction is provided that consists of eight variables. The main issue in this research is that by examining the financial statements o MoreIn this research, five major bankruptcy predictions model to study and among the components of the five models, redesigned bankruptcy prediction is provided that consists of eight variables. The main issue in this research is that by examining the financial statements of listed companies in Tehran Stock Exchange we can offer a model to predict corporate bankruptcy. In order to design data from two groups of companies accepted in the Tehran Stock Exchange use the first group consists companies surveyed non-bankrupt company and second group included bankrupt company. The study period financial statements of exchange data during the years have been 2005 to 2014. The study results in relation to the ability to predict model reflects the fact that the model could be two years before the bankruptcy of companies provide accurate predictions about the crisis and bankruptcy. The results show that the predictive power of the model for one year before bankruptcy 91% and two years before the bankruptcy 83%. Manuscript profile -
Open Access Article
222 - Comparing Different Feature Selection Methods in Financial Distress Prediction of the Firms Listed in Tehran Stock Exchange
Mohammad Namazi Mostafa Kazemnezhad M. Mahdi NematollahiResearch in financial distress and bankruptcy emphasize the design of more sophisticated classifiers, and less feature (variables) selection and their appropriate methods. In this regard, the purpose of this study is to compare performance of different feature selection MoreResearch in financial distress and bankruptcy emphasize the design of more sophisticated classifiers, and less feature (variables) selection and their appropriate methods. In this regard, the purpose of this study is to compare performance of different feature selection methods in financial distress prediction of the companies listed on Tehran Stock Exchange (TSE). In this regard, we investigated and compared five feature selection methods, including t-test, stepwise regression, factor analysis, relief, wrapper subset selection and RFE-SVM feature selection. To obtain comparable experimental results (reliable comparison), three different classifiers (including neural networks, support vector machine and AdaBoost) were used in this study. In overall, the experimental results confirmed the usefulness of variable selection methods and significant difference among level (amount) of different methods performance. In other words, the application of the feature selection methods increases the mean of accuracy, and reduces the occurrence of type I and type II errors. Furthermore, the results indicated that wrapper subset selection method outperforms the other feature selection methods. Manuscript profile -
Open Access Article
223 - Checking the accuracy of learning machines in predicting stock returns using the Rough set model, Nearest neighbor and decision tree.
mohammad reza karimi pouya mehrdad ghanbari babak jamshidinavid mansoor esmaeilpourPrediction is an essential component of short and medium term planning in any business. A precise prediction can be effective in generating returns, managing cash flows, and allocating resources, enabling an investor to estimate, within a given time frame, its business MorePrediction is an essential component of short and medium term planning in any business. A precise prediction can be effective in generating returns, managing cash flows, and allocating resources, enabling an investor to estimate, within a given time frame, its business revenue and its returns. Researchers have the idea to set aside old methods, which takes expense and time, and implement new methods such as the use of learning machines. This research is of the type of research, analytical-empirical, in terms of research design, post-event, in terms of purpose, applied, in terms of implementation logic, deductive and in terms of time, longitudinal and prospective type. In this research, the algorithm model of the nearest neighbor, the Rough method and the decision tree are used to improve predictive power, cost reduction, and time prediction of stock returns. For this purpose, a sample of 113 listed companies in the Tehran Stock Exchange during a 10-year period (2006-2015) was selected from the companies listed in the Tehran Stock Exchange. The results of the research showed that all the hypotheses of this research are based on a difference in the accuracy of estimating these models in the prediction of the three dependent variables. Manuscript profile -
Open Access Article
224 - Predicting the Direction of Stock Market Prices Using Random Forest
elham gholamian sayyed mohammad reza davoodiStock market activists are the acquiring and using methods to predict future stock prices, increasing their capital gains. Therefore, it seems necessary that appropriate, correct, and scientific principles are used to determine the future price of the stock of investor MoreStock market activists are the acquiring and using methods to predict future stock prices, increasing their capital gains. Therefore, it seems necessary that appropriate, correct, and scientific principles are used to determine the future price of the stock of investor stock options.stock price prediction is an important part of investment, and in most cases it is the field of research for researchers, because it ultimately leads to the choice of appropriate investment. Different methods have now been developed to achieve this goal. Have been introduced that are often statistical methods and artificial intelligence. In this research, using a randomized approach approach that is among artificial intelligence classification methods, along with technical indicators that include: power index Relative Price, Stochastic, Equilibrium Balance, Williams R%, Daily Returns, and Mac.d Series Markets, are looking for stock price trends. This model is compared with logistic regression method and completely randomized method (dice throw). The results of the research on daily data of Tehran Stock Exchange Index from 1393 to 1395 indicate that the accuracy of the proposed method in estimating market trend is 64%, which is more than two methods of logistic regressionand completely randomized method of accuracy Has a higher rate. Manuscript profile -
Open Access Article
225 - Tehran Stock Exchange Overal Index Prediction using Combined Approach of Metaheuristic Algorithms, Artificial Intelligence and Parametric Mother Wavelet
Alireza Saranj Madjid Ghods reza tehraniUnderstanding and the investigating the behavior of stock prices, has always been one of the major topics of interest to the investors and finance scholars. In recent years, various models for prediction using neural network and hybrid models have been proposed which ha MoreUnderstanding and the investigating the behavior of stock prices, has always been one of the major topics of interest to the investors and finance scholars. In recent years, various models for prediction using neural network and hybrid models have been proposed which have a better performance than the traditional models. Here a hybrid model of neural network and wavelet transform is proposed in which genetic algorithm has been used to improve the performance of wavelet transform in optimizing the wavelet function. Daily stock exchange rates of TSE from April 21, 2012 to April 19, 2017 are used to develop a prediction model. The results show that it is possible to find a wavelet basis, which will be appropriate to the intrinsic characteristics of time series for prediction and the prediction error in this model is reduced comparing to the neural network and hybrid neural network and wavelet models. Manuscript profile -
Open Access Article
226 - The Impact of Accounting Information Quality and Monetary Policy on Bankruptcy Prediction
Mohammad Hossein Setyesh milad rahimiAbstractThis study has investigated the effect of the accounting information quality and monetary policy on bankruptcy prediction. For this purpose, a sample of 135 companies was selected from the admitted companies in the stock exchange. In order to collect the needed MoreAbstractThis study has investigated the effect of the accounting information quality and monetary policy on bankruptcy prediction. For this purpose, a sample of 135 companies was selected from the admitted companies in the stock exchange. In order to collect the needed data to calculate variables in the research, Rahvardnovin database, Tehran Stock Exchange Organization database and Central Bank database were used. Eviews software and fixed effects panel data regression model have been used to analyze the collected data. This study is useful for financial analysts, managers, accountants and policy makers in order to evaluate the financial position and predict financial bankruptcy of companies. The results of the hypothesis test show that all three hypotheses are not rejected and indicate that the accounting information quality in interaction with monetary policy has a positive and significant effect on bankruptcy prediction. The estimated coefficient of the accounting information quality in the interaction with monetary policy on predicting premature bankruptcy is lower than the coefficients of the variables accounting information quality and monetary policy on predicting premature bankruptcy, and this shows that the interaction of the accounting information quality and monetary policy has a moderating role on It has predicted premature bankruptcy. Manuscript profile -
Open Access Article
227 - stock return prediction models; Estimating the distribution of total market returns and its fluctuations based on the Laplace distribution
Masoumeh Mohammadi Ledari Iman DadashiAbstractIn most return forecasting models, the return of the total market is used as one of the factors affecting the return of securities. In most of these models, such as the pricing model of capital assets and Black-Scholes, the data distribution is assumed to be nor MoreAbstractIn most return forecasting models, the return of the total market is used as one of the factors affecting the return of securities. In most of these models, such as the pricing model of capital assets and Black-Scholes, the data distribution is assumed to be normal. This is while the distribution of the total return is not necessarily normal and often has a significant difference from the normal distribution. If such a hypothesis is confirmed, the expected return predicted by these models will not be very effective in financial decisions. The purpose of this research is to model the total return of Tehran Stock Exchange based on the Laplace distribution and examine the dependence of the total return fluctuations on the desired distribution. In order to examine the distribution of the total daily return and its weekly fluctuations, data related to a 15-year period between 1387 and 1401 and R statistical software were used. The data analysis showed that the total daily return followed the Laplace distribution and the weekly fluctuations of the total return followed the distribution obtained based on the Laplace distribution. These findings make the use of models with the assumption of normality of total return to predict stock returns in Tehran Stock Exchange a major challenge and are a clear proof of the ineffectiveness of these models. . Manuscript profile -
Open Access Article
228 - Comparison of the Efficiency of Statistical Learning Algorithms and Artificial Neural Networks to Predict Stock Prices
Alireza Sadat Najafi Soheila Sardar -
Open Access Article
229 - Predicting the success of the investment projects of Aras and Maku commercial-industrial free zones and Salmas special economic zone using perceptron multilayer neural network technique
morteza shokrzadeh mojtaba shokrzadehTo analyze the data of this research descriptive statistics and inferential statistics were used and experts selection software, MATLAB SPSS and PLS software were employed.Using theoretical foundations and libraries, six effective factors and variables predicting the su MoreTo analyze the data of this research descriptive statistics and inferential statistics were used and experts selection software, MATLAB SPSS and PLS software were employed.Using theoretical foundations and libraries, six effective factors and variables predicting the success or failure of Investment projects in the free and special economic zones of the country were identified. After describing the variables and testing the normality,using the PLS software, a confirmatory factor analysis of the variables was carried out, in which all of the factors had a good confirmatory factor analysis and all the questions were approvedThen, using linear regression and ANOVA, the effect of each of the factors on the success or failure of investment projects was investigated, and the results of this test showed confirmation of the impact of each of the factors, and then the results of the hierarchical analysis indicated this was the first rank of product and service, followed by the second-rank ,that is geographical considerations, and the characteristics of the investor's psychology, the third rank, the product market characteristics, the fourth rank, the investor's ability to rank fifth, and financial considerations ,also, earned the last rank. Considering this prioritization, the neural network used in this research contained data from 6 variables as an input variable, with two intermediate layers with 30 nodes in the first layer, and three nodes in the second layer,which had one outlet. The results indicated that the neural network model had the power to predict the success of the investment projects to1.2%of the error Manuscript profile -
Open Access Article
230 - Prediction of growth stages of soybean cultivars and lines using climatologic parameters of photoperiod and temperature in karaj region
Sharareh Nasre Esfahani Jahanfar Daneshian Ebrahim Pazira Amir hosein Shirani RadSoybean development Reproductive stage werepredicted by climatic parameters as daylength and temperature . Therefore ,fifteen soybean lines and cultivars as name as Williams, Zane, M4, M12, S.R.F, Miandoab,A3935, A3237, L17, Union, Grangelb, Clark, Tns95,Elf, Calland we MoreSoybean development Reproductive stage werepredicted by climatic parameters as daylength and temperature . Therefore ,fifteen soybean lines and cultivars as name as Williams, Zane, M4, M12, S.R.F, Miandoab,A3935, A3237, L17, Union, Grangelb, Clark, Tns95,Elf, Calland were studied in four planting dates. Soybean genotipes were usedin a RCBD in each planting dates . Daylength and temperature effect wereevaluated by planting dates levels. Flowering occurance and developmentReproductive stage duration were fitted growth degree days, photoperiod andphotothermal units in a Multiple Regression Method. The results indicated thatphotoperiod had significant effect on Maturity in all of lines and cultivars.But photothermal had positive significant effect in Williams. Therefore Itcaused to delay in Maturity if It was increased. Photothermal was calculatedfrom growth degree days by photoperiod in each days. But the photoperiod wasmore effective than gdd in time of Maturity. Growth degree days affected ontime of Maturity by photothermal and caused to promote it. Reproductivedevelopment duration of Elf was affected by gdd Manuscript profile -
Open Access Article
231 - Prediction of the GC-MS Retention Indices for a Diverse Set of Terpenes as Constituent Components of Camu-camu (Myrciaria dubia (HBK) Mc Vaugh) Volatile Oil, Using Particle Swarm Optimization-Multiple Linear Regression (PSO-MLR)
Majid Mohammadhosseini -
Open Access Article
232 - The effect of information environment on the relationship between consistency in book-tax differences and analysts’ earnings forecasts
Mohammad Salari Abarghuoi Nasim ShahmoradiPurpose: Predicting accounting earnings and its changes as an economic event has long been of interest to financial analysts and researchers.The purpose of this study is to investigate the effect of information environment on the relationship between consistency in book MorePurpose: Predicting accounting earnings and its changes as an economic event has long been of interest to financial analysts and researchers.The purpose of this study is to investigate the effect of information environment on the relationship between consistency in book-tax differences and the accuracy of analysts’ of corporate earnings forecasts.Methodology: In this study, following the research of Choi, Hu and Khondkar (2020), the information environment and the quality of analysts' forecasts in two dimensions of predictive and prediction accuracy, consistency in book-tax differences in both temporary and Permanently dimensions were calculated and regression models were tested using the Generalized Torque Model (GMM). The selected sample consisted of 69 companies listed on the Tehran Stock Exchange between 2011 and 2020.Findings: : Generally findings indicate that consistency in book-tax differences has affected the prediction accuracy of analysts' forecasts. In a way, temporary consistency has led to an increase and permanent consistency has led to a decrease in analysts' forecast prediction accuracy. In addition, consistency in book-tax differences has also affected the analysts' forecasts predictive accuracy, which has been increasing for temporary consistency and decreasing for permanent predictive consistency.The information environment has also affected both the predictive and prediction accuracy of the analysts' forecasts.Originality / Value: The obtained results have led to the expansion of the theoretical foundations in relation to the factors affecting the quality of analysts' forecasts, in two aspects: accuracy and usefulness of forecasts. And besides that, it is useful for company managers in adopting the necessary policies to create a suitable information environment. The mentioned relationships are measured bilaterally and include temporary and permanent differences in the taxes paid by companies. which provides useful information in this field for standard setters and legislators. Manuscript profile -
Open Access Article
233 - Systematic review of bankruptcy prediction models
Jaber Zahmatkesh Akram Taftiyan Mahmoud Moeinadin Amin NezaratObjective: The current research aims to systematically examine bankruptcy prediction models with the goal of developing a model that serves as a guide for selecting the most suitable tools. These tools should ideally align with the existing data and quality criteria of MoreObjective: The current research aims to systematically examine bankruptcy prediction models with the goal of developing a model that serves as a guide for selecting the most suitable tools. These tools should ideally align with the existing data and quality criteria of bankruptcy prediction models.Research Methodology: To conduct this research, a systematic search was performed on the Web of Science database using keywords such as Bankruptcy, Default, Distress, Failure, Forecasting, Predicting, Prediction, and Insolvency, spanning the years 2015 to 2023. Based on defined inclusion and exclusion criteria, this search yielded 1000 articles, out of which 49 were ultimately selected and analyzed. The findings from these articles were then summarized in tables. Subsequently, major bankruptcy prediction models were compared based on nine key criteria, and final conclusions were drawn.Findings: Artificial neural networks and support vector machines were found to have the highest accuracy, while multiple personality analysis showed the lowest accuracy. Additionally, artificial neural networks, multiple personality analysis, decision trees, and logistic regression require a large training sample to logically identify and precisely classify patterns. However, case-based reasoning, rough sets, and support vector machines can work with smaller sample sizes.Originality/ Value: The outcomes of this research contribute to a comprehensive understanding of the characteristics of tools used in developing bankruptcy prediction models and the shortcomings associated with them. Manuscript profile -
Open Access Article
234 - پیشبینی مود II نرخ رهایی انرژی کرنشی در کامپوزیتهای چند لایهای چند جهته
افشین زینالدینی منصور علیزادهجهتگیری الیاف یکی از مهمترین پارامترهای مؤثر بر مود دوم نرخ رهایی انرژی کرنشی در شروع جدایش بینلایهای است. از تیری با شکاف انتهایی برای اندازهگیری نرخ رهایی انرژی کرنشی در شروع جدایش بینلایهای کامپوزیتهای لایهای استفاده شده است. در واقع هدف از این تحقیق، پیشبی Moreجهتگیری الیاف یکی از مهمترین پارامترهای مؤثر بر مود دوم نرخ رهایی انرژی کرنشی در شروع جدایش بینلایهای است. از تیری با شکاف انتهایی برای اندازهگیری نرخ رهایی انرژی کرنشی در شروع جدایش بینلایهای کامپوزیتهای لایهای استفاده شده است. در واقع هدف از این تحقیق، پیشبینی مقدار نرخ رهایی انرژی کرنشی نمونه­ی چندجهته از روی نتایج تجربی مربوط به قطعه­ی تکجهته میباشد، بدون اینکه مستقیماً به آزمایشهای تجربی و مدلسازی اجزاء محدود قطعه­ی کامپوزیتی لایهای چندجهته نیاز شود. در این زمینه، روشی پیشنهاد شده که ترکیبی از روشهای پیشبینی و تحلیلی است. ضمناً نتایج بدست آمده از این روش با نتایج مدلسازی عددی و نتایج تحلیلی مقایسه شده ­است. این روش، حجم محاسبات عددی و تحلیلی و نیز هزینه­ی مطالعات آزمایشگاهی را به مقدار چشمگیری کاهش می­دهد Manuscript profile -
Open Access Article
235 - ارائه یک مدل جدید پیش بینی عمر خستگی کم چرخه آلیاژ منیزیم براساس روش انرژی کرنش پلاستیک تصحیح شده
محمد آزادی غلامحسین فرهیامروزه تکنولوژی به سمت استفاده از موادی همچون آلیاژهای منیزیم، با نسبت استحکام به وزن بالا در قطعات موتوری، تمایل دارد. بطور معمول، از انواع چدن و آلیاژهای آلومینیوم در ساخت سرسیلندر و بلوک سیلندر موتورها استفاده می­شود. اما آلیاژهای منیزیم، خواص فیزیکی و مکانیکی نز Moreامروزه تکنولوژی به سمت استفاده از موادی همچون آلیاژهای منیزیم، با نسبت استحکام به وزن بالا در قطعات موتوری، تمایل دارد. بطور معمول، از انواع چدن و آلیاژهای آلومینیوم در ساخت سرسیلندر و بلوک سیلندر موتورها استفاده می­شود. اما آلیاژهای منیزیم، خواص فیزیکی و مکانیکی نزدیکی به آلیاژهای آلومینیوم داشته و تا حدود 40 درصد وزن را کاهش می­دهند. در این مقاله، یک مدل جدید پیش­بینی عمر خستگی کمچرخه برای آلیاژ منیزیم، بر اساس روش انرژی ارائه شده و به جهت تدوین آن، از نتایج آزمون خستگی کمچرخه روی نمونه­های منیزیمی استفاده شده است. این مدل در مقایسه با دیگر تئوریهای موجود، از پارامترهای مادی کمتری برخوردار است و دارای دقت مناسب­تری می­باشد؛ چراکه در روش انرژی، از رابطه عمر- کار پلاستیک که معادل با ضرب همزمان عددهای تنش و کرنش پلاستیک می­باشد، استفاده می­شود. با توجه به خواص نرم شوندگی آلیاژهای منیزیم و آلومینیوم, انرژی کرنش پلاستیک می­تواند انتخاب مناسبی باشد؛ چراکه در چرخه بارگذاری خستگی، عدد حاصل ضرب تنش در کرنش پلاستیک می­تواند ثابت بماند. همچنین، اثر تنش میانگین بصورت یک ضریب تصحیح در مدل پیش­بینی عمر خستگی کمچرخه اعمال شده است. نتایج حاصل از مدل ارائه شده، تطابق خوبی را با نتایج آزمون نشان می­دهد. Manuscript profile -
Open Access Article
236 - A hybrid bankruptcy prediction model based on GMDH-type neural network and genetic algorithm for Tehran Stock Exchange companies
hosain vazifehdost tayebeh zangenehThis paper proposes a Soft Computing model for effective bankruptcy prediction, based on the integration of Group Method of Data Handling (GMDH) neural network and genetic algorithm which is called here as GA-GMDH. Genetic algorithm (GA) designs the whole architec MoreThis paper proposes a Soft Computing model for effective bankruptcy prediction, based on the integration of Group Method of Data Handling (GMDH) neural network and genetic algorithm which is called here as GA-GMDH. Genetic algorithm (GA) designs the whole architecture of the GMDH network and optimizes its topology. In order to demonstrate the effectiveness of our proposed GA-GMDH model, its performance was compared with performance of the commonly used statistical techniques of logistic regression (LR) and a relatively new artificial intelligent technique of Adaptive Neuro-Fuzzy Inference System (ANFIS). Performance of the designed prediction models depends on the utilized variable selection technique. Therefore, we constructed 12 prediction models through combining the four filtering feature selection methods and the three prediction models. The four feature selection methods of independent samples T-test, correlation matrix (CM), stepwise multiple discriminant analysis (SDA) and principal component analysis (PCA)are combined with prediction models to generate four optimal feature subsets. Empirical data were collected one year prior to failure from Tehran Stock Exchange (TSE) during 1997-2008. For robust assessing of prediction models’ performance, we applied Type-I and Type-II errors, and area under the receiver operative characteristics curve (AUC) measures. Experimental results indicate that our proposed GA-GMDH model has high ability in bankruptcy prediction problem and significantly outperforms ANFIS and LR models in all combinations with four feature selection methods. Meanwhile, the CM method has the best ability in selecting predictive variables in comparison with other feature selection methods. Therefore, CM-GA-GMDH model is determined as the best constructed model for bankruptcy prediction using our particular dataset from TSE. Manuscript profile -
Open Access Article
237 - An Interval Type-2 Fuzzy LSTM Algorithm for Modeling Environmental Time-Series Prediction
Aref Safari Rahil Hosseini -
Open Access Article
238 - Possibility of the Economic Prediction Model based on the Smart Algorithm of the Smart City
mahsa khodadadi Larissa Khodadadi روزبه دبیریSmart cities make better use of space and have less traffic, cleaner air and more efficient city services and improve people's quality of life. The large number of vehicles that are constantly moving through congested areas in smart cities complicates the availability o MoreSmart cities make better use of space and have less traffic, cleaner air and more efficient city services and improve people's quality of life. The large number of vehicles that are constantly moving through congested areas in smart cities complicates the availability of a public parking space. This creates challenges for both traffic and residents. With such a large population, road congestion is a serious challenge. It wastes vital resources like fuel, money and most importantly time. Finding a suitable place to park is one of the reasons for traffic jams on highways. This paper proposes an economic forecasting model based on deep learning for long-term economic growth in smart cities. Traffic management is vital for cities in that it ensures that people can move freely around the city. Many cars trying to reach congested areas in smart cities make it difficult to find a public parking lot. This issue is inconvenient for both drivers and residents. A number of traffic management authorities have implemented an artificial neural network to solve this problem, and modern car systems have come with smart parking solutions. The experimental result of the economic forecasting model based on deep learning improves traffic estimation, accurate prediction of traffic flow, traffic management and intelligent parking compared to existing methods Manuscript profile -
Open Access Article
239 - Evaluation and Prediction of W/C Ratio vs. Compressive Concrete Strength Using A.I and M.L Based on Random Forest Algorithm Approach
R. JamalpourConcrete, an artificial stone composed of cement, aggregate, water, and additives, is extensively utilized in contemporary civil projects. A pivotal characteristic of concrete is its capacity to efficiently serve various purposes and structural requirements. Cement, wat MoreConcrete, an artificial stone composed of cement, aggregate, water, and additives, is extensively utilized in contemporary civil projects. A pivotal characteristic of concrete is its capacity to efficiently serve various purposes and structural requirements. Cement, water, aggregate, and additives are pivotal parameters wherein even minor alterations can significantly impact concrete strength. Among these parameters, the Water/Cement (W/C) ratio holds particular significance due to its inverse correlation with strength. Traditionally, predicting concrete strength solely based on the water-to-cement ratio has been challenging. However, with advancements in AI and machine learning techniques coupled with ample data availability, accurate strength prediction is achievable. This paper presents an analysis of a diverse dataset comprising various concrete tests utilizing machine learning methodologies, followed by a comparative examination of the outcomes. Furthermore, this study scrutinizes a renowned dataset encompassing 1030 experiments, featuring diverse combinations of cement, water, aggregate, etc., employing artificial intelligence and machine learning techniques. Model accuracy and result fidelity are evaluated through rigorous sampling methodologies. Initially, the dataset is subjected to analysis utilizing the linear regression algorithm, followed by validation employing the random forest algorithm. The random forest algorithm is employed to predict the water-to-cement ratio and corresponding compressive strength for concrete with a density of 300 kg/m3. Notably, the obtained results exhibit a high level of concordance with experimental and laboratory findings from prior studies. Hence, the efficacy of the random forest algorithm in concrete strength prediction is established, offering promising prospects for future applications in this domain. Manuscript profile
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