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      • Open Access Article

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

        2 - Face Detection based on Semantic Model for Mobile Banking
        leili nosrati Amir Massoud Bidgoli hamid hajseiedjavadi
        In this paper, a new authentication protocol for online banking based on the semantic model of features extracted from people's image is introduced. The proposed approach is presented using smart mobile phones for online digital imaging for customers. In this work, a fu More
        In this paper, a new authentication protocol for online banking based on the semantic model of features extracted from people's image is introduced. The proposed approach is presented using smart mobile phones for online digital imaging for customers. In this work, a fuzzy clustering has been used to categorize the characteristics of the images of different people and by applying them to different machine learning methods, a combined technique of machine learning classification methods has been presented to improve performance and increases strength against various attacks. Also to reduce the complexity of machine design for operational tasks, the technique of reducing features extracted from face images with the help of genetic algorithm has been used. In the last part, in order to make a decision for authentication selected by machine learning systems, a fuzzy logic system is presented based on the highest accuracy of identifying the desired person. Using a public dataset, the experimental results showed that the genetic algorithm-based technique is the best feature selection to create an implicit authentication method for the smartphone environment. The results showed an accuracy of about 99.80% using only 30 features out of 77 to authenticate users. At the same time, the results showed that the proposed method has a lower error rate compared to the related work. Manuscript profile
      • Open Access Article

        3 - Presenting a new model for ATM demand scenario
        Alireza Agha Gholizadeh Sayyar Mohamadreza Motadel Alireza Pour ebrahimi
        In today's competitive world, the ability to recognize predict customer demand is an important issue for the success of organizations. And since ATMs are one of the most important channels for cash distribution and one of the most fundamental criteria for assessing the More
        In today's competitive world, the ability to recognize predict customer demand is an important issue for the success of organizations. And since ATMs are one of the most important channels for cash distribution and one of the most fundamental criteria for assessing the level of service to banks,In this paper, the number of referrers to ATM devices is reviewed based on the timing and location of the devices. This article seeks to find a dynamic and functional model for predicting the number of referrers to each ATM depending on the time and location of the device. Hence, 378 ATM machines were used throughout the city of Tehran for a time period of one month, containing 69,418 records. Finally, with the help of clustering of statistical data in spatial and temporal dimensions, this model finally succeeds in learning the pattern in the macro data, and based on the decision tree, the predictor can predict the number of referents to each device, which after the algorithm is presented. In order to improve the quality of banking services and improve the performance of the ATM network, it is proposed to combine the optimal location of ATMs in spatial and temporal dimensions. Manuscript profile
      • Open Access Article

        4 - Presenting a Model for Predicting and Improving Production Quality Using Decision Tree Algorithms and Linear Planning (Case Study: TIBA Wave Generating Companies in Iran)
        Nadereh Sadat Rastghalam Roya Roya M.ahari Ahmad Reza Shekarchizadeh Atefeh Amindost
        10.30495/jmfr.2022.60091.2446
        Today, most industries and factories in the country use statistical quality control tools to improve product quality, but due to the high volume of data, now there is a need for a more powerful tool that can control statistical quality control processes, given the exten More
        Today, most industries and factories in the country use statistical quality control tools to improve product quality, but due to the high volume of data, now there is a need for a more powerful tool that can control statistical quality control processes, given the extent Data Mining Algorithms and Its Ability to Discover Rules In this research, data mining tools have been used to improve the quality control process and increase it. The method is that first the failure database is formed and after collecting quality control data, the accuracy of predicting the quality of parts is determined using different decision tree algorithms and in the next step using Modeling, Coverage Analysis, Data Each of the rules is evaluated, and finally the workstations are evaluated using the rules that apply to each workstation. Accordingly, in this study, the statistical population of all Tiba surge arresters in 1398. The attributes consist of 9 workstations. Based on the results, the best algorithm in predicting C5 failure is and the most important attributes selected by it are determined as the most important attributes, which are: Cooling quality, hole quality and cutting quality. Also, the evaluation of the rules has been done using the model of cover analysis, data and the most important rules have been extracted. Finally, based on solving the model, the devices that will be in the corrective priority for the current year are: Rowling , Solder and cutting Manuscript profile
      • Open Access Article

        5 - Using Clustering and Genetic Algorithm Techniques in Optimizing Decision Trees for Credit Scoring of Bank Customers
        Mahmood Alborzi Mohammad Khanbabaei M. E. Mohammad Pourzarandi
        Decision trees technique as one of the data mining techniques, is used in credit scoring ofbank customers to classify them in order to offer credit facilities. The main problem is incomplexity of decision trees, excessive size, lack of flexibility and low accuracy incla More
        Decision trees technique as one of the data mining techniques, is used in credit scoring ofbank customers to classify them in order to offer credit facilities. The main problem is incomplexity of decision trees, excessive size, lack of flexibility and low accuracy inclassification. The purpose of this paper is to propose a compound model in the optimization ofdecision trees by using genetic algorithm technique. It appears that genetic algorithm can chooseappropriate features and build decision trees to reduce complexity and increase flexibility indecision trees. In the proposed compound model, the credit data is initially divided into twoclusters by Simple means clustering technique. On the next step, the important credit scoringfeatures in the data set are selected using genetic algorithm and the five feature selectionalgorithm based on Filter, Wrapper and Embedded approaches. Subsequently, five decisiontrees based on C4.5 algorithm in each cluster are constructed with a set of the selected features.The best decision trees in each cluster, are selected and combined based on the desiredoptimality criteria, mentioned in this paper, to construct the final decision tree. WEKA machinelearning tool and GATree software were used to in this purpose. Results show that using theproposed compound model in building decision trees leads to increased classification accuracy,compared to other algorithms in this paper. However the algorithm complexity of the proposedcompound model is more than some of the classification algorithms compared in this paper. Manuscript profile
      • Open Access Article

        6 - Using Clustering and Genetic Algorithm Techniques in Optimizing Decision Trees for Credit Scoring of Bank Customers
        Mahmood Alborzi Mohammad Khanbabaei M. E. Mohammad Pourzarandi
        Decision trees technique as one of the data mining techniques, is used in credit scoring ofbank customers to classify them in order to offer credit facilities. The main problem is incomplexity of decision trees, excessive size, lack of flexibility and low accuracy incla More
        Decision trees technique as one of the data mining techniques, is used in credit scoring ofbank customers to classify them in order to offer credit facilities. The main problem is incomplexity of decision trees, excessive size, lack of flexibility and low accuracy inclassification. The purpose of this paper is to propose a compound model in the optimization ofdecision trees by using genetic algorithm technique. It appears that genetic algorithm can chooseappropriate features and build decision trees to reduce complexity and increase flexibility indecision trees. In the proposed compound model, the credit data is initially divided into twoclusters by Simple means clustering technique. On the next step, the important credit scoringfeatures in the data set are selected using genetic algorithm and the five feature selectionalgorithm based on Filter, Wrapper and Embedded approaches. Subsequently, five decisiontrees based on C4.5 algorithm in each cluster are constructed with a set of the selected features.The best decision trees in each cluster, are selected and combined based on the desiredoptimality criteria, mentioned in this paper, to construct the final decision tree. WEKA machinelearning tool and GATree software were used to in this purpose. Results show that using theproposed compound model in building decision trees leads to increased classification accuracy,compared to other algorithms in this paper. However the algorithm complexity of the proposedcompound model is more than some of the classification algorithms compared in this paper. Manuscript profile
      • Open Access Article

        7 - Scalable Fuzzy Decision Tree Induction Using Fast Data Partitioning and Incremental Approach for Large Dataset
        Somayeh Lotfi Mohammad Ghasemzadeh Mehran Mohsenzadeh Mitra Mirzarezaee
        Manuscript profile
      • Open Access Article

        8 - Provide financial policy by predicting financial statement fraud
        Seyed jalal Ahmadi Khosrow Faghani Makrani Naghi Fazeli
        Background: Management responsibility is creating the right organizational climate in which fraud is the worst crime. methods of identifying fraud play an important role in preventing fraud. Objective: To provide financial policy to management in predicting financial fr More
        Background: Management responsibility is creating the right organizational climate in which fraud is the worst crime. methods of identifying fraud play an important role in preventing fraud. Objective: To provide financial policy to management in predicting financial fraud by using neural network data mining Research method: Descriptive-applied research method and time domain is also from 2008 to 2017. In this study, financial ratios for both fraudulent and non-fraudulent samples and network data mining were analyzed. Pearson's correlation coefficient was then examined for the model linearity for financial ratios and the elimination of independent correlated variables. In the next step, the neural network method was used to provide financial policy to management regarding the prediction of financial statement fraud. Findings: The decision tree method is effective in providing financial policy to management in predicting financial statement fraud. Conclusion: Since the decision tree method has 65.4% correct forecast, it can be effective in providing financial policy to management to predict fraud. Manuscript profile
      • Open Access Article

        9 - Procedural S‌tudy on the Methods that Analyze the Design Process
        Shirin Aghayan Seyed Hadi Ghoddusifar Seyed Gholamreza Islami
        10.30495/ijaud.2021.16768
        Manuscript profile
      • Open Access Article

        10 - Using Data Mining and Three Decision Tree Algorithms to Optimize the Repair and Maintenance Process
        M. Izadikhah D. Garshasbi
        The purpose of this research is to predict the failure of devices using a data mining tool. For this purpose, at the outset, an appropriate database consists of 392 records of ongoing failures in a pharmaceutical company in 1394, in the next step, by analyzing 9 charact More
        The purpose of this research is to predict the failure of devices using a data mining tool. For this purpose, at the outset, an appropriate database consists of 392 records of ongoing failures in a pharmaceutical company in 1394, in the next step, by analyzing 9 characteristics and type of failure as a database class, analyzes have been used. In this regard, three decision tree algorithms have been used to determine the most important attributes and to determine the effective rules for the failure. Based on the results obtained from the feature selection of all the three used algorithms, the lifetime characteristics of the machine, the name of the machine and the duration until the last failure are recognized as the most important attributes. On this basis, the life of the device has a very special importance. Since the depreciation in the pharmaceutical industry is high, so the life of the devices used in maintenance and repair has a special effect. In this regard, machines with a life span of more than 20 years are subject to high depreciation and failure, and in addition to the usual repairs they need some special repairs. Manuscript profile
      • Open Access Article

        11 - Retaining Customers Using Clustering and Association Rules in Insurance Industry: A Case Study
        R. Samizadeh S. Mehregan
        Manuscript profile
      • Open Access Article

        12 - Identifying the Factors Affecting Marketing Success at One of the Branches of Tejarat Bank Using Data Mining Techniques
        mehdi ghazanfari aghdas badiee fatemeh moslehi
        Given the competitive market in the banking industry, the importance of customer relationship management in the industry is increasing day by day, one of the most important elements of which is active and effective marketing. Therefore, in this research it has been trie More
        Given the competitive market in the banking industry, the importance of customer relationship management in the industry is increasing day by day, one of the most important elements of which is active and effective marketing. Therefore, in this research it has been tried to identify and investigate the factors affecting the success of marketing activities, taking into account the application of data mining tools. Those factors that make bank customers more willing to take advantage of long-term deposits in the bank. In fact, the results of this study will help to increase the rate of return on direct marketing in the banking industry, which has been less considered in previous studies. To this end, the data set related to the telemarketing campaign conducted at one of the branches of Tejarat Bank in the period from May 2016 to September 2018. This paper is based on the type of applied research in sight of the objective of the research and from the perspective of the methodology of the research, is a mixed type, i.e., both qualitative and quantitative. The decision variable of this problem is the result of the success or failure of the telephone marketing activity. First, for identifying customers using the K-Means clustering algorithm, data are divided into six clusters. In the next step, C5 and CART decision tree algorithms were used to identify the factors affecting the success of the marketing campaign. As a general conclusion of the implementation of the three mentioned algorithms, it can be said that, in comparison with other variables, the time variable of the conversation with the client has the greatest effect on the decision of the person about the opening of the deposit. It should be noted that since the time period and place of data gathering in this research have been limited, the results of this study are not likely to be generalized to the other banks. Manuscript profile
      • Open Access Article

        13 - A Model for Identification Tax Fraud Based on Improved ID3 Decision Tree Algorithm and Multilayer Perceptron Neural Network
        Akbar Javadian Kootanaee Abbas Ali Poor aghajan Sarhamami Mirsaeid Hosseini Shirvani
        Tax revenues are one of the most important sources of governments and cover a large portion of government spending. In recent years, fraud in financial statements and tax returns has increasingly become a serious problem for businesses, governments and investors. Most t More
        Tax revenues are one of the most important sources of governments and cover a large portion of government spending. In recent years, fraud in financial statements and tax returns has increasingly become a serious problem for businesses, governments and investors. Most taxpayers are looking for a way to manipulate their financial statements and reduce their taxable profits. Therefore, identifying tax fraudsters and companies that cheat on financial statements has become a vital issue for the government.The purpose of this study is to present a model that uses the improved ID 3decision tree algorithm. Also, to improve its performance and accuracy, it was combined with multilayer perceptron neural networks optimized by genetic algorithm to select financial ratios associated with tax fraud and reduce computational overhead. The tree in the proposed model has the lowest depth possible, so it has high velocity and low computational overhead. For this purpose, the financial statements of 06companies listed in Tehran Stock Exchange during - 4330 4331were studied and 41financial ratios were extracted. By ANOVA test, 33ratios and finally by neural networks 7ratios related to tax fraud was selected as the model input data. The proposed model, with %4411accuracy, has been successful in identifying fraudulent companies with the highest accuracy and predictive power over the adaboost algorithms. Manuscript profile
      • Open Access Article

        14 - Forecasting the Type of Audit Opinions: A Data Mining Approach
        محمدحسین ستایش فهیمه ابراهیمی سیدمجتبی سیف مهدی ساریخانی
        Data mining methods can be used to assist auditors with providing audit opinions.The purpose of this research is to forecast the type of audit opinions using data miningmethods and compare the performance of these methods. Artificial neural networks,support vector machi More
        Data mining methods can be used to assist auditors with providing audit opinions.The purpose of this research is to forecast the type of audit opinions using data miningmethods and compare the performance of these methods. Artificial neural networks,support vector machines, nearest neighbors and decision tree methods were used toconduct the research. The sample consists of 842 observations between 2001 and 2010.The observations were divided in two groups: one group for training and the other forassessment of the method. A comparison of the performance of methods indicates thatsupport vector machines approach outperforms the other approaches with a predictiveability of 76%. Also measuring type I and type II error rates of each method shows thatthe performance of support vector machines is higher than the other methods. Manuscript profile
      • Open Access Article

        15 - Landslide susceptibility mapping using advanced machine learning algorithms (Case study: Sarovabad city, Kurdistan province)
        Baharak Motamedvaziri Hemen Rastkhadiv Seyed Akbar Javadi Hasan Ahmadi
        The occurrence of landslides in mountainous areas may cause serious damage to road infrastructure, and may also lead to human deaths. Therefore, the purpose of this study is to landslide susceptibility mapping using advanced machine learning algorithms in Sarovabad city More
        The occurrence of landslides in mountainous areas may cause serious damage to road infrastructure, and may also lead to human deaths. Therefore, the purpose of this study is to landslide susceptibility mapping using advanced machine learning algorithms in Sarovabad city. In this study, landslide susceptibility was determined using two advanced data mining algorithms including random forest (RF) and decision tree (DT). First, the point file of 166 landslides occurred in Sarovabad city was considered as the landslide inventory map. The landslide points are divided into training data (70%) and validation data (30%). A total of 16 parameters including slope, aspect, elevation, river proximity, road proximity, river density, fault proximity, fault density, road density, precipitation, land use, NDVI, lithology, earthquake, stream power index (SPI) and topographic wetness index (TWI) were used in order to landslide susceptibility mapping. Finally, the performance of the models was evaluated using the ROC curve. The results of the ROC showed that the decision tree and random forest models have AUC values of 0.942 and 0.951, respectively. Therefore, the random forest model has the highest AUC value compared to the decision tree and was the best model for predicting the risk of landslides in the future in the study area. Landslide potential maps are efficient tools; so that they can be used for environmental management, land use planning and infrastructure development. Manuscript profile
      • Open Access Article

        16 - Application of Machine Learning Models for flood risk assessment and producing map to identify flood prone areas: Literature Review
        Parisa Firoozishahmirzadi Shaghayegh Rahimi Zeinab Esmaeili Seraji
        10.30495/ijdea.2022.68987.1181
        Manuscript profile
      • Open Access Article

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

        18 - Comparison of Data Mining Models Performance in Rainfall Prediction Using Classification Approach (Case Study: Hamedan Airport Synoptic Weather Station)
        Morteza Salehi Sarbijan Hamidreza Dezfoulian
        10.30495/wsrcj.2024.75668.11411
        Background and Aim: Rainfall is one of the complex natural phenomena and one of the most crucial component of the water cycle, playing a significant role in assessing the climatic characteristics of each region. Understanding the amount and trends of rainfall changes is More
        Background and Aim: Rainfall is one of the complex natural phenomena and one of the most crucial component of the water cycle, playing a significant role in assessing the climatic characteristics of each region. Understanding the amount and trends of rainfall changes is essential for effective management and more precise planning in agricultural, economic, and social sectors, as well as for studies related to runoff, droughts, groundwater status, and floods. Additionally, rainfall prediction in urban areas has a significant impact on traffic control, sewage flow, and construction activities. Method: The objective of this study is to compare the accuracy of classification models, including Chi-squared Automatic Interaction Detector (CHAID), C5 decision tree, Naive Bayes (NB), Quest tree, and Random Forest, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) in predicting rainfall occurrence using 50 years of data from the synoptic station at Hamedan Airport. In this study, 80% of the data is used for training the models, and 20% for model validation and the results obtained from the model executions are compared using metrics such as confusion matrix, Receiver Operating Characteristic (ROC) curve, and the Area Under the Curve (AUC) index. To create the classification variable for rainfall and non-rainfall data, based on rainfall data, the days of the year are categorized into two classes: days with rainfall (y) and days without rainfall (n). Data preprocessing is performed using Automatic Data Preprocessing (ADP). Then, Principal Component Analysis (PCA) is employed to reduce the dimensions of the variables. Results: In this study, the PCA method reduces the dimensions of the variables to 5. Also, approximately 80% of the available data corresponds to rainless days, while 20% corresponds to rainy days. The research results indicated that the KNN model with an accuracy of 91.9% for training data and the SVM model with 89.13% for test data exhibit the best performance among the data mining models. The AUC index for the KNN model is 0.967 for training data and 0.935 for test data, while for the SVM algorithm, it is 0.967 for training data and 0.935 for test data. According to the ROC curve for Hamedan rainfall data, the KNN model outperforms other models. Considering the sensitivity index in the confusion matrix, the KNN and SVM models perform better in predicting non-rainfall occurrence for training data. In terms of the precipitation occurrence prediction, the RT and KNN models show better results according to the specificity index. Conclusion: The results demonstrated that for the RT, C5, ANN, SVM, BN, KNN, CHAID, QUEST, accuracy metrics was obtained 86.82%, 89.78%, 89.55%, 89.96%, 88.06%, 91.9%, 88.29%, 87.46%, 91.9%, respectively for training data. Moreover, for test data, the accuracy metrics for this model was obtained 83.82%, 87.9%, 88.12%, 89.13%, 87.12%, 89.13%, 87.12%, 88.19%, 86.93%, 86.76%, respectively. The AUC index in the training data for RT, C5, ANN, SVM, BN, KNN, CHAID QUEST models was 0.94%, 0.99%, 0.94%, 0.94%, 0.93%, 0.97%, 0.93%, 0.89%, respectively. In addition, for the test data, this metric was evaluated 0.89%, 0.89%, 0.93%, 0.94%, 0.92%, 0.90%, 0.92%, 0.88% respectively. As observed, considering accuracy metric and AUC index for training data KNN model and for test data SVM model were more sufficient in rainfall prediction.  Manuscript profile
      • Open Access Article

        19 - Investigation of Spatio-Temporal Dynamics of Parishan Land-Cover Using Decision Tree Model and Satellite Imagery
        golafarin zare Bahram Malek mohammadi Hamidreza Jafari Ahmad Reza Yavari Ahmad Nohegar
        Background and Objective: Wetlands, as one of the most important types of ecosystems in the world, are extremely threatened. In addition to being part of Iran's protected areas, the Parishan wetland is also known as an international wetland and biosphere reserve. Unders More
        Background and Objective: Wetlands, as one of the most important types of ecosystems in the world, are extremely threatened. In addition to being part of Iran's protected areas, the Parishan wetland is also known as an international wetland and biosphere reserve. Understanding the process of changing in this wetland, can be very helpful in improving its future status. Therefore, the purpose of the present study is to monitor the changes in the over a 30-year period. Material and Methodology: For the purpose of the research, Landsat satellite images were prepared for four time periods of 1987, 1998, 2007 and 2016 along with other required data. By performing the required preprocessing in ENVI 4.7 software, Parishan wetland land-cover maps was extracted using Normalized Difference Vegetation Index and Normalized Difference Water Index combining with Decision Tree method in three class including water-body, vegetation and others land-cover. Findings: The results showed that after 30 years only 13 hectares of 1963 hectares of Parishan wetland water-body remained. Monitoring of changes shows that Parishan wetland water-body has decreased by 1950 hectare in comparison to 1987, 3605 hectare in comparison to 1998 and 2272 hectare in comparison to 2007. Discussion and Conclusion: using satellite data and remote sensing techniques along with Decision Tree classification model indicate the capability of this method for identifying and classifying land-cover in wetland areas where vegetation and water are intertwined. Manuscript profile
      • Open Access Article

        20 - Presenting and explaining a model to create the value of the company according to the role of accounting standards management, financial reporting quality and audit quality using meta-innovative models
        saman khorshid yahya kamyabi mehdi khalilpour
        In the world of investment, decision making is the most important part of the investment process, in which investors need to make the most optimal decisions in order to achieve their maximum benefits and wealth. In this regard, the most important factor in the decision- More
        In the world of investment, decision making is the most important part of the investment process, in which investors need to make the most optimal decisions in order to achieve their maximum benefits and wealth. In this regard, the most important factor in the decision-making process is information. Information can have a significant impact on the decision-making process. Because it makes different decisions in different people. In the stock market, investment decisions are also affected by information. Therefore, this study seeks to provide and explain a model to create the value of the company according to the role of management of accounting standards, financial reporting quality and audit quality using meta-innovative models. To achieve this goal, the data of 101 companies listed on the Tehran Stock Exchange during the period 1392 to 1397 were collected, and the optimized algorithm method was used to analyze the data. The research findings indicate that all three meta-functional methods have the power to estimate economic value added and market value added. However, the estimated value of economic value added and market value added in the night cream algorithm is higher than the two decision tree algorithms and the regression machine-supporting algorithm algorithm. Is higher. Manuscript profile
      • Open Access Article

        21 - Applying Rough Developed theoretical Models (ERST), Interpretation-Structural Analysis (ISM) and Decision Tree (CART) for Help Auditors to Identify Fraud in the Financial Statements of Companies Listed on the Stock Exchange of Iran
        Davood Hasanpoor hasan valiyan mehdi safari griyly Reza Tahmasbizadeh
        The Purpose of this Research is Rough Set Theory Developed Using Model (ERST) to Assist Auditors to Identify Fraud in the Financial Statements of Iranian Companies Listed on Stock Exchange. The method of this combined research is based on the adaptation of theoretical f More
        The Purpose of this Research is Rough Set Theory Developed Using Model (ERST) to Assist Auditors to Identify Fraud in the Financial Statements of Iranian Companies Listed on Stock Exchange. The method of this combined research is based on the adaptation of theoretical foundations through the critical evaluation method to identify the characteristics and criteria of fraud in the financial statements (x) and the characteristics of committing fraud through them (y) and based on the decision tree (CART) and the developed Rough Theory Model (ERST) are seeking to determine the most effective criteria for fraud and how it can be applied in financial statements. The statistical population of the study consisted of 12 expert auditors selected through targeted and homogeneous sampling. In this study 18 indicators were identified as criteria for fraud and 5 attributes as ways of committing fraud. The results of this study showed that, based on the result of the management decision tree (CART) as the most important indicator of fraud, according to the developed Rough Theory Model (ERST), accounts receivable are considered as the most important feature of fraudulent behavior. Accordingly, in the conclusion of this research, for determining the fraud in the financial statements, we can use two indicators of low inventory sale (X12) and high management ownership (X17) based on changes in accounts receivable. Manuscript profile
      • Open Access Article

        22 - The Designing Early Warning System of Financial Crisis Outbreak in Tehran Stock Exchange by Decision Tree
        Alireza Gholizadeh mirfeiz Fallahshams Mohammad Ali Afsharkazemi
        The main purpose of this paper is to predict financial crisis in stock exchange market along with designing warning syetem by data analysis and then to present to financial policy makers for preventing the outbreak or decreasing the effects of crisis. Due to this purpos More
        The main purpose of this paper is to predict financial crisis in stock exchange market along with designing warning syetem by data analysis and then to present to financial policy makers for preventing the outbreak or decreasing the effects of crisis. Due to this purpose, it’s used the weekly datas during the years from 10.03.1997 to 03.22.2019. The mean of crises in this present paper is the falling more than 15% of stock price rather to last three months. Hence, it’s been used the dummy variable for operating the dependent variable. It’s been used the residual of autoregressive integrated moving average (ARIMA) for measuring the shocks caused by the index of stock price, exchange rate, price of oil and gold. Based on the results by the different data showed the financial crisis outbreak is the most important variable for predicting the crisis of Tehran stock exchange in weekly data in the last periods. Hence it’s claimed that falling the stock index is affected the value of index in the last periods more than external shock including shock of exchange rate as well as gold and oil. As it’s determined the detection accuracy of crisis is 81.82%, the same for all trees. It means that 36 crisises have been predicted and recognized from the 44 ocurred crisises during the mentioned periods (1121 weeks). Manuscript profile
      • Open Access Article

        23 - Optimal Portfolio Selection using Machine Learning Algorithms
        Mohammad baghar yazdani khodashahri Seyed Hossein Naslemousavi Mir Saeid Hoseini Shirvani
        Choosing the right portfolio is always one of the most important issues for investors. The price trend is predicted using technical analysis or basic analysis. Technical analysis focuses on market performance, while the focus of fundamental analysis is on the mechanism More
        Choosing the right portfolio is always one of the most important issues for investors. The price trend is predicted using technical analysis or basic analysis. Technical analysis focuses on market performance, while the focus of fundamental analysis is on the mechanism of supply and demand, and these changes prices. The existence of a solution to predict growth or decrease in stocks has been studied as a basic need in this study. In the present study, with the help of a monitoring dataset, a solution based on Raff collection algorithms and hierarchical analysis to reduce the feature and decision tree algorithms, backup vector machine, and business network have been used for prediction. This proposed solution has been implemented using language and compared with different solutions, and the research results have shown that the proposed method with 80% accuracy of prediction and 20 errors in prediction has the highest accuracy and the lowest error rate among the methods compared. Manuscript profile
      • Open Access Article

        24 - Predicting financial distress risk of firms listed in Tehran Stock Exchange using factor analysis, decision tree and logistic regression models
        Rasoul Tahmasebi Ali Asghar Anvary Rostamy Abbas Khorshidi Seyyed Jalal Sadeghi Sharif
        This research predicts financial distress of companies listed on Tehran Stock Exchange using Factor Analysis, Decision Tree, and Logistic Regression models. For this purpose, 33 financial ratios have been investigated in the 5-year time horizon. In order to reduce the d More
        This research predicts financial distress of companies listed on Tehran Stock Exchange using Factor Analysis, Decision Tree, and Logistic Regression models. For this purpose, 33 financial ratios have been investigated in the 5-year time horizon. In order to reduce the dimensions of the data and to find the internal relationship the variables, factor analysis model has been used. Then, the variables according to their relationships with financial distress are classified in eight factors. In the following, the results of the decision tree and the logistic regression models are compared with each other. The results show that both models have the ability to predict financial distress, but the decision tress model has a higher predictive power than the logistic regression model. Manuscript profile
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        25 - Optimizing Solar Radiation Prediction Based on The Internet of Things Platform in Photovoltaic Power Plant
        Maryam Mahmoudi Neda Ashrafi Khozani Shabnam Nasr Esfahani
        10.30495/jce.2023.1996606.1224
        The solar radiation value parameter is one of the most important parameters in determining the output power value of photovoltaic panels. Accurate prediction of this parameter is of special importance for planning in dispatching and load management units. Uncertainty in More
        The solar radiation value parameter is one of the most important parameters in determining the output power value of photovoltaic panels. Accurate prediction of this parameter is of special importance for planning in dispatching and load management units. Uncertainty in the amount of solar radiation and the difficulty of predicting it, managers and designers face economic and managerial challenges. In this research, a prediction method with high accuracy and generality is presented using tree-based methods and improving the performance of these methods with the help of meta-heuristic algorithms. The main emphasis in the proposed method is the lack of over-fitting and high reliability the ability to be used in Internet of Things systems. Meta-heuristic algorithms have been used not only in the optimization of tree-based methods, also in feature selection and instance selection. The use of meta-heuristic methods as the main innovative aspect of this research, not only to obtain the optimal settings of machine learning models, but also to reduce the effect of noises, outliers and low-effective inputs, has helped to improve the quality of the final output. Aadapting the prediction which is considered in this research which was done through innovative fitting function of this research in the optimization of models, makes the final output to be optimal in addition to high accuracy in terms of ease of implementation in the real environments of photovoltaic power plants. The final output is a strong model that has a score of 0.95 with the R-square criterion and is optimal model. Manuscript profile
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        26 - An Improved Decision Tree Classification Method based on Wild Horse Optimization Algorithm
        raheleh sharifi Mohammadreza Ramezanpour
        10.30486/mjtd.2023.1980072.1026
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        27 - Use data mining to identify factors affecting students' academic failure
        Mahmood Najafi Mehdi Afzali Mahmood Moradi
        Knowledge extraction is one of the most significant problems of data mining. The principles raised in if-then format can be turned into real numbers in each section- as values which could be included in dataset. The suggested method in the present dissertation is applic More
        Knowledge extraction is one of the most significant problems of data mining. The principles raised in if-then format can be turned into real numbers in each section- as values which could be included in dataset. The suggested method in the present dissertation is application of decision tree algorithms, clustering and forum rules for extraction of final rules. In the suggested method, extraction of rules is defined as an optimization problem and objective was obtaining a rule of high confidence, generalization and understandability. The suggested algorithm for extraction of rules was obtained from and tested based on a dataset of educational failure of 256 art school students living in Zanjan. The results suggested that the j48 algorithm in decision tree and accuracy of 0.95 is the choice for the dataset of educational failure. Data clustering was done by K-Main algorithm with confidence coefficient of 0.95. After all, obtaining rules of high confidence coefficient was done based on forum rules from Apriori algorithm for the whole datasets. The results of present study could be used for inhibition of educational failure of students, improved quality of relationship of parents and authorities with students and enhancing the education they receive. Manuscript profile
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        28 - Presenting a Model for Predicting Various Types of Stock Movement Trends in Water-Intensive Industries Using a Decision Tree
        Mohammad  Bahmani Ali Lalbar
        Stock market activists are trying to find and apply methods so that they can increase the profit of their capital by predicting the future stock price. Therefore, it seems necessary that appropriate, correct and scientific-base principles methods are used to determine t More
        Stock market activists are trying to find and apply methods so that they can increase the profit of their capital by predicting the future stock price. Therefore, it seems necessary that appropriate, correct and scientific-base principles methods are used to determine the future price of stocks for investors. Economists use econometric methods for forecasting in most cases. Therefore, the aim of this research was to present a new approach in predicting the types of stock movement trends in water-intensive industries using the decision tree approach. The local domain of this research was the companies listed on the Tehran Stock Exchange active in water-intensive industries and the time do-main was during 2016-2020. In the data section, the research was done by collecting the data of the sample companies by referring to the financial statements, explanatory notes and the stock exchange monthly journal; Based on the systematic elimination method, 72 companies active in water-intensive industries were selected as a statistical sample. In order to describe and summarize the collected data, descriptive and inferential statistics have been used using the decision tree approach; the results showed that by using the decision tree approach, it is possible to predict the profit, industry and stock price trends in water-intensive industries. The results obtained in this research are consistent with the documents mentioned in the theoretical framework of research and financial literature. Manuscript profile
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        29 - Developing a Prediction-Based Stock Returns and Portfolio Optimization Model
        Farzad Eivani Davood Jafari Seresht Abbas Aflatooni
        10.22034/amfa.2021.1877733.1304
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        30 - Forecasting Stock Trend by Data Mining Algorithm
        Sadegh Ehteshami Mohsen Hamidian Zohreh Hajiha Serveh Shokrollahi
        10.22034/amfa.2018.539138
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        31 - Online Mean Shift Detection in Multivariate Quality Control using Boosted Decision Tree learning
        Abbas Asadi Yaghoub Farjami
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        32 - Using Data Mining to Predict Bank Customers Churn
        parvin najmi abbas rad maryam shoar
        The intensity of finding competition in the industrial and economic space and the market move towards a complete competition market has made the inclination of firms to attract more customers and, instead, have increased the tendency to operate in various service and ma More
        The intensity of finding competition in the industrial and economic space and the market move towards a complete competition market has made the inclination of firms to attract more customers and, instead, have increased the tendency to operate in various service and manufacturing areas. This policy, which is known for increasing the share of wallet, makes it more important to maintain customer relationships and analyze their relationships, and it is necessary to conduct customer behavioral analysis, customer relationship analysis, and customer behavior forecasting. The present research seeks to identify customers who are turning away and anticipates the decline of customers in order to prevent customers from falling. In this regard, the variables associated with the reversal analysis are first identified and then the bank customers are clustered using a neural network and classified into three categories of loyal, regular, and negative clients. With the receipt of the above labels, a backup vector machine has been used to classify and reverse prediction. Based on the results, the proposed method has the ability to predict rotational deviation of up to 80% and, moreover, has a better performance than the classical decision tree. Manuscript profile
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        33 - Provide a Data Envelopment Analysis/Data Mining Integrated Model for Evaluation of Decision-making Units
        Alireza Alinezhad Javad Khalili
        Efficiency is an important issue for the managers of different companies and organizations, as well as customers who are interested in services of these companies and organizations. The aim of this research is to study the efficiency of pharmaceutical companies accepted More
        Efficiency is an important issue for the managers of different companies and organizations, as well as customers who are interested in services of these companies and organizations. The aim of this research is to study the efficiency of pharmaceutical companies accepted in stock exchange organization using Data Envelopment Analysis (DEA) and then, providing some rules using the decision tree. The Malmquist index partly resolves the problem of inadequacy of the observations by enabling the combination of time-series and cross-sectional observations. This method acts on the basis of moving average and it is of use for finding performance trends of a unit during the time. In this research, regarding the inputs and outputs and using the Malmquist index, efficiency evaluation of 22 pharmaceutical companies accepted in stock exchange organization has been done in the state of constant returns to scale during 2012-2016, and the results obtained were used as the class label of Decision-Making Units (DMUs) which are in fact inputs of decision tree method. Finally, using the decision tree, rules implicit in the data, are extracted. Manuscript profile
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        34 - A Survey on Employees Performance Appraisal Systems by Using the Fuzzy Tree Model
        Soleyman Iranzadeh Davood Norouzi Sadegh Babaei Heravi
        An improper performance appraisal discipline can be considered as the main incompetency in a world undergoing continuous development, the growth of added values, limited possibilities and finally promotion of efficacy era. In order to prevent consequences of improper ap More
        An improper performance appraisal discipline can be considered as the main incompetency in a world undergoing continuous development, the growth of added values, limited possibilities and finally promotion of efficacy era. In order to prevent consequences of improper appraisal discipline in Islamic Azad University in East Azerbaijan province, fuzzy tree approach was offered in this research to investigate employees’ performance appraisal methods. Population in this research which is a practical and statistical population investigation involves all 2079 employees at branches of Islamic Azad University at East Azerbaijan. The data collection tool is an author-made questioner. According to this model, the best performance appraisal method was investigated for each part. For doing so, the effective factors in the determination of performance appraisal method were defined and the employees’ performance was investigated according to these factors. In this relation, the viewpoints of masters and elites of the human resources were used and the database was established by using obtained knowledge. According to the knowledge database, algorithm ID3, and fuzzy approaches, the fuzzy tree was proposed. Then, the appraisal methods priorities were selected by using fuzzy multi-criteria decision making for each section based on decision value (goals obtain function).   Manuscript profile
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        35 - Optimal Prediction in the Diagnosis of Existing Heart Diseases using Machine Learning: Outlier Data Strategies
        Omid Rahmani Seyyed Amir Mahdi Ghoreishi Zadeh Mostafa Setak
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        36 - Presenting a data mining model based on sustainable development index in the urban management of Tehran metropolis affected by the covid-19 epidemic.
        Abbas Maleki sadegh abedi Alireza Irajpoor
        By applying the restrictions caused by the Covid-19 pandemic, it seems that changes in the concentrations of pollutants CO, O3, NO, NO2, SO2, PM2.5, PM10 and AQI can be seen in the periods before and after the epidemic. Therefore, the changes of air pollutants and traff More
        By applying the restrictions caused by the Covid-19 pandemic, it seems that changes in the concentrations of pollutants CO, O3, NO, NO2, SO2, PM2.5, PM10 and AQI can be seen in the periods before and after the epidemic. Therefore, the changes of air pollutants and traffic restrictions are investigated as one of the sub-categories of environmental indicators of sustainable urban development in the period of 2018/01/21 to 2022/03/20 in the stations under the supervision of Tehran city. First, the data is collected, processed and cleaned. Machine learning methods including decision tree, random forest, support vector machine, Bayesian network and perceptron neural network are applied to select the effective features using the particle swarm optimization method. Investigations showed that the prediction model using decision tree and random forest had the best performance for both precision and recall criteria. The results of the research showed that the concentration of pollutants in the period of Covid-19 compared to before, is increased in some stations and decreased in others, and also the application of traffic restrictions during the epidemic did not have a significant and noticeable effect in reducing the concentration of air pollutants. Also, by examining the trend of deaths during the epidemic period, it was found that the decrease or increase of pollutants has no significant relationship with the trend of deaths caused by Covid-19. Manuscript profile
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        37 - Modelling and Predicting Earnings Quality Using Decision Tree and Support Vector Machine
        Loghman Hatami Shirkouhi Soghra Barari Nokashti Maryam Ooshaksarae
        10.22094/jdem.2023.708161
        Earnings and its quality are one of the most important decision-making com-ponents for users. Therefore, earnings quality prediction is very important for investors and other stakeholders. To this aim, decision tree and support vec-tor machine (SVM) were used to predict More
        Earnings and its quality are one of the most important decision-making com-ponents for users. Therefore, earnings quality prediction is very important for investors and other stakeholders. To this aim, decision tree and support vec-tor machine (SVM) were used to predict earnings quality. The statistical population of the study included companies listed in Tehran Stock Exchange from 2011 to 2021 for 10 years. After screening, 113 companies and 1130 observations were selected as statistical samples. In order to identify and predict earnings quality, indicators related to corporate governance (board independence, audit committee independence, organizational ownership), dividend policy, debt financing, and conservatism were considered as inde-pendent variables and discretionary accruals quality representing profit quali-ty index was considered as a dependent variable. Data analysis was done according to CRISP-DM data mining standards and implementation of four decision tree algorithms including CHAID, C5.0, C&R, QUEST, and SVM. As the results showed, board independence had the greatest effect on earn-ings profit quality. Considering the accuracy value for the created SVM, which is equal to 98.5%, it indicates the high capability of this method to predict earnings quality. Manuscript profile
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        38 - Presenting a model for predicting tax evasion guilds based on Data mining techniques
        Mohammad Ghasemi Sadegh Abedi Ali Mohtashami
        In 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 More
        In 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
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        39 - Classifying the Customers of Telecommunication Company in order to Identify Profitable Customers Based on Their First Transaction, Using Decision Tree: A Case Study of System 780
        Mohammad Velayati Mohammadreza Shahriari Farhad Hosseinzadeh Lotfi
        10.22094/joie.2018.572783.1575
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        40 - A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements
        Akbar Javadian Kootanaee Abbas ali Poor Aghajan Mirsaeid Hosseini Shirvani
        10.22094/joie.2020.1877455.1685
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        41 - Short-Term Tuberculosis Incidence Rate Prediction for Europe using Machine learning Algorithms
        Jamilu Yahaya Maipan-uku Nadire Cavus Boran Sekeroglu
        10.22094/joie.2023.1988443.2079
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        42 - A New Algorithm for Optimization of Fuzzy Decision Tree in Data Mining
        Abolfazl Kazemi Elahe Mehrzadegan
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        43 - Modelling Customer Attraction Prediction in Customer Relation Management using Decision Tree: A Data Mining Approach
        Abolfazl Kazemi Mohammad Esmaeil Babaei
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        44 - A Fuzzy Decision-Making Methodology for Risk Response Planning in Large-Scale Projects
        Seyed Meysam Mousavi Sadigh Raissi Behnam Vahdani Seyed Mohammad Hossein
        Manuscript profile
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        45 - Fuzzy tree model for evaluating employee performance evaluation system and Selection evaluation system for branch of University of West Azarbaijan
        Ebrahim Mollazadeh Ebrahim Ali Mohammadi Asl Ghader Zemestani
        This study evaluates the fuzzy tree model to evaluate employee performance evaluation systemand Selection evaluation system for the University of West Azarbaijan units has been done.The study staff units are the University of West Azarbaijan province. In this discourse, More
        This study evaluates the fuzzy tree model to evaluate employee performance evaluation systemand Selection evaluation system for the University of West Azarbaijan units has been done.The study staff units are the University of West Azarbaijan province. In this discourse, in order to prevent the effects of the unfavorable appraisal system in west Azerbaijan Branches of Islamic Azad University, it has been intended to present a model of fuzzy tree for investigating the methods of the performance appraisal of the employees. on the basis of this model, the right performance appraisal method for each sector will be chosen. First, the researcher recognize the effective factors in identifyiny the method of the performance appraisal, then by using these factors the performance oppraisal of employees are investigated. In this case, the viewpoints of professors and the experts of human resources management are being used. By using the obtained knowledge from the experts of humsn resources management, knowledge base is created and by using knowledge base and algorithm  and fuzzy sets, the model of fuzzy tree is offered. Then by distributing the guestionnaire, the values of each variable (the effective factorse in identifying the performance appraisal method) for each sector are identified. In the end, by using these obtained values, the priority of the methods of performance appraisal for each sector in identified. At the next step, by using the fuzzy multi-criterion decision making, the researcher identifies the priority of selecting the appraisal methods for each of these sectors on the basis of decision value Manuscript profile
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        46 - Presenting a Fuzzy Tree Appraisal Model to Investigate the Systems of the Performance Appraisal of the Employees and to Select the Right Appraisal System for East Azerbaijan Branches of Islamic Azad University
        Soleyman Iranzadeh Davood Bagheri Asgar Azarkasb
        At the age of continuous advances, the times of value added, the age of numerous activities with limited facilities, and finally, the times of productivity enhancement, an improper system of performance appraisal could be an important deficiency. In this article, in ord More
        At the age of continuous advances, the times of value added, the age of numerous activities with limited facilities, and finally, the times of productivity enhancement, an improper system of performance appraisal could be an important deficiency. In this article, in order to prevent the cosequences of the unfavorable appraisal system in East Azerbaijan Branches of Islamic Azad University, we have tried to present a model of fuzzy tree to investigate the methods of the performance appraisal of the employees and to select, on the basis of this model, the right performance appraisal method for each branch. To do this, first the researchers recognized the effective factors in identifying the method of the performance appraisal, and then, by using these factors, the performance appraisal methods of employees was investigated. In this regard, the viewpoints of professors and the experts of human resources management have been used. By using the knowledge obtained from the experts of human resources management, a knowledge base was created, and through the use of the knowledge base and algorithm  and fuzzy sets, the model of fuzzy tree was offered. Then by distributing the questionnaire, the values of each variable (the effective factors in identifying the performance appraisal method) for each branch were identified. Ultimately, by using these obtained values, the priority of the methods of performance appraisal for each branch were specified. Later, by applying the fuzzy multi-criterion decision making, the researchers identified the priority of selecting the appraisal methods for each of these branches on the basis of decision value. Manuscript profile
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        47 - Saderat Bank customer classification using decision tree Based on customer value
        Hosein Beyorani Mahram Azimi
        Customer satisfaction in today's business environment has gained importance. Many companies to increase profits and customer satisfaction are focused on customer value. Customer relationship Management (CRM) is tools to enhance customer relationships has emerged as the More
        Customer satisfaction in today's business environment has gained importance. Many companies to increase profits and customer satisfaction are focused on customer value. Customer relationship Management (CRM) is tools to enhance customer relationships has emerged as the principal competing companies. Successful customer relationship management in companies start from identifying customer value beacuse Customer value provides important information for the development and management. Techniques such as data mining has led to the development of customer relationship management in new competition areas so that companies can be profitable in business competition. Through data mining -the discovery of hidden knowledge database- organizations can identify valuable customer and predict their future behavior and take useful and knowledge-based decisions. The purpose of this research is to gain the effective features to select valuable customer that can classify customers based on population characteristics and other variables relating to transactions in classes very low profit, low profit, high profit and very high profit. In this study the influence of demographic characteristics including age, education and job level also affect the degree of branch, bank branch location and number of transactions on customer value will be checked. Dependent variable in this research is customer value that is classified into four categories. Statistical population is the all customers have an active checking account with Bank Saderat Iran in Tabriz city. To review the case, the CHAID decision tree data mining algorithms were used.Results showed that the variables age, education level of customer and bank branches have no significant effect on customer value and the number of customer transactions with bank has most effective in identifying the class of customer. Manuscript profile
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        48 - Forecasting Of Tehran Stock Exchange Index by Using Data Mining Approach Based on Artificial Intelligence Algorithms
        Mohammad Mahmoodi Akbar Ghasemi
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        49 - Long-Term Demand Forecasting in Electrical Energy Supply Chain of Espidan Ironstone Industry using Deep Learning and Extreme Learning Machine
        Sepehr Moalem Roya M. Ahari Ghazanfar Shahgholian Majid Moazzami Seyed Mohammad Kazemi
        Espidan ironstone industries is one of the most consumed power industries in the electricity supply chain of Isfahan province as the second industrial hub of the country and one of the main suppliers of raw materials in the supply chain of the country's steel industry. More
        Espidan ironstone industries is one of the most consumed power industries in the electricity supply chain of Isfahan province as the second industrial hub of the country and one of the main suppliers of raw materials in the supply chain of the country's steel industry. Planning in a large-scale electricity supply chain, in a space full of uncertainty, is begin with electricity demand forecasting.In this paper, a hybrid long-term demand forecasting method in the electricity supply chain of Isfahan's ironstone industries using a combined data mining method including wavelet transform,deep learning and intensive learning machine is proposed. The used data in this study is according to the recorded information from the electrical energy demand signal of Espidan ironstone industries in a period of 40 months in the form of 24-hours. The data in a part of the study period due to the lack of production of this industry in some hours are interrupted. So that only 40% of the data had a value and the remaining, 60% were zero. This subject led to information deficiencies and increases the forecasting error up to 40% in the first step of the proposed algorithm. By completing the first step of the proposed model with intense learning machine (ELM) the forecasting error is reduced and it was possible to create an improved forecasting model for supervised training. Finally, simulation results are compared with other available approaches such as support vector machine and decision tree. The results show the improvement and reduction of error and a significant increase in the accuracy of the proposed method in long-term demand forecasting in the electricity supply chain of Espidan ironstone industries. Manuscript profile
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        50 - Online Farsi Character Recognition Using Structural Features
        Vahid Ghods Ehsanollah Kabir
        In this paper, grouping and recognition of online Farsi discrete characters are presented according to their structural features. The letters are divided into 9 groups based on the form and structure of their main bodies. After feature extraction, grouping is performed More
        In this paper, grouping and recognition of online Farsi discrete characters are presented according to their structural features. The letters are divided into 9 groups based on the form and structure of their main bodies. After feature extraction, grouping is performed using a decision tree. Final recognition of letters is carried out in each group by delayed strokes. The proposed method is a rapid method in character recognition because time-consuming methods have not been used. Our proposed method was tested on TMU-OFS dataset, and a recognition rate of 94% and 92% was achieved for character grouping and recognition, respectively. The mean processing time for recognizing a letter was 3ms. Manuscript profile
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        51 - Eco-Efficiency Evaluation in Two-Stage Network Structure: Case Study: Cement Companies
        Mirpouya Mirmozaffari
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        52 - Improving Accuracy in Intrusion Detection Systems Using Classifier Ensemble and Clustering
        Ensieh Nejati Hassan Shakeri Hassan Raei
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        53 - Presenting a New Text-Independent Speaker Verification System Based on Multi Model GMM
        Mohammad Mosleh Faraz Forootan Najmeh Hosseinpour
        Manuscript profile
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        54 - Intrusion Detection System in Computer Networks Using Decision Tree and SVM Algorithms
        Zeinab Kermansaravi Hamid Jazayeriy Soheil Fateri
        Manuscript profile
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        55 - Proposing a Method to Classify Texts Using Data Mining
        Mohammad Rostami Seyed Saeed Ayat Iman Attarzadeh Farid Saghari
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        56 - Predicted Increase Enrollment in Higher Education Using Neural Networks and Data Mining Techniques
        Behzad Nakhkob Maryam Khademi
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        57 - Clustering Social Progress and its Impact on Economic Growth
        hoda Rezaei Roshan ali rahmani Amir Mansour Tehranchian Reza Ali Mohseni
        The present paper intends to test the impact of these two variables of social progress and economic freedom on economic growth. Statistics related to the social progress, economic freedom and per capita gross domestic product in 128 countries are based on the Social Dev More
        The present paper intends to test the impact of these two variables of social progress and economic freedom on economic growth. Statistics related to the social progress, economic freedom and per capita gross domestic product in 128 countries are based on the Social Development Index, the Heritage Foundation and the World Bank in 2017, and are used the methods of decision tree and multiple regression. The results of the decision tree method show that there are significant differences in the rate of social progress and economic freedom between countries with different revenues. Also, the results of multiple regression estimations in the study year show that the variables of social progress and economic freedom in these countries have been effective and have a positive and significant effect on GDP per capita. Thus, with the effectiveness of these indicators, society will come toward development and economic and social well-being, and this society will be an ideal society. Manuscript profile
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        58 - Reviewing the websites of Tehran Municipality and providing appropriate data mining solutions
        shaysteh shojaei karizaki sudabeh Shapoori hajar zarei
        Objective: The main purpose of this study is to identify and analyze different types of data on the website of Tehran Municipality and to provide appropriate data mining solutions. Method: This research is fundamental and in terms of nature it can be considered analytic More
        Objective: The main purpose of this study is to identify and analyze different types of data on the website of Tehran Municipality and to provide appropriate data mining solutions. Method: This research is fundamental and in terms of nature it can be considered analytical. The data collection method was field and the statistical population of 47 sites were selected among 220 domains of Tehran Municipality and data mining techniques were used for analysis and the source of data collection is web analytics and tools used by Google Analytics. Results: The accuracy of the normal neural network algorithm is equal to 99.25% and the RMS standard of the normal neural network algorithm is equal to 0.159. The accuracy of the decision tree algorithm is 99.80% and the MSI criterion of the decision tree algorithm is 0.003 and finally the RMS criterion of the decision tree algorithm is 0.045. The accuracy of the CNN algorithm is equal to 99.81% and finally the RMS criterion of the CNN algorithm is equal to 0.035. Conclusion: Based on the obtained findings, the DB Scan method is equal to other basic methods for analyzing data of Tehran Municipality websites and has a higher accuracy than other methods. Manuscript profile
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        59 - Predicting Emotional Tendency of Investors Using Support Vector Machine (SVM) and Decision Tree (DT) Techniques
        reza taghavi iman dadashi mohammad javad zare bahnamiri hasmidreza gholamnia roshan
        Investor's emotional tendencies indicate the margin of shareholder's optimism and pessimism towards a stock. Investors' emotions, under the influence of psychological phenomena, direct people's behavior and, in many cases, make people to deviate from the rational behavi More
        Investor's emotional tendencies indicate the margin of shareholder's optimism and pessimism towards a stock. Investors' emotions, under the influence of psychological phenomena, direct people's behavior and, in many cases, make people to deviate from the rational behavior. The purpose of this study is to use meta-innovative methods to predict the emotional tendencies of investors. In this study, using 97 financial ratios related to 176 companies listed on the Tehran Stock Exchange during the period between 2006 and 2018, investors' emotional tendencies have been predicted with the help of support vector machine (SVM) and decision tree (DT) techniques.To measure the emotional tendencies of investors, four indicators of relative strength, psychological line, trading volume and stock turnover adjustment rate have been applied. Finally, we have combined these indicators with the help of PCA method. Mean absolute error (MAE) and root mean square error (RMSE) values were used to compare predicting methods. The results of data analysis indicate that the prediction error of the support vector machine method is less than the decision tree. Manuscript profile
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        60 - The Compare of Power Fire Flies Algorithm Prediction, Decision Making Tree Algorithm and the Support Vector Machine Regression Algorithm for Systematic Risk Predicti
        alireza eslampour roya darabi
        Financial and economic decisions are always at risk due to future uncertainties. Therefore, one of the ways to help investors is to provide investment risk forecasting patterns. The more predictions are closer to reality, the decisions made on the basis of such predicti More
        Financial and economic decisions are always at risk due to future uncertainties. Therefore, one of the ways to help investors is to provide investment risk forecasting patterns. The more predictions are closer to reality, the decisions made on the basis of such predictions will be correct. In this research, the goal of predicting the systematic risk of companies admitted to Tehran Stock Exchange using artificial neural network software and three night-worm algorithms, decision tree algorithm and backup vector machine regression algorithm. For this research, a sample of 92 companies from listed companies in Tehran Stock Exchange during the period 2013 to 2018 has been used. The results obtained from the research hypothesis test showed that the predictive power of systematic risk in the night cream algorithm is more than the decision tree algorithm and the support vector machine regression algorithm, as well as the predictive power of the decision tree algorithm in relation to the backup vector machine regression algorithm It is higher for systematic risk prediction Manuscript profile
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        61 - Corporates Manner and Comparing its Prediction Accuracy with Decision Tree and Bayes Models
        zohre arefmanesh vahid zare mehrjardi Alireza Mohammadi nodooshan
        The 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 More
        The 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

        62 - 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 esmaeilpour
        Prediction 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 More
        Prediction 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

        63 - Dimension Reduction of Big Data and Deleting Noise and Its Efficiency in the Decision Tree Method and Its Use in Covid 19
        Fazel Badakhshan Farahabadi Kianoush Fathi Vajargah Rahman Farnoosh
        10.30495/ijm2c.2022.1947200.1239
        Manuscript profile

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