-
Open Access Article
1 - 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
2 - Forecasting the Type of Audit Opinions: A Data Mining Approach
محمدحسین ستایش فهیمه ابراهیمی سیدمجتبی سیف مهدی ساریخانیData mining methods can be used to assist auditors with providing audit opinions.The purpose of this research is to forecast the type of audit opinions using data miningmethods and compare the performance of these methods. Artificial neural networks,support vector machi MoreData mining methods can be used to assist auditors with providing audit opinions.The purpose of this research is to forecast the type of audit opinions using data miningmethods and compare the performance of these methods. Artificial neural networks,support vector machines, nearest neighbors and decision tree methods were used toconduct the research. The sample consists of 842 observations between 2001 and 2010.The observations were divided in two groups: one group for training and the other forassessment of the method. A comparison of the performance of methods indicates thatsupport vector machines approach outperforms the other approaches with a predictiveability of 76%. Also measuring type I and type II error rates of each method shows thatthe performance of support vector machines is higher than the other methods. Manuscript profile -
Open Access Article
3 - 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 -
Open Access Article
4 - 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
5 - Evaluation of wavelet – least square support vector machine hybrid model to rainfall time series spatiotemporal disaggregation
nima farboudfam Vahid Nourani babak aminnejadThe need to simulate rainfall time series at different scales for engineering purposes on the one hand and lack of recording such parameters in small scales because of administrative and economic problems, on the other hand, disaggregation of rainfall time series to the MoreThe need to simulate rainfall time series at different scales for engineering purposes on the one hand and lack of recording such parameters in small scales because of administrative and economic problems, on the other hand, disaggregation of rainfall time series to the desired scale is an essential topic. In this study, for disaggregating the Tabriz and Sahand rain gauges time series, according to nonlinear characteristics of time scales, wavelet- Least Square Support Vector Machine (WLSSVM) hybrid model is proposed and daily data of four rain gauges and monthly data of six rain gauges from Urmia Lake Basin for ten years were decomposed with wavelet transform and then by using mutual information and correlation coefficient criteria, the subseries were ranked and superior subseries were used as input data of Least Square Support Vector Machine (LSSVM) model for disaggregating the Tabriz and Sahand rain gauges monthly rainfall time series to the daily time series. Results obtained from the WLSSVM disaggregation model were compared with the results of LSSVM and traditional multiple linear regression models. The results of WLSSVM model to LSSVM and multiple linear regression models at validation stage in the optimized case for Tabriz rain gauge were increased 10% and 37.5% and in the optimized case for Sahand rain gauge were increased 24.5% and 46.7% respectively. It was concluded that hybrid WLSSVM model has a higher accuracy than two other methods and can be considered as an accurate disaggregation model to disaggregate the rainfall time series. Manuscript profile -
Open Access Article
6 - 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
7 - Forecasting Municipal Solid Waste Quantity by Intelligent Models and Their Uncertainty Analysis
Maryam Abbasi Malihe Fallah Nezhad Rooholah Noori Maryam MirabiBackground and Objective: The first step in design of municipal waste management systems is complete understanding of waste generation quantity. Forecasting waste generation is one of the most complex engineering problems due to the effect of various and out of control MoreBackground and Objective: The first step in design of municipal waste management systems is complete understanding of waste generation quantity. Forecasting waste generation is one of the most complex engineering problems due to the effect of various and out of control parameters on waste generation. Therefore, it is obvious that it is necessary to develop approaches to a model such complex events. The objective of this study is forecasting waste generation quantity using intelligent models as well as their comparisons and uncertainty analysis.Method: In this study, Mashhad city was selected as a case study and waste generation time series of waste generation in 1380 to 1390 were used for weekly prediction. Intelligent models including artificial neural network, support vector machine, adaptive neuro-fuzzy inference system as well as K-nearest neighbors were used for modelling. After optimizing the models’ parameters, models’ accuracy were compared by statistical indices. Finally, result uncertainty of the models was done by Mont Carlo technique.Findings: Results showed that coefficient of determination (R2) of artificial neural network adaptive neuro-fuzzy inference system, support vector machine, and K-nearest neighbor models were 0.67, 0.69, 0.72 and 0.64 respectively. Uncertainty analysis was also justified the results and demonstrates that support vector machine model had the lowest uncertainty among other models and the lowest sensitivity to input variables.Conclusion: Intelligent models were successfully able to forecast waste quantity and among the studied models, support vector machine was the best predictive model. Moreover, support vector machine produced the results with the lowest uncertainty the other models. Manuscript profile -
Open Access Article
8 - Estimation of Aquifer Qualitative Parameters in Guilans Plain Using Gamma Test and Support Vector Machine and Artificial Neural Network Models
Mohammad Isazadeh seyedmostafa Biazar Afshin Ashrafzadeh Rezvan KhanjaniAbstract Background and Objective: Having information about qualitative and quantitative parameters distribution of groundwater supplies is one of most important parameters in integrated groundwater management. Thus, in this study it has been attempted to determine a pr MoreAbstract Background and Objective: Having information about qualitative and quantitative parameters distribution of groundwater supplies is one of most important parameters in integrated groundwater management. Thus, in this study it has been attempted to determine a proper model and input combination for estimation of quality parameters including electrical conductivity (EC), calcium (Ca) and sodium (Na) ions in aquifers of Guilans plain. Method: In this study, the data from 132 observation wells during 2001 to 2013 were used and artificial neural network (ANN) and support vector model (SVM) were applied. In the first approach, estimations were conducted according to five different combinations, including water level, distance from see, total precipitation of six months and coordinates of observation wells. In the second approach, estimations were conducted based on combination of the selected qualitative parameters of gamma test with combinations of the best input in the first part. Findings: Comparison of the results from the first part indicated that SVM model outperformed the ANN mode in the estimation of Ca, Na and EC parameters. Support vector machine error values for estimating Ca, Na and EC variables at the test period were 1.218 (meq/l), 0.867(meq/l), and 175.742 (µmos/cm), while for artificial neural network these values were 1.268 (meq/l), 0.933 (meq/l), and 186/448 (µmos/cm) respectively. The results from this part showed that adding the distance from see input improves the estimation of models in all cases. In the second part, using gamma test for measuring the nine quality parameters, the best combination of quality parameters was determined to estimate the three parameters: Ca, Na and EC. The results from the second part show that both ANN and SVM models have an excellent performance in the estimation of the three qualitative parameters. ANN model error values in estimating Ca, Na and EC variables in validation period were 0.662 (meq/l), 0.305(meq/l), and 47.346 (µmos/cm), while these values were 0.671 (meq/l), 0.356 (meq/l), and 55.412 (µmos/cm) for SVM model respectively. Obviously, the results from ANN model in this section were better than those from SVM model. Discussion and Conclusion:Results showed that both ANN and SVM models have a great ability in predicting qualitative parameters in the aquifers. Also, in less inputs, the results of SVM model are better than those of ANN model and in more inputs it is vice versa. Results of the second section showed that gamma test is fully practical and accurate in determining the effective input combinations. Manuscript profile -
Open Access Article
9 - 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 khalilpourIn 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- MoreIn 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
10 - A hybrid metaheuristic model in the Forex market to optimize investment strategies based on market trend forecasting
alireza sadeghi mehdi madanchi zaj amir daneshvarDetermining the appropriate strategy for buying or selling in the foreign exchange market is very important for companies to cover exchange rate fluctuations against the national currency. This study proposes a new approach based on genetic algorithms and support vector MoreDetermining the appropriate strategy for buying or selling in the foreign exchange market is very important for companies to cover exchange rate fluctuations against the national currency. This study proposes a new approach based on genetic algorithms and support vector machines for trading in the foreign exchange market.In this research, a new algorithm with the ability to generate technical rules for investment based on forecast certainty is presented. For prediction, a combination of the Combined Support Vector Machine (HSVM) algorithm for classifying the market into three different classes (uptrend, downtrend, sideway) and a dynamic genetic algorithm for optimizing trading rules based on several technical indicators Different has been used. Rials-dollar pair data is used as training and test data for the period between 1392 and 1398. The proposed architecture for machine learning, as well as the implementation and study of the proposed trading system are fully described. The research shows promising results during the test period in which the return on investment was 129%. Manuscript profile -
Open Access Article
11 - Optimal Portfolio Selection using Machine Learning Algorithms
Mohammad baghar yazdani khodashahri Seyed Hossein Naslemousavi Mir Saeid Hoseini ShirvaniChoosing 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 MoreChoosing 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
12 - Weekly crude oil price forecasting by hybrid support vector machine model and Autoregressive Integrated Moving Average
Shapor Mohammadi Reza Raeie Hossein karamiFluctuations in crude oil prices in addition to affect the economy of the exporting countries, is one of the sources of disruption in oil-dependent economy. Always predict the price and volatility has been of the challenges facing traders in oil markets and price foreca MoreFluctuations in crude oil prices in addition to affect the economy of the exporting countries, is one of the sources of disruption in oil-dependent economy. Always predict the price and volatility has been of the challenges facing traders in oil markets and price forecast is raised as an imperative and functional however, should be noted forecasts that will take place in more accurate and less error than the observed actual results. In order to predict the weekly price of Brent crude oil as an oil indicator given the difficulty of accurately identifying linear and nonlinear models in economic and financial time series from combining Autoregressive Integrated Moving Average models (ARIMA) by the assumption that the time series have a linear pattern and support vector machine (SVM) which has great potential in modeling nonlinear model is used to enhance the accuracy of prediction. Given two paired comparison performance criteria of root mean square error test (RMSE) and the mean absolute magnitude percentage error (MDAPE) which are resulting from the predicted values and actual values for each model, this indicates that in most cases the hybrid model provide smaller errors in predicting the future price of crude oil as compared to the individual applications of autoregressive integrated moving average models and the support vector machine. Manuscript profile -
Open Access Article
13 - Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT
Sepehr Sharifi Soulmaz GheisariComputer networks play an important and practical role in communication and data exchange, and they also share resources with complete ease. Today, various types of computer networks have emerged, one of which is the Internet of Things. In the Internet of Things, networ MoreComputer networks play an important and practical role in communication and data exchange, and they also share resources with complete ease. Today, various types of computer networks have emerged, one of which is the Internet of Things. In the Internet of Things, network nodes can be smart objects, and in this sense, this network has many nodes and there is a lot of traffic in this network. Like any computer network, it faces its own challenges and problems, one of which is the issue of network intrusion and disruption. This dissertation focuses on detecting anomaly-based intrusion into the Internet of Things using data mining. In this study, after collecting and preparing data, the improved support vector machine with grasshopper optimization algorithm is used as a proposed method to detect anomaly-based intrusion in the Internet of Things. The bagging and k-nearest neighbor classifiers and Basic SVM are compared based on error types and standard performance criteria. The simulation results show 97.2% accuracy in the proposed method and better performance compared to other methods. Manuscript profile -
Open Access Article
14 - Short-Term Load Forecasting using an Ensemble of Artificial Neural Networks: Chaharmahal Bakhtiari Case
E. Faraji M. Mirzaeian H. Parvin A. Chamkoorii Majid MohammadpourShort-term load forecasting is very important in electrical marketing. Load forecasting is dependent on climatic condition of every region and the previous structures of electrical consumption in that region; so we have accomplished this through employing climatic data MoreShort-term load forecasting is very important in electrical marketing. Load forecasting is dependent on climatic condition of every region and the previous structures of electrical consumption in that region; so we have accomplished this through employing climatic data (including temperature and pressure) and real load consumption of Chaharmahal Bakhtiari. We have evaluated our method using four machine learning algorithms: artificial neural networks (multilayer perceptron), ensemble of artificial neural networks, support vector machine and ensemble of support vector machine. Experimental results indicates that ensemble of artificial neural networks is superior to the others in the field of load consumption forecasting of Chaharmahal Bakhtiari. Manuscript profile -
Open Access Article
15 - Offering a model for persian texts classify by combination of classification methods
iman jamali Seyed Javad Mirabedini علی HarounabadiTo classify text information extraction techniques, natural language processing and machine learning has been widely used general purpose of categories of documents, classified documents in the form of a certain number of categories are pre-determined. Each document can MoreTo classify text information extraction techniques, natural language processing and machine learning has been widely used general purpose of categories of documents, classified documents in the form of a certain number of categories are pre-determined. Each document can be in one, several or no category is placed. In the case of any document to this question will be placed the document on which of the categories. This can be in the form of an automatic learning to use it any document can be automatically assigned to a category. In this thesis, data collection and cleanup after you select text using the normal method of word frequency -inverse document frequency (norm TF-IDF) is the weight features and features in two stages using document frequency (DF) and Chi square (SChi) are selected, and then using principal component analysis (PCA) features reduced dimensions, and at a later stage by combining 21 support vector machine (SVM) the proposed model we have implemented, and the accuracy of the model to assess the 10-step method validation. Experimental results show that this model can text classification accuracy of 91.86 for the seven categories do, which has a higher accuracy than the earlier work done. Manuscript profile -
Open Access Article
16 - Quantitative Structure-Property Relationship Study for Prediction of the Solvent Polarity Using Quantum Mechanics Descriptors and Support Vector Machine
mehdi nekoei بهزاد چهکندیQuantitative structure-property relationship (QSPR) study for prediction of the polarity some of solvents using quantum mechanics descriptors and support vector machine. Experimental S′ values for 69 solvents were assembled. This set included saturated and unsatur MoreQuantitative structure-property relationship (QSPR) study for prediction of the polarity some of solvents using quantum mechanics descriptors and support vector machine. Experimental S′ values for 69 solvents were assembled. This set included saturated and unsaturated hydrocarbons, solvents containing halogen, cyano, nitro, amide, sulfide, mercapto, sulfone, phosphate, ester, ether, etc. After drawing the structure of the molecules, the suitable molecular descriptors were calculated. Then, the stepwise multiple linear regressions (SW-MLR) variable selection method was subsequently employed to select and implement the prominent descriptors having the most significant contributions to the polarity of the molecules. At first, multiple linear regressions (MLR) model was constructed. Then, support vector machine (SVM) model was used for to obtain better results. A comparison of results by the two methodologies indicated the superiority of SW-SVM over the SW-MLR method. Manuscript profile -
Open Access Article
17 - Modeling and quantitative structure-property relationship (QSPR) study to predict the acidic constants of some chemical compounds using multiple linear regression and support vector machine
mehdi nekoei Abbass Taheri Majid MohammadhosseiniModeling and studying the structure-property quantitative relationship (QSPR) to predict the acidic constants of some chemical compounds were performed using multiple linear regression (MLR) and support vector machine (SVM). First, the structure of chemical compounds wa MoreModeling and studying the structure-property quantitative relationship (QSPR) to predict the acidic constants of some chemical compounds were performed using multiple linear regression (MLR) and support vector machine (SVM). First, the structure of chemical compounds was plotted and a suitable group of descriptors was calculated. Then, the step selection method was used to obtain the best descriptors that were most related to the chemical properties of the compounds. Then, linear multiple linear regression (MLR) model and nonlinear vector machine (SVM) model were used to predict the acid constants of the compounds. Statistical data showed that the SVM method was superior to the MLR method. Manuscript profile -
Open Access Article
18 - Comparative Study of LS-SVM, RVM and ELM for Modelling of Electro-Discharge Coating Process
Morteza Taheri Nader Mollayi Seyyed Amin Seyyedbarzani Abolfazl Foorginejad Vahide Babaiyan -
Open Access Article
19 - Centroid Distance Shape Recognition for Real Time Low Complexity Traffic Sign Recognition
Hamidreza Emami Ramin Shaghaghi Kandowan Seyyed Abolfazl Hosseini -
Open Access Article
20 - Spectral discrimination of important orchard species using hyperspectral indices and artificial intelligence approaches
Mohsen Mirzaie Mozhgan Abbasi Safar Marofi Eisa Solgi Roohollah KarimiStudy spectral reflectance through spectral indices allows the optimal use of the wide range of spectral wavelengths in hyperspectral data. The purpose of this study was to introduce and evaluate the performance of spectral indices to discriminate dominant orchard speci MoreStudy spectral reflectance through spectral indices allows the optimal use of the wide range of spectral wavelengths in hyperspectral data. The purpose of this study was to introduce and evaluate the performance of spectral indices to discriminate dominant orchard species in Chaharmahal Bakhtiari province. In this study, 150 spectral curves were measured in the range of 350 to 2500 mm, from grapes, walnuts and almond trees. After the initial correction, 30 of the most important spectral indices were extracted. Analysis of variance and comparisons of meanings was applied to identify the optimal indices for species discrimination at a 99% confidence level. Then, an artificial neural network (ANN) and support vector machine (SVM) approaches were used to evaluate the performance of indices in species discrimination. ANOVA results indicated that the Moisture Stress Index (MSI), Band ratio at 1,200 nm, normalized phaepophytiniz index (NPQI) and cellulose absorption index (CAI) indices are optimal for discrimination of the studied species. The performance evaluation of the introduced indicators in some of the ANN and SVM enhancement structures has been associated with 100% accuracy in both education and testing, which shows the effectiveness of these studies in distinguishing orchard species. The performance evaluation of the introduced indicators has been validated at 100% in both training and testing stages. This result emphasizes the necessity of performing spectroscopic studies to separate the orchard species before analyzing the hyperspectral images due to their large data volume, high cost and huge data analysis. Manuscript profile -
Open Access Article
21 - Comparison of different algorithms for land cover mapping in sensitive habitats of Zagros using Sentinel-2 satellite image: (Case study: a part of Ilam province)
Saeedeh EskandariThe western forests and rangelands of Iran in Zagros habitats have mainly been destroyed by various reasons in recent years. The preparation of the land cover map in these sites is the first step to protect them and to prevent further destruction. The aim of this resear MoreThe western forests and rangelands of Iran in Zagros habitats have mainly been destroyed by various reasons in recent years. The preparation of the land cover map in these sites is the first step to protect them and to prevent further destruction. The aim of this research was to select the best algorithm for land cover mapping in a part of Ilam site using the Sentinel-2 image. After providing Sentinel-2 the supervised classification of it was performed by seven different algorithms (maximum likelihood, minimum distance from the average, mahalanobis distance, spectral angle mapper, spectral correlation mapper, support vector machine, neural network). For accuracy assessment of the land cover maps, the stratified random points were created and found in the field. In the field visit, after determining the current land cover of each point in the plot area, the real land cover of each point was compared with the defined land cover of the same point in the pixel area based on classification results and the accuracy of the algorithms was evaluated. The results showed that the support vector machine algorithm had the highest accuracy in providing the land cover map with a general accuracy of 79% and a Kappa index of 0.70. The analysis of the land cover map obtained from this algorithm showed that the dense forest area was 319.64 ha, semi-dense forest area was 361.44 ha and sparse forest area was 1832.36 ha from the total area of the study area (16085.31 ha). Also, the rangeland area was 7352.78 ha, the garden area was 62.32 ha, the agricultural area was 658.42 ha and understorey agriculture was 4504.64 ha. For optimal management of this sensitive ecosystem, land cover mapping using this algorithm in certain temporal intervals is essential to investigate the forests and rangelands change and to control the human-made land uses. Manuscript profile -
Open Access Article
22 - Three-dimensional calibration of land use changes using the integrated model of Markov chain automatic cell in Gorgan-rud river basin
Mahboobeh Hajibigloo Vahed berdi Sheikh Hadi Memarian Chooghi Bairam komakiBackground and ObjectiveLand use/cover changes (LU/LC) are considered as one of the most important issues in natural resource management, sustainable development and the environmental changes on a local, national, regional and global scale. Changing uses into each other MoreBackground and ObjectiveLand use/cover changes (LU/LC) are considered as one of the most important issues in natural resource management, sustainable development and the environmental changes on a local, national, regional and global scale. Changing uses into each other and changing permissible uses into impermissible uses such as changing agricultural lands into residential regions or changing rangelands into eroded and low-yielding dry farming lands are always considered as importand issues in natural resources. Detection of the patterns of the land use changes and prediction of the changes in the future to carry out suitable planning for optimal utilization of uses in natural resource management reveal the need for modeling spatial and temporal changes of LU/LC. This study aims to assess the efficiency of the integrated model of Markov chain automatic cell (CA-Markov model) in simulation and prediction of spatial and temporal changes of Land use/Land cover (LU/LC) in Gorgan-rud river basin by applying three-dimensional Pentius-Melinus analysis in calibration of land use changes by using three assessment indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit as new indices in the assessment of the accuracy of CA-Markov model. Materials and Methods In this research, the Earth observing sensor images of Landsat-5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) acquired from the U.S. geographical site dependent on the U.S. Geographical Survey (USGS) were used to predict land use changes by using the integrated model of Markov chain automatic cell in Gorgan-rud river basin. Seven land use classes were separated for Gorgan-rud river basin including forest land class with the use code 1, agricultural land class with the use code 2, rangeland class (a mixture of shrubbery,langeland,agriculture) with the use code 3, water bodies class with the use code 4, barren land class (barren, rangeland, agriculture) with the use code 5, residential and industrial region class with the use code 6, streambed class with the use code 7. In this study, object-oriented classification method and Support Vector Machine (SVM) algorithm were used to classify Landsat 5 and 8 satellite images and extract the land use classes of Gorgan-rud river basin. Segmentation scale in this algorithm on a 50 unit scale (SL 50) was selected to classify the satellite images of 1987, 2000, 2009 and 2017. The assessment of the accuracy of Support Vector Machine algorithm in the object-based classification of satellite images was done by representing overall accuracy, Kappa cefficient, user accuracy, producer accuracy, commission error and omission error for four study periods. To understand how the changes in the region were created during the period of the study three decades and which classes had the area expansion and which classes had the area decrease, changes in the limits of the classes were revealed and percent of the changes in each class were obtained by using the classification maps and IDRISI software. CA-Markov model predicts the changes of different groups of LU/LC units based on spatial neighbourhood concept, transition probability matrix. Preparing land suitability maps is necessary to predict land use changes so that spatial changes can be controlled for each use by probability rules via filtering suitability maps. Validation of Markov model was performed by using three-dimensional Pentius-Melinus analysis with three assessment indices of Figure of Merit, Quantity Disagreement and Allocation Disagreement. Results and Discussion Support Vector Machine algorithm in the classification of the land use based on object-oriented showed that the highest rate of commission error and omission error were observed in rangelands and agricultural lands with 19.12 and 18.55 percent respectively in the land use map of the year 2009. The lowest accuracy of the producer with 71.49 percent belongs to the rangeland use class in the land use map of the year 2009 and the lowest use accuracy with 71.45 percent belongs to agricultural land use class in the land use map of the year 2017. In keeping with the obtained results, the highest positive change belongs to the agricultural land use increase and the highest negative changes belong to rangeland and forest land use decrease during the period of three decades from 1987 to 2017. The highest forest land decrease with 4.8 percent, the highest agricultural land increase with 5.3 percent, the highest rangeland decrease with 9 percent, the highest barren land increase with 4.6 percent and the highest residential and industrial land increase with 0.8 happened during the periods of 2000-2017, 1987-2017, 2009-2017, 2009-2017, and 1987-2017 respectively. After validating the predicted land use chnges in CA-Markov model, based on the analysis of the 5 existing states in three-dimensional Pentius-Melinus analysis, the CA-Markov model with the accurate prediction of simulation of 89.92 percent showed the high efficiency of CA-Markov model in simulation process. After the implementation of the CA-Markov model analysis on the obtained land use map from the classification of the satellite images, one transition probability matrix and one transitioned area matrix were created. In predictions made by using CA-Markov model in 2017 to 2033, the most changes relate to barren and forest land expansion decrease to 16966 and 6961 hectare respectively and in contrast to the use decrease, rangeland, residential and agricultural land expansion increase will be observed to 20397, 3913 and 3825 hectare respectively. Conclusion Detecting land use changes by using LCM tool for the period of three decades 1987-2017 in Gorgan-rud river basin showed that the forest, agricultural and residential use has had significant changes in this region. The obtained results of the prediction of the land use changes during the coming eighteen years by using the integrated model of Markov chain automatic cell following the detected changes by LCM tool show that we will face extreme deforestation phenomenon in this area. Investigation of the obtained results from the implementation of the future use network model by using Markov transition estimator showed that the future use changes can be predicted based on the existing environmental conditions showing that the agriculture will extremely increase in Gorgan-rud river basin during the coming eighteen years. Thus we can protect water and soil resources with comprehensive and long-term management and prevent the degradation of these valuable resources. Three indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit in three-dimensional Pentius-Melinus analysis had an important role in representation of the accuracy rate and calibration of the land use classification and the land use prediction corresponding with the obtained results from the carried out studies concerning the accuracy assessment with indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit. The results of the studied land use changes by using LCM tool and the integrated model of Markov chain automatic cell during the period of 1987 to 2035 show the degradation of more than 24309 hectare of the forest lands and agriculture increase in an area about 62421 hectare indicating human interfernces and deforestation we face in this area. Manuscript profile -
Open Access Article
23 - Efficiency of mangrove indices in mapping some mangrove forests using Landsat 8 imagery in southern Iran
Yousef Erfanifard Mohsen Lotfi NasirabadBackground and Objective Mangrove forests are one of the important plant ecosystems established across the intertidal zones and consist of evergreen species. According to Food and Agriculture Organization (FAO) reports, the area of world mangrove forests is almost 14.6 MoreBackground and Objective Mangrove forests are one of the important plant ecosystems established across the intertidal zones and consist of evergreen species. According to Food and Agriculture Organization (FAO) reports, the area of world mangrove forests is almost 14.6 million ha and more than 40% of them are located in Asia. Indonesia has the largest mangrove forests with 2.3 million ha with the highest richness. Moreover, Iran with approximately 10,000 ha of mangrove forests in northern parts of the Persian Gulf and Oman Sea is one of the countries with mangrove ecosystems. The ecological and socio-economic importance of mangrove forests is evident to researchers and managers, however, an annual quantitative and qualitative decrease in these forests happens due to natural (e.g., storm) and anthropogenic (e.g., overexploitation) factors. Therefore, it seems essential to develop a practical approach in order to protect the present sites and improve the management, monitoring, and assessment of mangrove forests. The first step in every management and conservation plan in mangrove forests is mapping their spatial distribution and monitoring the spatial changes. It is important to find efficient methods for mensuration and assessment of temporal and spatial changes of mangrove forests for their efficient management and conservation. Field measurement difficulties in these ecosystems result in the rapid development of remote sensing data in mangrove mapping. However, previous studies have shown that common vegetation indices are not efficient in mangrove classification because of the high greenness and moisture content of leaves. Assessing the spectral signature of mangrove forests, researchers have designed specific indices for mangrove classification on satellite imagery. Since the mangrove indices have been recently developed, their efficiency in similar conditions has not been investigated, while they have been compared to some vegetation indices or individually investigated in case studies. Additionally, the mangrove indices have not been applied in mapping mangrove forests of southern Iran. Therefore, the aim of this study was a comparison of eight mangrove indices in mapping mangrove forests of Nayband Gulf (Bushehr province), Sirik (Hormozgan province), and Govatr Gulf (Sistan-Baluchestan province) on Landsat 8 imagery. Materials and Methods Previous studies have shown that mangrove forests in Iran are distributed in 21 sites in 10 cities in Bushehr, Hormozgand, and Sistan-Baluchestan provinces. In order to assess the mangrove indices, a region was selected in each province. Mangroves in Nayband Gulf are concentrated in Bidkhun and Basatin Creeks. In Sirik, mangroves are located in the Azini wetland, and in Govatr Gulf, they are established in Baho and Govatr Creeks. Low- and high-tide Landsat imagery of each study area related to 2020 was downloaded. After pre-processing, the images were used to compute MI (Mangrove Index), NDMI (Normalized Difference Mangrove Index), CMRI (Combined Mangrove Recognition Index), MDI (Mangrove Discrimination Index), MMRI (Modular Mangrove Recognition Index), L8MI (Landsat 8 Mangrove Index), and MVI (Mangrove Vegetation Index). Moreover, low- and high-tide images were implemented in making SMRI (Submerged Mangrove Recognition Index). The classification of soil, water, and mangrove was performed by a support vector machine (SVM) algorithm. In addition to common accuracy criteria (i.e., overall accuracy, Kappa coefficient, mangrove producer's and user's accuracies), the results were evaluated by area under the curve (AUC) of receiver operating characteristic (ROC).Results and Discussion The efficiency of 10 mangrove indices was evaluated in similar conditions. The number of selected indices was eight; however, two of them (i.e., L8MI, MDI) were calculated two times, once with SWIR1 and once with SWIR2, and in total, 10 mangrove indices were used in three regions to classify mangrove forests. Between the indices, SMRI was selected as the most efficient mangrove index. One of the likely reasons for the efficiency of the index can be the application of low- and high-tide imagery to detect mangroves. In addition to PAmangrove and UAmangrove, the overall accuracy and kappa coefficient of soil, water, and mangrove of SMRI were more than other indices. The results of MDI and L8MI showed that they were more efficient with SWIR2 in Nayband Gulf. One of the reasons that likely caused the result can be urban areas and non-mangrove vegetation cover in Nayband Gulf. However, both indices were more accurate in mangrove discrimination when calculated with SWIR1 in Govatr Gulf. Investigation of AUC values proved that SMRI was the most efficient index between all studied indices in mangrove mapping within three study areas. The AUC of mangroves in Nayband Gulf, Sirik, and Govatr Gulf were 0.94, 0.92, and 0.93, respectively. The area of mangrove forests was estimated in Nayband Gulf (260.1 ha), Sirik (1049.2 ha), and Govatr Gulf (649.5 ha) using SMRI.Conclusion In general, the results showed that all mangrove indices were reliable in mangrove discrimination in three study areas and no weak results were achieved. The AUC values of mangroves using SMRI were more than 0.9 in three regions and the index was known as the most reliable index in all regions. The outcome in the study areas revealed that the efficiency of mangrove indices was less in Nayband Gulf compared to two other regions (The AUC of 0.6 for NDMI and L8MI-1). The area of mangrove forests in Nayband Gulf, Sirik, and Govatr Gulf was estimated on Landsat 8 imagery of 2020. The results indicated that between the study sites Sirik (1049.2 ha) and Basatin Creek (43.3 ha) had the highest and the lowest area covered by mangroves. It is suggested to use SMRI in other mangrove forests in southern Iran to approve the achievements of the present study. Manuscript profile -
Open Access Article
24 - Modeling of Aboveground Carbon stock using Sentinel -1, 2 satellite Imagery and Parametric and Nonparametric Relationships (Case Study: District 3 of Sangdeh Forests)
Seyed Mahdi Rezaei Sangdehi Asghar Fallah Homan Latifi Nastaran NazarianiIn this study, the goal is; Find suitable statistical and experimental models for estimating ground carbon storage by combining spectral and radar data from Sentinel 1, 2. There are 150 random circular samples with an area of 10 acres and a total of 150 samples. With gl MoreIn this study, the goal is; Find suitable statistical and experimental models for estimating ground carbon storage by combining spectral and radar data from Sentinel 1, 2. There are 150 random circular samples with an area of 10 acres and a total of 150 samples. With global coverage, all height classes were selected. Species of species type, the total height of trees, and diameter equal to the chest of trees with more than 7.5 cm were recorded in each sample plot. After that, the amount of biomass at the surface of the sample parts was calculated based on the FAO global model and the amount of carbon storage on the ground by applying a coefficient. Radar and spectral images were subjected to various preprocessing operations and necessary processing. Then, the numerical values corresponding to the ground sample plots were extracted from the spectral bands and considered as independent variables. Modeling was performed by non-parametric methods of RF, SVM, kNN, and parametric methods of multiple linear regressions. The results showed that the average ground biomass was 469.07 tons per hectare and carbon storage was 234.53 tons per hectare. Also, the highest correlation was obtained between the main and artificial bands with the two characteristics related to the near-infrared band. The results of modeling validation showed the combination of optical and radar data of Sentinel 1, 2 satellites with biomass and surface carbon storage; Random forest method with the RMSE%, and percentage of bias. The studied characteristics (32.79, -2.24) and (30.79 and 0.01), respectively, have had a better performance in modeling. In general, the results obtained from the validation showed that in estimating the two characteristics the RF method showed better results if the Sentinel 1, 2 data were combined, and in contrast to the SVM. Manuscript profile -
Open Access Article
25 - Identification and separation of land covers by optical and RADAR image fusion
Mostafa Kabolizadeh Sajad Zareie Rahman KhanaferehClassification and separation of land cover is one of the most important applications of remote sensing. To perform classification, multispectral satellite data are an efficient tool, but unfortunately, they are not available in some conditions, such as cloudy weather. MoreClassification and separation of land cover is one of the most important applications of remote sensing. To perform classification, multispectral satellite data are an efficient tool, but unfortunately, they are not available in some conditions, such as cloudy weather. Also, most remote sensing data classification algorithms operate based on the characteristics and spectral information of pixels, which causes the useful spatial information that can be extracted from the images to be ignored, including; The texture of the pictures. The simultaneous use of texture and spectral information is a topic that has been less discussed. Therefore, considering this idea, two methods were used to select the optimal features for preparing the land cover map. The first method is the normalized reflection of complications according to the extracted features and the second method is applying the Optimum Index Factor on the extracted textural and spectral features. For this purpose, the classification process using the Support Vector Machine method, on the Sentinel-1 radar image and the Sentinel-2 multispectral image, the optimal features selected by the two methods and the combination of image bands with the optimal features selected by It was done by two methods and finally by combining the best combination of radar and optical bands. According to the obtained results, the classification using spectral features is more accurate than the classification using texture features. By combining the optical and radar features and obtaining values of 97.07% for the overall accuracy and 0.96% for the Kappa coefficient, the classification accuracy was improved to a great extent. This research showed that by choosing optimal features and combining spectral and radar data, different features of each data can be used and better results can be achieved. Manuscript profile -
Open Access Article
26 - Comparison of the effectiveness of machine learning methods in modeling fire-prone areas (Ilam Province, Darehshahr City)
maryam mohammadian Maryam Morovati Reza OmidipourFire is one of the most important natural hazards that has a great impact on the structure and dynamics of natural ecosystems. Due to Iran's location in the arid and semi-arid belt of the world, a large number of human-made and natural fires occur in different regions o MoreFire is one of the most important natural hazards that has a great impact on the structure and dynamics of natural ecosystems. Due to Iran's location in the arid and semi-arid belt of the world, a large number of human-made and natural fires occur in different regions of the country every year. Therefore, determining sensitive areas to fire occurrence plays an important role in fire management in natural resources. To do so, the current study aims to identify fire-prone areas in Dere Shahr city in Ilam province using two machine learning of random forest (RF) and support vector machine (SVM) and 2024 fire occurrence points. Environmental factors were prepared in categories including topographical factors (altitude, slope direction, slope anlgle), climatic factors (rainfall, relative humidity, wind, temperature), biological factors (vegetation and soil moisture) and man-made factors (distance from residential areas, distance from road, distance from agricultural land, distance from river). The model’s accuracy was evaluated using the area under the curve (AUC) in the ROC curve and cross-validation statistics. Examining the AUC index showed that both models had good accuracy, although the RF model (AUC = 0.97) had higher accuracy than the support vector machine model (AUC = 0.86). According to the results of RF model, about 60% are in the low-risk class and about 20% are in the high fire risk class. Investigating the contribution of the factors affecting the occurrence of fire showed that man-made factors (distance from residential areas) and climatic factors (temperature) played a more important role in areas with a history of fire. Therefore, increasing public culture and reducing dangerous behaviors in nature can reduce the occurrence of fire in this area and contribute greatly to the protection of the environment and preservation of natural resources. Manuscript profile -
Open Access Article
27 - Comparing artificial neural network, support vector machine and object-based methods in preparation land use/cover mapsusing landSat-8 images
Farnoush Aslami Ardavan Ghorbani Behrouz Sobhani Mohsen PanahandehPreparing the maps of land use/cover for spatial planning and management is essential. Nowadays, satellite images and remote sensing techniques have widespread applications according to their capabilities to produce the updated data and analyze the images in all discipl MorePreparing the maps of land use/cover for spatial planning and management is essential. Nowadays, satellite images and remote sensing techniques have widespread applications according to their capabilities to produce the updated data and analyze the images in all disciplines such as agriculture and natural resources. In the present study, Artificial Neural Network, Support Vector Machines and Object-Based techniques wereutilized for drawing the land use and vegetation maps in Ardabil, Namin, and Nir counties. The images of LandSat-8 Operational Land Imager (OLI) (2013) were usedafter geometric correction and topographic normalization and classified into 9 land use/cover classes including water bodies, irrigated farming, rainfed farming, meadows, outcrops, forests, rangelands, residential and airport areas. After the accuracy assessment, overall accuracy for the produced maps of ANN, Support Vector Machine (SVM) and Object-based (OB) techniques was estimated as 89.91, 85.68 and 94.37%, respectively and Kappa's coefficients were 0.88, 0.82 and 0.93, respectivelyindicating that the object-based method in comparison with two other methods has more advantages;on the other hand, all three methods could provide the desirable accuracy for the land use/covermaps. Overally, three advanced classification methods were examined in the heterogeneous area with elevation changes up to 3600m using the images of new lunched Landsat 8 and the most appropriate land use/cover mapping method was introduced. Manuscript profile -
Open Access Article
28 - Evaluating non-parametric supervised classification algorithms in land cover map using LandSat-8 Images
Vahid Mirzaei Zadeh Maryam Niknejad Jafar Oladi QadikolaeiThe aim of this study was to evaluate the efficiency of three support vector machine algorithms, fuzzy decision trees and neural networks for mapping land vegetation map of Arakvaz watershed using OLI sensor of Landsat images (2014). Geometric correction and image pre-p MoreThe aim of this study was to evaluate the efficiency of three support vector machine algorithms, fuzzy decision trees and neural networks for mapping land vegetation map of Arakvaz watershed using OLI sensor of Landsat images (2014). Geometric correction and image pre-processing were utilized to determine the training samples of land vegetation classes for the classification operations. Sample resolution in the vegetation classes has been evaluated using a statistical divergence index. On the next stage, to evaluate the accuracy of algorithms' classification results, ground truth map with the dimensions of 550 m was designed using systematic approach and land vegetation types in the sampling plots were determined. Finally, the efficiency of each classification methodwas investigated bysuch criteria as overall accuracy, kappa coefficient, producer accuracy and user accuracy.Comparing the accuracy and kappa coefficient obtained for three categories with a proper band set in comparison with the ground truth map indicates that the Support Vector Machine (SVM) classifier with overall accuracy of 91.26% and kappa coefficient of 0.8731 has had more appropriate results than other algorithms. The results showed that the separation and classification of forest landswith high accuracy have beenperformedas compared to the other land use classes. Manuscript profile -
Open Access Article
29 - Parkinson’s disease detection using EEG signals analysis based on Walsh Hadamard transform
Yasamin Ezazi Peyvand GhaderyanBackground: Parkinson's disease (PD) is one of the most important diseases of the nervous system that occurs due to the degeneration of dopaminergic neurons in the substantia nigra. Because of increasing prevalence rate, lack of specific treatment, and aggravation sympt MoreBackground: Parkinson's disease (PD) is one of the most important diseases of the nervous system that occurs due to the degeneration of dopaminergic neurons in the substantia nigra. Because of increasing prevalence rate, lack of specific treatment, and aggravation symptoms over time, PD detection is very important for the optimal control of patients' life. Therefore, the development of non-invasive, low-cost and reliable clinical diagnostic methods play an essential role to help doctors in diagnosis, slowing progressions of the disease and providing better control strategies to improve the quality of patients' life. Among diagnostic methods, recording and analyzing the electroencephalogram (EEG) signal as a low-cost and non-invasive approach has attracted a lot of attention.Method: EEG signal analysis in the time domain contains important information, but does not include the frequency information. Hence, this study is based on extracting new frequency features from the EEG signal using Walsh-Hadamard transform (WHT). WHT converts the signal from the time domain into the frequency domain and decompose it into orthogonal and rectangular waves. In this method, after calculating the Walsh coefficients, a set of features such as entropy, impulsive metrics, basic and high-order statistical features have been extracted from these coefficients. Subsequently, the discriminating capability of the presented method has been assessed using two classifiers namely support vector machine and k-nearest neighbor to classify PD patients from the healthy group.Results: The proposed method has been evaluated using the EEG signals of 28 healthy individuals and 28 patients with PD in two medication states (ON and OFF) during the reinforcement learning task. The obtained results have shown that this method is able to detect PD by using the entropy feature, support vector machine, and k nearest neighbor with acceptable accuracy of 99.95% and 99.98%, respectively. The good performance of entropy feature in comparison of other ones can be attributed to non-linear and non-stationary nature of EEG signal.Conclusion: In this study, a non-invasive, low-cost, and reliable method for PD detection using EEG signal analysis has been proposed. This algorithm is a multi-stage technique with a feature extraction approach based on WHT, entropy feature, and support vector machine and k-nearest neighbor classifiers. The reported results indicate that this method is effective in PD detection while being simple and easy, as well as being robust to the clinical factor of medication status. Manuscript profile -
Open Access Article
30 - Differential Information Extraction of Electroencephalogram Signals for Obsessive-Compulsive Disorder Detection
Farzaneh Manzari Peyvand GhaderyanIntroduction: Obsessive-Compulsive Disorder (OCD) is a chronic mental and social disease that is prevalent in about 2 to 3% of the human population leading to cognitive impairments and affected quality of patient's life. Therefore, a reliable and timely diagnosis can he MoreIntroduction: Obsessive-Compulsive Disorder (OCD) is a chronic mental and social disease that is prevalent in about 2 to 3% of the human population leading to cognitive impairments and affected quality of patient's life. Therefore, a reliable and timely diagnosis can help psychiatrists in better treating or controlling this disease.Method: Previous studies have demonstrated interdependence impairments between different brain regions in patients with OCD. Hence, this study has provided a new approach based on the decomposition of signals into intrinsic components and extraction of differential transient changes in amplitude envelope and phase spectra of the EEG signal recorded during Flanker tasks. The proposed algorithm has been evaluated using 19 healthy subjects and 11 patients by the Support Vector Machine (SVM) classifier.Result: The obtained results have confirmed the capability of the proposed method in diagnosing the disease with high accuracy of 93.89% using amplitude differential information of the electroencephalogram signal.Conclusion: In comparison between different regions, the statistical features extracted from the frontal lobe, the frontal-parietal network, and the inter-hemispheric features have offered better detection ability. Manuscript profile -
Open Access Article
31 - The opinion mining of Digikala reviews by semi-supervised support vector machine
zohre Karimi Hadis HaghiriIntroduction: The widespread use of the internet and social media platforms has led to an explosion of digital data, including users' opinions about various services and products. These opinions are valuable sources of information for businesses and organizations to und MoreIntroduction: The widespread use of the internet and social media platforms has led to an explosion of digital data, including users' opinions about various services and products. These opinions are valuable sources of information for businesses and organizations to understand the needs and preferences of their customers. Supervised machine learning models have been proven to be effective in analyzing users' opinions. However, to achieve efficient results, a sufficient amount of labeled training data is necessary. Labeling data requires a considerable amount of time and resources, which can be a significant challenge for many organizations. This is where the concept of semi-supervised learning comes in, which utilizes both labeled and unlabeled data to improve the performance of the model.Method: In this paper, a semi-supervised approach to analyze users' Persian opinions has been proposed. The method takes advantage of the abundant unlabeled data available in addition to a small number of labeled data in the training phase. The proposed method uses the support vector machine (SVM) algorithm, which has been shown to be effective in opinion mining in related research. The proposed method extracts emotional words from comments using sentiment lexicons and then extracts term frequency-inverse of document frequency vectors. The semi-supervised SVM algorithm is then applied to these vectors to estimate the polarity of sentiments.Results: To evaluate the performance of the proposed method, it has been tested on the Digikala comments dataset and compared with the supervised SVM algorithm and semi-supervised self-training method for different numbers of labeled data based on accuracy, precision, recall, and F1 criteria. The results indicate that the proposed semi-supervised method outperforms the supervised SVM algorithm and the semi-supervised method of self-training. The impact of the size of unlabeled data is also investigated in the experiments.Discussion: One of the advantages of the proposed method is that it can estimate the polarity of opinions that have not been trained in the training phase, which is not possible in some graph-based methods. Furthermore, it is not affected by the error of training with labeled data in self-training methods. In conclusion, the proposed semi-supervised method provides an efficient solution for analyzing users' opinions in Persian. This method can be used by businesses and organizations to gain insights into their customers' opinions and improve their products and services accordingly. Manuscript profile -
Open Access Article
32 - Forecasting the Tehran Stock market by Machine Learning Methods using a New Loss Function
Mahsa Tavakoli Hassan Doosti -
Open Access Article
33 - Interval Forecasting of Stock Price Changes using the Hybrid of Holt’s Exponential Smoothing and Multi-Output Support Vector Regression
Sayyed Mohammadreza Davoodi Mahdi Rabiei -
Open Access Article
34 - 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
35 - Remote Sensing and Land Use Extraction for Kernel Functions Analysis by Support Vector Machines with ASTER Multispectral Imagery
E. Akbari N. Amiri H. AziziLand use is being considered as an element in determining land change studies, environmental planning and natural resource applications. The Earth’s surface Study by remote sensing has many benefits such as, continuous acquisition of data, broad regional coverage, MoreLand use is being considered as an element in determining land change studies, environmental planning and natural resource applications. The Earth’s surface Study by remote sensing has many benefits such as, continuous acquisition of data, broad regional coverage, cost effective data, map accurate data, and large archives of historical data. To study land use / cover, remote sensing as an efficient technology, is always desired by experts. In this case, classification could be considered as one of the most important methods of extracting information from digital satellite images. Selecting the best classification method and applying the proper values for parameters extremely influence the trust level of extracted land use maps. This research is an applied study which attempts to introduce Support Vector Machines (SVM) classification method, a recent development from the machine learning community. Moreover, we prove its potential for structure–activity relationship analysis on Aster multispectral data of central county of Kabodar-Ahang region in Hamedan, Iran. Accuracy of SVMs method is varied by the type of kernel functions and its parameters. The purpose of this research is to find the accuracy of Land use extraction by SVM method by Polynomial and radial basis functions kernel with their estimated optimum parameters in addition to compare the results with Maximum Likelihood method. Most of the scientists imply that Maximum Likelihood method is suitable for classification. Therefore, we try to compare SVM with ML method and to deliberate the efficiency of this new method in classification progress on Aster multispectral data. The accuracy of SVM method by Polynomial and radial basis functions kernel with optimum parameters and ML classification methods achieved 93.18%, 91.77% and 88.35 % respectively as an overall accuracy. By comparing the accuracy of these methods, SVM method by Polynomial kernel was evaluated as suitable. Therefore, we can suggest using SVM method especially with the use of Polynomial kernel to determine land use. In general, the results of this research are very practical in natural resources conservation planning and studies. Also, this study verifies the effectiveness and robustness of SVMs in the classification of remotely sensed images. Manuscript profile -
Open Access Article
36 - Using Data Mining to Predict Bank Customers Churn
parvin najmi abbas rad maryam shoarThe 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 MoreThe 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 -
Open Access Article
37 - Explaining the categories of support vector machine and neural network for Ranking of bank branches
davod khosroanjom mohamamd elyasi behzad keshanchi Bahare Boobanian shovana abdollahiThere is a lot of information in the banking industry that is of particular importance in identifying it. The use of data mining techniques not only improves quality but also leads to competitive advantages and market positioning. By using data mining and in order to an MoreThere is a lot of information in the banking industry that is of particular importance in identifying it. The use of data mining techniques not only improves quality but also leads to competitive advantages and market positioning. By using data mining and in order to analyze patterns and trends, banks can predict the accuracy of how bank branches are ranked. In this paper, the branches of one of the large commercial banks (number of selected branches 1825 branches and the number of features used 57 features) were performed on real data using support vector machine categories and multi layer perceptron neural network. The evaluation results related to the support vector machine showed that this classifier has lower efficiency for the proposed method. However, the use of neural networks and its combination with PCA showed that it has high performance criteria. Values related to efficiency and accuracy were obtained using neural network with very high accuracy. Manuscript profile -
Open Access Article
38 - Assessment of Adaptive neural fuzzy inference systems and support vector regression in runoff estimation(A case study:Dez Basin)
Ghazaleh Ahmadian Ahmadabad Mahmoud Zakeri Niri Saber Moazami GoudarziEstimation of discharge flow in basin due to impact on water resource management can have an important economic role.In this research several computationals intelligence techniques suchas:ANN,SVR and ANFIS have been used to prediction the runoff dez basin.correlation be MoreEstimation of discharge flow in basin due to impact on water resource management can have an important economic role.In this research several computationals intelligence techniques suchas:ANN,SVR and ANFIS have been used to prediction the runoff dez basin.correlation between stations was investigated and stations of kamandan,zoorabad and daretakht were eliminated due to small correlation with around stations.then due to lack of human intervention with using xlstat software were evaluated trend of stations and were selected stations without trend.Inorder to evaluate the performance of models were used correlation,RMSE and NSE.Results of this research showed that ANFISwith clustering approach gives better estimation than grid partitioning approach.ANN, ANFIS and SVR have agood ability to simulate the flow of dez basin. Manuscript profile -
Open Access Article
39 - Performance evaluation of FFT_PCA Method based on dimensionality reduction algorithms in improving classification accuracy of OLI data
Parviz Zeaiean Firooz Abadi1 Hasan Hasani MoghaddambFusions of panchromatic and multispectral images create new permission to gainspatial and spectral information together. This paper focused on hybrid image fusionmethod FFT-PCA, to fuse OLI bands to apply Dimensionality Reduction (DR)methods (PCA, ICA and MNF) on this f MoreFusions of panchromatic and multispectral images create new permission to gainspatial and spectral information together. This paper focused on hybrid image fusionmethod FFT-PCA, to fuse OLI bands to apply Dimensionality Reduction (DR)methods (PCA, ICA and MNF) on this fused image to evaluate the effect of thesemethods on final classification accuracy. A window of OLI images from ArdabilCounty was selected to this purpose and preprocessing method like atmospheric andradiometric correction was applied on this image. Then panchromatic (band8) andmultispectral bands of OLI were fused with FFT-PCA method. Three dimensionalityreduction algorithms were applied on this fused image and the training data forclassification were selected from DRs Output. A total of eight classes include bareland, rich range land, water bodies, settlement, snow, agricultural land, fallow andpoor range land were selected and classified with support vector machine algorithm.The results showed that classification based on dimensionality reduction algorithmswas quite good on OLI data classification. Overall accuracy and kappa coefficient ofclassification images showed that ICA, PCA and MNF methods 86.9%, 89%, 96.8%and 0.84, 0.91, 0.96 respectively. The MNF based image classification has higherclassification accuracy between two others. PCA and ICA have lower accuracy thanMNF respectively. Manuscript profile -
Open Access Article
40 - Detection of Cardiac Hypertrophy by RVM and SVM Algorithms
fereshte morsali -
Open Access Article
41 - Landslide susceptibility modelling using integrated application of computational intelligence in Ahar County, Iran
Solmaz Abdollahizad Mohammad Ali Balafar Bakhtiar Feizizadeh Amin Babazadeh Sangar Karim Samadzamini -
Open Access Article
42 - Automatic Face Recognition via Local Directional Patterns
Maryam Moghaddam Saeed Meshgini -
Open Access Article
43 - Face Recognition using Eigenfaces , PCA and Supprot Vector Machines
Parvaneh Shayghan Gharamaleki Hadi Seyedarabi -
Open Access Article
44 - Facial expression recognition based on Local Binary Patterns
Saeede Jabbarzadeh Reyhani Saeed Meshgini -
Open Access Article
45 - Protein Secondary Structure Prediction: a Literature Review with Focus on Machine Learning Approaches
Leila Khalatbari Mohammad Reza Kangavari -
Open Access Article
46 - Feature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine
Mojgan Elikaei Ahari Babak Nasersharif -
Open Access Article
47 - Visual Tracking using Learning Histogram of Oriented Gradients by SVM on Mobile Robot
Iman Zabbah Shima Foolad Ali Maroosi Alireza Pourreza -
Open Access Article
48 - Modelling and Predicting Earnings Quality Using Decision Tree and Support Vector Machine
Loghman Hatami Shirkouhi Soghra Barari Nokashti Maryam OoshaksaraeEarnings 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 MoreEarnings 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 -
Open Access Article
49 - 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 -
Open Access Article
50 - A Neural Network Model Based on Support Vector Machine for Conceptual Cost Estimation in Construction Projects
Behnam Vahdani Seyed Meysam Mousavi Morteza Mousakhani Mani Sharifi Hassan Hashemi -
Open Access Article
51 - Presenting a Hybrid Model based on the Machine Learning for the Classification of Banking and Insurance Industry Common Customers
Hamidreza Amirhassankhani Abbas Toloie Eshlaghy reza radfar Alireza pourebrahimiGlobal competition, dynamic markets, and rapidly shrinking innovation and technology cycles, all have imposed significant challenges on the financial, banking, and insurance industries and the need to data analysis for improving decision-making processes in these organi MoreGlobal competition, dynamic markets, and rapidly shrinking innovation and technology cycles, all have imposed significant challenges on the financial, banking, and insurance industries and the need to data analysis for improving decision-making processes in these organizations has become increasingly important. In this regard, the data stored in the databases of these organizations are considered as valuable sources of information and knowledge needed for organizational decisions. In the present research, the researchers focus on the common customers of the bank and insurance industry. The purpose is to provide a methodology to predict the performance of new customers based on the behavior of previous customers. To this end, a hybrid model based on support vector machine and genetic algorithm is used. The support vector machine is responsible for modeling the relationship between customer performance and their identity information and the genetic algorithm is responsible for tuning and optimizing the parameters of the support vector machine. The results obtained from customer classification using the proposed model in this research led to customer classification with a high accuracy of 99%. Manuscript profile -
Open Access Article
52 - Evaluation of soil loss rate in land uses of Nirchai watershed using RUSLE model and Landsat satellite images (OLI)
mousa Abedini AmirHesam Pasban Behrouz Nezafat takleThe purpose of this research is to evaluate the amount of soil loss in the land uses of the Nirchai watershed using the RUSLE model in Ardabil province. In order to carry out this research, first, the satellite image of the studied area related to the year 1400 and the MoreThe purpose of this research is to evaluate the amount of soil loss in the land uses of the Nirchai watershed using the RUSLE model in Ardabil province. In order to carry out this research, first, the satellite image of the studied area related to the year 1400 and the month of June was received from the American Geological Research Center, and after atmospheric and radiometric corrections, a land use map was prepared using the supervised classification method using the support vector machine method. Then the RUSLE model was used to estimate the erosion rate. SPSS 21, Excel, ArcGIS 5.4, Archydro and ENVI 5.3 software were used to analyze and produce maps in this research. RUSLE model parameter layer includes rain erosion layer, soil layer, topography layer, vegetation layer and soil protection factor as well as various statistics related to rain gauge stations, hydrometry, topographic maps 1:50000, geology 1:100000 as well as DEM (20 meters area) and GIS geographic information system and remote sensing have been used. The results of this study showed that the average amount of annual soil erosion for the whole basin ranges from 0.5 to 14.25 tons per hectare per year. Also, the investigation of the regression relationships between the factors of RUSLE model and the amount of annual soil erosion showed that the topography factor (LS) with the highest value of the coefficient of determination R^2=0.93 is the most important in estimating the annual soil erosion using the RUSLE model. Manuscript profile -
Open Access Article
53 - Investigating the Effect of Land Use Change on Soil Erosion and Sediment Yield in Razeychay Watershed During Past 20 Years
Mousa Abedini Farydeh Bahramnia Gojabeiglo Raoof Mostafazadeh AmirHesam PasbanSoil erosion is a global problem that threatens water and soil resources and land use change is one of the important factors in soil erosion intensification. The aim of this study was to evaluate the effect of land use change on soil erosion in Razeychay watershed of Me MoreSoil erosion is a global problem that threatens water and soil resources and land use change is one of the important factors in soil erosion intensification. The aim of this study was to evaluate the effect of land use change on soil erosion in Razeychay watershed of Meshginshahr located in Ardabil province. First, Landsat images of the study area in May 1999, and 2019 and were obtained from USGS website. In the image processing stage, atmospheric and radiometric corrections have been conducted, and then the land use maps of the study area has been prepared for study years using support vector machine (SVM) as a supervised classification method. Then, the RUSLE model was used to estimate the amount of erosion in the two time span. SPSS, Excel, Arc GIS 5.4, Archydro and ENVI 5.3 software were used to spatial analysis and data processing.The results showed that, rangeland, irrigated farming and bare lands have decreased during the last twenty years. While, the extent of dry farming and residential area have increased. Meanwhile, the highest change is related to dry farming (an increase of 27.69 hectares). According to the results of erosion modeling, the rate of erosion from 1999 to 2019 has decreased from 6.49 to 6.46 tons per hectare per year. Manuscript profile -
Open Access Article
54 - Evaluation and assessment of changes in forest area Harra (mangrove) Using remote sensing techniques Case Study: Bandar Abbas
محمد علی زنگنه اسدی ابراهیم تقوی مقدم elahe akbariKnowledge of changes is first, most important action planners, and authority’s natural and human environment. Satellite images and satellite image processing techniques and methods very precise tool for navigation and assessment of changes in forest areas is the p MoreKnowledge of changes is first, most important action planners, and authority’s natural and human environment. Satellite images and satellite image processing techniques and methods very precise tool for navigation and assessment of changes in forest areas is the purpose of of this study is assess the changes in forest areas mangrove in Bandar using the technique of remote sensing. To achieve this purpose of we used the information and topographic maps, satellite images and the algorithm of maximum likelihood and minimum distance 1989, 2005 and 2015 years of area. The results show that the maximum likelihood method with 98/32% overall accuracy and kappa coefficient 0/978 accurate method than using support vector machine and the minimum distance for mapping land cover changes and monitoring changes in forest. According to calculations forest surface area’ of 76/09 sq km in 1989 has increased to 125/08 square kilometers in 2015. Which indicates the shores of the Strait of Hormuz is the hydrodynamic change. Thus adopting every environmental protection measures in the area is necessary, any facilities and infrastructure projects must comply with environmental considerations and ecological. Manuscript profile -
Open Access Article
55 - Identifying Factors Affecting Non-curent Debts of Banks Using Neural Networks and Support Vector Machine Algorithm
sajjad kordmanjiri iman dadashi zahra Khoshnood hamid reza gholamnia roshanThe main purpose of this paper is to identify the factors influencing the creation and increase of non-current debts to make a more appropriate decision in granting facilities. For this purpose, to select effective variables, from the analysis algorithms of correlation MoreThe main purpose of this paper is to identify the factors influencing the creation and increase of non-current debts to make a more appropriate decision in granting facilities. For this purpose, to select effective variables, from the analysis algorithms of correlation and Lasso components; And to classify the samples, neural networks and support machine were used. In this study, a sample of 660 legal customers of Sepah Bank for the years 2006-2017 was selected and focused on the characteristic variables extracted from the facility contracts of these customers along with financial, non-financial, auditing and economic variables. The results showed that the Lasso algorithm focused on financial, economic and auditing variables, performed better than the neighboring component analysis algorithm, and based on this algorithm, 10 key variables affecting non-current debts were identified. Due to the better performance of support vector machines with radial cores, its use in modeling non-current debts is recommended. Manuscript profile -
Open Access Article
56 - Emotion Recognition of Speech Signals Based on Filter Methods
Narjes Yazdanian Hamid MahmoodianAbstract: Speech is the basic mean of communication among human beings.With the increase of transaction between human and machine, necessity of automatic dialogue and removing human factor has been considered. The aim of this study was to determine a set of affective fe MoreAbstract: Speech is the basic mean of communication among human beings.With the increase of transaction between human and machine, necessity of automatic dialogue and removing human factor has been considered. The aim of this study was to determine a set of affective features the speech signal is based on emotions. In this study system was designs that include three mains sections, features extraction, features selection and classification. After extraction of useful features such as, mel frequency cepstral coefficient (MFCC), linear prediction cepstral coefficients (LPC), perceptive linear prediction coefficients (PLP), ferment frequency, zero crossing rate, cepstral coefficients and pitch frequency, Mean, Jitter, Shimmer, Energy, Minimum, Maximum, Amplitude, Standard Deviation, at a later stage with filter methods such as Pearson Correlation Coefficient, t-test, relief and information gain, we came up with a method to rank and select effective features in emotion recognition. Then Result, are given to the classification system as a subset of input. In this classification stage, multi support vector machine are used to classify seven type of emotion. According to the results, that method of relief, together with multi support vector machine, has the most classification accuracy with emotion recognition rate of 93.94%. Manuscript profile -
Open Access Article
57 - 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
58 - Proposing an Automated System for Differentiating between Healthy Individuals and Patients with Diabetic Retinopathy
Mina Ghayoor Hossein PourghassemDiabetes is one of the most common diseases in the world, adversely affects different body organs. One of the most common causes of eye problems is diabetes. Analyzing retinal damage is one of the best ways to diagnose diabetes so one of the best ways to diagnose diabet MoreDiabetes is one of the most common diseases in the world, adversely affects different body organs. One of the most common causes of eye problems is diabetes. Analyzing retinal damage is one of the best ways to diagnose diabetes so one of the best ways to diagnose diabetes is to look at the damage to the retina. Hence, first, a highly applicable and effective method, which is a combination of the Wiener filter and the discrete wavelet transform (DWT), is used for the removal of noise from images. Afterward, the k-means clustering algorithm is used to remove the bad image sections including very light and very dark areas of the image. Next, the image color and shape features are extracted. We transfer the images to the lab space, which fits the eye more, to extract the image color features. To extract the image shape features, first the images are converted into grey images and then the shape features are extracted. After extracting the features, the number of features is reduced using the Principal Component Analysis (PCA) algorithm. Besides, the best and most effective features are also selected. Finally, the support vector machine classifier with different kernel is used to classify the features and images into two categories, namely the healthy participants and patients. The accuracy resulting from this algorithm using the test images is over 90%. Manuscript profile -
Open Access Article
59 - 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 KazemiEspidan 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. MoreEspidan 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 -
Open Access Article
60 - Improved Intrusion Detection System Based On Distributed Self-Adaptive Genetic Algorithm to Solve Support Vector Machine in Form of Multi Kernel Learning with Auto Encoder
Elaheh Faghihnia Seyed Reza Kamel Tabakh Farizni Maryam KheirabadiIntrusion into systems through network infrastructure and the Internet is one of the security challenges facing the world of information and communication technology and can lead to the destruction of systems and access to data and information. In this paper, a support MoreIntrusion into systems through network infrastructure and the Internet is one of the security challenges facing the world of information and communication technology and can lead to the destruction of systems and access to data and information. In this paper, a support vector machine model with weighted and parameters of SVM kernels are presented to detect the intrusion. Due to the high complexity of this problem, conventional optimization methods are not able to solve it. Therefore, we propose a Distributed Self Adaptive Genetic Algorithm (DSAGA). On the other hand, due to the high volume of data in such issues, Auto encoder has been used to reduce data. The proposed approach is a hybrid method based on Auto encoder, improved Support Vector Machine and Distributed Self Adaptive Genetic Algorithm (DSAGA) that it is evaluated by its execution on DARPA data set. Manuscript profile -
Open Access Article
61 - Evaluation of Deep Neural Networks in Emotion Recognition Using Electroencephalography Signal Patterns
Azin Kermanshahian Mahdi KhezriIn this study, the design of a reliable detection system that is able to identify different emotions with the desired accuracy has been considered. To reach this goal, two different structures for the emotion recognition system include 1) using linear and non-linear fea MoreIn this study, the design of a reliable detection system that is able to identify different emotions with the desired accuracy has been considered. To reach this goal, two different structures for the emotion recognition system include 1) using linear and non-linear features of the electroencephalography (EEG) signal along with common classifiers and 2) using EEG signal in a deep learning structure is considered to identify emotional states. To design the system, the EEG signals of the DEAP database which were recorded by displaying emotional videos from 32 subjects were used. After the preparation and noise removal, linear and non-linear features such as: Skewness, Kurtosis, Hjorth parameters, Lyapunov exponent, Shannon entropy, correlation and fractal dimension and time reversibility were extracted from the alpha, beta and gamma subbands of the EEG signals. Then according to structure 1, the features were applied as input to common classifiers such as decision tree (DT), k nearest neighbor (kNN) and support vector machine (SVM). Also in structure 2, the EEG signal was considered as the input of the convoloutional neural network (CNN). The goal is to evaluate the results of deep learning networks and other methods for emotion recognition. According to the obtained results, the SVM achieved the best performance for identifying four emotional states with 94.1 % accuracy. Also, the proposed CNN identified the desired emotional states with the accuracy of 86%. Deep learning methods are superior to simple classifiers because they do not require the features of the signals and are resistant to different noises. Using a short period of time for the signals and performing near optimal preprocessing and conditioning, can further improve the results of deep neural networks. Manuscript profile -
Open Access Article
62 - Brain Stroke Classification Based on Deep Learning Approach in Microwave Brain Imaging System
Majid Roohi Jalil Mazloum Mohammad Ali Pourmina Behbod GhalamkariOne of the main reasons of death in the world, mostly affecting seniors, is brain stroke. Almost 85% of all brain strokes are ischemic due to internal bleeding in a part of the brain. Due the high mortality rate, quick diagnosic and treatment of ischemic and hemorrhagic MoreOne of the main reasons of death in the world, mostly affecting seniors, is brain stroke. Almost 85% of all brain strokes are ischemic due to internal bleeding in a part of the brain. Due the high mortality rate, quick diagnosic and treatment of ischemic and hemorrhagic strokes are of utmost importance. In this paper, to realize microwave brain imaging system, a circular array-based of modified bowtie antennas located around the multilayer head phantom with a spherical target with radius of 1 cm as intracranial hemorrhage target aresimulated in CST simulator. To obtain satisfied radiation characteristics in the desired band (from 0.5-5 GHz) an appropriate matching medium is designed. First, in the processing section, a confocal image-reconstructing method based using delay and sum (DAS) and delay, multiply and sum (DMAS) beam-forming algorithms is used. The reconstructed images generated shows the usefulness of the proposed confocal method in detecting the spherical target in the range of 1 cm. The main purpose of this paper is stroke classification using deep learning approaches. For this, an image classification algorithm is developed to estimate the stroke type from reconstructed images. By using the proposed deep learning method, the reconstructed images are classified into different categories of cerebrovascular diseases using a multiclass linear support vector machine (SVM) trained with convol­uti­onal neural networks (CNN) features extracted from the images. The simulated results show the suitability of the proposed image reconstruction method for precisely localizing bleeding targets, with 89% accuracy in 9 seconds. In addition, the proposed deep-learning approach shows good performance in terms of classification, since the system does not confuse between different classes. Manuscript profile -
Open Access Article
63 - Evaluation of Surface Electromyogram Signal Decomposition Methods in the Design of Hand Movement Recognition System
Maryam Karami Mahdi KhezriOne method for determining motor commands to control hand prostheses is to use surface electr­omy­ogr­am (sEMG) signal patterns. Due to the random and non-stationary nature of the signal, the idea of using signal information in small time intervals was inves MoreOne method for determining motor commands to control hand prostheses is to use surface electr­omy­ogr­am (sEMG) signal patterns. Due to the random and non-stationary nature of the signal, the idea of using signal information in small time intervals was investigated. In this study, with the aim of more accurate and faster detection of hand movements, two signal decomposition methods, namely discrete wavelet transform (DWT) and empirical mode decomposition (EMD) were evaluated. The sEMG sign­als of the Ninapro-DB1 dataset, which were extracted from 27 healthy subjects while performing hand and finger movements, were used to design the system. Simple time domain features with fast calculation capability were extracted for each subband of the decomposed signals. Also, support vector machine (SVM) using different kernel functions was applied as a classifier. The results show that the use of DWT and EMD methods with the ability to access the information of time and frequency sub-intervals of the signals, provides better results in identifying hand movements compared to previous studies. With the EMD method and eight intrinsic mode functions (IMF), the highest recognition accuracy of 83.3% was obtained for six movements. Also, the DWT with the Bior5.5 mother wavelet and five levels of decomposition, achieved 80% recognition accuracy for ten movements and with the Coif2 mother wavelet and six levels of decomposition, the accuracy was 83.33% for eight movements. The results show the better performance of the DWT decomposition method compared to EMD for the design of the hand movement recognition system using sEMG signal patterns. Manuscript profile -
Open Access Article
64 - Content-Based Medical Image Retrieval Based on Image Feature Projection in Relevance Feedback Level
Mohammad Behnam Hossein PourghasemThe purpose of this study is to design a content-based medical image retrieval system and provide a new method to reduce semantic gap between visual features and semantic concepts. Generally performance of the retrieval systems based on only visual contents decrease bec MoreThe purpose of this study is to design a content-based medical image retrieval system and provide a new method to reduce semantic gap between visual features and semantic concepts. Generally performance of the retrieval systems based on only visual contents decrease because these features often fail to describe the high level semantic concepts in user’s mind. In this paper this problem is solved using a new approach based on projection of relevant and irrelevant images in to a new space with low dimensionality and less overlapping in relevance feedback level. For this purpose, first we change the feature space using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques and then classify the feedback images applying Support Vector Machine (SVM) classifier. The proposed framework has been evaluated on a database consisting of 10,000 medical X-ray images of 57 semantic classes. The obtained results show that the proposed approach significantly improves the accuracy of retrieval system. Manuscript profile -
Open Access Article
65 - 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
66 - Wavelet Packet Entropy in Speaker-Independent Emotional State Detection from Speech Signal
Mina Kadkhodaei Elyaderani Hamid Mahmoodian Ghazaal SheikhiIn this paper, wavelet packet entropy is proposed for speaker-independent emotion detection from speech. After pre-processing, wavelet packet decomposition using wavelet type db3 at level 4 is calculated and Shannon entropy in its nodes is calculated to be used as featu MoreIn this paper, wavelet packet entropy is proposed for speaker-independent emotion detection from speech. After pre-processing, wavelet packet decomposition using wavelet type db3 at level 4 is calculated and Shannon entropy in its nodes is calculated to be used as feature. In addition, prosodic features such as first four formants, jitter or pitch deviation amplitude, and shimmer or energy variation amplitude besides MFCC features are applied to complete the feature vector. Then, Support Vector Machine (SVM) is used to classify the vectors in multi-class (all emotions) or two-class (each emotion versus normal state) format. 46 different utterances of a single sentence from Berlin Emotional Speech Dataset are selected. These are uttered by 10 speakers in sadness, happiness, fear, boredom, anger, and normal emotional state. Experimental results show that proposed features can improve emotional state detection accuracy in multi-class situation. Furthermore, adding to other features wavelet entropy coefficients increase the accuracy of two-class detection for anger, fear, and happiness. Manuscript profile -
Open Access Article
67 - GIS-based support vector machine model in shallow landslide hazards prediction: A case study on Ilam dam watershed, Iran
Yaghoub Niazi Manuel E Mendoza Ali Talebi Hasti Bidaki -
Open Access Article
68 - Crack Detection in Structures Using Modal Strain Energy and Frequency
SIyamak ghadimi seyyed sina kourehliIn this paper a new method for crack detection in structures based on first three mode frequencies and modal strain energies using least square support vector machine has been proposed. Since the mode shape vectors are equivalent to nodal displacements of a vibrating st MoreIn this paper a new method for crack detection in structures based on first three mode frequencies and modal strain energies using least square support vector machine has been proposed. Since the mode shape vectors are equivalent to nodal displacements of a vibrating structure, therefore in each element of the structure strain energy is stored. The strain energy of a structure due to mode shape vector are usually referred to as modal strain energy (MSE) and can be considered as a valuable parameter for crack identification. Also, change of natural frequencies is effective, inexpensive, and fast tool for non-destructive testing. So, the proposed method uses the first three natural frequencies and modal strain energies as the input parameters and crack states as output to train the least squares support vector machine model. Manuscript profile -
Open Access Article
69 - Facial Expression Recognition Using Texture and Edge Descriptors
Davar Giveki Nastaran Mirzaei -
Open Access Article
70 - To Present Method for Rice Variety Identification with Fuzzy-imperialist Competitive Algorithm
Zeinab Faraji Farhad Ramezani Homayun Motameni -
Open Access Article
71 - Intrusion Detection System in Computer Network Using Hybrid Algorithms (SVM and ABC)
Bahareh Gholipour Goodarzi Hamid Jazayeri Soheil Fateri -
Open Access Article
72 - A New Hierarchical Architecture Based on SVM for Persian License Plate Character Recognition
Amir Ebrahimi Ghahnavieh Abolghasem A. Raie -
Open Access Article
73 - RISE Feedback Control Design for RLED Robot Manipulator Using Bees Algorithm
Behnaz Hadi Alireza Khosravi Abolfazl Ranjbar N. Pouria Sarhadi -
Open Access Article
74 - Presenting a New Text-Independent Speaker Verification System Based on Multi Model GMM
Mohammad Mosleh Faraz Forootan Najmeh Hosseinpour -
Open Access Article
75 - Intrusion Detection System in Computer Networks Using Decision Tree and SVM Algorithms
Zeinab Kermansaravi Hamid Jazayeriy Soheil Fateri -
Open Access Article
76 - A New Approach in Persian Handwritten Letters Recognition Using Error Correcting Output Coding
Maziar Kazemi Muhammad Yousefnezhad Saber Nourian -
Open Access Article
77 - Building Semantic Kernel for Persian Text Classification with a Small Amount of Training Data
Amir H. Jadidinejad Venus Marza -
Open Access Article
78 - Improvement of Face Recognition Approach through Fuzzy-Based SVM
Amir Hooshang Mazinan لیلا یار محمدی -
Open Access Article
79 - A Novel Method Based on Support Vector Machines to Classify Bank Transactions
Melika Tojjari Razieh Farazkish -
Open Access Article
80 - An Automatic Model Combining Descriptors of Gray-Level Co-Occurrence Matrix and HMAX Model for Adaptive Detection of Liver Disease in CT Images
Sanaz Bagheri Somayeh Saraf Esmaili -
Open Access Article
81 - A New Approach in Epilepsy Diagnosis using Discrete Wavelet Transformation and Analysis of Variance
Tayebeh Iloon Ramin Barati Hamid Azad -
Open Access Article
82 - Investigating the Effects of Land Use Changes on Trend of Desertification Using Remote Sensing (Case Study: Abarkooh Plain, Yazd, Iran)
Mohammad Ali Hakimzadeh Ardakani Fatemeh Cheshmberah Mohammad Hossein Mokhtari -
Open Access Article
83 - Designing Credit Risk Early-warning System for Individual and Corporate Customers of the Banks using Neural Network Models, Survival Probability Function and Support Vector Machine
Roya Derakhshani Mirfeiz Fallah hosein jahangirnia Reza Gholami jamkarani Hamidreza kordlouieCredit risk is the probability of default of the borrower or the counterparty of the bank in fulfilling its obligations, according to the agreed terms. In other words, uncertainty about receiving future investment income is called risk, which is of great importance in b MoreCredit risk is the probability of default of the borrower or the counterparty of the bank in fulfilling its obligations, according to the agreed terms. In other words, uncertainty about receiving future investment income is called risk, which is of great importance in banks. The purpose of this article is to estimate the credit risk of individual and corporate customers. In this study, the statistical information of 400 individual customers and7500 corporate customers was used. In this regard, the results of neural network model and support vector machine model have been compared. The obtained results have shown that the components considered in this study based on their personal, financial and economic characteristics had significant effects on the probability of customer default and credit risk calculation. Also, the results of this study showed that the application of control policies at the beginning of the repayment period suggests facilities that have the highest probability of default with long life and high repayment. The comparison of the results of the prediction accuracy shows the higher explanatory power of the support vector machine model and the use of the survival probability function than the simple neural network model for both groups of customers. Manuscript profile -
Open Access Article
84 - Robot control system using SMR signals detection
faeze asadi -
Open Access Article
85 - Fault diagnosis in a distillation column using a support vector machine based classifier
ebrahim mirakhorli -
Open Access Article
86 - Bionic Wavelet Transform Entropy in Speaker-Independent and Context-Independent Emotional State Detection from Speech Signal
Mina Kadkhodaei Elyaderani Hamid Mahmoodian -
Open Access Article
87 - 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 roshanInvestor'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 MoreInvestor'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 -
Open Access Article
88 - Development of an intelligent method based on fuzzy technical indicators for predicting and trading the euro-dollar exchange rate
alireza sadeghi amir Daneshvar Mahdi Madanchi ZajToday, the Forex market is the largest financial market in the world. Determining the right strategy for buying or selling in the Forex market is based on predicting the price trend. Therefore, to choose a suitable strategy in Forex, complex meta-heuristic models are us MoreToday, the Forex market is the largest financial market in the world. Determining the right strategy for buying or selling in the Forex market is based on predicting the price trend. Therefore, to choose a suitable strategy in Forex, complex meta-heuristic models are used. In this research, by predicting the market trend and based on trading rules based on fuzzy technical indicators, a new method for investing in the Forex market is presented. For forecasting, a combination of hyper support vector machine (HSVM) algorithm is used and for market classification in three different classes (uptrend, downtrend, sideway) and a dynamic genetic algorithm is used to optimize trading rules. Five fuzzy technical indicators have been used to determine the trading rules. Euro-dollar pair data is used as daily training and test data for a daily period between 2010 and 2019. The results obtained compared to traditional methods have had promising results. Manuscript profile -
Open Access Article
89 - 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
90 - Portfolio Formation Using Diagonal Quadratic Discriminant Analysis and Weighting Based on Posterior Probability
Saeid Fallahpour H. Pirayesh ShirazinejadStock return forecasting is one of the most important question for investing in Stock markets. Because of the effects of policy, economic, etc., we need moderns and intelligent models to forecast the returns. The main idea in this research is classifying the stocks int MoreStock return forecasting is one of the most important question for investing in Stock markets. Because of the effects of policy, economic, etc., we need moderns and intelligent models to forecast the returns. The main idea in this research is classifying the stocks into high and low return groups, for this purpose support vector machine (SVM) was used. To elect the best variables for models we used sequential feature selection and in order to evaluate the accuracy of SVM we do the same forecasting with diagonal quadratic discriminant analysis (DQDA). By using paired t-test, we conclude that models have no significant difference. Equal weighted portfolios were created for each models with and without feature selection also, we used posterior probability to weight the portfolio of DQDA with feature selection. The returns were calculated for each portfolio during the years 1388-1391. The simulating results are satisfying and all portfolios’ returns are better than market portfolio. Manuscript profile -
Open Access Article
91 - Novel QSPR Study on the Melting Points of a Broad Set of Drug-Like Compounds Using the Genetic Algorithm Feature Selection Approach Combined With Multiple Linear Regression and Support Vector Machine
Alireza Jalali Mehdi Nekoei Majid Mohammadhosseini -
Open Access Article
92 - Ability of Machine Learning Algorithms and Artificial Neural Networks in Predicting Accounting Profit Information Content Before Announcing
Hossein Alizadeh Majid Zanjirdar Gholam Ali HajiPurpose: The aim of this research is to investigate the capability of artificial neural networks and machine learning algorithms, including Support Vector Machine and Random Forest, in predicting the information content of accounting profits before its announcement in a MorePurpose: The aim of this research is to investigate the capability of artificial neural networks and machine learning algorithms, including Support Vector Machine and Random Forest, in predicting the information content of accounting profits before its announcement in accepted companies on the Tehran Stock Exchange during the period from 2015 to 2020.Methodology: Daily data required for the research were collected using Rahnaward-e-Novin software, and a systematic random sampling method was used to select 88 companies. MATLAB was used for modeling artificial neural networks and machine learning algorithms, and Python code was employed to calculate abnormal returns in neural networks and machine learning algorithms. The information content of profits was measured through the test of the relationship between profits and abnormal returns, based on the model by Porti et al. (2018). The input variables for artificial neural networks and machine learning algorithms are technical indicators. Accuracy, precision, recall, and F-score metrics were used for performance evaluation.Findings: The results of predicting with three models of artificial neural networks, Support Vector Machine, and Random Forest showed that Support Vector Machine and Random Forest had higher accuracy than artificial neural networks in predicting buy, sell, and hold strategies, and only Support Vector Machine had the ability to predict the information content of profits among the three models.Originality / Value: Designing a predictive model for stock price movements in the next trading day using artificial neural networks, Support Vector Machine, and Random Forest as the main innovation of the research. The research findings can increase the speed of information dissemination to the market and attract it, which will reduce the impact of informational asymmetry and information-based trading and ultimately enhance market efficiency. Manuscript profile -
Open Access Article
93 - Application and Comparison of Simple Additive Weighting method, Fuzzy Analytic Hierarchy Process and Support Vector Machine in identifying the internal and external factors in SWOT’s analysis
Ali HaeriaAn Ardekani Hamidreza Koosha fatemeh mirsaeediAll organizations, must determine their future path; in other words, they must understand where they stand and where they are heading to. Strategic management is one of the most recognized management approaches for this purpose. One of the most important steps in strate MoreAll organizations, must determine their future path; in other words, they must understand where they stand and where they are heading to. Strategic management is one of the most recognized management approaches for this purpose. One of the most important steps in strategic management is recognizing organization’s internal and external factors. If these factors are recognized correctly, they can be used to establish correct and optimal strategies. So far, few researchers have used exact methods for identifying and prioritizing internal and external factors. In this article, we try to use multi criteria (Sample Additive Weighting and Fuzzy Analytic Hierarchy Process techniques) and data mining (support vector machine) for reorganization of internal and external factors. The case study in this research is Water and Sewerage Company of Mashhad. First, organization’s internal and external factors are identified and classified by organization’s higher managers and experts. For applying Sample Additive Weighting and Fuzzy Analytic Hierarchy Process, first, the criteria according to internal and external factor’s definition are determined and criteria’s weights are identified by Fuzzy Analytic Hierarchy Process also Sample Additive Weighting. Then, by using these weights, the values for all factors are calculated and classified. Using these criteria (attributes) and WEKA software, after data preprocess, factors classified by Support Vector Machine that is one of the most accurate data mining approaches. The results show Support Vector Machine prediction more accurately compared to other techniques. Manuscript profile -
Open Access Article
94 - An Intrusion Detection System for Network Cyber Security Using Hybrid Feature Selection Algorithms
Golnaz Aghaee Ghazvini zahra Oday KamilOne of the most important challenges of the expansion of the Internet and virtual space is cyber-attacks. These attacks are becoming new every day and it is becoming more difficult to deal with them. As a result, methods should be used to detect them, which can detect a MoreOne of the most important challenges of the expansion of the Internet and virtual space is cyber-attacks. These attacks are becoming new every day and it is becoming more difficult to deal with them. As a result, methods should be used to detect them, which can detect all types of cyber-attacks in the shortest possible time and with proper accuracy. Nowadays, machine learning methods are usually used to detect cyber-attacks. But since the data related to cyber-attacks have many characteristics and are kind of bulky data, as a result, the accuracy of conventional machine learning methods to detect them is usually low. In this research, we have used a hybrid feature selection method to select optimal features from the database related to cyber-attacks, which increases the accuracy of attack detection by classification models. In the proposed feature selection method, first the features that have the least redundancy with each other and at the same time are most related to the category variables (labels) are selected by the MRMR algorithm. Then, using a wrapper feature selection method based on the gray wolf optimization (GWO) algorithm to select a subset of the features selected from the previous step, which maximizes the accuracy of the SVM classifier model, is used this subset has optimal features by which the SVM model is trained. As a result, the accuracy of detecting cyber-attacks by the SVM model increases. According to the simulation results, the average accuracy of the proposed method for detecting cyber-attacks is 99.84%, which has improved compared to the intrusion detection methods of the reference article. Manuscript profile
Sanad
Sanad is a platform for managing Azad University publications
Links
Related Centers
Technical Support
Official pages
The rights to this website are owned by the Raimag Press Management System.
Copyright © 2021-2024