decision tree

Open Access Article

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

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

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

Open Access Article

2 - Face Detection based on Semantic Model for Mobile Banking
leili nosrati Amir Massoud Bidgoli hamid hajseiedjavadi

In this paper, a new authentication protocol for online banking based on the semantic model of features extracted from people's image is introduced. The proposed approach is presented using smart mobile phones for online digital imaging for customers. In this work, a fu More

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

Open Access Article

3 - Presenting a new model for ATM demand scenario
Alireza Agha Gholizadeh Sayyar Mohamadreza Motadel Alireza Pour ebrahimi

In today's competitive world, the ability to recognize predict customer demand is an important issue for the success of organizations. And since ATMs are one of the most important channels for cash distribution and one of the most fundamental criteria for assessing the More

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

Open Access Article

4 - Scalable Fuzzy Decision Tree Induction Using Fast Data Partitioning and Incremental Approach for Large Dataset
Somayeh Lotfi Mohammad Ghasemzadeh Mehran Mohsenzadeh Mitra Mirzarezaee

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Open Access Article

5 - A Model for Identification Tax Fraud Based on Improved ID3 Decision Tree Algorithm and Multilayer Perceptron Neural Network
Akbar Javadian Kootanaee Abbas Ali Poor aghajan Sarhamami Mirsaeid Hosseini Shirvani

Tax revenues are one of the most important sources of governments and cover a large portion of government spending. In recent years, fraud in financial statements and tax returns has increasingly become a serious problem for businesses, governments and investors. Most t More

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

Open Access Article

6 - Landslide susceptibility mapping using advanced machine learning algorithms (Case study: Sarovabad city, Kurdistan province)
Baharak Motamedvaziri Hemen Rastkhadiv Seyed Akbar Javadi Hasan Ahmadi

The occurrence of landslides in mountainous areas may cause serious damage to road infrastructure, and may also lead to human deaths. Therefore, the purpose of this study is to landslide susceptibility mapping using advanced machine learning algorithms in Sarovabad city More

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

Open Access Article

7 - Investigation of Spatio-Temporal Dynamics of Parishan Land-Cover Using Decision Tree Model and Satellite Imagery
golafarin zare Bahram Malek mohammadi Hamidreza Jafari Ahmad Reza Yavari Ahmad Nohegar

Background and Objective: Wetlands, as one of the most important types of ecosystems in the world, are extremely threatened. In addition to being part of Iran's protected areas, the Parishan wetland is also known as an international wetland and biosphere reserve. Unders More

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

Open Access Article

8 - Presenting and explaining a model to create the value of the company according to the role of accounting standards management, financial reporting quality and audit quality using meta-innovative models
saman khorshid yahya kamyabi mehdi khalilpour

In the world of investment, decision making is the most important part of the investment process, in which investors need to make the most optimal decisions in order to achieve their maximum benefits and wealth. In this regard, the most important factor in the decision- More

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

Open Access Article

9 - Optimal Portfolio Selection using Machine Learning Algorithms
Mohammad baghar yazdani khodashahri Seyed Hossein Naslemousavi Mir Saeid Hoseini Shirvani

Choosing the right portfolio is always one of the most important issues for investors. The price trend is predicted using technical analysis or basic analysis. Technical analysis focuses on market performance, while the focus of fundamental analysis is on the mechanism More

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

Open Access Article

10 - Online Mean Shift Detection in Multivariate Quality Control using Boosted Decision Tree learning
Abbas Asadi Yaghoub Farjami

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Open Access Article

11 - Presenting a data mining model based on sustainable development index in the urban management of Tehran metropolis affected by the covid-19 epidemic.
Abbas Maleki sadegh abedi Alireza Irajpoor

By applying the restrictions caused by the Covid-19 pandemic, it seems that changes in the concentrations of pollutants CO, O3, NO, NO2, SO2, PM2.5, PM10 and AQI can be seen in the periods before and after the epidemic. Therefore, the changes of air pollutants and traff More

By applying the restrictions caused by the Covid-19 pandemic, it seems that changes in the concentrations of pollutants CO, O3, NO, NO2, SO2, PM2.5, PM10 and AQI can be seen in the periods before and after the epidemic. Therefore, the changes of air pollutants and traffic restrictions are investigated as one of the sub-categories of environmental indicators of sustainable urban development in the period of 2018/01/21 to 2022/03/20 in the stations under the supervision of Tehran city. First, the data is collected, processed and cleaned. Machine learning methods including decision tree, random forest, support vector machine, Bayesian network and perceptron neural network are applied to select the effective features using the particle swarm optimization method. Investigations showed that the prediction model using decision tree and random forest had the best performance for both precision and recall criteria. The results of the research showed that the concentration of pollutants in the period of Covid-19 compared to before, is increased in some stations and decreased in others, and also the application of traffic restrictions during the epidemic did not have a significant and noticeable effect in reducing the concentration of air pollutants. Also, by examining the trend of deaths during the epidemic period, it was found that the decrease or increase of pollutants has no significant relationship with the trend of deaths caused by Covid-19. Manuscript profile

Open Access Article

12 - A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements
Akbar Javadian Kootanaee Abbas ali Poor Aghajan Mirsaeid Hosseini Shirvani

10.22094/joie.2020.1877455.1685

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