List of Articles Classification

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Determination of Optimum SVMs Based on Genetic Algorithm in Classification of Hyper spectral Imagery
  farhad samadzadegan hadise hassani
  Hyper spectral remote sensing imagery, due to its rich source of spectral information provides an efficient tool for ground classifications in complex geographical areas with similar classes. Referring to robustness of Support Vector Machines (SVMs) in high dimensional More

  Hyper spectral remote sensing imagery, due to its rich source of spectral information provides an efficient tool for ground classifications in complex geographical areas with similar classes. Referring to robustness of Support Vector Machines (SVMs) in high dimensional space, they are efficient tool for classification of hyper spectral imagery. However, there are two optimization issues which strongly effect on the SVMs performance: Optimum SVMs parameters determination and optimum feature subset selection. Traditional optimization algorithms are appropriate in limited search space but they usually trap in local optimum in high dimensional space, therefore it is inevitable to apply meta-heuristic optimization algorithms such as Genetic Algorithm to obtain global optimum solution. This paper evaluates the potential of different proposed optimization scenarios in determining of SVMs parameters and feature subset selection based on Genetic Algorithm (GA). Obtained results on AVIRIS Hyper spectral imagery demonstrate superior performance of SVMs achieved by simultaneously optimization of SVMs parameters and input feature subset. In Gaussian and Polynomial kernels, the classification accuracy improves by about 5% and15% respectively and more than 90 redundant bands are eliminated. For comparison, the evaluation is also performed by applying it to Simulated Annealing (SA) that shows a better performance of Genetic Algorithm especially in complex search space where parameter determination and feature selection are solve simultaneously. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - The Participation of Three Brain Tissues in Alzheimer’s disease Diagnosis from Structural MRI
  Shima Tajeddini Habibollah  Danyali Mohammad Sadegh  Helfroush Yaghoub  Fatemi
  20.1001.1.27170411.1396.9.33.1.6
  Alzheimer’s disease (AD) is a progressive and irreversible disease which gradually makes patients unable to do their daily routines. Although the present treatments can not cure the disease completely, its early detection can reduce symptoms and enhance the patients’ li More

  Alzheimer’s disease (AD) is a progressive and irreversible disease which gradually makes patients unable to do their daily routines. Although the present treatments can not cure the disease completely, its early detection can reduce symptoms and enhance the patients’ life quality. In the current literature, using the grey matter (GM) tissue which is known as an appropriate biomarker is highly common in AD diagnosis. However, two other brain tissues known as cerebrospinal fluid (CSF) and white matter (WM) seem to reveal beneficial information about the patients’ brain changes. The aim of the present study is to develop an automatic system for the early diagnosis of Alzheimer’s disease from structural MRI by simultaneously considering suitable features of all GM, CSF and WM tissues. A SVM-RBF classifier is trained and evaluated on the OASIS database to separate AD from healthy control (HC) subjects. The obtained results represent higher accuracy and sensitivity of the proposed algorithm in comparison with similar method. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - Modified orthogonal chaotic colonial competition algorithm and its application in improving pattern recognition in multilayer perceptron neural network
  Payman Moallem mehrdad sadeghi hariri MAHDI hashemi
  20.1001.1.27170411.1397.10.35.1.7
  Despite the success of the Colonial Competition Algorithm (ICA) in solving optimization problems, this algorithm still suffers from repeated entrapment in the local minimum and low convergence speed. In this paper, a new version of this algorithm, called Modified Orthog More

  Despite the success of the Colonial Competition Algorithm (ICA) in solving optimization problems, this algorithm still suffers from repeated entrapment in the local minimum and low convergence speed. In this paper, a new version of this algorithm, called Modified Orthogonal Chaotic Colonial Competition (COICA), is proposed. In the policy of absorbing the proposed version, each colony seeks the space to move towards the colonizer through the definition of a new orthogonal vector. Also, the possibility of selecting powerful empires is defined through the boltzmann distribution function, and the selection operation is performed through the roulette wheel method. The proposed multilevel perceptron neural network (MLP) algorithm is used to classify standard datasets, including ionosphere and sonar. To evaluate the performance of this algorithm and to evaluate the generalizability of the trained neural network with the proposed version, the K-Fold cross-validation method has been used. The results obtained from the simulations confirm the reduction of network training error as well as the improved generalizability of the proposed algorithm. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  4 - Integrating Data Envelopment Analysis and Decision Tree Models in Order to Evaluate Information Technology-Based Units
  Amir Amini ali alinezhad somaye shafaghizade
  In order to evaluate the performance and desirability of the activities of its units each organization needs an evaluation system to assess this desirability and it is more important for financial institutions, including information technology-based companies. Data enve More

  In order to evaluate the performance and desirability of the activities of its units each organization needs an evaluation system to assess this desirability and it is more important for financial institutions, including information technology-based companies. Data envelopment analysis (DEA) is a non-parametric method to measure the effectiveness and efficiency of decision-making units (DMUs). On the other hand, data mining technique allows DMUs to explore and discover meaningful information, which had previously been hidden in large databases. . This paper presents a general framework for combining DEA and regression tree for evaluating the effectiveness and efficiency of the DMUs. Resulting hybrid model is a set of rules that can be used by policy makers to discover reasons behind efficient and inefficient DMUs. Using the proposed method for examining factors related to productivity, a sample of 18 branches of Iran insurance in Tehran was elected as a case study. After modeling based on advanced model the input oriented LVM model with weak disposability in data envelopment analysis was calculated using undesirable output, and by use of decision tree technique deals with extracting and discovering the rules for the cause of increased productivity and reduced productivity. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  5 - New Method to Improve Illumination Variations in Adult Images Based on Fuzzy Deep Neural Network
  Sasan Karamizadeh abouzar arabsorkhi
  20.1001.1.27170411.1398.11.41.3.8
  In the era of the Internet, recognition of adult images is important to children's physical and mental protection. It is a challenge to recognize adult images with changes in the illumination and skin color. In this paper, we proposed a new method for solving illumi More

  In the era of the Internet, recognition of adult images is important to children's physical and mental protection. It is a challenge to recognize adult images with changes in the illumination and skin color. In this paper, we proposed a new method for solving illumination normalization with skin color classification in the diagnosis of the adult image. In this paper, the deep fuzzy neural network method is utilized to improve the illumination normalization of adult images, which has improved the recognization of adult images is utilized. Using Xception to dividing the images and reduce the illumination variations in each part separately, which makes it possible to reduce the illumination variation in the whole image without losing details. In addition, the advanced color combination algorithm based on Gaussian-KNN algorithm is used for skin color classification, a non-parametric method is used for classifications and regressions. Finally, the SVM algorithm is utilized for image classification. In this paper, 33,000 different types of images are collected from the Internet. The results show that the proposed method of 1/3 has improved the accuracy of the recognization. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  6 - Improving performance of probe-based rate control mechanisms using classification: evaluation on an experimental testbed for High Throughput WLANs
  ghalibaf ali Mohammad Nassiri mohammadhassan daei mahdi sakhaei
  20.1001.1.27170414.1399.12.43.9.0
  MIMO technology offers a wide range of transmission rates for modern wireless LANs. In order to improve the performance of the rate control module, statistical information on the history of state and usage of each transmission rate is maintained at the MAC layer to help More

  MIMO technology offers a wide range of transmission rates for modern wireless LANs. In order to improve the performance of the rate control module, statistical information on the history of state and usage of each transmission rate is maintained at the MAC layer to help determine the rate at which future packets are sent. However, the great diversity of transmission rates in the 802.11n and 802.11ac standards imposes an overhead for updating this information. In this article, to reduce the state space of transmission rates while keeping statistics approximately up to date for each rate, a method for clustering rates is presented so that when sending a packet over a transmission rate, statistical information relating to all the rates belonging to the same cluster is updated. As a result, statistics for a greater number of rates can be updated even when sending a fewer number of packets. We implemented our proposed mechanism in the Linux kernel environment and evaluated its performance under different conditions on an experimental testbed deployed in our research laboratory. The results show that the proposed method outperforms the de-facto Minstrel-HT rate control mechanism in terms of throughput and number of successful transmissions. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  7 - Converting protein sequence to image for classification with convolutional neural network
  reza ahsan mansour ebrahimi dianat dianat
  20.1001.1.27170414.1399.12.43.11.2
  Since methods for sequencing machine learning sequences were not successful in classifying healthy and cancerous proteins, it is imperative to find a way to represent these sequences to classify healthy and ill individuals with deep learning approaches. In this study di More

  Since methods for sequencing machine learning sequences were not successful in classifying healthy and cancerous proteins, it is imperative to find a way to represent these sequences to classify healthy and ill individuals with deep learning approaches. In this study different methods of protein sequence representation for classification of protein sequence of healthy individuals and leukemia have been studied. Results showed that conversion of amino acid letters to one-dimensional feature vectors in classification of 2 classes was not successful and only one disease class was detected. By changing the feature vector to colored numbers, the accuracy of the healthy class recognition was slightly improved. The binary protein sequence representation method was more efficient than the previous methods with the initiative of sequencing the sequences in both one-dimensional and two-dimensional (image by Gabor filtering). Protein sequence representation as binary image was classified by applying Gabor filter with 100% accuracy of the protein sequence of healthy individuals and 98.6% protein sequence of those with leukemia. The findings of this study showed that the representation of protein sequence as binary image by applying Gabor filter can be used as a new effective method for representation of protein sequences for classification Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  8 - Analysing students' learning through morning exercise using data mining techniques
  behzad lak narges abbasi
  20.1001.1.27170411.1400.13.49.11.6
  Since school has identified as one of the major agents in the socialization process, it has found remarkable position in the educational system of any country. Improving student learning is also a key factor to enhance the educational system quality in schools. As regul More

  Since school has identified as one of the major agents in the socialization process, it has found remarkable position in the educational system of any country. Improving student learning is also a key factor to enhance the educational system quality in schools. As regular exercise has profoundly positive impact on learning, this paper mainly aims to provide an approach to enhance students' learning process through morning exercise based on artificial neural network (ANN) technique and intelligent water drop optimization algorithm. This study is a quantitative research, which is purposefully a descriptive-analytical and methodologically a practical study. To that end, ANN technique was used to classify and extract the results, as well as, intelligent water drop optimization algorithm was employed for feature selection. In ANN, eleven neurons were selected as the appropriate number of hidden layer neurons; a combination of two linear and sigmoidal activation functions were employed as interlayer transmission functions; a training function was applied to train the network; and a maximum 3000 duplicates was proposed for the training algorithm on dataset. The accuracy of the proposed method was 68%, which has improved by about 2.2% compared to the basic method, i.e., exercise has a positive effect on students' learning. The results showed a proper performance of the optimal classification on the dataset with homogeneous parameters as well as a better performance of the artificial neural networks than the novel methods. Accordingly, the proposed method can have an appropriate improvement in terms of output accuracy in strengthening the learning process. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  9 - Application identification through intelligent traffic classification
  Shaghayegh Naderi
  Traffic classification and analysis is one of the big challenges in the field of data mining and machine learning, which plays an important role in providing security, quality assurance and network management. Today, a large amount of transmission traffic in the network More

  Traffic classification and analysis is one of the big challenges in the field of data mining and machine learning, which plays an important role in providing security, quality assurance and network management. Today, a large amount of transmission traffic in the network is encrypted by secure communication protocols such as HTTPS. Encrypted traffic reduces the possibility of monitoring and detecting suspicious and malicious traffic in communication infrastructures (instead of increased security and privacy of the user) and its classification is a difficult task without decoding network communications, because the payload information is lost, and only the header information (which is encrypted too in new versions of network communication protocols such as TLS1.03) is accessible. Therefore, the old approaches of traffic analysis, such as various methods based on port and payload, have lost their efficiency, and new approaches based on artificial intelligence and machine learning are used in cryptographic traffic analysis. In this article, after reviewing the traffic analysis methods, an operational architectural framework for intelligent traffic analysis and classification has been designed. Then, an intelligent model for Traffic Classification and Application Identification is presented and evaluated using machine learning methods on Kaggle141. The obtained results show that the random forest model, in addition to high interpretability compared to deep learning methods, has been able to provide high accuracy in traffic classification compared to other machine learning methods. Finally, tips and suggestions about using machine learning methods in the operational field of traffic classification have been provided. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  10 - Application Identification Through Intelligent Traffic Classification
  Shaghayegh Naderi
  Traffic classification and analysis is one of the big challenges in the field of data mining and machine learning, which plays an important role in providing security, quality assurance and network management. Today, a large amount of transmission traffic in the network More

  Traffic classification and analysis is one of the big challenges in the field of data mining and machine learning, which plays an important role in providing security, quality assurance and network management. Today, a large amount of transmission traffic in the network is encrypted by secure communication protocols such as HTTPS. Encrypted traffic reduces the possibility of monitoring and detecting suspicious and malicious traffic in communication infrastructures (instead of increased security and privacy of the user) and its classification is a difficult task without decoding network communications, because the payload information is lost, and only the header information (which is encrypted too in new versions of network communication protocols such as TLS1.03) is accessible. Therefore, the old approaches of traffic analysis, such as various methods based on port and payload, have lost their efficiency, and new approaches based on artificial intelligence and machine learning are used in cryptographic traffic analysis. In this article, after reviewing the traffic analysis methods, an operational architectural framework for intelligent traffic analysis and classification has been designed. Then, an intelligent model for Traffic Classification and Application Identification is presented and evaluated using machine learning methods on Kaggle141. The obtained results show that the random forest model, in addition to high interpretability compared to deep learning methods, has been able to provide high accuracy in traffic classification (95% and 97%) compared to other machine learning methods. Finally, tips and suggestions about using machine learning methods in the operational field of traffic classification have been provided. Manuscript profile