List of Articles یادگیری ماشین

• Open Access Article
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  1 - Optimized Modeling for satisfaction in the relationship between a physician and patient based on machine learnin methods
  Fatemeh Saghafi mojtaba shadmehr Zainabolhoda Heshmati Hadi Veisi
  Health has always been one of the most important concerns of human. The goal in this research is to know what factors cause and affect patient satisfaction in the relationship between a physician and patient. Since this relationship is a form of healthcare service, the More

  Health has always been one of the most important concerns of human. The goal in this research is to know what factors cause and affect patient satisfaction in the relationship between a physician and patient. Since this relationship is a form of healthcare service, the SERVQUAL service quality assessment method has been used as a framework. However the questions have been reviewed based on the previous literature and the experts’ views, leading to a questionnaire designed for the healthcare domain. Data collection has been performed using the questionnaires on subjects selected amongst clients of Rhinoplasty Centers in Tehran. To analyze the data, three machine-learning approaches have been implemented namely Decision Tree, Support Vector Machine and Artificial Neural Networks. A number of possible factors affecting the patient-physician relationship have been used as input and patient satisfaction has been taken as output. Comparing the results of these three methods, Artificial Neural Networks method is shown to have better performance, which has therefore been used for prioritizing the effective factors in this relationship. The results indicate that reaching the information which the patient expects their physician to give is the most effective characteristic in patient satisfaction. The rank of gained features were compared with similar researches. The outcome was very similar and approved the results. Manuscript profile
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  2 - A new algorithm based on ensemble learning for learning to rank in information retrieval
  Azadeh Shakery elham ghanbari
  Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank has been shown to be useful in many applications of information retrieval, natural language processing, and data mining. Learning to rank can be described by More

  Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank has been shown to be useful in many applications of information retrieval, natural language processing, and data mining. Learning to rank can be described by two systems: a learning system and a ranking system. The learning system takes training data as input and constructs a ranking model. The ranking system then makes use of the learned ranking model for ranking prediction. In this paper, a new learning algorithm based on ensemble learning for learning ranking models in information retrieval is proposed. This algorithm iteratively constructs weak learners using a fraction of the training data whose weight distribution is determined based on previous weak learners. The proposed algorithm combines the weak rankers to achieve the final ranking model. This algorithm constructs a ranking model on a fraction of the training data to increase the accuracy and reduce the learning time. Experimental results based on Letor.3 benchmark dataset shows that the proposed algorithm significantly outperforms other ensemble learning algorithms. Manuscript profile
• Open Access Article
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  3 - Optimum modeling of patient satisfaction with the doctor based on machine learning methods
  - شادمهر Zainabolhoda Heshmati Fatemeh saghafi Hadi Veisi
  20.1001.1.27170411.1394.7.24.5.8
  The patient-centered approach in the field of health has recently been proposed in the field of the medical system of our country, but until now there is no published scientific research on the factors of patient satisfaction with doctors. The present article aims to co More

  The patient-centered approach in the field of health has recently been proposed in the field of the medical system of our country, but until now there is no published scientific research on the factors of patient satisfaction with doctors. The present article aims to cover the stated gap with a scientific evaluation based on the real information obtained from the field study. A questionnaire was designed for the health sector and was approved by the opinion of experts. In order to get the opinions of patients, a questionnaire was distributed among 500 people who underwent rhinoplasty in Tehran, and 395 questionnaires were collected. Three methods of decision tree, support vector machine and neural networks were used for data analysis. The analysis of the results according to the accuracy criteria showed that the most efficient method, in priority, the importance of the factors affecting the patient's satisfaction; Neural network method. The results of the analysis with this method indicate that the most effective feature in the patient's satisfaction with the doctor is the information that the patient expects the doctor to provide. The results of ranking factors in comparison with other studies that only used statistical methods for analysis showed that the results were relatively similar and confirmed each other. But the strengths of the neural network method in modeling is the strength of this method compared to the mentioned studies. Manuscript profile
• Open Access Article
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  4 - Intrusion Detection Based on Cooperation on the Permissioned Blockchain Platform in the Internet of Things Using Machine Learning
  Mohammad Mahdi  Abdian majid ghayori Seyed Ahmad  Eftekhari
  Intrusion detection systems seek to realize several objectives, such as increasing the true detection rate, reducing the detection time, reducing the computational load, and preserving the resulting logs in such a way that they cannot be manipulated or deleted by unauth More

  Intrusion detection systems seek to realize several objectives, such as increasing the true detection rate, reducing the detection time, reducing the computational load, and preserving the resulting logs in such a way that they cannot be manipulated or deleted by unauthorized people. Therefore, this study seeks to solve the challenges by benefiting from the advantages of blockchain technology, its durability, and relying on IDS architecture based on multi-node cooperation. The proposed model is an intrusion detection engine based on the decision tree algorithm implemented in the nodes of the architecture. The architecture consists of several connected nodes on the blockchain platform. The resulting model and logs are stored on the blockchain platform and cannot be manipulated. In addition to the benefits of using blockchain, reduced occupied memory, the speed, and time of transactions are also improved by blockchain. In this research, several evaluation models have been designed for single-node and multi-node architectures on the blockchain platform. Finally, proof of architecture, possible threats to architecture, and defensive ways are explained. The most important advantages of the proposed scheme are the elimination of the single point of failure, maintaining trust between nodes, and ensuring the integrity of the model, and discovered logs. Manuscript profile
• Open Access Article
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  5 - 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
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  6 - An Intrusion Detection System based on Deep Learning for CAN Bus
  Fatemeh Asghariyan Mohsen Raji
  20.1001.1.27170411.1402.15.57.2.7
  In recent years, with the advancement of automotive electronics and the development of modern vehicles with the help of embedded systems and portable equipment, in-vehicle networks such as the controller area network (CAN) have faced new security risks. Since the CAN bu More

  In recent years, with the advancement of automotive electronics and the development of modern vehicles with the help of embedded systems and portable equipment, in-vehicle networks such as the controller area network (CAN) have faced new security risks. Since the CAN bus lacks security systems such as authentication and encryption to deal with cyber-attacks, the need for an intrusion detection system to detect attacks on the CAN bus seem to be very necessary. In this paper, a deep adversarial neural network (DACNN) is proposed to detect various types of security intrusions in CAN buses. For this purpose, the DACNN method, which is an extension of the CNN method using adversarial learning, detects intrusion in three stages; In the first stage, CNN acts as a feature descriptor and the main features are extracted, and in the second stage, the discriminating classifier classifies these features and finally, the intrusion is detected using the adversarial learning. In order to show the efficiency of the proposed method, a real open source dataset was used in which the CAN network traffic on a real vehicle during message injection attacks is recorded on a real vehicle. The obtained results show that the proposed method performs better than other machine learning methods in terms of false negative rate and error rate, which is less than 0.1% for DoS and drive gear forgery attack and RPM forgery attack while this rate is less than 0.5% for fuzzy attack. Manuscript profile
• Open Access Article
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  7 - Presenting a web recommender system for user nose pages using DBSCAN clustering algorithm and machine learning SVM method.
  reza molaee fard Mohammad mosleh
  20.1001.1.27170411.1402.15.57.6.1
  Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can s More

  Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can solve the problem of the cold start of the system and improve the quality of the search. In this research, a new method is presented in order to improve recommender systems in the field of the web, which uses the DBSCAN clustering algorithm to cluster data, and this algorithm obtained an efficiency score of 99%. Then, using the Page rank algorithm, the user's favorite pages are weighted. Then, using the SVM method, we categorize the data and give the user a combined recommender system to generate predictions, and finally, this recommender system will provide the user with a list of pages that may be of interest to the user. The evaluation of the results of the research indicated that the use of this proposed method can achieve a score of 95% in the recall section and a score of 99% in the accuracy section, which proves that this recommender system can reach more than 90%. It detects the user's intended pages correctly and solves the weaknesses of other previous systems to a large extent. Manuscript profile
• Open Access Article
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  8 - Intrusion Detection Based on Cooperation on the Permissioned Blockchain Platform in the Internet of Things Using Machine Learning
  Mohammad Mahdi  Abdian majid ghayori Seyed Ahmad  Eftekhari
  Intrusion detection systems seek to realize several objectives, such as increasing the true detection rate, reducing the detection time, reducing the computational load, and preserving the resulting logs in such a way that they cannot be manipulated or deleted by unauth More

  Intrusion detection systems seek to realize several objectives, such as increasing the true detection rate, reducing the detection time, reducing the computational load, and preserving the resulting logs in such a way that they cannot be manipulated or deleted by unauthorized people. Therefore, this study seeks to solve the challenges by benefiting from the advantages of blockchain technology, its durability, and relying on IDS architecture based on multi-node cooperation. The proposed model is an intrusion detection engine based on the decision tree algorithm implemented in the nodes of the architecture. The architecture consists of several connected nodes on the blockchain platform. The resulting model and logs are stored on the blockchain platform and cannot be manipulated. In addition to the benefits of using blockchain, reduced occupied memory, the speed, and time of transactions are also improved by blockchain. In this research, several evaluation models have been designed for single-node and multi-node architectures on the blockchain platform. Finally, proof of architecture, possible threats to architecture, and defensive ways are explained. The most important advantages of the proposed scheme are the elimination of the single point of failure, maintaining trust between nodes, and ensuring the integrity of the model, and discovered logs. 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 (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