Volume-9 , Issue-3 , Jun 2021, ISSN 2321-3256 (Electronic Version) Go Back

• Open Access   Article

  Providing a Web Recommender System Using Markov Chains and Registration Files Structure

  M. Mojarad, M.A. Mohammadshahi, M. Saberi Nasab, A. Shamsi

  Research Paper | Journal (IJSRNSC)

  Vol.9 , Issue.3 , pp.1-8, Jun-2021

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  Today, due to the increasing growth of the internet and the huge amount of information we need systems to be able to recommend the most appropriate services and products to the user. The systems that do this are recommender systems. These systems are intelligently using artificial intelligence techniques to identify the interests of your users on the internet and suggest tailored offers to the user’s preferences and interests. Today, Markov models commonly used to predict web pages. For this purpose, in this research, we use a new Markov model and use the structure of registration files to predict the next pages obtained by the user. The proposed Markov model is based on a matrix of 1 to k and in the form of a Markov model which predicts the next pages. In order to reduce the complexity of the search space, as well as to better navigate to the recommender system, we use k-means clustering to group users. The results of the evaluations of the proposed method on the NASA web server log file and in the F-Measure criterion shows 0.57% superiority over BCF.

  Key-Words / Index Term
  Markov chains, File structure, Recommendation system, K-means clustering

  References
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  Citation
  M. Mojarad, M.A. Mohammadshahi, M. Saberi Nasab, A. Shamsi, "Providing a Web Recommender System Using Markov Chains and Registration Files Structure," International Journal of Scientific Research in Network Security and Communication, Vol.9, Issue.3, pp.1-8, 2021

• Open Access   Article

  Design of an IoT-based management and monitoring system for intelligent irrigation

  M. Mojarad, H. Ahmadi, A. Behjathaghighi, M. Kakaei Karji

  Research Paper | Journal (IJSRNSC)

  Vol.9 , Issue.3 , pp.9-15, Jun-2021

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  Abstract
  Agriculture plays a major role in the world economy and most people depend on it for their livelihood. This makes water an important resource that must be conserved using the latest available technologies. Today, the Internet of Things has expanded its capabilities to smart agriculture. In this research, an automated and low-cost system for intelligent irrigation based on a fuzzy-based energy-aware routing approach is presented. In addition, a neural network is trained to determine the best irrigation program, based on information received from sensors (such as temperature, soil moisture, etc.). The user in the system can monitor the data collection process with mobile phones, mobile computers, etc. and manage the irrigation of agricultural products. The proposed system proves its intelligence, low cost and portability, its suitability for greenhouses, farms, etc. The simulation results show that the proposed method offers better results compared to the LEACH protocol as well as the WSN-IoT algorithm in various criteria such as grid life and power consumption

  Key-Words / Index Term
  Internet of Things, Intelligent Irrigation, Intelligent Agriculture, Monitoring, LEACH Protocol

  References
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  Citation
  M. Mojarad, H. Ahmadi, A. Behjathaghighi, M. Kakaei Karji, "Design of an IoT-based management and monitoring system for intelligent irrigation," International Journal of Scientific Research in Network Security and Communication, Vol.9, Issue.3, pp.9-15, 2021

• Open Access   Article

  Diagnosis of Diabetes Using Bee Colony Algorithm and Fuzzy Decision Tree

  M. Mojarad, E. Hajizadegan, M. Gurkani

  Research Paper | Journal (IJSRNSC)

  Vol.9 , Issue.3 , pp.16-21, Jun-2021

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  Abstract
  M. Mojarad1*, E. Hajizadegan2, M. Gurkani2

  Key-Words / Index Term
  Diagnosis of diabetes, bee colony algorithm, fuzzy decision tree

  References
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  [2] Rezaeipanah, A., Mojarad, M., & Sechin Matoori, S. (2021). Intrusion Detection in Computer Networks Through Combining Particle Swarm Optimization and Decision Tree Algorithms. Journal of Business Data Science Research, 1(1), 14-22.
  [3] Rezaeipanah, A., & Ahmadi, G. (2020). Breast Cancer Diagnosis Using Multi-Stage Weight Adjustment In The MLP Neural Network. The Computer Journal.
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  [13] Cheruku, R., Edla, D. R., & Kuppili, V. (2017). SM-RuleMiner: Spider monkey based rule miner using novel fitness function for diabetes classification. Computers in biology and medicine, 81, 79-92.

  Citation
  M. Mojarad, E. Hajizadegan, M. Gurkani, "Diagnosis of Diabetes Using Bee Colony Algorithm and Fuzzy Decision Tree," International Journal of Scientific Research in Network Security and Communication, Vol.9, Issue.3, pp.16-21, 2021