Volume-9 , Issue-3 , Jun 2021, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Optimal Task Assignment to Heterogeneous Cores in Cloud Computing Using Particle Swarm Optimization

  Musa Mojarad, Nafiseh Sadat Hosseini, Tahere Lalesangi

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.3 , pp.1-6, Jun-2021

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  Recently, mobile heterogeneous embedded systems have developed rapidly and significantly due to hardware upgrades. These systems support multiple processor cores, and their energy consumption is increasing as computing capacity increases. Cloud computing is a way to reduce energy costs. In this paper, the issue of energy dissipation when assigning tasks to heterogeneous processors or cloud servers is considered. The objective is to minimize energy cost from all embedded heterogeneous mobile systems via optimally assigning tasks to mobile clouds and heterogeneous cores. The suggested method is an energy-conscious heterogeneous resource management approach that is supported by the heterogeneous task allocation approach. Here, to solve this problem, a combined method according to Particle Swarm Optimization (PSO) and greedy algorithm is used. Experiments performed provide a heterogeneous mobile embedded system with more efficient energy savings in mobile cloud computing.

  Key-Words / Index Term
  Energy Consumption, Heterogeneous Cores, Particle Swarm Optimization, Cloud Computing

  References
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  Citation
  Musa Mojarad, Nafiseh Sadat Hosseini, Tahere Lalesangi, "Optimal Task Assignment to Heterogeneous Cores in Cloud Computing Using Particle Swarm Optimization," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.3, pp.1-6, 2021

• Open Access   Article

  Development of Katz Similarity Index to Improve Link Prediction in Social Networks Based on Reliable Routes

  Musa Mojarad, Omid Ranjbar Dehghan, Amin Ranjbar Dehghan, Alireza Salar

  Research Paper | Journal-Paper (IJSRCSE)

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

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  Abstract
  Online social networks are a new generation of databases that are in the spotlight of Internet users these days. In fact, social networks have a multi-layered architecture. This means that there can be links between users on several different networks. Predicting links in social networks is one of the most important activities in social media analysis. In this article, link prediction is extracted by extracting different features between users in social networks through multiple layers. Most real-world social networks promote communication in multiple layers (i.e., multiplex social networks). Here, problem of link prediction on several networks, namely Foursquare and Twitter, is examined. Here, only users who have a shared account on both networks are considered. The link prediction process for the Foursquare social network is based on connection information from both layers. By extracting structural features between users and using reliable routes, a new method according to Katz similarity index is suggested to compute final similarity between users. Experiments show that the suggested algorithm can successfully predict links for the Foursquare social network through multilayer information.

  Key-Words / Index Term
  Social Networks; Link Prediction; Multiplex; Reliable Routes; Katz Index

  References
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  Citation
  Musa Mojarad, Omid Ranjbar Dehghan, Amin Ranjbar Dehghan, Alireza Salar, "Development of Katz Similarity Index to Improve Link Prediction in Social Networks Based on Reliable Routes," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.3, pp.16-21, 2021

• Open Access   Article

  A Review on Recommendation Systems Based On Fuzzy Logics in E-Commerce

  Nivedita Das

  Review Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.3 , pp.22-31, Jun-2021

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  Abstract
  Recommender systems have turned into a significant web-based recommendation methodology and are popularly used to endorse various items. Huge amounts of data are available on the internet on the web, the need for analyzing and personalizing systems is continuously increasing. The recommendation systems have a vast range of applications in the field of e-commerce. This paper discusses the types of recommender systems based on fuzzy logic, and adaptive and flexible methods are specifically grouped into three clusters: collaborative filtering, content-based filtering, and hybrid filtering. This paper also addresses recommender system growth following the –eCommerce sector challenges. Each approach has its relative strengths and weaknesses relating to the domain. The main aim of this review paper is to analyze the different types of recommendation systems along with their techniques based on fuzzy logic and used in e-commerce.

  Key-Words / Index Term
  Recommendation system (RS), E-commerce, Fuzzy logics, user behavior data, filtering, behavioral matrix

  References
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  Citation
  Nivedita Das, "A Review on Recommendation Systems Based On Fuzzy Logics in E-Commerce," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.3, pp.22-31, 2021

• Open Access   Article

  Flood Forecasting, using Artificial Neural Network (ANN) and Predict Values of Flood condition Derived using River Water Level Data

  Rajesh Kumar Sharma

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.3 , pp.32-35, Jun-2021

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  Abstract
  This paper focuses on flood forecasting, using Artificial Neural Network (ANN) and predicts the values of flood condition derived using Narmada River Water level data of Hoshangabad (M.P). We have used the water level data as input data of ANN model for flood forecasting, and determine Standardized Water Level Index (SWLI). Artificial Neural networks operate on the principle of learning from a training set. There is a large variety of neural network models and learning procedures. Two classes of neural networks that are usually used for prediction applications are feed-forward networks and recurrent networks. They often train both of these networks using back-propagation algorithm.

  Key-Words / Index Term
  Artificial Neural Networks (ANNs), SPI

  References
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  [4]. Mu¨ller, B., and Reinhardt, J.: Neural Networks: An Introduction, the Physics of Neural Networks Series, Springer-Verlag, 2, pp.266, 1991.

  Citation
  Rajesh Kumar Sharma, "Flood Forecasting, using Artificial Neural Network (ANN) and Predict Values of Flood condition Derived using River Water Level Data," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.3, pp.32-35, 2021