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Cloud Services User’s Recommendation System Using Random Iterative Fuzzy-Based Trust Computation and Support Vector Regression

Author

Listed:
  • Janjhyam Venkata Naga Ramesh

    (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India)

  • Syed Khasim

    (School of Computer Science & Engineering, VIT-AP University, Amaravati 522237, India)

  • Mohamed Abbas

    (Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia)

  • Kareemulla Shaik

    (School of Computer Science & Engineering, VIT-AP University, Amaravati 522237, India)

  • Mohammad Zia Ur Rahman

    (Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India)

  • Muniyandy Elangovan

    (Department of R&D, Bond Marine Consultancy, London EC1V 2NX, UK)

Abstract

Cloud computing is now a fundamental type of computing due to technological innovation and it is believed to be a benefit for mid-scale enterprises. The use of cloud computing is increasing daily, which improves service quality but also gives rise to security concerns. Finding trustworthy service can be very challenging, take a great deal of time, or produce subpar services. Due to these difficulties, the client needs a service that is dependable, suitable, time-saving, and trustworthy. As a result, from the end user’s perspective, adopting a cloud service’s trustworthiness becomes crucial. Trust is a measure of how well users’ expectations about a service’s capabilities are realized. In this research, a recommendation system for cloud service customers based on random iterative fuzzy computation (RIFTC) is proposed. RIFTC focuses on the assessment of trust using Quality of Service (QoS) characteristics. RIFTC calculates trust using the machine learning approach Support Vector Regression (SVR). RIFTC can helpfully recommend a cloud service to the end user and anticipate the trust values of cloud services.. Precision (97%), latency (51%), throughput (25.99 mbps), mean absolute error (54%), and re-call (97%) rates are used to assess how well this recommendation system performs. RIFTC’s average F-measure rate is calculated by adjusting the number of users from 200 to 300, and it is 93.46% more accurate on average with less time spent than the current methodologies.

Suggested Citation

  • Janjhyam Venkata Naga Ramesh & Syed Khasim & Mohamed Abbas & Kareemulla Shaik & Mohammad Zia Ur Rahman & Muniyandy Elangovan, 2023. "Cloud Services User’s Recommendation System Using Random Iterative Fuzzy-Based Trust Computation and Support Vector Regression," Mathematics, MDPI, vol. 11(10), pages 1-14, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2332-:d:1148668
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    References listed on IDEAS

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    1. Krishna Kumar Gupta & Kanak Kalita & Ranjan Kumar Ghadai & Manickam Ramachandran & Xiao-Zhi Gao, 2021. "Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective," Energies, MDPI, vol. 14(4), pages 1-16, February.
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