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Article type: Research Article
Authors: Arulkumar, V.a; * | Sandana Karuppan, A.b | Alex, Sini Annac | Lathamanju, R.d
Affiliations: [a] Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, Tamilnadu, India | [b] Department of Information Technology, SSN College of Engg, Rajiv Gandhi Salai, OMR, Chennai, India | [c] Ramaiah Institute of Technology, Bangalore, Karnataka, India | [d] SRM Institute of Science and Technology, Ramapuram, Chennai, TamilNadu, India
Correspondence: [*] Corresponding author. V. Arulkumar, Assistant Professor, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, 603110, Chennai, Tamilnadu, India. E-mail: arulkumarv@ssn.edu.in.
Abstract: In an era marked by the widespread adoption of cloud services, individuals and businesses face the daunting task of navigating a complex landscape to make informed choices. The inherent opacity of the cloud service environment underscores the need for methods that can effectively handle imprecise information. This research presents a novel and superior approach to aid customers in selecting the most suitable cloud services. Our work introduces a distinctive fuzzy decision-making paradigm, surpassing current methodologies. We leverage an innovative analytic hierarchy process technique to quantify the semantic similarity between concepts and employ a fuzzy ontology to elucidate the uncertain relationships among database items, facilitating precise service matching. Furthermore, we present a multi-faceted evaluation framework for ranking cloud services. To substantiate the efficacy of our similarity matching based on the fuzzy ontology, we conduct comprehensive testing. The results of our experiments provide compelling evidence of the viability and effectiveness of the proposed method. This research offers a valuable contribution to the challenging realm of cloud service selection, empowering individuals and organizations to make well-informed decisions amidst the cloud service abundance.
Keywords: Semantic information retrieval, fuzzy ontology, ontology, the Semantic Web
DOI: 10.3233/JIFS-235130
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3811-3826, 2024
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