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Melfi Alrasheedi

Researcher at King Faisal University

Publications -  31
Citations -  913

Melfi Alrasheedi is an academic researcher from King Faisal University. The author has contributed to research in topics: Fuzzy logic & Computer science. The author has an hindex of 10, co-authored 28 publications receiving 303 citations. Previous affiliations of Melfi Alrasheedi include Ton Duc Thang University.

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A novel approach to extended fuzzy TOPSIS based on new divergence measures for renewable energy sources selection

TL;DR: A new divergence measure is proposed for ranking and choosing the renewable energy sources in multi-criteria decision-making problems based on fuzzy TOPSIS, and it is compared to some existing methods to show the thorough execution process of the introduced method.
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Novel Multi-Criteria Intuitionistic Fuzzy SWARA–COPRAS Approach for Sustainability Evaluation of the Bioenergy Production Process

TL;DR: An integrated approach, based on stepwise weight assessment ratio analysis (SWARA) and complex proportional assessment (COPRAS) approaches, for the selection of BPT alternatives is presented, confirming that the proposed framework is more useful than and consistent with previously developed methods using the IFSs environment.
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Healthcare evaluation in hazardous waste recycling using novel interval-valued intuitionistic fuzzy information based on complex proportional assessment method

TL;DR: A novel method based on the Complex Proportional Assessment (COPRAS) with multi-criteria decision making for interval-valued intuitionistic fuzzy sets is proposed for hazardous waste recycling facility selection, which proves the validity and feasibility of the proposed approach.
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Internet of Things (IoT) adoption barriers for the circular economy using Pythagorean fuzzy SWARA-CoCoSo decision-making approach in the manufacturing sector

TL;DR: A novel method integrating the Stepwise Weight Assessment Ratio Analysis (SWARA) and Combined Compromise Solution (CoCoSo) methods based on Pythagorean Fuzzy Sets (PFSs) was proposed, indicating that the developed approach was able to suggest more realistic performance under PFSs, hence providing a wide range of applications.