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Shahzaib Ashraf
Researcher at Abdul Wali Khan University Mardan
Publications - 91
Citations - 2372
Shahzaib Ashraf is an academic researcher from Abdul Wali Khan University Mardan. The author has contributed to research in topics: Fuzzy logic & Computer science. The author has an hindex of 22, co-authored 52 publications receiving 1238 citations. Previous affiliations of Shahzaib Ashraf include Bacha Khan University.
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Solid Waste Collection System Selection Based on Sine Trigonometric Spherical Hesitant Fuzzy Aggregation Information
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A Novel Spherical Fuzzy Rough Aggregation Operators Hybrid with TOPSIS Method and Their Application in Decision Making
TL;DR: This paper aims to develop aggregation operators that can be used to address the decision-making problems based on a spherical fuzzy rough environment and shows that the conventional approach has a larger area for information representation, better adaptability to the evaluation environment, and higher reliability of the evaluation results.
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Emergency Decision-Making Based on q-Rung Orthopair Fuzzy Rough Aggregation Information
TL;DR: A new EDM method based on the novel concept of q-rung orthopair fuzzy rough (q-ROPR) set is proposed, and the q-ROFR entropy measure method is used to determine criteria and experts' weights objectively in the EDM process.
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Novel EDAS Methodology Based on Single-Valued Neutrosophic Aczel-Alsina Aggregation Information and Their Application in Complex Decision-Making
TL;DR: Wang et al. as discussed by the authors proposed an extended EDAS (Evaluation based on Distance from Average Solution) method based on the single-valued neutrosophic (SVN) Aczel-Alsina aggregation information.
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Cyber security control selection based decision support algorithm under single valued neutrosophic hesitant fuzzy Einstein aggregation information
TL;DR: In this paper , a decision-making algorithm in the single-valued neutrosophic hesitant fuzzy set (SV-NHFS) environment is presented, which is designed for some incomplete, uncertain, and inconsistent situations in which each element has a few different values designed by the truth membership hesitant function.