Uncertainty and decision-making with multi-polar interval-valued neutrosophic hypersoft set: A distance, similarity measure and machine learning approach
The issue of decision-making (DM) is intricate due to the environment's ambiguous, imprecise, and uncertain nature, particularly when multiple attributes are involved and further subdivided. To address such complex problems, the concept of the hypersoft set has been employed. This article shows...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682300978X |
| _version_ | 1827593626394296320 |
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| author | Muhammad Saqlain Harish Garg Poom Kumam Wiyada Kumam |
| author_facet | Muhammad Saqlain Harish Garg Poom Kumam Wiyada Kumam |
| author_sort | Muhammad Saqlain |
| collection | DOAJ |
| description | The issue of decision-making (DM) is intricate due to the environment's ambiguous, imprecise, and uncertain nature, particularly when multiple attributes are involved and further subdivided. To address such complex problems, the concept of the hypersoft set has been employed. This article shows how to combine the multi-polar interval-valued neutrosophic set with the hypersoft set. This can help you solve DM problems that have more than one attribute. Additionally, we define similarity measures for multipolar interval-valued neutrosophic hypersoft sets (mPIVNHSs). We discuss the proposed extensions' definitions and mathematical operations and present an algorithm to solve DM problems related to everyday life using the proposed operators under mPIVNHSs. A machine learning method called K-Nearest Neighbor (KNN) is also employed to calculate ranking in the selection of sites for a new store. By utilizing the potential of data point similarity, K-Nearest Neighbors (KNN) and similarity measurements have practical value in daily life. KNN mimics the human propensity to draw from related experiences, resulting in applications like customized suggestions. Parallel to this, similarity metrics statistically evaluate resemblance to support tasks like personalized advice across fields like biology and social networks. Finally, we conclude the present study by comparing it with the existing studies, and future directions are also given. |
| first_indexed | 2024-03-09T02:15:32Z |
| format | Article |
| id | doaj.art-19ec7a3d517a4131ae696a54ee165d9a |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-09T02:15:32Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-19ec7a3d517a4131ae696a54ee165d9a2023-12-07T05:27:57ZengElsevierAlexandria Engineering Journal1110-01682023-12-0184323332Uncertainty and decision-making with multi-polar interval-valued neutrosophic hypersoft set: A distance, similarity measure and machine learning approachMuhammad Saqlain0Harish Garg1Poom Kumam2Wiyada Kumam3Departments of Mathematics, Faculty of Science, the King Mongkuta's University of Technology Thonburi (KMUTT), Bangkok 10140, ThailandSchool of Mathematics, Thapar Institute of Engineering and Technology (Deemed University), Patiala 147004, Punjab, IndiaDepartments of Mathematics, Faculty of Science, the King Mongkuta's University of Technology Thonburi (KMUTT), Bangkok 10140, Thailand; Corresponding author.Department of Mathematics and Computer Science, Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi (RMUTT), Pathum Thani 12110, ThailandThe issue of decision-making (DM) is intricate due to the environment's ambiguous, imprecise, and uncertain nature, particularly when multiple attributes are involved and further subdivided. To address such complex problems, the concept of the hypersoft set has been employed. This article shows how to combine the multi-polar interval-valued neutrosophic set with the hypersoft set. This can help you solve DM problems that have more than one attribute. Additionally, we define similarity measures for multipolar interval-valued neutrosophic hypersoft sets (mPIVNHSs). We discuss the proposed extensions' definitions and mathematical operations and present an algorithm to solve DM problems related to everyday life using the proposed operators under mPIVNHSs. A machine learning method called K-Nearest Neighbor (KNN) is also employed to calculate ranking in the selection of sites for a new store. By utilizing the potential of data point similarity, K-Nearest Neighbors (KNN) and similarity measurements have practical value in daily life. KNN mimics the human propensity to draw from related experiences, resulting in applications like customized suggestions. Parallel to this, similarity metrics statistically evaluate resemblance to support tasks like personalized advice across fields like biology and social networks. Finally, we conclude the present study by comparing it with the existing studies, and future directions are also given.http://www.sciencedirect.com/science/article/pii/S111001682300978XDecision-makingSoft setDistance measuresSimilarity measuresNeutrosophic setHypersoft set |
| spellingShingle | Muhammad Saqlain Harish Garg Poom Kumam Wiyada Kumam Uncertainty and decision-making with multi-polar interval-valued neutrosophic hypersoft set: A distance, similarity measure and machine learning approach Alexandria Engineering Journal Decision-making Soft set Distance measures Similarity measures Neutrosophic set Hypersoft set |
| title | Uncertainty and decision-making with multi-polar interval-valued neutrosophic hypersoft set: A distance, similarity measure and machine learning approach |
| title_full | Uncertainty and decision-making with multi-polar interval-valued neutrosophic hypersoft set: A distance, similarity measure and machine learning approach |
| title_fullStr | Uncertainty and decision-making with multi-polar interval-valued neutrosophic hypersoft set: A distance, similarity measure and machine learning approach |
| title_full_unstemmed | Uncertainty and decision-making with multi-polar interval-valued neutrosophic hypersoft set: A distance, similarity measure and machine learning approach |
| title_short | Uncertainty and decision-making with multi-polar interval-valued neutrosophic hypersoft set: A distance, similarity measure and machine learning approach |
| title_sort | uncertainty and decision making with multi polar interval valued neutrosophic hypersoft set a distance similarity measure and machine learning approach |
| topic | Decision-making Soft set Distance measures Similarity measures Neutrosophic set Hypersoft set |
| url | http://www.sciencedirect.com/science/article/pii/S111001682300978X |
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