Development of an Investment Sector Selector Using a TOPSIS Method Based on Novel Distances and Similarity Measures for Picture Fuzzy Hypersoft Sets
The selection of an optimal investment sector is of critical importance not only for individual financial success but also to drive economic development. The allocation of capital into sectors with high potential for growth, innovation, and job creation is key. In addressing the complexity of decisi...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10477422/ |
| _version_ | 1797231179811782656 |
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| author | Muhammad Imran Harl Muhammad Saeed Muhammad Haris Saeed Sanaa Ahmed Bajri Alhanouf Alburaikan Hamiden Abd El-Wahed Khalifa |
| author_facet | Muhammad Imran Harl Muhammad Saeed Muhammad Haris Saeed Sanaa Ahmed Bajri Alhanouf Alburaikan Hamiden Abd El-Wahed Khalifa |
| author_sort | Muhammad Imran Harl |
| collection | DOAJ |
| description | The selection of an optimal investment sector is of critical importance not only for individual financial success but also to drive economic development. The allocation of capital into sectors with high potential for growth, innovation, and job creation is key. In addressing the complexity of decision-making scenarios associated with investment sector exploration, we introduce a novel data structure known as Picture fuzzy hypersoft set (<inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}_{\mathbb {HSS}s}$ </tex-math></inline-formula>). This specialized approach within computational intelligence and decision-making aims to categorize data into various attributes and sub-attributes, considering the significant role of neutrality. The study stems from the need for a comprehensive framework (<inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}{\mathbb {HSS}s}$ </tex-math></inline-formula>) that can effectively handle intricate decision-making scenarios involving attributes, subattributes, and nuanced factors such as neutrality. Traditional tools such as TOPSIS and its extensions of fuzzy sets, while robust in Multiple Criteria Decision Making (MCDM), may face challenges in modeling and analyzing decision-making information within a <inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}{\mathbb {HSS}s}$ </tex-math></inline-formula> environment. The rationale behind this study lies in enhancing the accuracy and efficiency of decision-making processes when dealing with complex, fuzzy, and multi-criteria data. By introducing newly proposed distances and similarity measures tailored to <inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}{\mathbb {HSS}s}$ </tex-math></inline-formula>, and constructing a <inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}{\mathbb {HSS}s}$ </tex-math></inline-formula>-TOPSIS method, we aim to address the limitations faced by existing models in the <inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}_{\mathbb {HSS}s}$ </tex-math></inline-formula> environment. The application of Hamming distance-based similarity measures further distinguishes our method by determining the weights assigned to each decision maker. The proposed <inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}_{\mathbb {HSS}s}$ </tex-math></inline-formula>-TOPSIS method is practically applied in designing an optimal investment sector exploration tool for investors. This method has the potential to establish a crucial connection between alternatives and attributes, providing value across various fields and industries. The research emphasizes bridging the gap in decision-making scenarios where alternatives and attributes need to be effectively connected and analyzed, thereby contributing to the advancement of decision-making processes in complex domains. |
| first_indexed | 2024-04-24T15:40:16Z |
| format | Article |
| id | doaj.art-12ba4557c2584ff9814a47b2c0e770c1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T15:40:16Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-12ba4557c2584ff9814a47b2c0e770c12024-04-01T23:00:32ZengIEEEIEEE Access2169-35362024-01-0112451184513310.1109/ACCESS.2024.338002510477422Development of an Investment Sector Selector Using a TOPSIS Method Based on Novel Distances and Similarity Measures for Picture Fuzzy Hypersoft SetsMuhammad Imran Harl0https://orcid.org/0000-0002-5584-6017Muhammad Saeed1https://orcid.org/0000-0002-7284-6908Muhammad Haris Saeed2https://orcid.org/0000-0002-7913-951XSanaa Ahmed Bajri3https://orcid.org/0009-0008-4187-5205Alhanouf Alburaikan4https://orcid.org/0000-0002-4420-7287Hamiden Abd El-Wahed Khalifa5Department of Mathematics, University of Management and Technology, Lahore, Punjab, PakistanDepartment of Mathematics, University of Management and Technology, Lahore, Punjab, PakistanDepartment of Chemistry, University of Management and Technology, Lahore, Punjab, PakistanDepartment of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaDepartment of Mathematics, College of Science, Qassim University, Buraydah, Saudi ArabiaDepartment of Mathematics, College of Science, Qassim University, Buraydah, Saudi ArabiaThe selection of an optimal investment sector is of critical importance not only for individual financial success but also to drive economic development. The allocation of capital into sectors with high potential for growth, innovation, and job creation is key. In addressing the complexity of decision-making scenarios associated with investment sector exploration, we introduce a novel data structure known as Picture fuzzy hypersoft set (<inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}_{\mathbb {HSS}s}$ </tex-math></inline-formula>). This specialized approach within computational intelligence and decision-making aims to categorize data into various attributes and sub-attributes, considering the significant role of neutrality. The study stems from the need for a comprehensive framework (<inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}{\mathbb {HSS}s}$ </tex-math></inline-formula>) that can effectively handle intricate decision-making scenarios involving attributes, subattributes, and nuanced factors such as neutrality. Traditional tools such as TOPSIS and its extensions of fuzzy sets, while robust in Multiple Criteria Decision Making (MCDM), may face challenges in modeling and analyzing decision-making information within a <inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}{\mathbb {HSS}s}$ </tex-math></inline-formula> environment. The rationale behind this study lies in enhancing the accuracy and efficiency of decision-making processes when dealing with complex, fuzzy, and multi-criteria data. By introducing newly proposed distances and similarity measures tailored to <inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}{\mathbb {HSS}s}$ </tex-math></inline-formula>, and constructing a <inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}{\mathbb {HSS}s}$ </tex-math></inline-formula>-TOPSIS method, we aim to address the limitations faced by existing models in the <inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}_{\mathbb {HSS}s}$ </tex-math></inline-formula> environment. The application of Hamming distance-based similarity measures further distinguishes our method by determining the weights assigned to each decision maker. The proposed <inline-formula> <tex-math notation="LaTeX">$\mathbb {PF}_{\mathbb {HSS}s}$ </tex-math></inline-formula>-TOPSIS method is practically applied in designing an optimal investment sector exploration tool for investors. This method has the potential to establish a crucial connection between alternatives and attributes, providing value across various fields and industries. The research emphasizes bridging the gap in decision-making scenarios where alternatives and attributes need to be effectively connected and analyzed, thereby contributing to the advancement of decision-making processes in complex domains.https://ieeexplore.ieee.org/document/10477422/Soft sethypersoft setpicture fuzzy hypersoft setsimilarity measureTOPSISalgorithms |
| spellingShingle | Muhammad Imran Harl Muhammad Saeed Muhammad Haris Saeed Sanaa Ahmed Bajri Alhanouf Alburaikan Hamiden Abd El-Wahed Khalifa Development of an Investment Sector Selector Using a TOPSIS Method Based on Novel Distances and Similarity Measures for Picture Fuzzy Hypersoft Sets IEEE Access Soft set hypersoft set picture fuzzy hypersoft set similarity measure TOPSIS algorithms |
| title | Development of an Investment Sector Selector Using a TOPSIS Method Based on Novel Distances and Similarity Measures for Picture Fuzzy Hypersoft Sets |
| title_full | Development of an Investment Sector Selector Using a TOPSIS Method Based on Novel Distances and Similarity Measures for Picture Fuzzy Hypersoft Sets |
| title_fullStr | Development of an Investment Sector Selector Using a TOPSIS Method Based on Novel Distances and Similarity Measures for Picture Fuzzy Hypersoft Sets |
| title_full_unstemmed | Development of an Investment Sector Selector Using a TOPSIS Method Based on Novel Distances and Similarity Measures for Picture Fuzzy Hypersoft Sets |
| title_short | Development of an Investment Sector Selector Using a TOPSIS Method Based on Novel Distances and Similarity Measures for Picture Fuzzy Hypersoft Sets |
| title_sort | development of an investment sector selector using a topsis method based on novel distances and similarity measures for picture fuzzy hypersoft sets |
| topic | Soft set hypersoft set picture fuzzy hypersoft set similarity measure TOPSIS algorithms |
| url | https://ieeexplore.ieee.org/document/10477422/ |
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