Analysis of deep learning technique using a complex spherical fuzzy rough decision support model
Deep learning (DL), a branch of machine learning and artificial intelligence, is nowadays considered as a core technology. Due to its ability to learn from data, DL technology originated from artificial neural networks and has become a hot topic in the context of computing, it is widely applied in v...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-07-01
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| Series: | AIMS Mathematics |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.20231188?viewType=HTML |
| _version_ | 1797745344049577984 |
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| author | Muhammad Ali Khan Saleem Abdullah Alaa O. Almagrabi |
| author_facet | Muhammad Ali Khan Saleem Abdullah Alaa O. Almagrabi |
| author_sort | Muhammad Ali Khan |
| collection | DOAJ |
| description | Deep learning (DL), a branch of machine learning and artificial intelligence, is nowadays considered as a core technology. Due to its ability to learn from data, DL technology originated from artificial neural networks and has become a hot topic in the context of computing, it is widely applied in various application areas. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. The aim of this work was to develope a new method for appropriate DL model selection using complex spherical fuzzy rough sets (CSFRSs). The connectivity of two or more complex spherical fuzzy rough numbers can be defined by using the Hamacher t-norm and t-conorm. Using the Hamacher operational laws with operational parameters provides exceptional flexibility in dealing with uncertainty in data. We define a series of Hamacher averaging and geometric aggregation operators for CSFRSs, as well as their fundamental properties, based on the Hamacher t-norm and t-conorm. Further we have developed the proposed aggregation operators and provide here a group decision-making approach for solving decision making problems. Finally, a comparative analysis with existing methods is given to demonstrate the peculiarity of our proposed method. |
| first_indexed | 2024-03-12T15:21:55Z |
| format | Article |
| id | doaj.art-802b367db4fe40a19f42f94936daa917 |
| institution | Directory Open Access Journal |
| issn | 2473-6988 |
| language | English |
| last_indexed | 2024-03-12T15:21:55Z |
| publishDate | 2023-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj.art-802b367db4fe40a19f42f94936daa9172023-08-11T01:32:49ZengAIMS PressAIMS Mathematics2473-69882023-07-01810233722340210.3934/math.20231188Analysis of deep learning technique using a complex spherical fuzzy rough decision support modelMuhammad Ali Khan0Saleem Abdullah 1Alaa O. Almagrabi21. Department of Mathematics, Abdul Wali Khan Univesity Mardan, KP 23200, Pakistan1. Department of Mathematics, Abdul Wali Khan Univesity Mardan, KP 23200, Pakistan2. Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz Univesity, Jeddah, Saudi ArabiaDeep learning (DL), a branch of machine learning and artificial intelligence, is nowadays considered as a core technology. Due to its ability to learn from data, DL technology originated from artificial neural networks and has become a hot topic in the context of computing, it is widely applied in various application areas. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. The aim of this work was to develope a new method for appropriate DL model selection using complex spherical fuzzy rough sets (CSFRSs). The connectivity of two or more complex spherical fuzzy rough numbers can be defined by using the Hamacher t-norm and t-conorm. Using the Hamacher operational laws with operational parameters provides exceptional flexibility in dealing with uncertainty in data. We define a series of Hamacher averaging and geometric aggregation operators for CSFRSs, as well as their fundamental properties, based on the Hamacher t-norm and t-conorm. Further we have developed the proposed aggregation operators and provide here a group decision-making approach for solving decision making problems. Finally, a comparative analysis with existing methods is given to demonstrate the peculiarity of our proposed method.https://www.aimspress.com/article/doi/10.3934/math.20231188?viewType=HTMLcomplex spherical fuzzy rough sethamacher aggregation operatorsdecision making problemsdeep learning technique |
| spellingShingle | Muhammad Ali Khan Saleem Abdullah Alaa O. Almagrabi Analysis of deep learning technique using a complex spherical fuzzy rough decision support model AIMS Mathematics complex spherical fuzzy rough set hamacher aggregation operators decision making problems deep learning technique |
| title | Analysis of deep learning technique using a complex spherical fuzzy rough decision support model |
| title_full | Analysis of deep learning technique using a complex spherical fuzzy rough decision support model |
| title_fullStr | Analysis of deep learning technique using a complex spherical fuzzy rough decision support model |
| title_full_unstemmed | Analysis of deep learning technique using a complex spherical fuzzy rough decision support model |
| title_short | Analysis of deep learning technique using a complex spherical fuzzy rough decision support model |
| title_sort | analysis of deep learning technique using a complex spherical fuzzy rough decision support model |
| topic | complex spherical fuzzy rough set hamacher aggregation operators decision making problems deep learning technique |
| url | https://www.aimspress.com/article/doi/10.3934/math.20231188?viewType=HTML |
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