Pattern recognition and medical diagnosis based on trigonometric similarity measures for bipolar complex fuzzy soft sets
Abstract Bipolar complex fuzzy soft sets, one of the substantial notions, generalize the notions of bipolar complex fuzzy sets and soft sets. The notion of bipolar complex fuzzy soft sets is a significant tool to cope with the awkward and complicated information involving the second dimension, i.e.,...
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| Autor*in: |
Mahmood, Tahir [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
Bipolar complex fuzzy soft set |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 27(2023), 16 vom: 03. Juni, Seite 11125-11154 |
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| Übergeordnetes Werk: |
volume:27 ; year:2023 ; number:16 ; day:03 ; month:06 ; pages:11125-11154 |
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| DOI / URN: |
10.1007/s00500-023-08176-y |
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| 520 | |a Abstract Bipolar complex fuzzy soft sets, one of the substantial notions, generalize the notions of bipolar complex fuzzy sets and soft sets. The notion of bipolar complex fuzzy soft sets is a significant tool to cope with the awkward and complicated information involving the second dimension, i.e., unreal part, both positive and negative opinions, and the parameters. Further, similarity measures play an important role in various fields such as data mining, machine learning, information retrieval, and natural language processing. They are used to measure the degree of similarity or dissimilarity between two or more objects, documents, images, or any other type of data. Consequently, in this script, we diagnose trigonometric similarity measures like generalized cosine similarity measure, generalized tangent similarity measure and generalized cotangent similarity measure, and generalized hybrid trigonometric similarity measure in the environment of bipolar complex fuzzy soft sets. Furthermore, we also discuss the weighted generalized cosine similarity measure, weighted generalized tangent similarity measure, weighted generalized cotangent similarity measure, and weighted generalized hybrid trigonometric similarity measure for bipolar complex fuzzy soft sets. After that, through the diagnosed similarity measures, we analyze real-life dilemmas like pattern recognition and medical diagnosis to portray the applicability and benefits of the diagnosed similarity measures in the real world. At the end of this study, we show the superiority and supremacy of the analyzed trigonometric similarity measures by doing their analysis with certain prevailing similarity measures. | ||
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10.1007/s00500-023-08176-y doi (DE-627)SPR052033678 (SPR)s00500-023-08176-y-e DE-627 ger DE-627 rakwb eng Mahmood, Tahir verfasserin aut Pattern recognition and medical diagnosis based on trigonometric similarity measures for bipolar complex fuzzy soft sets 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bipolar complex fuzzy soft sets, one of the substantial notions, generalize the notions of bipolar complex fuzzy sets and soft sets. The notion of bipolar complex fuzzy soft sets is a significant tool to cope with the awkward and complicated information involving the second dimension, i.e., unreal part, both positive and negative opinions, and the parameters. Further, similarity measures play an important role in various fields such as data mining, machine learning, information retrieval, and natural language processing. They are used to measure the degree of similarity or dissimilarity between two or more objects, documents, images, or any other type of data. Consequently, in this script, we diagnose trigonometric similarity measures like generalized cosine similarity measure, generalized tangent similarity measure and generalized cotangent similarity measure, and generalized hybrid trigonometric similarity measure in the environment of bipolar complex fuzzy soft sets. Furthermore, we also discuss the weighted generalized cosine similarity measure, weighted generalized tangent similarity measure, weighted generalized cotangent similarity measure, and weighted generalized hybrid trigonometric similarity measure for bipolar complex fuzzy soft sets. After that, through the diagnosed similarity measures, we analyze real-life dilemmas like pattern recognition and medical diagnosis to portray the applicability and benefits of the diagnosed similarity measures in the real world. At the end of this study, we show the superiority and supremacy of the analyzed trigonometric similarity measures by doing their analysis with certain prevailing similarity measures. Bipolar complex fuzzy soft set (dpeaa)DE-He213 Trigonometric similarity measures (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 Medical diagnosis (dpeaa)DE-He213 Jaleel, Abdul (orcid)0000-0002-7446-6060 aut Rehman, Ubaid Ur aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 16 vom: 03. Juni, Seite 11125-11154 (DE-627)SPR006469531 nnns volume:27 year:2023 number:16 day:03 month:06 pages:11125-11154 https://dx.doi.org/10.1007/s00500-023-08176-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 16 03 06 11125-11154 |
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10.1007/s00500-023-08176-y doi (DE-627)SPR052033678 (SPR)s00500-023-08176-y-e DE-627 ger DE-627 rakwb eng Mahmood, Tahir verfasserin aut Pattern recognition and medical diagnosis based on trigonometric similarity measures for bipolar complex fuzzy soft sets 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bipolar complex fuzzy soft sets, one of the substantial notions, generalize the notions of bipolar complex fuzzy sets and soft sets. The notion of bipolar complex fuzzy soft sets is a significant tool to cope with the awkward and complicated information involving the second dimension, i.e., unreal part, both positive and negative opinions, and the parameters. Further, similarity measures play an important role in various fields such as data mining, machine learning, information retrieval, and natural language processing. They are used to measure the degree of similarity or dissimilarity between two or more objects, documents, images, or any other type of data. Consequently, in this script, we diagnose trigonometric similarity measures like generalized cosine similarity measure, generalized tangent similarity measure and generalized cotangent similarity measure, and generalized hybrid trigonometric similarity measure in the environment of bipolar complex fuzzy soft sets. Furthermore, we also discuss the weighted generalized cosine similarity measure, weighted generalized tangent similarity measure, weighted generalized cotangent similarity measure, and weighted generalized hybrid trigonometric similarity measure for bipolar complex fuzzy soft sets. After that, through the diagnosed similarity measures, we analyze real-life dilemmas like pattern recognition and medical diagnosis to portray the applicability and benefits of the diagnosed similarity measures in the real world. At the end of this study, we show the superiority and supremacy of the analyzed trigonometric similarity measures by doing their analysis with certain prevailing similarity measures. Bipolar complex fuzzy soft set (dpeaa)DE-He213 Trigonometric similarity measures (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 Medical diagnosis (dpeaa)DE-He213 Jaleel, Abdul (orcid)0000-0002-7446-6060 aut Rehman, Ubaid Ur aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 16 vom: 03. Juni, Seite 11125-11154 (DE-627)SPR006469531 nnns volume:27 year:2023 number:16 day:03 month:06 pages:11125-11154 https://dx.doi.org/10.1007/s00500-023-08176-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 16 03 06 11125-11154 |
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10.1007/s00500-023-08176-y doi (DE-627)SPR052033678 (SPR)s00500-023-08176-y-e DE-627 ger DE-627 rakwb eng Mahmood, Tahir verfasserin aut Pattern recognition and medical diagnosis based on trigonometric similarity measures for bipolar complex fuzzy soft sets 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bipolar complex fuzzy soft sets, one of the substantial notions, generalize the notions of bipolar complex fuzzy sets and soft sets. The notion of bipolar complex fuzzy soft sets is a significant tool to cope with the awkward and complicated information involving the second dimension, i.e., unreal part, both positive and negative opinions, and the parameters. Further, similarity measures play an important role in various fields such as data mining, machine learning, information retrieval, and natural language processing. They are used to measure the degree of similarity or dissimilarity between two or more objects, documents, images, or any other type of data. Consequently, in this script, we diagnose trigonometric similarity measures like generalized cosine similarity measure, generalized tangent similarity measure and generalized cotangent similarity measure, and generalized hybrid trigonometric similarity measure in the environment of bipolar complex fuzzy soft sets. Furthermore, we also discuss the weighted generalized cosine similarity measure, weighted generalized tangent similarity measure, weighted generalized cotangent similarity measure, and weighted generalized hybrid trigonometric similarity measure for bipolar complex fuzzy soft sets. After that, through the diagnosed similarity measures, we analyze real-life dilemmas like pattern recognition and medical diagnosis to portray the applicability and benefits of the diagnosed similarity measures in the real world. At the end of this study, we show the superiority and supremacy of the analyzed trigonometric similarity measures by doing their analysis with certain prevailing similarity measures. Bipolar complex fuzzy soft set (dpeaa)DE-He213 Trigonometric similarity measures (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 Medical diagnosis (dpeaa)DE-He213 Jaleel, Abdul (orcid)0000-0002-7446-6060 aut Rehman, Ubaid Ur aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 16 vom: 03. Juni, Seite 11125-11154 (DE-627)SPR006469531 nnns volume:27 year:2023 number:16 day:03 month:06 pages:11125-11154 https://dx.doi.org/10.1007/s00500-023-08176-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 16 03 06 11125-11154 |
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10.1007/s00500-023-08176-y doi (DE-627)SPR052033678 (SPR)s00500-023-08176-y-e DE-627 ger DE-627 rakwb eng Mahmood, Tahir verfasserin aut Pattern recognition and medical diagnosis based on trigonometric similarity measures for bipolar complex fuzzy soft sets 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bipolar complex fuzzy soft sets, one of the substantial notions, generalize the notions of bipolar complex fuzzy sets and soft sets. The notion of bipolar complex fuzzy soft sets is a significant tool to cope with the awkward and complicated information involving the second dimension, i.e., unreal part, both positive and negative opinions, and the parameters. Further, similarity measures play an important role in various fields such as data mining, machine learning, information retrieval, and natural language processing. They are used to measure the degree of similarity or dissimilarity between two or more objects, documents, images, or any other type of data. Consequently, in this script, we diagnose trigonometric similarity measures like generalized cosine similarity measure, generalized tangent similarity measure and generalized cotangent similarity measure, and generalized hybrid trigonometric similarity measure in the environment of bipolar complex fuzzy soft sets. Furthermore, we also discuss the weighted generalized cosine similarity measure, weighted generalized tangent similarity measure, weighted generalized cotangent similarity measure, and weighted generalized hybrid trigonometric similarity measure for bipolar complex fuzzy soft sets. After that, through the diagnosed similarity measures, we analyze real-life dilemmas like pattern recognition and medical diagnosis to portray the applicability and benefits of the diagnosed similarity measures in the real world. At the end of this study, we show the superiority and supremacy of the analyzed trigonometric similarity measures by doing their analysis with certain prevailing similarity measures. Bipolar complex fuzzy soft set (dpeaa)DE-He213 Trigonometric similarity measures (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 Medical diagnosis (dpeaa)DE-He213 Jaleel, Abdul (orcid)0000-0002-7446-6060 aut Rehman, Ubaid Ur aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 16 vom: 03. Juni, Seite 11125-11154 (DE-627)SPR006469531 nnns volume:27 year:2023 number:16 day:03 month:06 pages:11125-11154 https://dx.doi.org/10.1007/s00500-023-08176-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 16 03 06 11125-11154 |
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10.1007/s00500-023-08176-y doi (DE-627)SPR052033678 (SPR)s00500-023-08176-y-e DE-627 ger DE-627 rakwb eng Mahmood, Tahir verfasserin aut Pattern recognition and medical diagnosis based on trigonometric similarity measures for bipolar complex fuzzy soft sets 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bipolar complex fuzzy soft sets, one of the substantial notions, generalize the notions of bipolar complex fuzzy sets and soft sets. The notion of bipolar complex fuzzy soft sets is a significant tool to cope with the awkward and complicated information involving the second dimension, i.e., unreal part, both positive and negative opinions, and the parameters. Further, similarity measures play an important role in various fields such as data mining, machine learning, information retrieval, and natural language processing. They are used to measure the degree of similarity or dissimilarity between two or more objects, documents, images, or any other type of data. Consequently, in this script, we diagnose trigonometric similarity measures like generalized cosine similarity measure, generalized tangent similarity measure and generalized cotangent similarity measure, and generalized hybrid trigonometric similarity measure in the environment of bipolar complex fuzzy soft sets. Furthermore, we also discuss the weighted generalized cosine similarity measure, weighted generalized tangent similarity measure, weighted generalized cotangent similarity measure, and weighted generalized hybrid trigonometric similarity measure for bipolar complex fuzzy soft sets. After that, through the diagnosed similarity measures, we analyze real-life dilemmas like pattern recognition and medical diagnosis to portray the applicability and benefits of the diagnosed similarity measures in the real world. At the end of this study, we show the superiority and supremacy of the analyzed trigonometric similarity measures by doing their analysis with certain prevailing similarity measures. Bipolar complex fuzzy soft set (dpeaa)DE-He213 Trigonometric similarity measures (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 Medical diagnosis (dpeaa)DE-He213 Jaleel, Abdul (orcid)0000-0002-7446-6060 aut Rehman, Ubaid Ur aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 16 vom: 03. Juni, Seite 11125-11154 (DE-627)SPR006469531 nnns volume:27 year:2023 number:16 day:03 month:06 pages:11125-11154 https://dx.doi.org/10.1007/s00500-023-08176-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 16 03 06 11125-11154 |
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Mahmood, Tahir Jaleel, Abdul Rehman, Ubaid Ur |
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pattern recognition and medical diagnosis based on trigonometric similarity measures for bipolar complex fuzzy soft sets |
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Pattern recognition and medical diagnosis based on trigonometric similarity measures for bipolar complex fuzzy soft sets |
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Abstract Bipolar complex fuzzy soft sets, one of the substantial notions, generalize the notions of bipolar complex fuzzy sets and soft sets. The notion of bipolar complex fuzzy soft sets is a significant tool to cope with the awkward and complicated information involving the second dimension, i.e., unreal part, both positive and negative opinions, and the parameters. Further, similarity measures play an important role in various fields such as data mining, machine learning, information retrieval, and natural language processing. They are used to measure the degree of similarity or dissimilarity between two or more objects, documents, images, or any other type of data. Consequently, in this script, we diagnose trigonometric similarity measures like generalized cosine similarity measure, generalized tangent similarity measure and generalized cotangent similarity measure, and generalized hybrid trigonometric similarity measure in the environment of bipolar complex fuzzy soft sets. Furthermore, we also discuss the weighted generalized cosine similarity measure, weighted generalized tangent similarity measure, weighted generalized cotangent similarity measure, and weighted generalized hybrid trigonometric similarity measure for bipolar complex fuzzy soft sets. After that, through the diagnosed similarity measures, we analyze real-life dilemmas like pattern recognition and medical diagnosis to portray the applicability and benefits of the diagnosed similarity measures in the real world. At the end of this study, we show the superiority and supremacy of the analyzed trigonometric similarity measures by doing their analysis with certain prevailing similarity measures. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Bipolar complex fuzzy soft sets, one of the substantial notions, generalize the notions of bipolar complex fuzzy sets and soft sets. The notion of bipolar complex fuzzy soft sets is a significant tool to cope with the awkward and complicated information involving the second dimension, i.e., unreal part, both positive and negative opinions, and the parameters. Further, similarity measures play an important role in various fields such as data mining, machine learning, information retrieval, and natural language processing. They are used to measure the degree of similarity or dissimilarity between two or more objects, documents, images, or any other type of data. Consequently, in this script, we diagnose trigonometric similarity measures like generalized cosine similarity measure, generalized tangent similarity measure and generalized cotangent similarity measure, and generalized hybrid trigonometric similarity measure in the environment of bipolar complex fuzzy soft sets. Furthermore, we also discuss the weighted generalized cosine similarity measure, weighted generalized tangent similarity measure, weighted generalized cotangent similarity measure, and weighted generalized hybrid trigonometric similarity measure for bipolar complex fuzzy soft sets. After that, through the diagnosed similarity measures, we analyze real-life dilemmas like pattern recognition and medical diagnosis to portray the applicability and benefits of the diagnosed similarity measures in the real world. At the end of this study, we show the superiority and supremacy of the analyzed trigonometric similarity measures by doing their analysis with certain prevailing similarity measures. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Bipolar complex fuzzy soft sets, one of the substantial notions, generalize the notions of bipolar complex fuzzy sets and soft sets. The notion of bipolar complex fuzzy soft sets is a significant tool to cope with the awkward and complicated information involving the second dimension, i.e., unreal part, both positive and negative opinions, and the parameters. Further, similarity measures play an important role in various fields such as data mining, machine learning, information retrieval, and natural language processing. They are used to measure the degree of similarity or dissimilarity between two or more objects, documents, images, or any other type of data. Consequently, in this script, we diagnose trigonometric similarity measures like generalized cosine similarity measure, generalized tangent similarity measure and generalized cotangent similarity measure, and generalized hybrid trigonometric similarity measure in the environment of bipolar complex fuzzy soft sets. Furthermore, we also discuss the weighted generalized cosine similarity measure, weighted generalized tangent similarity measure, weighted generalized cotangent similarity measure, and weighted generalized hybrid trigonometric similarity measure for bipolar complex fuzzy soft sets. After that, through the diagnosed similarity measures, we analyze real-life dilemmas like pattern recognition and medical diagnosis to portray the applicability and benefits of the diagnosed similarity measures in the real world. At the end of this study, we show the superiority and supremacy of the analyzed trigonometric similarity measures by doing their analysis with certain prevailing similarity measures. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Pattern recognition and medical diagnosis based on trigonometric similarity measures for bipolar complex fuzzy soft sets |
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