Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease
Dengue fever is a serious public health issue worldwide, particularly in tropical and subtropical areas. Early detection and accurate diagnosis are essential for effective management and control of the disease. In this study, we present a fuzzy hybrid approach (F-TLBO-APSO) for the detection and dia...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000899 |
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| author | Nivedita Riddhi Garg Seema Agrawal Ajendra Sharma M.K. Sharma |
| author_facet | Nivedita Riddhi Garg Seema Agrawal Ajendra Sharma M.K. Sharma |
| author_sort | Nivedita |
| collection | DOAJ |
| description | Dengue fever is a serious public health issue worldwide, particularly in tropical and subtropical areas. Early detection and accurate diagnosis are essential for effective management and control of the disease. In this study, we present a fuzzy hybrid approach (F-TLBO-APSO) for the detection and diagnosis of dengue disease using an advanced teaching-learning technique with adaptive particle swarm optimization. The proposed method combines the strengths of fuzzy logic, teaching learning-based optimization (TLBO), and adaptive particle swarm optimization (APSO) to improve the accuracy and efficiency of dengue detection based on symptoms. A key challenge addressed is the management of uncertain information existing in the problem. To validate the proposed technique, we applied it to a case study, demonstrating its robustness. The results indicate the versatility of the F-TLBO-APSO algorithm and highlight its value in detecting dengue based on symptoms. Our numerical computations reveal the advantages of the F-TLBO-APSO algorithm compared to TLBO and APSO. |
| format | Article |
| id | doaj-art-381250d92cc54f95bbcd41305ca0ab7c |
| institution | DOAJ |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-381250d92cc54f95bbcd41305ca0ab7c2025-08-20T02:52:23ZengElsevierSystems and Soft Computing2772-94192024-12-01620016010.1016/j.sasc.2024.200160Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease Nivedita0Riddhi Garg1Seema Agrawal2Ajendra Sharma3M.K. Sharma4Department of Mathematics, SSV (PG) College, Hapur, Chaudhary Charan Singh University Meerut IndiaDepartment of Mathematics (SOS) IFTM University, Lodhipur Rajput, Delhi Road, Moradabad 244102, Meerut IndiaDepartment of Mathematics, SSV (PG) College, Hapur, Chaudhary Charan Singh University Meerut IndiaDepartment of Mathematics, NAS College, Meerut, Chaudhary Charan Singh University Meerut IndiaDepartment of Mathematics, Chaudhary Charan Singh University Meerut 250004, India; Corresponding author.Dengue fever is a serious public health issue worldwide, particularly in tropical and subtropical areas. Early detection and accurate diagnosis are essential for effective management and control of the disease. In this study, we present a fuzzy hybrid approach (F-TLBO-APSO) for the detection and diagnosis of dengue disease using an advanced teaching-learning technique with adaptive particle swarm optimization. The proposed method combines the strengths of fuzzy logic, teaching learning-based optimization (TLBO), and adaptive particle swarm optimization (APSO) to improve the accuracy and efficiency of dengue detection based on symptoms. A key challenge addressed is the management of uncertain information existing in the problem. To validate the proposed technique, we applied it to a case study, demonstrating its robustness. The results indicate the versatility of the F-TLBO-APSO algorithm and highlight its value in detecting dengue based on symptoms. Our numerical computations reveal the advantages of the F-TLBO-APSO algorithm compared to TLBO and APSO.http://www.sciencedirect.com/science/article/pii/S2772941924000899Fuzzy hybrid approachDengue diseaseTeaching learning techniqueAdaptive particle swarm optimization |
| spellingShingle | Nivedita Riddhi Garg Seema Agrawal Ajendra Sharma M.K. Sharma Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease Systems and Soft Computing Fuzzy hybrid approach Dengue disease Teaching learning technique Adaptive particle swarm optimization |
| title | Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease |
| title_full | Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease |
| title_fullStr | Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease |
| title_full_unstemmed | Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease |
| title_short | Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease |
| title_sort | fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease |
| topic | Fuzzy hybrid approach Dengue disease Teaching learning technique Adaptive particle swarm optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000899 |
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