Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease

•Introduce a fuzzy hybrid approach: the paper aims to propose a hybrid approach that integrates fuzzy logic with advanced teaching-learning techniques and PSO. This hybrid approach leverages the strengths of each technique to effectively address the complexities and uncertainties associated with den...

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Bibliographic Details
Published inSystems and soft computing Vol. 6; p. 200160
Main Authors Nivedita, Garg, Riddhi, Agrawal, Seema, Sharma, Ajendra, Sharma, M.K.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2024
Elsevier
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Summary:•Introduce a fuzzy hybrid approach: the paper aims to propose a hybrid approach that integrates fuzzy logic with advanced teaching-learning techniques and PSO. This hybrid approach leverages the strengths of each technique to effectively address the complexities and uncertainties associated with dengue disease control.•Enhance optimization capabilities: the objective is to enhance the optimization capabilities of the proposed approach by utilizing advanced teaching-learning techniques. This technique stimulates the teaching and learning interactions between students and teachers to improve the search process and convergence to optimal solutions.•Incorporate fuzzy logic: the paper aims to incorporate fuzzy logic to handle the inherent uncertainties and imprecise information associated with dengue disease control. Fuzzy logic allows for the representation and manipulation of vague and uncertain data, enabling more robust decision-making and control strategies.•Utilize particle swarm optimization: the objective is to utilize the particle swarm optimization technique to optimize the parameters and variables involved in dengue disease control. PSO is a population-based optimization algorithm inspired by social behavior, which facilitates efficient exploration and exploitation of the solution space.•Evaluate the proposed approach: the paper intends to evaluate the effectiveness and performance of the fuzzy hybrid approach using real-world data and case studies related to dengue disease control. The objective is to compare the results obtained from the proposed approach with existing approaches or techniques to demonstrate its superiority in terms of accuracy, efficiency, and effectiveness. 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.
ISSN:2772-9419
2772-9419
DOI:10.1016/j.sasc.2024.200160