ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Fuzzy Logic Systems with Data Classification - a Cooperative Approach for Intelligent Decision Support


Affiliations
1 Department of Computer Science, RVS Agricultural College, India
2 Department of Mathematics, Government Science College, Hassan, India
3 Department of Geography, Bhairab Ganguly College, India
4 Department of Computer Science and Engineering, Symbiosis Institute of Technology, India
5 College of Computing and Information Sciences, University of Technology and Applied Sciences, Sohar, Oman
     

   Subscribe/Renew Journal


In intelligent decision support, the integration of fuzzy logic systems with data classification has emerged as a promising avenue. This cooperative approach seeks to enhance decision-making processes by leveraging the strengths of both fuzzy logic and data classification techniques. However, a critical gap exists in the literature concerning the seamless integration of fuzzy logic systems and data classification for effective decision support. Existing approaches often treat these methodologies in isolation, overlooking the synergies that can arise from their collaborative utilization. Bridging this gap is essential for developing robust decision support systems capable of handling the intricacies of modern datasets. The research aims to address this gap by proposing a cooperative approach that seamlessly integrates fuzzy logic systems and data classification methods. By doing so, it seeks to overcome the limitations of traditional decision support systems and enhance their adaptability to real-world scenarios characterized by uncertainty and complexity. The method involves the development of a hybrid system that combines fuzzy logic rules and data classification algorithms. The fuzzy logic component captures and processes imprecise information, while the data classification component identifies patterns and trends within the data. The cooperative nature of the approach ensures that each method complements the other, resulting in a more robust and effective decision support system. The results demonstrate the improved performance of the proposed cooperative approach compared to traditional decision support systems. The system exhibits enhanced accuracy and adaptability, showcasing its potential to address the challenges posed by modern datasets.

Keywords

Fuzzy Logic, Decision Support, Data Classification, Cooperative Approach, Intelligent Systems.
Subscription Login to verify subscription
User
Notifications
Font Size

  • H. Kasugai, A. Kawano, K. Honda and A. Notsu, “A Study on Applicability of Fuzzy K-Member Clustering to Privacy Preserving Pattern Recognition”, Proceedings of IEEE International Conference on Fuzzy Systems, pp. 1-6, 2013.

  • S. Sundaramurthy, A. Thangavelu and K. Sekaran, “Predicting Rheumatoid Arthritis from the Biomarkers of Clinical Trials using Improved Harmony Search Optimization with Adaptive Neuro-Fuzzy Inference System”, Journal of Intelligent and Fuzzy Systems, Vol. 44, No. 1, pp. 125-137, 2023.

  • S. Goktepe Yıldız, “Prediction of Students’ Perceptions of Problem Solving Skills with a Neuro-Fuzzy Model and Hierarchical Regression Method: A Quantitative Study”, Education and Information Technologies, Vol. 67, pp. 1-39, 2023.

  • J. Florez Lozano and M. Gongora, “Cooperative and Distributed Decision-Making in a Multi-Agent Perception System for Improvised Land Mines Detection”, Information Fusion, Vol. 64, pp. 32-49, 2020.

  • L.C. Voumik and A. Dutta, “A Study on Mathematics Modeling using Fuzzy Logic and Artificial Neural Network for Medical Decision Making System”, Proceedings of International Conference on Computational Intelligence and Sustainable Engineering Solutions, pp. 492-498, 2022.

  • M.J. Aqel, O.A. Nakshabandi and A. Adeniyi, “Decision Support Systems Classification in Industry”, Periodicals of Engineering and Natural Sciences, Vol. 7, No. 2, pp. 774-785, 2019.

  • B. Wu, T.L. Yip and Y. Wang, “Fuzzy Logic Based Dynamic Decision-Making System for Intelligent Navigation Strategy within Inland Traffic Separation Schemes”, Ocean Engineering, Vol. 197, pp. 1-12, 2023.

  • G. Improta, S. Santini and M. Triassi, “Fuzzy Logic-Based Clinical Decision Support System for the Evaluation of Renal Function in Post‐Transplant Patients”, Journal of Evaluation in Clinical Practice, Vol. 26, No. 4, pp. 1224-1234, 2020.

  • J.M. Florez-Lozano and M. Gongora, “A Robust Decision-Making Framework based on Collaborative Agents”, IEEE Access, Vol. 8, pp. 150974-150988, 2020.

  • W. Ren and Y. Hu, “Intelligent Decision Making for Service Providers Selection in Maintenance Service Network: An Adaptive Fuzzy-Neuro Approach, Knowledge-Based Systems, Vol. 190, pp. 105263-105268, 2020.

  • S. Gupta, S. Bhattacharyya and I. Bose, “Artificial Intelligence for Decision Support Systems in the Field of Operations Research: Review and Future Scope of Research”, Annals of Operations Research, Vol. 89, No. 2, pp. 41-60, 2022.

  • R.X. Ding, Y. Dong and F. Herrera, “Large-Scale Decision-Making: Characterization, Taxonomy, Challenges and Future Directions from an Artificial Intelligence and Applications Perspective”, Information Fusion, Vol. 59, pp. 84-102, 2020.


Abstract Views: 152

PDF Views: 1




  • Fuzzy Logic Systems with Data Classification - a Cooperative Approach for Intelligent Decision Support

Abstract Views: 152  |  PDF Views: 1

Authors

A. Alagu Karthikeyan
Department of Computer Science, RVS Agricultural College, India
R. D. Jagadeesha
Department of Mathematics, Government Science College, Hassan, India
Shrinwantu Raha
Department of Geography, Bhairab Ganguly College, India
Harshal Patil
Department of Computer Science and Engineering, Symbiosis Institute of Technology, India
Prince Williams
College of Computing and Information Sciences, University of Technology and Applied Sciences, Sohar, Oman

Abstract


In intelligent decision support, the integration of fuzzy logic systems with data classification has emerged as a promising avenue. This cooperative approach seeks to enhance decision-making processes by leveraging the strengths of both fuzzy logic and data classification techniques. However, a critical gap exists in the literature concerning the seamless integration of fuzzy logic systems and data classification for effective decision support. Existing approaches often treat these methodologies in isolation, overlooking the synergies that can arise from their collaborative utilization. Bridging this gap is essential for developing robust decision support systems capable of handling the intricacies of modern datasets. The research aims to address this gap by proposing a cooperative approach that seamlessly integrates fuzzy logic systems and data classification methods. By doing so, it seeks to overcome the limitations of traditional decision support systems and enhance their adaptability to real-world scenarios characterized by uncertainty and complexity. The method involves the development of a hybrid system that combines fuzzy logic rules and data classification algorithms. The fuzzy logic component captures and processes imprecise information, while the data classification component identifies patterns and trends within the data. The cooperative nature of the approach ensures that each method complements the other, resulting in a more robust and effective decision support system. The results demonstrate the improved performance of the proposed cooperative approach compared to traditional decision support systems. The system exhibits enhanced accuracy and adaptability, showcasing its potential to address the challenges posed by modern datasets.

Keywords


Fuzzy Logic, Decision Support, Data Classification, Cooperative Approach, Intelligent Systems.

References