Research Article
Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection
@INPROCEEDINGS{10.1007/978-3-031-77075-3_3, author={Ankit Dogra and Vinayak Kumawat and Neetu Gupta}, title={Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection}, proceedings={Cognitive Computing and Cyber Physical Systems. 5th EAI International Conference, IC4S 2024, Bhimavaram, India, April 5--7, 2024, Proceedings, Part-I}, proceedings_a={IC4S}, year={2025}, month={2}, keywords={Fuzzy Theorem K-Nearest Neighbours Fault Detection Anomaly Detection Machine Learning}, doi={10.1007/978-3-031-77075-3_3} }- Ankit Dogra
Vinayak Kumawat
Neetu Gupta
Year: 2025
Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection
IC4S
Springer
DOI: 10.1007/978-3-031-77075-3_3
Abstract
Fault and anomaly detection systems are important to ensure the reliability of complex systems in various fields. This study presents a comprehensive comparative analysis between two main fault and anomaly detection strategies: fuzzy systems and k-nearest neighbour methods. Our study investigates the efficacy of each approach in identifying anomalies in a complex dataset. Our research aims to identify ideal data scenarios in which each technology excels, providing valuable insights across all technology sectors. By examining predefined parameters and input-based performance variations, we aim to guide domain-specific choices for error and anomaly detection systems. This research lays the foundation for identifying sectors that will benefit most from implementing these approaches, helping to improve the flexibility and reliability of complex systems.
