A Study On The Fault Recognition Utilizing Svm Classifier


  • Manidipa Acharjya and Dr. Rahul Mishra


WSNs should focus on fault-tolerant event detection and change detection. According to recent articles, 1.21 percent and 2.12 percent of work has been done in detection and estimation, wireless radio, and link characteristics, respectively. The goal of this research is to contribute to the event region detection in WSN by using the suggested technique to optimize the network's fault tolerance capacity. This research is being expanded to investigate the effect of a noisy communication channel on event detection. The research contributes to the detection of non-binary fault-tolerant event regions by taking into account symmetric and non-symmetric sensor fault probability. Because mushrooms are sensitive to temperature fluctuations, this research could be applied to temperature monitoring in mushroom cultivation. For detecting systematic errors, the third section proposes a sequential change detection algorithm. This work will be useful in time-critical applications, particularly in industrial applications where data must be handled before processing.



How to Cite

Manidipa Acharjya and Dr. Rahul Mishra. (2023). A Study On The Fault Recognition Utilizing Svm Classifier. SJIS-P, 35(3), 90–97. Retrieved from http://sjisscandinavian-iris.com/index.php/sjis/article/view/633