An Ontology Based Multilayer Perceptron for Object Detection

In object detection, spatial knowledge assisted systems are effective. Object detection is a main and challenging issue to analyze object-related information. Several existing techniques were developed consider the problem as classification perform feature selection classification. But these still face, less computational efficiency high time consumption. This paper resolves above limitations using Fuzzy Tversky index Ontology-based Multi-Layer Perception method which improves accuracy of with minimum time. The proposed uses multilayer for finding similarity score. A fuzzy membership function used validate score predicting burned non-burned zone. Experimental assessment performed different factors such rate, complexity, error space precision by forest fire dataset. results show that this novel technique can help improve rate reduce complexity well than conventional methods.