Hybrid deep learning – picture fuzzy set model for monitoring human behaviour in forest protection

Abstract

In conventional monitoring forest protection, detection methods use optical sensors or RGB cameras combine features including smokes, fires and human-destroyed forests at national forests. This paper has presented a new approach using Deep learning integrated with Picture Fuzzy Set for the surveillance monitoring system to be activated to confirm human behaviour in real-time in forest protection. Picture Fuzzy Graph (PFG) are applied to solve many complex problems in the real-world problems. The paper has presented a novel approach using deep learning with knowledge graphs to find a human profile including the detection of humans in large data. In the proposed model, digital human profiles are collected from conventional databases combination with social networks in real-time, and a knowledge graph is created to represent complex-relational user attributes of human profile in large datasets. PFG is applied to quantify the degree centrality of nodes. To confirm the proposed model, the proposed model has been tested with data sets through case studies of a forest. Experimental results show that the proposed model has been validated on real world datasets to demonstrate this method’s effectiveness.