Hybrid deep learning : picture fuzzy set model for monitoring human behaviour in forest protection
Introduction: This article is a product of the research “Monitoring human behaviour in forest protection” deve-loped at the Hanoi University of Science and Technology in the year 2021,
Objective: This paper presents a new approach using Deep learning, integrated with Picture Fuzzy Set, for a surveillance monitoring system to identify human behaviour in real-time for the pupose of forest protection.
Methodology: The paper has presented a novel approach using deep learning with knowledge graphs to detect humans in large data sets, including finding a human profile. In the proposed model, digital human profiles are collected from conventional databases combined with social networks in real-time, and a knowledge graph is created to represent complex-relational user attributes of human profiles in large data sets. Picture Fuzzy Graphs (PFGs) are applied to quantify the degree of centrality of nodes. The proposed model has been tested with data sets through case studies of a forest.
Results:Experimental results show that the proposed model has been validated on real-world datasets to demonstrate this method’s effectiveness. The dataset includes 93,979 identities out of a total of 2,830,146 processed images that identify face detection. In a case study of forest protection in this video, a human is considered to behave normally in the proposed system.
Conclusion:The effectiveness of the theoretical basis for Deep learning, integrated with a graph database, to demonstrate human behaviours by tracking human profiles, for the pupose of forest protection, has been demonstrated.
Originality: The study has presented a new approach using a deep learning model integrated with Picture Fuzzy Sets for the surveillance monitoring system to identify human behaviour in real-time in eco-tourism areas or national forests.
Limitations: This research could be extended by integrating the models of Deep learning with knowledge gra-phs in reasoning to track in big data
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