Dynamic Neural Network Based Theft Detection Algorithm For Smart Security Systems
Volume: 13 - Issue: 07 - Date: 01-07-2024
Approved ISSN: 2278-1412
Published Id: IJAECESTU412 | Page No.: 112-118
Author: Sonawane Rahul Shivaji
Co- Author: Jeetendra Singh Yadav
Abstract:-– This paper explores the development and implementation of a dynamic neural network-based
theft detection algorithm tailored for smart security systems. Leveraging the advanced capabilities of neural
networks, the proposed algorithm aims to enhance the accuracy and reliability of detecting theft-related
activities in various environments. The model is trained and tested using the XGBoost method, demonstrating
an overall accuracy of 95%. This high level of performance is reflected in the precise identification of
multiple event classes within the security system. For instance, the algorithm achieved near-perfect
precision, recall, and F1-scores in most event categories, with Class 1 and Class 3 recording perfect metrics
across the board. Despite some variance in performance, particularly in Class 5, the overall results affirm
the effectiveness of the dynamic neural network approach in improving theft detection capabilities. The
macro and weighted average precision, recall, and F1-scores further support the robustness of the model.
This research contributes to the growing field of smart security systems by providing a sophisticated and
reliable solution for real-time theft detection, offering significant potential for practical application in
enhancing security measures.
Key Words:-Dynamic Neural Networks, Theft Detection Algorithm, Smart Security Systems, XGBoost Method, Security Enhancement
Area:-Engineering
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