• Volume: 1
• Issue:03
• Date: 01-03-2024

Title : Review of Theft Detection Algorithms for Smart Security Systems

Abstract: This review paper provides a comprehensive analysis of various theft detection algorithms implemented in smart security systems. With the increasing integration of artificial intelligence and machine learning into security technologies, the ability to accurately detect and prevent theft has become a critical focus. This paper examines a range of algorithms, including traditional methods and advanced machine learning approaches like neural networks, decision trees, and ensemble models such as XGBoost. The review highlights the strengths and limitations of each algorithm in terms of accuracy, precision, recall, and overall effectiveness in real-time theft detection. It also discusses the challenges faced in implementing these algorithms, including data quality, computational complexity, and the adaptability of models to different environments and scenarios. Through this analysis, the paper aims to identify the most promising theft detection algorithms and propose directions for future research to enhance the capabilities of smart security systems

Key Words: Theft Detection, Smart Security Systems, Machine Learning, Neural Networks, XGBoost, Algorithm Performance, Artificial Intelligence, Real-time Detection, Security Technology

Area: Engineering

• Approved ISSN: ----
• Paper Id: IJREISTU9
• Page No: 7-11

• Author: Sonawane Rahul Shivaji

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• Co- Author: Jeetendra Singh Yadav