SMART IOT SOLUTIONS: LEVERAGING MACHINE LEARNING FOR ANOMALY DETECTION AND FAULT PREDICTION
Qusay Abdullah Abed*
Polytechnic College of Karbala,
Al-Furat Al-Awsat Technical University,
56001, Kerbala, Iraq
Abstract.This research aims to develop innovative smart IoT solutions by leveraging advanced machine learning techniques for anomaly detection and fault prediction. The novelty of this study lies in proposing a hybrid framework that integrates Support Vector Machines (SVM), Random Forest, and Neural Networks to analyze the vast, real-time data streams generated by IoT devices. Unlike existing approaches, this method enhances the accuracy and efficiency of early fault detection by combining the strengths of these algorithms. The study addresses the critical need for scalable, intelligent systems across industries such as healthcare, manufacturing, and smart homes, where IoT adoption is widespread. Experimental results demonstrate the effectiveness of the proposed method, achieving an anomaly detection accuracy of 96% and a fault prediction precision of 93% across multiple datasets. In addition, the consequences indicate that the proposed hybrid approach outperforms traditional strategies throughout all metrics, ensuing in higher detection accuracy and reduced fake positives. These findings underline the potential of this approach to improve preventive maintenance systems, reduce unexpected failures, and optimize device performance. This research contributes to the development of robust IoT systems by offering practical insights into integrating machine learning techniques into diverse IoT applications, paving the way for smarter, more reliable technological ecosystems.
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Source: International Journal of
Applied Mathematics
ISSN printed version: 1311-1728
ISSN on-line version: 1314-8060
Year: 2025
Volume: 38
Issue: 1
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