Adaptive AI Systems for Personalized Learning in Virtual Classrooms
Authors:-Manmohan
Abstract-: The exponential growth of Internet of Things (IoT) ecosystems has significantly enhanced automation, efficiency, and connectivity across various industries. However, this complexity has also increased vulnerability to faults and failures, impacting performance and reliability. Traditional fault management mechanisms are reactive and often inadequate for managing dynamic and large-scale IoT environments. To address these challenges, this paper explores the concept of self-healing networks integrated with Artificial Intelligence (AI)-based fault prediction models, forming a resilient and proactive solution. The proposed framework leverages machine learning techniques to predict potential failures in real time and autonomously initiate recovery protocols without human intervention. By analyzing data streams from diverse IoT devices, AI models identify anomalies, predict faults, and dynamically reconfigure network components to ensure seamless operations. This self-healing approach minimizes downtime, optimizes resource utilization, and improves overall network efficiency. The paper discusses the design architecture, fault prediction algorithms, and healing strategies used in developing AI-driven self-healing IoT networks. Experimental evaluations demonstrate the effectiveness of this methodology in real-world scenarios, showcasing reduced recovery time and increased reliability. Moreover, the integration of edge and cloud computing further enhances the scalability and responsiveness of the system. The findings suggest that AI-enabled self-healing networks offer a transformative advancement for sustainable and intelligent IoT infrastructures. The paper concludes with insights into current limitations, potential applications across critical sectors, and directions for future research. This research paves the way for next-generation fault-tolerant systems that can autonomously learn, adapt, and recover from disruptions in highly interconnected environments.
