Category Archives: Uncategorized

Self-Healing Networks with AI-Based Fault Prediction in IoT Ecosystems

Authors:-Manohar Jain

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.

DOI: 10.61137/ijsret.vol.11.issue2.410

Published by: vikaspatanker

AI for Chronic Disease Management: A Remote Monitoring and Predictive Analytics Approach

Authors:-Nagendra Kumar

Abstract-: In the evolving landscape of modern management, the integration of Remote Monitoring and Predictive Analytics (RMPA) has revolutionized how organizations operate, strategize, and make decisions. With the growing reliance on digital technologies, data-driven tools are becoming vital in managing operations, workforce, equipment, and customer interactions. Remote Monitoring (RM) enables real-time oversight of various assets and processes from a distance, minimizing the need for physical intervention. Simultaneously, Predictive Analytics (PA) harnesses historical and real-time data using machine learning and statistical models to forecast future events and inform strategic actions. This review explores the convergence of RM and PA as a comprehensive management approach, applicable across diverse sectors including healthcare, manufacturing, IT, and infrastructure. It discusses how these technologies enhance efficiency, reduce costs, ensure safety, and drive proactive decision-making. By analyzing current applications, benefits, limitations, and future directions, this article provides a detailed understanding of the role of RMPA in modern management practices. The sections delve into the architecture of remote monitoring systems, data analytics frameworks, sector-specific implementations, challenges, ethical implications, and innovations shaping this domain. The review concludes with reflections on the transformative potential of RMPA and recommendations for sustainable and scalable integration into business ecosystems.

DOI: 10.61137/ijsret.vol.11.issue2.409

Published by: vikaspatanker

AI-Integrated Blockchain Systems for Transparent Supply Chain Management
Authors:-Rajkumar

Abstract-The integration of Artificial Intelligence (AI) and Blockchain technology has opened up transformative possibilities across various sectors, particularly in supply chain management. This paper explores the synergistic combination of AI and Blockchain in the context of enhancing transparency, security, and efficiency within supply chains. With the increasing complexity and globalization of supply chains, maintaining transparency, reducing fraud, and improving operational efficiency have become crucial challenges. AI offers data-driven insights, predictive capabilities, and automation, while Blockchain provides a decentralized, immutable ledger that ensures the integrity and security of transactions. The paper discusses the architecture of AI-integrated Blockchain systems and their application in streamlining processes such as traceability, smart contracts, and decision-making. Additionally, the study examines real-world case studies where AI and Blockchain integration has proven successful, highlighting the benefits and challenges. By delving into the technical, operational, and economic aspects of AI-Blockchain systems, this paper aims to demonstrate how this convergence can revolutionize supply chain management, providing actionable recommendations for businesses seeking to leverage these technologies for a more transparent, efficient, and resilient supply chain.

DOI: 10.61137/ijsret.vol.11.issue2.408

Published by: vikaspatanker

Advancing Predictive Maintenance with Edge AI and IoT Integration in Industrial Systems

Authors:-Ruksana

Abstract-The advent of Neuro-Symbolic AI models has revolutionized the approach to complex decision-making in autonomous environments. These models combine the strengths of neural networks and symbolic reasoning to tackle problems that require both data-driven learning and human-like reasoning. In this paper, we explore the integration of these two paradigms and their potential for improving decision-making in dynamic, real-world autonomous systems. We begin by outlining the fundamental principles of Neuro-Symbolic AI, discussing how it bridges the gap between purely data-driven deep learning models and rule-based symbolic systems. We highlight key challenges in autonomous decision-making, such as uncertainty, partial observability, and the need for interpretability. The paper then presents a framework for applying Neuro-Symbolic models to decision-making tasks, illustrating their capabilities in handling complex environments such as robotics, self-driving cars, and smart grids. Furthermore, we examine case studies that demonstrate the practical applications of these models in various autonomous systems, showcasing their potential to outperform traditional AI approaches. The paper concludes by discussing the future prospects of Neuro-Symbolic AI, including the challenges that need to be addressed, such as scalability, learning efficiency, and integration with existing autonomous systems. Ultimately, the paper aims to provide a comprehensive understanding of how Neuro-Symbolic AI models can significantly enhance decision-making processes in autonomous environments.

DOI: 10.61137/ijsret.vol.11.issue2.407

Published by: vikaspatanker

Neuro-Symbolic AI Models for Complex Decision Making in Autonomous Environments
Authors:-Surya.S

Abstract-The advent of Neuro-Symbolic AI models has revolutionized the approach to complex decision-making in autonomous environments. These models combine the strengths of neural networks and symbolic reasoning to tackle problems that require both data-driven learning and human-like reasoning. In this paper, we explore the integration of these two paradigms and their potential for improving decision-making in dynamic, real-world autonomous systems. We begin by outlining the fundamental principles of Neuro-Symbolic AI, discussing how it bridges the gap between purely data-driven deep learning models and rule-based symbolic systems. We highlight key challenges in autonomous decision-making, such as uncertainty, partial observability, and the need for interpretability. The paper then presents a framework for applying Neuro-Symbolic models to decision-making tasks, illustrating their capabilities in handling complex environments such as robotics, self-driving cars, and smart grids. Furthermore, we examine case studies that demonstrate the practical applications of these models in various autonomous systems, showcasing their potential to outperform traditional AI approaches. The paper concludes by discussing the future prospects of Neuro-Symbolic AI, including the challenges that need to be addressed, such as scalability, learning efficiency, and integration with existing autonomous systems. Ultimately, the paper aims to provide a comprehensive understanding of how Neuro-Symbolic AI models can significantly enhance decision-making processes in autonomous environments.

DOI: 10.61137/ijsret.vol.11.issue2.406

Published by: vikaspatanker

AI and Robotics in Healthcare Surgery: A Framework for Precision and Outcome Optimization

Authors:-Nithin

Abstract-This study explores the statistical landscape of billionaires worldwide, examining trends in wealth accumulation, geographic distribution, industry dominance, and demographic patterns. Drawing from global wealth reports and billionaire indexes, the analysis highlights the exponential growth in billionaire wealth over the past decade, with significant concentration in sectors such as technology, finance, and real estate. The report also delves into disparities by region, revealing the dominance of the United States and China in billionaire count, alongside the emergence of billionaires in developing economies. Additionally, demographic insights underscore a persistent gender gap and a gradual generational shift as younger entrepreneurs enter the billionaire ranks. The findings underscore the growing influence of billionaires on global economics, politics, and philanthropy, prompting further inquiry into wealth inequality and regulatory frameworks.

DOI: 10.61137/ijsret.vol.11.issue2.405

Published by: vikaspatanker

Data Visualization for Billionaires Statistics

Authors:-P. Surya Visahal, G. Humsika, Dr Diana Moses

Abstract-This study explores the statistical landscape of billionaires worldwide, examining trends in wealth accumulation, geographic distribution, industry dominance, and demographic patterns. Drawing from global wealth reports and billionaire indexes, the analysis highlights the exponential growth in billionaire wealth over the past decade, with significant concentration in sectors such as technology, finance, and real estate. The report also delves into disparities by region, revealing the dominance of the United States and China in billionaire count, alongside the emergence of billionaires in developing economies. Additionally, demographic insights underscore a persistent gender gap and a gradual generational shift as younger entrepreneurs enter the billionaire ranks. The findings underscore the growing influence of billionaires on global economics, politics, and philanthropy, prompting further inquiry into wealth inequality and regulatory frameworks.

DOI: 10.61137/ijsret.vol.11.issue2.404

Published by: vikaspatanker

Design and Development of Iot Prototype for Real-Time Theft Detection and Optimization of Electricity Using Machine Learning Techniques/strong>
Authors:-Assistant Professor Lakshmi G, Associate Professor Dr. M Charles Arockiaraj

Abstract-The pervasive issue of electricity theft poses a substantial challenge to power utilities globally, resulting in significant financial losses and operational inefficiencies. This paper presents the plan and growth of an IoT-based prototype for real-time electricity theft detection and optimization of electricity distribution using advanced machine-learning practices. By integrating smart meters and IoT sensors, the system continuously monitors electricity consumption, providing accurate, real-time data. Utilizing Deep Neural Networks (DNNs), the prototype identifies anomalous usage patterns indicative of theft, ensuring swift and precise detection. Additionally, the structure influences machine-learning procedures to optimize electricity distribution, enhancing overall efficiency and reducing waste. This complete method not only mitigates the risk of theft but also improves the dependability and sustainability of electricity supply. The proposed solution demonstrates important possibilities for enhancing the operational effectiveness of power utilities, offering a scalable, robust, and efficient framework for modern energy management.

DOI: 10.61137/ijsret.vol.11.issue2.404

Published by: Kajal Tripathi