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

Improving Energy Consumption in Q-Learning based Routing Protocol for Flying Ad-hoc Networks (FANETs)

Authors:-Devashri Anwekar,Vikas Sakalle

Abstract-The aviation technology known as Flying Ad-hoc Networks (FANETs) demonstrates potential for disaster response scenarios and border safety operations and agricultural observation tasks. Unmanned Aerial Vehicles (UAVs) encounter major obstacles in their routing protocols because of their fluctuating topology design along with their continually moving position and their constrained energy capacity. A new Q-Learning routing protocol enhances FANET energy efficiency by applying an advanced reward system which maintains packet delivery ratio and end-to-end delay alongside network operational duration. The proposed framework adopts an energy-conscious reward structure in Q-Learning combined with state variables for tracking UAV energy reservoirs and connection range together with connection stability indicators. The simulation results prove that our proposed routing protocol offers reduced energy usage by 27% against present Q-Learning mechanisms alongside increased network operation span to 32%. The protocol maintains high performance in both packet delivery ratio and end-to-end delay measurements which makes it ready for energy-efficient FANET implementations.

DOI: 10.61137/ijsret.vol.11.issue2.403

Published by: vikaspatanker

The Impact of AI in Reducing Environmental Pollution: A Data-Driven Approach

Authors:-Raghav. B

Abstract-Artificial Intelligence (AI) has emerged as a powerful tool for tackling complex global challenges, and one of its most promising applications lies in addressing environmental pollution. As pollution continues to pose significant threats to ecosystems, human health, and climate stability, innovative and intelligent approaches are needed to monitor, predict, and mitigate its impacts. AI technologies, including machine learning, deep learning, and data analytics, offer data-driven solutions for real-time monitoring, pollution source detection, emissions forecasting, and sustainable policy development. This paper explores the role of AI in reducing environmental pollution through advanced data analysis, predictive modeling, and automation. It examines case studies, practical implementations, and the challenges associated with integrating AI into environmental management. The discussion concludes by highlighting future prospects and ethical considerations for responsible AI usage in creating cleaner, more sustainable environments.

DOI: 10.61137/ijsret.vol.11.issue2.402

Published by: vikaspatanker

Machine Learning in Financial Risk Management: Enhancing Decision-Making in Uncertain Markets

Authors:-Manoj Kumar

Abstract-The dynamic nature of financial markets, marked by volatility, uncertainty, and the influence of diverse global factors, necessitates robust and adaptive risk management strategies. Machine learning (ML), as a subset of artificial intelligence (AI), is increasingly being adopted in financial risk management to analyze large volumes of data, detect patterns, and make informed predictions. This paper explores the integration of ML techniques in financial risk assessment and management, emphasizing their role in improving decision-making, identifying potential threats, and optimizing portfolio strategies in uncertain environments. The study examines various machine learning models, such as supervised learning, unsupervised learning, and reinforcement learning, and their applications in credit scoring, fraud detection, market risk forecasting, and stress testing. Furthermore, the paper addresses challenges related to data quality, model interpretability, regulatory compliance, and ethical concerns, highlighting the need for transparent and responsible AI implementation. Through a comprehensive analysis, this paper underscores the transformative potential of machine learning in advancing financial resilience and decision-making efficiency in complex and fluctuating markets.

DOI: 10.61137/ijsret.vol.11.issue2.401

Published by: vikaspatanker