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

The Role of AI in Optimizing Renewable Energy Systems for Sustainable Development

Authors:-Hemanth Kumar

Abstract-As the world faces the growing challenges of climate change and energy insecurity, the transition to renewable energy has become a global imperative. Artificial Intelligence (AI) is playing a crucial role in optimizing renewable energy systems by improving efficiency, reducing costs, and enhancing the integration of renewable energy sources into existing power grids. AI-driven technologies, such as machine learning algorithms, predictive analytics, and optimization models, are being used to forecast energy demand, optimize energy production, manage energy storage, and improve grid stability. This paper explores the role of AI in optimizing renewable energy systems, focusing on its applications in wind, solar, and energy storage. It also examines the challenges and opportunities that AI presents in the context of sustainable development, highlighting the potential for AI to contribute to a cleaner, more sustainable energy future.

DOI: 10.61137/ijsret.vol.11.issue2.400

Published by: vikaspatanker

Deep Learning in Video Surveillance: Enhancing Security and Threat Detection

Authors:-Deepthi. P

Abstract-The increasing demand for public safety and the growing concerns around security threats have driven the adoption of advanced surveillance technologies. Among these, deep learning has emerged as a transformative approach in video surveillance systems, enabling real-time and intelligent analysis of visual data. By leveraging neural networks, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), deep learning enables accurate detection, recognition, and classification of human behaviors, faces, vehicles, and other objects of interest. This paper explores how deep learning enhances video surveillance systems for threat detection, anomaly identification, and predictive analytics. It delves into the technical aspects of integrating deep learning with video surveillance, the advantages over traditional systems, the challenges in implementation, and its application in various sectors such as law enforcement, transportation, and smart cities. The study concludes by addressing the ethical and privacy concerns and discusses the future direction of deep learning in surveillance.

DOI: 10.61137/ijsret.vol.11.issue2.399

Published by: vikaspatanker

Blockchain and AI Integration for Secure Data Management in Healthcare

Authors:-Chethan Swamy

Abstract-The healthcare industry generates massive amounts of sensitive data daily, including medical records, patient information, diagnostic results, and treatment histories. Ensuring the security, privacy, and integrity of this data is a critical concern. Both Blockchain technology and Artificial Intelligence (AI) offer potential solutions to address these challenges, each excelling in different aspects of data management. This paper explores the integration of Blockchain and AI in the healthcare sector, focusing on how the combination of these technologies can enhance data security, improve healthcare delivery, and streamline administrative tasks. By utilizing Blockchain’s decentralized and immutable ledger system alongside AI’s capabilities in data analysis and decision-making, healthcare systems can ensure secure data management, improve patient outcomes, and reduce operational inefficiencies. The paper examines real-world applications of Blockchain and AI in healthcare, addresses the challenges in their integration, and discusses the future potential of these technologies in transforming healthcare data management.

DOI: 10.61137/ijsret.vol.11.issue2.398

Published by: vikaspatanker