Daily Archives: May 3, 2025

Research Paper on Working of Advanced Gas Leakage Detection System with Auto Cutoff Regulator and Exhaust Fan

Authors:-Dr. Vijay R. Tripathi, Mr. Shobhit B. Khandare, Mr. Akshay G. Khillare, Mr Ronit S. Rathod, Mr. Mohammed Musaddique Ahmad

Abstract-:Gas leakages in domestic, industrial, and commercial environments pose significant safety threats, including the risk of fire, explosion, and suffocation. An advanced gas detection system integrated with an automatic cutoff regulator and exhaust fan can significantly mitigate these risks by immediately detecting gas leaks, cutting off the gas supply, and ventilating the affected area. This paper presents a comprehensive study on the working principles, system architecture, components, technologies involved, and implementation challenges of such a system.

DOI: 10.61137/ijsret.vol.11.issue2.415

Published by: vikaspatanker

Red Chili Defect Detection and Removal System using Raspberry Pi and Pi camera

Authors:-Shanmukha Priya Kurre, Bapanapalli Naga Bhargavi, Guntur Pushpa, Dabbugottu Thirupathi Vani, Chinthabttina Meghamala, Dhulipudi Chinmayi Sai Sri.

Abstract-:The Red Chilli Defect Detection and Removal System is a robust and automated solution designed to identify and remove defective red chillies on a conveyor belt. Utilizing a Raspberry Pi 3 B V1.2 and a Pi Camera module, the system captures real-time images of chillies as they move along the belt. Advanced image processing algorithms analyze these images to detect defects based on predefined parameters such as color, shape, and texture. Upon detection of a defective chilli, a control signal is sent to a DC motor-controlled ejection mechanism powered by an L293D motor driver to remove the defective chilli from the conveyor. This automated approach enhances the efficiency and precision of the quality control process in industries handling red chilli sorting, ensuring higher throughput and consistent product quality.

DOI: 10.61137/ijsret.vol.11.issue2.414

Published by: vikaspatanker

Emotional AI in Customer Experience: Adaptive Interfaces for Real-Time Sentiment Response

Authors:-Ganesh.M

Abstract-:This paper explores the integration of Emotional AI in customer experience management, particularly focusing on adaptive interfaces that can respond in real-time to customer sentiment. Emotional AI, a subset of artificial intelligence, uses machine learning models to detect and interpret human emotions through various data sources such as facial expressions, voice tone, and text. By leveraging this technology, businesses can create more personalized and engaging interactions with customers, improving satisfaction and fostering loyalty. Real-time sentiment response allows interfaces to adjust dynamically, offering tailored solutions and responses based on the emotional state of the customer. This paper delves into the applications, challenges, and future prospects of Emotional AI in transforming customer service and user interfaces. Furthermore, the study examines the ethical considerations, potential privacy concerns, and the effectiveness of adaptive interfaces in enhancing user engagement.
The research is presented in a structured manner, providing an overview of the evolution of Emotional AI, its technical foundations, and how it is reshaping customer experience strategies. Case studies and real-world examples are used to highlight the practical implications and the tangible benefits that businesses can gain by adopting such technology. Additionally, the paper outlines key methodologies for implementing adaptive emotional interfaces, with a focus on human-computer interaction and user-centered design principles.

DOI: 10.61137/ijsret.vol.11.issue2.413

Published by: vikaspatanker

Optimizing Urban Noise Control Using AI-Driven Acoustic Mapping

Authors:-Krishnaraj. S

Abstract-:Urban noise pollution has emerged as a pressing public health concern, affecting millions of city dwellers globally. Traditional methods of noise assessment and control have often fallen short due to limitations in spatial coverage, temporal resolution, and adaptability to dynamic urban environments. This paper presents a comprehensive exploration of AI-driven acoustic mapping as a transformative approach to optimize urban noise control. By integrating machine learning algorithms, real-time sensor data, and geospatial analytics, AI can generate high-resolution acoustic maps that capture the complex soundscape of urban environments. These maps not only provide detailed spatial distribution of noise levels but also reveal patterns, predict future noise trends, and guide mitigation strategies more efficiently than conventional models.
The objective of this study is to evaluate how artificial intelligence can revolutionize noise monitoring by enabling more accurate, scalable, and cost-effective solutions. Key components such as neural networks, edge computing, Internet of Things (IoT) sensor networks, and predictive analytics are examined for their role in data collection, processing, and interpretation. Case studies from leading smart cities illustrate successful implementations and potential pitfalls. In addition, we propose a conceptual framework for urban policymakers to adopt AI-driven acoustic mapping as part of sustainable urban planning. The paper concludes with a critical discussion of ethical, privacy, and technological challenges, alongside recommendations for future research and deployment strategies. Through this study, we aim to contribute to the growing discourse on leveraging AI for environmental sustainability and public health in urban ecosystems.

DOI: 10.61137/ijsret.vol.11.issue2.412

Published by: vikaspatanker

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.

DOI: 10.61137/ijsret.vol.11.issue2.411

Published by: vikaspatanker

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