Category Archives: Uncategorized

Augmented Reality Interfaces Enhanced with AI for Industrial Training
Authors:-Manasa. M

Abstract-:Augmented Reality (AR) is rapidly transforming the landscape of industrial training by offering immersive, context-aware, and interactive environments. However, its full potential is unlocked when integrated with Artificial Intelligence (AI), resulting in highly adaptive, intelligent, and user-centric training systems. This paper explores the convergence of AR and AI technologies for industrial training applications, emphasizing their collective impact on skill development, operational efficiency, and safety compliance. The objective is to analyze how AI enhances AR interfaces through real-time decision-making, behavior prediction, personalized learning paths, and automated performance assessments. We present a structured review of recent advancements in AI-powered AR systems, detailing machine learning algorithms, natural language processing (NLP), computer vision, and sensor data fusion techniques used to create intuitive training experiences. Furthermore, the paper discusses the architecture and components of AR-AI interfaces, including hardware configurations, software frameworks, and integration pipelines that support training in complex industrial settings such as manufacturing, construction, and maintenance. Our study includes several case examples where AI-enhanced AR systems have been deployed successfully, demonstrating measurable improvements in learning outcomes, retention, and procedural accuracy among industrial workers. A comparative analysis with traditional and standalone AR training models highlights the added value of AI, particularly in scenarios requiring adaptive feedback, predictive error correction, and cognitive load balancing. Moreover, we address the challenges in scaling and maintaining such systems, including data privacy, hardware limitations, user acceptance, and standardization across industries. We propose a framework for evaluating the effectiveness of AR-AI solutions based on metrics such as engagement, efficiency, error rates, and knowledge retention.

DOI: 10.61137/ijsret.vol.11.issue2.420

Published by: Kajal Tripathi

Zero-Shot Learning in AI: Enabling Machines to Understand the Unseen
Authors:-Kumarswamy. S

Abstract-:Zero-Shot Learning (ZSL) has emerged as a transformative paradigm in artificial intelligence (AI), aiming to bridge the gap between learning models and their capacity to understand and classify previously unseen data. Unlike traditional supervised learning models that rely heavily on extensive labeled datasets, ZSL leverages auxiliary information such as semantic attributes, word embeddings, and ontologies to enable recognition of novel classes without explicit examples. This paper explores the foundational principles, theoretical underpinnings, and practical advancements in zero-shot learning, highlighting its potential in enabling machines to infer knowledge and perform intelligent tasks beyond their training scope. We examine the diverse methodologies utilized in ZSL, including embedding-based models, generative approaches, and hybrid architectures, and analyze their respective strengths and limitations. Real-world applications across fields such as computer vision, natural language processing, and healthcare diagnostics demonstrate ZSL’s value in scenarios where data labeling is infeasible or data collection is restricted due to privacy, cost, or rarity constraints. Additionally, the paper discusses challenges that hinder the widespread adoption of ZSL, such as semantic gap issues, domain shift, and generalization. Through a comprehensive review and synthesis of existing literature and current innovations, this work provides a roadmap for future research and development in zero-shot learning. The paper concludes by envisioning a future where AI systems with zero-shot capabilities can achieve deeper understanding, enhanced adaptability, and higher autonomy, fundamentally shifting how machines interact with and learn from the real world.

DOI: 10.61137/ijsret.vol.11.issue2.419

Published by: Kajal Tripathi

Predicting Migration Trends Using AI Models on Geopolitical and Climate Data
Authors:-Ashwini.M

Abstract-:Migration trends have always been influenced by a variety of factors, including political, economic, and environmental conditions. In recent years, the role of artificial intelligence (AI) in predicting migration patterns has garnered increasing attention. This paper explores the application of AI models in predicting migration trends by incorporating geopolitical and climate data. With the rapid advancements in machine learning and data analytics, AI models have proven to be powerful tools in analyzing complex, multidimensional datasets, providing insights into the potential movements of populations under various scenarios. This research aims to combine geopolitical factors such as conflict, political instability, and governance with climate-related data, including temperature changes, natural disasters, and resource scarcity, to generate more accurate migration forecasts. By applying machine learning algorithms, especially supervised and unsupervised techniques, the study integrates a wide range of datasets, including real-time geopolitical shifts and projected climate patterns, to create predictive models. The paper discusses the methodology of integrating AI algorithms with spatial and temporal data, while also evaluating the reliability and robustness of these models in forecasting migration flows across different regions. Furthermore, it addresses the challenges and limitations of using AI in this context, including the availability of high-quality data, ethical considerations, and the uncertainties inherent in predicting human behavior. The findings of this study will offer valuable insights for policymakers, international organizations, and humanitarian agencies in planning for future migration scenarios and managing related risks. By leveraging AI’s potential, migration forecasting can be more nuanced, timely, and context-aware, ultimately enabling better-informed decision-making in the face of global challenges.

DOI: 10.61137/ijsret.vol.11.issue2.418

Published by: Kajal Tripathi

AI-Powered Zero Trust Architectures for Secure Government Cloud Systems
Authors:-Arun Kumar

Abstract-:AI-powered Zero Trust architectures are emerging as a pivotal approach for securing government cloud systems, addressing the increasing complexity and sophistication of cybersecurity threats. This paper explores the concept of Zero Trust Architecture (ZTA), its integration with Artificial Intelligence (AI), and how these combined technologies can bolster the security of cloud environments within government sectors. Zero Trust is grounded in the principle that trust should never be implicit, even within trusted networks, and demands continuous authentication, authorization, and monitoring to ensure secure access to resources. When AI is embedded within Zero Trust models, it enhances threat detection, risk assessment, and response capabilities by enabling automated, data-driven security decisions. The dynamic nature of cloud environments necessitates robust, adaptive security frameworks. Traditional perimeter-based defenses, such as firewalls and intrusion detection systems, no longer provide sufficient protection against modern cyber threats, including insider attacks, data breaches, and advanced persistent threats. As government organizations increasingly adopt cloud services to store and manage sensitive data, ensuring the security of these systems becomes paramount. AI offers the ability to analyze vast amounts of data in real time, predict potential vulnerabilities, and respond to incidents faster and more accurately than manual methods. This paper discusses the principles behind Zero Trust, the role of AI in its implementation, and examines several use cases within the government sector. It also highlights the challenges faced when adopting AI-powered Zero Trust frameworks and offers solutions to mitigate these challenges.

DOI: 10.61137/ijsret.vol.11.issue2.417

Published by: Kajal Tripathi

Cloud-Based ETL Pipelines for Social Media Analytics
Authors:-Parth Yangandul, Sakshi Soni

Abstract-:The rapid expansion of social media has resulted in massive volumes of user-generated content, offering valuable insights for businesses, researchers, and policymakers. However, extracting, processing, and analysing this data presents challenges in scalability, efficiency, and cost. This research proposes a cloud-based ETL (Extract, Transform, Load) pipeline designed for handling large-scale social media data, ensuring efficient extraction, transformation, and structured storage for further analysis. The study will explore data extraction techniques using the Reddit API, optimizing for rate limits and scalability. The transformation process will involve text cleaning, metadata structuring, and sentiment classification to enhance data quality. For storage, AWS S3, Redshift, and NoSQL databases will be evaluated based on performance, query speed, and cost efficiency. To handle real-time and batch processing, the research will implement Apache Spark, comparing their effectiveness in different analytics scenarios. Orchestration tools like Apache Airflow and Docker will automate ETL workflows, while Terraform will enable infrastructure provisioning. Performance will be assessed through processing speed, cost, scalability, and accuracy. Additionally, Power BI and Google Data Studio will be used for visualization and reporting. This research aims to provide a scalable, cloud-native ETL solution that enhances social media data analytics, benefiting data engineers, businesses, and researchers. Index Terms—ETL, Cloud Infrastructure, Social Media Analytics, Data Pipelines, Automation.

DOI: 10.61137/ijsret.vol.11.issue2.416

Published by: Kajal Tripathi

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