Decentralized Voting System
Authors:-Tanisha Gaikwad, Aditya Jangam, Alish Firasta, Professor Vivek More, Assistant Professor Ajeenkya D Y Patil

Abstract-:This research paper explores the development and implementation of a decentralized voting system using blockchain technology. Traditional electoral systems face challenges such as voter fraud, lack of transparency, and centralized control. Blockchain offers a potential solution by providing a secure, transparent, and immutable platform for voting. In this paper, we propose a decentralized voting system built on Ethereum smart contracts, where votes are recorded securely, and election results are tamper-proof. We focus on key functionalities, including voter registration, vote casting, result tallying, and ensuring voter anonymity. The system was designed and tested using Ethereum’s test networks, with MetaMask integration for voter authentication. While the prototype demonstrated a functional and secure system for small-scale elections, challenges such as scalability, voter privacy, and legal compliance remain. Future work includes investigating layer-2 solutions, zero- knowledge proofs, and decentralized identity systems to address these limitations and scale the system for larger elections.

DOI: 10.61137/ijsret.vol.11.issue2.428

Published by: Kajal Tripathi

A Comprehensive Anti-Theft Vehicle Protection Framework Using Embedded Electronics and Python
Authors:-Assistant Professor Kandimalla Mallikarjuna Rao, Pinapala Sai Pavan, Loya Ravi Teja, Vennapusa Chinna Lingareddy, Tatanaboina Johny

Abstract-:Vehicle theft remains a significant concern, necessitating the development of advanced security systems that provide real- time monitoring and effective deterrence. This project, “A Comprehensive Anti-Theft Vehicle Protection Framework Using Embedded Electronics and Python,” presents a robust solution integrating biometric authentication, sensor-based detection, image capture, and remote communication to ensure comprehensive vehicle safety. The system is built around an ESP32 microcontroller that coordinates with various components, including a fingerprint sensor for authorized access, a vibration sensor for detecting unauthorized tampering, and a camera module to capture images of potential intruders, which are stored and emailed using Wi-Fi. A display module (OLED or LCD) provides real-time feedback on system status, while a mosquito module emits ultrasonic waves to deter intruders. Upon verified access, a DC motor enables vehicle operation, and in the event of a breach, GSM and GPS modules send SMS alerts with the vehicle’s live location. The GSM module also supports remote control, allowing the user to activate or deactivate the motor via SMS. Additionally, a buzzer and display work in tandem to notify nearby individuals and the owner of a security threat. This integrated system leverages embedded electronics and IoT technologies to deliver an intelligent, multi-layered defense against vehicle theft.

DOI: 10.61137/ijsret.vol.11.issue2.427

Published by: Kajal Tripathi

AI-Based Ecosystem Monitoring for Climate-Sensitive Biodiversity Conservation
Authors:-Varun.P

Abstract-:The escalating impacts of climate change pose a serious threat to global biodiversity, necessitating innovative and adaptive approaches to conservation. This paper explores the integration of artificial intelligence (AI) into ecosystem monitoring frameworks, specifically tailored to address climate-sensitive biodiversity conservation. As traditional biodiversity monitoring methods struggle with limitations in spatial and temporal scales, AI-driven technologies such as machine learning, computer vision, and remote sensing are increasingly employed to bridge these gaps. This study highlights the potential of AI to process vast environmental data in real time, detect ecological changes, and predict species vulnerabilities with high precision. The implementation of AI not only enhances monitoring efficiency but also fosters proactive conservation strategies by enabling early warnings and predictive insights. We present a multidisciplinary framework that synthesizes AI tools with ecological modeling to facilitate data-driven decision-making in biodiversity conservation under changing climatic conditions. Case studies are discussed where AI-based monitoring has successfully supported conservation initiatives, particularly in ecologically sensitive zones. Furthermore, we examine the ethical, technical, and logistical challenges associated with deploying AI in remote and fragile ecosystems. Emphasis is placed on ensuring data transparency, stakeholder collaboration, and equitable access to AI technologies, especially in biodiversity hotspots within developing countries. The study concludes that AI holds transformative potential in reshaping conservation paradigms but requires strategic investments in infrastructure, capacity building, and policy alignment. By leveraging AI for ecosystem monitoring, conservation efforts can become more responsive, scalable, and resilient in the face of climate uncertainties, ultimately contributing to global sustainability goals. This paper offers a comprehensive outlook on how AI can drive systemic change in biodiversity monitoring and policy planning, setting the stage for future research and collaboration in the emerging field of AI-driven conservation science.

DOI: 10.61137/ijsret.vol.11.issue2.426

Published by: Kajal Tripathi

AI in Emotional Robotics: Building Companion Systems for Elderly Care
Authors:-Siddesh Gowda

Abstract-:AI in Emotional Robotics has emerged as a revolutionary field in elderly care, where the integration of advanced machine learning algorithms and robotics aims to provide emotional and physical support to aging individuals. The elderly population is growing globally, and with it comes the challenge of providing quality care that meets both their physical and emotional needs. Emotional robotics focuses on creating companion robots that can simulate emotions, interact meaningfully with humans, and provide psychological support. These systems are designed to recognize and respond to the emotional states of the elderly, fostering a sense of connection and reducing loneliness, which is a common issue in older adults. This paper explores the development, challenges, and potential applications of AI-driven emotional robotics in elderly care. It examines various approaches to designing these systems, from affective computing to machine learning models that enable robots to understand and adapt to human emotions. It also highlights the ethical considerations, social impact, and future directions for this technology, aiming to enhance the quality of life for elderly individuals while addressing the growing demand for care services. The paper concludes with an analysis of the key challenges faced in the development of companion robots, including the need for more natural and empathetic interactions, the importance of cultural sensitivity, and the integration of safety protocols for elderly users.

DOI: 10.61137/ijsret.vol.11.issue2.425

Published by: Kajal Tripathi

Automated Journalism: Generative AI and the Future of News Reporting
Authors:-Ragavendra.M

Abstract-:The field of journalism is undergoing a transformation as artificial intelligence (AI) continues to advance. Specifically, generative AI has emerged as a critical tool in reshaping how news is reported, written, and consumed. Automated journalism, which involves the use of algorithms and AI models to generate news content, promises to increase the speed and scale of news reporting, reduce human biases, and streamline editorial processes. However, this transformation also raises concerns about the future of journalism, including issues of accuracy, credibility, and the potential for job displacement. This paper explores the impact of generative AI on news reporting, its potential benefits and challenges, and the ethical considerations that must be addressed to ensure its responsible use in the media industry. Through an examination of current AI technologies in journalism, real-world applications, and theoretical frameworks, the paper highlights both the opportunities and the risks associated with AI-driven news production. As AI continues to evolve, the question arises: can machines truly replace the human element in journalism, or will they work in conjunction with journalists to enhance reporting? This paper provides a nuanced analysis of the evolving role of AI in newsrooms, outlining how this technological shift could redefine what it means to be a journalist in the 21st century. The discussion includes not only technological advancements but also societal implications, exploring the ethical dimensions of an AI-driven future for journalism. Through in-depth research and case studies, the paper offers valuable insights into how automated journalism can shape the future of news reporting while emphasizing the importance of human oversight to maintain journalistic integrity.

DOI: 10.61137/ijsret.vol.11.issue2.424

Published by: Kajal Tripathi

Crisis Mapping with AI: Real-Time Crowd-Sourced Intelligence for Relief Coordination
Authors:-Prakash Nayak

Abstract-:The concept of crisis mapping has gained immense attention in recent years, particularly in response to the increasing frequency and intensity of natural disasters and humanitarian crises around the world. This paper explores the use of artificial intelligence (AI) to enhance the process of crisis mapping, focusing on the integration of real-time crowd-sourced intelligence for efficient relief coordination. The ability to leverage AI-driven technologies for mapping and analyzing crisis data allows for more accurate and timely decision-making, which is crucial in the chaotic environment of a crisis. This paper reviews current technologies, methodologies, and applications related to AI-powered crisis mapping, with an emphasis on the real-time collection, analysis, and visualization of data from diverse sources such as social media, mobile apps, satellite imagery, and sensors. It also discusses the integration of machine learning and deep learning models to process and interpret large volumes of unstructured data, facilitating quicker response times and better-targeted relief efforts. Moreover, ethical considerations, challenges, and future developments are addressed, offering insights into the evolving role of AI in crisis management and disaster relief. The combination of crowd-sourced data and advanced AI algorithms enhances the accuracy, speed, and reliability of crisis response systems, which are vital for ensuring that humanitarian aid reaches those in need in a timely manner. This paper also delves into potential future applications of AI in crisis mapping, exploring innovations such as autonomous data collection methods and more refined prediction models, and how these can further streamline disaster relief coordination.

DOI: 10.61137/ijsret.vol.11.issue2.423

Published by: Kajal Tripathi

AI and IoT Synergy for Intelligent Cold Chain Logistics
Authors:-Natraja

Abstract-:The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in logistics has significantly transformed the operational dynamics of various industries, especially in the context of cold chain logistics. The cold chain is a vital component in sectors such as food, pharmaceuticals, and biotechnology, where maintaining precise temperature conditions is critical for the preservation of goods. This paper explores the synergy between AI and IoT technologies to enhance the efficiency and reliability of intelligent cold chain logistics. IoT provides real-time monitoring through sensors that track the temperature, humidity, and location of goods during transit. Meanwhile, AI processes this vast amount of data to predict anomalies, optimize routes, and automate decision-making processes, ensuring that goods are stored and transported under optimal conditions. The combination of these technologies can improve supply chain transparency, reduce wastage, lower operational costs, and enhance customer satisfaction. Furthermore, the paper addresses the challenges and potential risks involved in integrating AI and IoT into cold chain logistics, offering solutions to mitigate these issues. By delving into the practical applications, benefits, and future prospects of this technological convergence, this paper provides a comprehensive overview of how AI and IoT can revolutionize cold chain logistics, making it more intelligent, efficient, and sustainable. Furthermore, the paper investigates the scalability of these technologies and their ability to meet the growing demands of global cold chain logistics, particularly in response to market dynamics, regulatory changes, and consumer expectations. It also considers the long-term impact of AI and IoT on sustainability and how these technologies contribute to reducing carbon footprints, improving energy efficiency, and ensuring compliance with global environmental standards.

DOI: 10.61137/ijsret.vol.11.issue2.422

Published by: Kajal Tripathi

Multilingual Voice AI Assistants for Bridging Language Gaps in Rural Healthcare
Authors:-Nagesh Algondi. A

Abstract-:The integration of multilingual voice AI assistants in rural healthcare has the potential to address significant language barriers that hinder effective communication between healthcare providers and patients in underserved regions. In many rural areas, healthcare professionals often face challenges in delivering quality care due to language differences, which may result in miscommunication, improper diagnosis, and inefficient treatment plans. The lack of effective communication in healthcare settings has been a persistent issue, and the multilingualism gap exacerbates this problem, leading to distrust and dissatisfaction among patients. By employing multilingual voice-based AI assistants, these gaps can be bridged, enabling seamless interactions between patients and healthcare providers who may not share a common language. This paper explores the development and implementation of AI-driven systems capable of translating medical information across multiple languages, with a focus on voice recognition and natural language processing technologies. These AI assistants can process spoken language and convert it into actionable healthcare data, providing accurate translations in real-time, which can significantly improve healthcare outcomes in rural regions. Additionally, the paper discusses the challenges, benefits, and future prospects of multilingual AI assistants in rural healthcare settings. The effectiveness of these tools in enhancing patient care, improving diagnosis accuracy, and promoting inclusivity in healthcare delivery are examined. This paper also explores the potential for such systems to improve healthcare access, reduce linguistic barriers, and empower rural communities with better health outcomes. This advancement in healthcare accessibility could foster increased community trust and make healthcare systems more inclusive, ensuring a more equitable healthcare environment across regions and languages. The analysis includes a look at ethical considerations, technological advancements, and the scalability of such solutions in different global contexts, highlighting how multilingual AI assistants can become a cornerstone of rural healthcare improvement worldwide.

DOI: 10.61137/ijsret.vol.11.issue2.421

Published by: Kajal Tripathi

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