Authors: P. Pradeep Kumar, A. Archana G, Usha Sree A, Jayachandra C. , Mohan Krishna

Abstract: Public transport buses in India face critical safety challenges, with over 130 fatalities recorded in bus fire accidents since 2013 and hundreds of fog-related collisions occurring annually during winter months. Current safety systems are inadequate, with non-functional fire extinguishers, blocked emergency exits, and poor visibility conditions contributing to preventable deaths. This paper proposes an integrated multi-sensor safety architecture that addresses three primary hazards: fog-induced collisions, onboard fire emergencies, and delayed evacuation during accidents. The proposed system employs LiDAR (Light Detection and Ranging) technology for real-time obstacle detection and collision avoidance in low-visibility conditions caused by dense fog or heavy rainfall. Unlike conventional camera-based systems that fail in adverse weather, LiDAR sensors penetrate fog particles and provide accurate distance measurements up to 300 meters, triggering graduated visual and audible alerts to prevent collisions. For fire safety, the system integrates multi-zone automatic fire detection and suppression using temperature sensors and smoke detectors connected to solenoid-controlled water mist nozzles distributed throughout the passenger compartment. Upon detecting fire conditions, the system automatically activates suppression mechanisms within 3-10 seconds while simultaneously triggering emergency evacuation protocols. The automated emergency evacuation system features motorized rear-frame emergency doors designed to open upward using linear actuators, eliminating manual operation delays during panic situations. Additionally, the system incorporates an automated hydrophobic coating spray mechanism for the driver's windshield that dispenses nano-coating solution to create water-beading effects,significantly improving driver visibility. The complete system is controlled by an ESP32 microcontroller with modular firmware architecture, enabling real-time sensor fusion and decision-making algorithms. This integrated approach provides comprehensive safety enhancement at an estimated implementation cost significantly lower than deploying separate commercial systems.

DOI: https://doi.org/10.5281/zenodo.18753773

 

Published by: vikaspatanker

Authors: Mrs.S.Subha, Jeeva Pandiyan S, Kaviyarasan E, Najeeb Ahmed S

Abstract: The AI-Powered Platform for Personal Finance aims to simplify and enhance individual financial management through intelligent, data-driven solutions. The platform utilizes artificial intelligence and machine learning algorithms to examine user financial records such as income, expenditures, savings, and investment behavior. Based on these analyses, the system generates personalized budgeting strategies, spending insights, and predictive forecasts to support better financial decision-making. Natural language processing is incorporated to enable intuitive, conversational interaction, allowing users to access financial guidance in real time. In addition, advanced analytics help identify potential financial risks and opportunities, assisting users in optimizing savings and investment plans. Strong security measures and privacy-aware data handling techniques are embedded to protect sensitive financial information and ensure compliance with regulatory standards. Performance evaluation indicates that the platform significantly improves financial awareness, encourages disciplined spending habits, and enhances long-term financial planning. Overall, the proposed AI-based personal finance platform provides a scalable, secure, and intelligent approach to managing personal finances, empowering users with actionable insights for achieving financial stability and sustainable economic growth.

DOI: https://doi.org/10.5281/zenodo.18753748

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Authors: Akshada Ashok Bhor, Vaishnavi Arun Jadhav, Pranali Suresh Vadaje, Urvashi Raosaheb Mahajan, Prof Dr. Monika Deshmukh

Abstract: Women’s safety is a major socio-technological concern globally, with increasing cases of harassment, assault, and threats both in public and private spaces. There is a growing need for innovative solutions that ensure protection, provide quick assistance, and evaluate environmental risks. This project introduces a comprehensive Women Safety and Evaluation System that utilizes modern technologies such as GPS tracking, emergency communication, real-time alert generation, and data-based safety evaluation. The system allows users to trigger emergency alerts, share live location with trusted contacts, and notify authorities instantly. Additionally, it incorporates evaluation mechanisms to analyze unsafe zones based on past incidents, user feedback, and contextual factors. By integrating these features, the project provides a proactive and reactive safety framework aimed at minimizing response time, preventing harm, and enhancing awareness. The objective of this system is to create a secure environment where women feel protected, confident, and supported. The solution serves as a technological bridge between victims and responders, promoting safety, empowerment, and a more secure society.

DOI: https://doi.org/10.5281/zenodo.18753530

Published by: vikaspatanker

Dr. Madhuri Abhijit Darekar
Affiliation	Assistant Professor,MAEER’s MIT Arts, Commerce and Science College
Email-Id:	madhuriadarekar@gmail.com
Publication: Patents: Co-inventor of “Child-Friendly AI Learning Device with Emotive LED Display” (Design Registration No. 456722-001, granted 26 April 2025. Books: Applied ML & DL in Speech, Music and Education” (ISBN: 978- 81-972623-3-3), published by Kindle Edition, Amazon, October 2025. Operating System (CA-403), S.Y.B.B.A.(CA) (Sem-IV)” (ISBN: 978-93-24457-35-2), published by Success Publications, Pune, 2020. Publications: Deep Learning in Musical Instrument Classification: Revolutionizing Music Information Retrieval” in the National Conference on " Emerging India: Navigating Opportunities & Challenges” held on 20th April , 2024 organized by Institute of Social-Sciences ( Department of History, Department of PoliticalScience, Department of Geography and Department of Economics, JJTU Journal of Renewable Energy Exchange, Volume 12 Issue 4 (2024), PP 49-54, ISSN: 2321-1067. Observe How IOT in Schooling is creating a Massive Effect” at International Journal of Computing and Technology (IJCAT) with Impact Factor : 7.97 in Volume 9 Issue 12 on December 2021. IOT in Advance Education” in International Journal of Creative Research thoughts (IJCRT) with Impact Factor : 7.97 in Volume 9 Issue 6 on 12 June 2021. Secure File Transfer Using BlockChain Technology” at International Journal of Computing and Technology (IJCAT) with Impact Factor : 0.835 in Volume 7 Issue 4 on April 2020. Study of Social Media Marketing” at International Journal of Research and Analytical Reviews (IJRAR) with Impact Factor : 5.75 in Volume 7 Issue 1 on March 2020.

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Authors: Osorio Lolo iii, Daniel Adrian Labadan, Ginrie P. Villaruel, Kathrina Clariss P. Duliente, Jesson H. Cinto

Abstract: This study utilized sulfur, waste cooking canola oil (WCCO), and sodium chloride, a waste-derived polysulfide sorbent synthesized for potential use in oil spill recovery and remediation. Through thermal copolymerization at 170°C , washed, and dried. Three concentrations were produced (15–15–70, 20–20-60, and 25–25–50 wt%). Oil absorption capacity, reusability retention across three cycles, and oil removal efficiency were tested for the three concentrations of polysulfide sorbent. Based on the findings, all concentrations showed successful results in absorbing oil, with 15-15-70 wt% achieving the highest mean absorption (1.42 g/g) and reusability retention (38.73%). 25-25-50 wt% performed the highest in terms of oil removal efficiency (96.0%), followed by 15-15-70 wt% (95.0%). Among three different concentrations, one-way ANOVA showed no statistically significant difference at α = 0.05 in terms of absorption capacity. The polysulfide sorbent showed effective absorption, moderate reusability, and high removal efficiency generally, indicating for its potential as a low-cost and eco-friendly sorbent. To help improve durability and performance, enhancement and conducting extended testing under simulated environmental conditions are recommended for future studies.

DOI: https://doi.org/10.5281/zenodo.18742276

Published by: vikaspatanker

Authors: Dr Anuranjita Dixit

Abstract: IoT has disrupted supply chains worldwide by incorporating smart sensors, RFID tags, cloud-based visibility systems, autonomous tracking devices, robotics, and data-driven logistics. IoT-SC provides considerable operational benefits, including real-time tracking, predictive maintenance, inventory automation, transportation optimization, and responsive decision-making. However, it simultaneously brings in serious cybersecurity challenges due to the distributed nature of IoT ecosystems, resource-constrained devices, heterogeneous communication protocols, and exposure to public networks, vulnerabilities in every supply chain layer-from procurement and manufacturing to warehousing, distribution, and last-mile delivery.This research paper will comprehensively analyze the threats, vulnerabilities, and risks in IoT-enabled supply chains in regard to cybersecurity. It reviews the existing literature, maps attack surfaces, and evaluates major cyberattacks affecting supply chain IoT infrastructure, such as malware propagation, DDoS attacks, side-channel attacks, data tampering, firmware manipulation, RFID spoofing, GPS jamming, and supply chain infiltration via compromised vendor devices. The paper will also propose a multi-layer security framework for IoT-based supply chains that includes device authentication, lightweight encryption, blockchain-based integrity, intrusion detection systems, AI-driven anomaly detection, ZTA, and post-quantum cryptography.The goal is to emphasize the importance of robust cybersecurity strategies that would effectively protect IoT-enabled supply chains against emerging threats without compromising efficiency, scalability, and interoperability. The paper concludes with some future research directions, emphasizing dynamic security adaptation powered by AI, threat simulation using digital twin concepts, and advanced cryptographic techniques appropriate for next-generation IoT ecosystems.

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Authors: Aditya Kale, Srushti Dani, Sonali Gawali, Om Menkudle

Abstract: In last few years low power electronic devices have been increased rapidly. The devices are used in a large number to comfort our daily lives. With the increase in energy consumption of these portable electronic devices, the concept of harvesting alternative renewable energy in human surroundings arises a new interest among us. In this project we try to develop a piezoelectric generator. That can produce energy from vibration and pressure available on some other term(Like people walking ). This project describes the use of piezoelectric materials in order to harvest energy from people walking vibration for genera ting and accumulating the energy. This concept is also applicable to some large vibration sources which can find from nature. Thisproject also represents a footstep of piezoelectric energy harvesting model which is cost effective and easy to implement.

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Authors: Zaibindah Rafeeq Pandit, Sabit Aslam, Rehana Jan, Irfan Rasool

Abstract: Conversational agents, or more popularly called virtual assistants or chatbots, are now a unifying interface for modern digital ecosystems, enabling seamless human-computer interaction. Fueled by unprecedented accelerations in Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP), these agents evolved from script-based rules to sentient agents with the capability to understand context, sentiment, and intent. Transformer-based models such as GPT and BERT have significantly improved fluency, coherence, and chatbot response flexibility so that conversations could be more human-like. The present paper follows the historical progression of conversational agents from the initial symbolic systems such as ELIZA to modern-day deep learning models. It covers significant architectural components like intent recognition, conversation management, and response generation with emphasis placed on the intersection of speech-to-text (STT) and text-to-speech (TTS) for voice interaction. The book also looks into popular frameworks and toolkits used to develop and deploy chatbots into real-world applications across healthcare, education, customer support, and mental health. Moreover, the paper highlights major challenges hindering the robustness of current systems, including data bias, hallucination, context limitations, and lack of emotional intelligence. Moral implications—particularly of fairness, privacy, and explainability—are argued against in terms of novel guidelines and mitigation strategies. A modular, LLM-assisted architecture is suggested to demonstrate practical implementation with inherent evaluation metrics. Finally, the paper outlines guidance for subsequent research and development, calling for emotionally smart, multi-lingual, and culturally sensitive conversational agents that are ethics-compliant and highly accessible and performing.

DOI: https://doi.org/10.5281/zenodo.18739755

 

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Authors: Durgesh Nishad, Purvesh Patil, Vedant Chaudhary, Ghansham Bordekar, Dr.Umesh Pawar

Abstract: The MealSphere Management System is a modular, automated platform designed to streamline food ordering, inventory tracking, billing, and administrative operations. Traditional restaurant workflows reliant on handwritten logs and disconnected tools often result in delays, errors, and poor visibility. MealSphere resolves these inefficiencies through a centralized system that integrates authentication, menu management, inventory deduction, digital billing, and analytics. It supports both offline and online modes, ensuring operational continuity and real-time synchronization. This paper presents the system’ s architecture, implementation, and performance evaluation, demonstrating its scalability across restaurants, hostels, cloud kitchens, and canteens.

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Authors: Apurva Tanaji Bhosale, Gaurav Prabhakar Pandhare, Suman Ravi Rathod, Nikhil Megharaj Tikande

Abstract: The Fully Automatic Solar Grass Cutter is an innovative, eco-friendly solution designed to automate lawn maintenance while utilizing renewable energy. This system operates entirely on solar power, eliminating the need for conventional fuel or external electrical supply. The solar panel mounted on the device captures sunlight and converts it into electrical energy, which is stored in a rechargeable battery to power the DC motors and control unit.The grass cutter is equipped with automated navigation and obstacle detection mechanisms using sensors, enabling it to move independently across the lawn while avoiding collisions. A microcontroller is used to control the movement of the wheels and the cutting blade motor, ensuring efficient and uniform grass trimming. The automation reduces human effort, operational cost, and environmental pollution compared to traditional petrol-powered grass cutters.This project emphasizes sustainability, energy efficiency, and smart automation. It is suitable for residential lawns, gardens, parks, and institutional grounds. By integrating renewable energy with robotic technology, the Fully Automatic Solar Grass Cutter provides a cost-effective, low-maintenance, and environmentally friendly alternative for modern lawn care applications.energy, which is stored in a rechargeable battery. This stored energy powers the DC motors responsible for blade rotation and vehicle movement. A microcontroller-based control unit manages the overall operation of the system.The machine is equipped with sensors for obstacle detection and autonomous navigation, enabling it to operate without human intervention. The automatic mechanism ensures uniform grass cutting while reducing manual labor, fuel consumption, and environmental pollution. Compared to conventional petrol-driven grass cutters, this system offers low operational cost, minimal maintenance, and zero carbon emissions.

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