Published papersfrom researchers in 60+ countries.
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Smart Fire Detection and Response System
This paper presents the design and development of an IoT-based smart fire detection and autonomous fire-fighting robot using the ESP32 microcontroller. The system aims to reduce human risk in hazardous environments by enabling early fire detection and automatic suppression. It integrates multiple sensors, including flame sensors, a temperature sensor, and an ultrasonic sensor, to accurately detect fire and navigate obstacles. The ESP32 processes real-time data and controls the robot’s movement through a motor driver while activating a water pump via a relay module to extinguish fire. A servo motor ensures directional water spraying for effective suppression. Additionally, IoT connectivity allows remote monitoring and manual control through a mobile application. Experimental results demonstrate reliable performance, fast response, and improved safety, making the system suitable for residential and industrial applications.
srijan manna · mumbai
Attendo: An IoT-based Smart Attendance System Using Biometric Edge-to-Cloud Architecture and LBPH Textural Analysis
The administrative workflow for student attendance tracking in higher educational institutions has historically relied on manual roll calls and physical sign-in sheets, methods that are inherently inefficient, time-consuming, and prone to the pervasive security vulnerability of “proxy attendance” (buddy-punching). This project presents “Attendo,” an integrated IoT-enabled smart attendance system designed to automate the authentication process using contactless facial recognition. The research addresses the “Security Usability-Cost Trilemma” by synthesizing low-cost edge-computing hardware with enterprise-grade cloud-native software architectures. The hardware architecture centers on a custom-fabricated Printed Circuit Board (PCB) integrating a standard ESP32-CAM microcontroller, a Passive Infrared (PIR) sensor for energy-efficient presence detection, dual LED status indicators, and a dedicated push-button triggered biometric capture pipeline. By leveraging a hardware-interrupt driven state machine, the edge node minimizes baseline power consumption to approximately 2.5mA in deep sleep while ensuring deterministic acquisition of VGA-resolution (640×480) JPEG matrices via the onboard OV2640 CMOS sensor. The software ecosystem is engineered on a distributed PERN (PostgreSQL, Express, React, Node.js) stack, utilizing AWS S3 buckets for scalable and persistent image blob storage. To accommodate the resource constraints of edge environments, computationally intensive bio metric processing is offloaded to a dedicated Python-based vision microservice. This microservice employs Haar Cascade classifiers for precise facial localization and the Local Binary Pattern Histogram (LBPH) algorithm for texture based feature extraction. By projecting micro textural patterns into concatenated spatial histograms, identity verification is executed efficiently using a strict Chi-Square (χ2) distance metric against pre-computed vectors securely stored in the PostgreSQL database.
Tejas Shinde · Vishwaniketan's Institute of Management Entrepreneurship and Engineering Technology
- Computer ScienceIndia
Snakebite Detection and Treatment Using AI
Snakebite envenomation remains a critical public health challenge, particularly in rural and resource-limited regions where timely diagnosis and treatment are often unavailable. Traditional diagnostic methods rely heavily on clinical symptoms and patient history, which can lead to delays and misclassification. This research introduces an AI-driven snakebite detection and treatment system that leverages machine learning and computer vision to classify snakebite images into specific snake types and recommend appropriate treatment protocols. The model is trained using convolutional neural networks (CNNs) and deployed via the Django framework, ensuring accessibility through a user-friendly interface.
KOMAL SINGH MARKAM · Parul Institute of Engineering and Technology, Parul University, Vadodara, India
- EngineeringIndia
Smart Healthcare Blockchain Platform
Our project is on blockchain which is used in healthcare , as we know people travel over different country , if in case any accident happens and patient runs to the near by hospital .In that case our app will help them in medical history of patient . it provides the records of patients to doctors which patient will give access from our app .. then doctors will know about the patients history is they have any serious problems previously , and according to that the treatment will perform to patient .
Vidhi Prashant Patil · A.C .Patil College of Engineering / Mumbai University
Enhancing Manufacturing Supply Chains through Improved Visibility and Collaboration Using Artificial Intelligence within the Industry 5.0 Paradigm
This paper seeks to explore and transition Industry 4.0 to Industry 5.0 concept among Indian manufacturing industries and their supply chains. Many industries have forecast that the future of operations in supply chain management (SCM) may change steadily dramatically, from planning, scheduling, optimisation, to transportation, with the presence and integration of artificial intelligence (AI). Although the integration of Artificial Intelligence (AI) into supply chain management has emerged a pivotal role in enhancing efficiency and resilience in industries operations. The paper delves into integrating of AI in SCM processes strengthening supply chain resilience including improving data sharing, enhancing real time visibility and ensuring smooth communication among partners. With the technological advancement we tried to build a AI interface which uses real time analytics and secures collaboration to fix poor visibility and drive efficiency across complex manufacturing supply chain. It ensures the interface is User Centric and Effective in solving the core visibility and collaboration problem.
Keshav Arun Tyagi · Vishwaniketan's Institute of Management Entrepreneurship and Engineering Technology
Swastyasathi AI: An ABHA-Aligned, AI-Enabled Digital Health Platform for Integrated Clinical Documentation, e-Prescribing, and Longitudinal Patient Record Access
Swastyasathi AI is an ABHA Card revolutionary Initiative. It aims to make Abha card to be practiced in each and every hospital. Making it accessible for each and every individual to track health record.
Divyesh Potawde · Mumbai University
- Computer ScienceIndia
Expansion.AI: A Globally Scalable AI-Driven Decision-Support Framework for Autonomous International Market
International business expansion is a high-stakes strategic process requiring the synthesis of fragmented data across regulatory, economic, and competitive domains. Traditional expansion strategies rely on human-centric consultancy, which is often cost-prohibitive for Small and Medium Enterprises (SMEs) and prone to cognitive bias. This paper introduces Expansion AI, an integrated decision-support framework that leverages Multi-Criteria Decision Making (MCDM) and Explainable Artificial Intelligence (XAI) to automate global market evaluation. Utilizing real-time data from the World Bank, WTO, and UN Comtrade, Expansion.AI generates transparent, data-driven recommendations that reduce the time-to-insight for strategic planning by over 70%.
Shivani Mangesh Bhosale · University of Mumbai
- Computer ScienceIndia
Sketch2Animate
Sketch2Animate is an AI-driven system designed to automatically transform hand-drawn sketches into animated sequences with minimal human intervention.
DHIRAJ MANGESH RUPVATE · UNIVERSITY OF MUMBAI
- Computer Science India
Titanium: AI Based Fraud Detection System
The increasing use of digital payment systems and online financial services has led to a significant rise in fraudulent transactions. Traditional rule-based fraud detection methods often fail to identify complex and evolving fraud patterns. This paper presents an AI-powered fraud detection system that utilizes machine learning techniques to analyze transaction data and identify suspicious activities. The proposed system processes historical transaction datasets, performs data preprocessing, and applies machine learning algorithms to classify transactions as legitimate or fraudulent. Experimental results demonstrate that the system can effectively detect fraudulent transactions with improved accuracy and reduced false positives compared to traditional approaches. The proposed model enhances the security of digital financial systems and helps financial institutions minimize financial losses caused by fraud.
Pranav Mahesh Khamitkar · Vishwaniketan Institute of Engineering Technology
AI Powered Student Assistance Chatbot for Department of Technical Education
This paper presents Saviour, an AI-powered multilingual student assistance chatbot designed for institutes under the Directorate of Technical Education (DOTE), Maharashtra. The system uses a hybrid NLP approach combining rule-based FAQ matching with a large language model to deliver fast and accurate responses. It supports multiple Indian languages and integrates features such as result checking, timetable access, and syllabus retrieval. Experimental results show high accuracy (~91%), low response latency (<2 seconds), and improved student satisfaction, demonstrating its effectiveness in enhancing student support services.
Nilima Mahendra Patil · Mumbai
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