International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | QUAFIDE: Quantum – Assisted Fire Detection and Alert System |
|---|---|
| ABSTRACT | QUAFIDE is a quantum-based fire detection and alert application designed to bridge the gap between fire accident victims and emergency services. It is an automated mobile and desktop application with a two-layered architecture. It takes 3-5 seconds to detect fire and another 5 seconds to inform the emergency contacts. The first layer captures real-time CCTV footage, divides it into 10-second video clips, and monitors them for visual cues of fire or smoke. These clips are processed through a Convolutional Neural Network (CNN), which analyzes the frames and identifies regions with a high probability of fire. The CNN serves as the initial detection mechanism that filters potential fire instances and forwards only the suspected regions to the next layer for deeper analysis. The second layer is the quantum layer, which employs a Variational Quantum Circuit (VQC) to analyze the highlighted regions and verify the possibility of fire. This hybrid approach ensures more accurate detection and minimizes false positives. Unlike traditional fire detection systems that depend on physical smoke or heat sensors, QUAFIDE leverages visual data and quantum enhanced computation to deliver faster and more precise results. Additionally, when fire is confirmed, the system automatically initiates emergency alerts via calls and emails, shares geolocation coordinates with responders, and triggers local alarms to warn nearby individuals. By combining classical deep learning with quantum intelligence, QUAFIDE offers a smarter, more reliable, and responsive fire detection solution. |
| AUTHOR | PALADUGU LAVA KUMAR, MUCHERLA ABHINAV REDDY, DR. K. MADHUBABU, S. VIJAYA LAKSHMI Student, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Gandipet, Telangana, India Assistant Professor, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Gandipet, Telangana, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405084 |
| pdf/84_QUAFIDE Quantum – Assisted Fire Detection and Alert System.pdf | |
| KEYWORDS | |
| References | [1] S M Naveed Masroor et al. – “FlameCure: An Autonomous Indoor Fire Detection and Extinguishing Surveillance Car Using Image Processing”, International Conference on Electrical, Computer and Communication Engineering (ECCE), 2025 - Link [2] Jagruthi D Waje et al. – “Smoke and Fire Detection Using YOLOv8”, Third International Conference on Networks, Multimedia and Information Technology (NMITCON), 2025 - Link [3] Van Nguyen Thanh, et al. – “A Deep Learning-Based Fire Detection System for Ships Using CNN-LSTM Networks”, International Russian Automation Conference (RusAutoCon), 2025 - Link [4] Ruoyun Ho et al. – “Forest Fire Detection and Analysis Based on Joint MLP and CNN”, 13th International Conference of Information and Communication Technology (ICTech), 2024 - Link [5] Balaji V R et al. – “Fireguard: Deep CNN Video Surveillance for Efficient Fire Detection”, International Conference on Science Technology Engineering and Management (ICSTEM), 2024 - Link |
