Real-time Smoke Detection with AI-Based Sensor Fusion
Keywords:
Artificial Intelligence (AI); Sensor Fusion; Smoke Detection; Fire Alarm System; Binary Classification
Abstract
A Fire Alarm System is sensitive to smoke, fire, carbon monoxide (CO2), or general notification emergencies. This research paper, previous experimental data were collected to develop artificial intelligence (AI) models as an indicator of fire that detects smoke. Binary Classification 1 means Positive (fire), and zero means Not Positive (no fire). It was evaluated based on twelve input parameters related to indoor and outdoor environments. This task evaluated three different classifiers: Decision Tree Classifier (DTC), Random Forest Classifier (RFC), and Gradient Boosting Classifier (GBC). The results indicated that every model was giving 99.99% accuracy. A smoke detection device can be designed according to the highaccuracy AI models validated in the current study.
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B. Sarwar, I. S. Bajwa, N. Jamil, S. Ramzan, and N. Sarwar, "An intelligent fire warning application using IoT and an adaptive neuro-fuzzy inference system," Sensors, vol. 19, no. 14, p. 3150, 2019.
C.-C. Ho, "Nighttime fire/smoke detection system based on a support vector machine," Mathematical Problems in Engineering, vol. 2013, 2013.
T. X. Truong and J.-M. Kim, "Fire flame detection in video sequences using multi-stage pattern recognition techniques," Engineering Applications of Artificial Intelligence, vol. 25, no. 7, pp. 1365-1372, 2012.
I. Chakraborty and T. K. Paul, "A hybrid clustering algorithm for fire detection in video and analysis with color-based thresholding method," in 2010 International Conference on Advances in Computer Engineering, 2010: IEEE, pp. 277-280.
K. Muhammad, J. Ahmad, and S. W. Baik, "Early fire detection using convolutional neural networks during surveillance for effective disaster management," Neurocomputing, vol. 288, pp. 30-42, 2018.
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A. Gaur et al., "Fire sensing technologies: A review," IEEE Sensors Journal, vol. 19, no. 9, pp. 3191-3202, 2019.
F. Rachman et al., "Design of the early fire detection based fuzzy logic using multisensor," in IOP Conference Series: Materials Science and Engineering, 2020, vol. 732, no. 1: IOP Publishing, p. 012039.
R. Sowah, K. O. Ampadu, A. Ofoli, K. Koumadi, G. A. Mills, and J. Nortey, "Design and Implementation of a Fire Detection and Control System for Automobiles using fuzzy logic," in 2016 IEEE industry applications society annual meeting, 2016: IEEE, pp. 1-8.
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M. Nakıp and C. Güzeliş, "Multi-sensor fire detector based on trend predictive neural network," in 2019 11th International Conference on Electrical and Electronics Engineering (ELECO), 2019: IEEE, pp. 600-604.
K. Lee, Y.-S. Shim, Y. G. Song, S. D. Han, Y.-S. Lee, and C.-Y. Kang, "Highly sensitive sensors based on metal-oxide nanocolumns for fire detection," Sensors, vol. 17, no. 2, p. 303, 2017.
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J. Fonollosa, A. Solórzano, and S. Marco, "Chemical sensor systems and associated algorithms for fire detection: A review," Sensors, vol. 18, no. 2, p. 553, 2018.
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R. G. Poola and L. Pl, "COVID-19 diagnosis: A comprehensive review of pre-trained deep learning models based on feature extraction algorithm," Results in Engineering, p. 101020, 2023.
Published
2024-12-09
How to Cite
Mohammed N Ali, Faris Hassan Taha, Rusul Taha Hussain Abd, & Muhannad Khalil Ibrahim. (2024). Real-time Smoke Detection with AI-Based Sensor Fusion. Central Asian Journal of Medical and Natural Science, 6(1), 129-136. Retrieved from https://cajmns.centralasianstudies.org/index.php/CAJMNS/article/view/2679
Section
Articles
