Smart City Gnosys

Smart city article details

Title	Low-Cost Real-Time Fire Detection In Smart Cities: Logistic Regression Is All You Need
ID_Doc	35731
Authors	Said O.; Hamza B.M.; Hibatallah M.; Ali E.M.; Abdellatif E.A.
Year	2024
Published	Proceedings of 2024 1st Edition of the Mediterranean Smart Cities Conference, MSCC 2024
DOI	http://dx.doi.org/10.1109/MSCC62288.2024.10697062
Abstract	Fire detection systems play a significant role in guaranteeing public safety and limiting property damage in smart city environments. This study analyzes the viability of installing low-cost, real-time fire detection systems using typical machine learning algorithms. Specifically, we examine the performance of Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Multinomial Naive Bayes, Gradient Boosting, K-Nearest Neighbors, Multi-Layer Perceptron, and deep learning model VGG16 in classifying fire and non-fire classes based on color histogram features. Our results suggest that Logistic Regression beats alternative models in terms of efficiency and efficacy for deployment on resource-constrained devices like Raspberry Pi. We address the relevance of our findings for boosting fire detection skills in smart cities while optimizing computational resources and operational expenses. ©2024 IEEE.
Author Keywords	Artificial intelligence (AI); Deep learning; Fire detection; Internet of Things (IoT); Smart cities

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