Predicting Fire Alarms in Smoke Detection using Neural Networks

International Journal of Academic Information Systems Research (IJAISR) 7 (10):26-33 (2023)
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Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.

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Samy S. Abu-Naser
North Dakota State University (PhD)


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