Title: Predicting Fire Alarms in Smoke Detection using Neural Networks
Authors: Maher Wissam Attia, Baraa Akram Abu Zaher, Nidal Hassan Nasser, Ruba Raed Al-Hour, Aya Haider Asfour, Samy S. Abu-Naser
Volume: 7
Issue: 10
Pages: 26-33
Publication Date: 2023/10/28
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.