Predicting Fire Alarms in Smoke Detection using Neural Networks

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International Journal of Academic Information Systems Research (IJAISR) 7 (10):26-33 (2023)
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Abstract

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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Leveraging Artificial Neural Networks for Cancer Prediction: A Synthetic Dataset Approach.Mohammed S. Abu Nasser & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (11):43-51.

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References found in this work

Potato Classification Using Deep Learning.Abeer A. Elsharif, Ibtesam M. Dheir, Alaa Soliman Abu Mettleq & Samy S. Abu-Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):1-8.
A Proposed Knowledge Based System for Desktop PC Troubleshooting.Ahmed Wahib Dahouk & Samy S. Abu-Naser - 2018 - International Journal of Academic Pedagogical Research (IJAPR) 2 (6):1-8.
Expert System for Castor Diseases and Diagnosis.Fatima M. Salman & Samy S. Abu-Naser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (3):1-10.

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