Predicting Fire Alarms in Smoke Detection using Neural Networks

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

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.

Author's Profile

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

Analytics

Added to PP
2023-11-11

Downloads
528 (#35,488)

6 months
430 (#3,988)

Historical graph of downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.
How can I increase my downloads?