Volume 8 , Issue 2 , PP: 23-31, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Hatip 1 * , Karla Zayood 2
Doi: https://doi.org/10.54216/IJWAC.080202
The reassessment of alarm systems’ role in this regard has led to the search for improved ways of detecting fire. In this study, sensor fusion is explored to improve the accuracy and reliability of smoke detection. Since individual sensors are limited in their capabilities, this research seeks to merge different sensor data using complex fusion techniques. This paper gives a detailed analysis of several types of sensors that are used indoors and outdoors as well as firefighter training grounds that have multiple fire sources. To work around this problem, the Adaboost algorithm was used as an ensemble learning technique where sensor data were combined iteratively to form a strong classification model. The study then goes on to meticulously plot variable distribution graphs/bar charts, carry out correlation analyses, and make comparisons with other studies done previously; these findings give insight into how effective sensor fusion methods could be when it comes to smoke detection. The research results indicate that incorporating multiple sensors can significantly enhance detection accuracy and reliability. Thus, the findings obtained from this study identify a promising path for creating more efficient smoke detection systems.
Information fusion , Smoke detection , Machine Learning , Sensor Timeseries , Data Analytics.
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