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Interval Type-2 Fuzzy Model for Intelligent Fire Intensity Detection Algorithm with Decision Making in Low-Power Devices

Emmanuel Lule1,2,*, Chomora Mikeka3, Alexander Ngenzi4, Didacienne Mukanyiligira5

1 African Center of Excellence in Internet of Things (ACEIoT), College of Science & Technology, University of Rwanda, Nyarugenge-Kigali, 3900, Rwanda
2 Department of Computer Science, College of Computing & Information Sciences, Makerere University, Kampala, 7062, Uganda
3 Directorate of Science, Technology & Innovation (DSTI), Ministry of Education, Malawi, Lilongwe, 328, Malawi
4 Department of Computer & Software Engineering, College of Science & Technology, University of Rwanda, Nyarugenge-Kigali, 3900, Rwanda
5 National Council for Science & Technology (NCST), Government of Rwanda, Kigali, 2285, Rwanda

* Corresponding Author: Emmanuel Lule. Email: email

Intelligent Automation & Soft Computing 2023, 38(1), 57-81. https://doi.org/10.32604/iasc.2023.037988

Abstract

Local markets in East Africa have been destroyed by raging fires, leading to the loss of life and property in the nearby communities. Electrical circuits, arson, and neglected charcoal stoves are the major causes of these fires. Previous methods, i.e., satellites, are expensive to maintain and cause unnecessary delays. Also, unit-smoke detectors are highly prone to false alerts. In this paper, an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach. A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy model. Two crisp input parameters, namely: and are used. Results show that the Interval Type-2 model achieved an accuracy value of  = 98.2%, MAE = 1.3010, MSE = 1.6938 and RMSE = 1.3015 using regression analysis. The study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire danger. As a result, the proposed solution can be fully implemented in low-cost, low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false alerts. Through informed decision-making in low-cost fire detection devices, early warning notifications can be provided to aid in the rapid evacuation of people, thereby improving fire safety surveillance, management, and protection for the market community.

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Cite This Article

E. Lule, C. Mikeka, A. Ngenzi and D. Mukanyiligira, "Interval type-2 fuzzy model for intelligent fire intensity detection algorithm with decision making in low-power devices," Intelligent Automation & Soft Computing, vol. 38, no.1, pp. 57–81, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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