Machine learning hybrid dynamic best model selection algorithm for real-time fire prediction using IoT-enabled multi-sensor data in buildingsOA
Machine learning hybrid dynamic best model selection algorithm for real-time fire prediction using IoT-enabled multi-sensor data in buildings
Mujeeb Ali Khan;Weiguo Song;Abbas Khan;Mazhar Ali;Rehmat Karim;Jun Zhang
State Key Laboratory of Fire Sciences,University of Science and Technology of China,Hefei,230026,China||Hefei Keda Li'an Safety Technology Co.Ltd.,Hefei,230088,ChinaState Key Laboratory of Fire Sciences,University of Science and Technology of China,Hefei,230026,ChinaState Key Laboratory of Fire Sciences,University of Science and Technology of China,Hefei,230026,ChinaTelecommunication System Research Laboratory(TSLR),Chulalongkorn University,Bangkok 10330,ThailandState Key Laboratory of Fire Sciences,University of Science and Technology of China,Hefei,230026,ChinaState Key Laboratory of Fire Sciences,University of Science and Technology of China,Hefei,230026,China
Machine learning optimizationAdaptive classifier selectionSensor fusionFire hazard mitigationReal-time analytics
Machine learning optimizationAdaptive classifier selectionSensor fusionFire hazard mitigationReal-time analytics
《安全科学与韧性（英文）》 2026 (2)
125-142,18
This work was supported by the National Natural Science Foundation of China(52321003)and the China Scholarship Council(CSC).The authors thank Hefei Keda Li'an Safety Technology Co.,Ltd.,for providing the experimental devices and facilities.Additionally,we are profoundly grateful to Prof.Weiguo Song for his valuable comments and guidance throughout this research.
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