{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T03:47:32Z","timestamp":1766807252573,"version":"3.41.2"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","funder":[{"name":"scientific and technological research projects of Henan Province","award":["232102321021"],"award-info":[{"award-number":["232102321021"]}]},{"name":"scientific and technological research projects of Henan Province","award":["222102220071"],"award-info":[{"award-number":["222102220071"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:p> The rapid development of modern society and continuous urbanization have resulted in a proliferation of functional buildings, which offer significant convenience to individuals, but pose significant fire hazards as well. How to detect the fire at the early stage is always the focus of research. This paper proposes a multi-information source fusion fire recognition method based on particle swarm optimization (PSO)-backpropagation (BP) neural networks and ResNet50. The PSO algorithm is applied to optimize the initial parameters of a BP neural network model, while data from three sensors\u00a0\u2014 temperature, humidity and smoke\u00a0\u2014 are integrated, through iterative training of the system, accurate recognition of sensor data can be achieved. Additionally, a method is proposed for the recognition of infrared fire images using ResNet50 and transfer learning. By improving the ResNet50 network model and migrating the ResNet50 pre-trained network weight, infrared fire image recognition accuracy is further enhanced. Then the sensor information recognition results and image information recognition results are input into the fuzzy system for fusion reasoning again, and the final decision is output according to the set fuzzy rules. Experimental findings demonstrate that the multi-information source fusion approach utilizing the PSO-BP neural network and ResNet50 significantly enhances the accuracy and response time of fire recognition, and achieves a remarkable recognition effect. <\/jats:p>","DOI":"10.1142\/s0218001424500228","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T09:56:27Z","timestamp":1726221387000},"source":"Crossref","is-referenced-by-count":2,"title":["Fire Recognition Method Based on PSO-BP Neural Network and ResNet50"],"prefix":"10.1142","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2986-0533","authenticated-orcid":false,"given":"Jing","family":"Ren","sequence":"first","affiliation":[{"name":"School of Building and Environmental Engineering, Zhengzhou University of Light Industry, Henan Engineering Research Center for Intelligent, Buildings and Human Settlements No. 136, Science Avenue, High-Tech Zone, Zhengzhou 450000, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4100-997X","authenticated-orcid":false,"given":"Xiaoyan","family":"Shi","sequence":"additional","affiliation":[{"name":"AVIC Jonhon Optronic Technology Co. Ltd., No. 60, Qianjing South Road, Jianxi Zone, Luoyang 471000, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1667-9077","authenticated-orcid":false,"given":"Xianghong","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Building and Environmental Engineering, Zhengzhou University of Light Industry, Henan Engineering Research Center for Intelligent, Buildings and Human Settlements No. 136, Science Avenue, High-Tech Zone, Zhengzhou 450000, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"S0218001424500228BIB002","doi-asserted-by":"publisher","DOI":"10.1088\/1402-4896\/aca0cd"},{"key":"S0218001424500228BIB003","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-189639"},{"key":"S0218001424500228BIB004","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-211816"},{"issue":"06","key":"S0218001424500228BIB005","first-page":"1257","volume":"43","author":"Duan X.","year":"2020","journal-title":"Chin. 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