[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124730-en":3,"doc-seo-124730-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124730,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Based Fire Detection - A Comprehensive Review and Evaluation of Classification Models","Fires, whether natural or human-induced, create major economic and environmental risks, making efficient fire detection essential. This study systematically reviews machine learning–based fire detection research and organizes it by dataset type, including image datasets, Wireless Sensor Network (WSN)–derived data, or hybrid combinations. The work evaluates four classification models—Support Vector Machines, Decision Trees, Logistic Regression, and Multi-Layer Perceptron (MLP)—using experimental comparison of accuracy and ROC performance. Results show the MLP model achieves the highest overall accuracy (0.997), with analysis of learning dynamics and scalability to support deployment in real-world scenarios.","INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION  \n[journal homepage :](journal homepage : www.joiv.org/index.php/joiv)[ www.joiv.org/index.php/joiv](journal homepage : www.joiv.org/index.php/joiv)  \nMachine Learning Based Fire Detection: A Comprehensive Review and Evaluation of Classification Models  \nAdildabay Secilmis a, Nurullah Aksu a, Fares A. Daelb,*, Ibraheem Shayea a, Ayman A. El-Saleh c  \na Electronics and Communication Engineering Department, Istanbul Technical University (ITU), 34469 Istanbul, Turkey b Management Information Systems Department, İzmir Bakırçay University, 35665, İzmir, Turkey c Department of Electronics and Communication Engineering, A’Sharqiyah University (ASU), Ibra 400, Oman  \nCorresponding author:*[faresalariqi@gmail.com](faresalariqi@gmail.com)  \nAbstract—Fires, regardless of their origin being natural events or human-induced, provide substantial economic and environmental hazards. Therefore, the development of efficient fire detection systems is of utmost importance. This study provides a comprehensive examination of the extant body of literature about studies on fire detection utilizing machine learning techniques. Significantly, the studies employed three distinct categories of datasets: pictures, data derived from Wireless Sensor Networks (WSNs), or a hybrid amalgamation of both. Our work mainly aims to categorize fire-related data utilizing four distinct classification models: Support Vector Machines (SVMs), Decision Trees, Logistic Regression, and Multi-Layer Perceptron (MLP). The model with the highest accuracy and ROC curve performance was identified through experimental analysis. The results of our study indicate that the MLP model exhibits the highest overall accuracy, achieving a score of 0.997. In this study, we analyze the learning curves to showcase the positive training dynamics ofour model. Additionally, we explore the scalability of our model to ensure its suitability in real-world situations. In general, our research underscores the possibility of employing machine learning methodologies for fire detection, specifically emphasizing the effectiveness of the Multilayer Perceptron (MLP) model. This study contributes to the existing literature by offering valuable insights into the performance of several categorization models and conducting a comprehensive investigation of the Multilayer Perceptron (MLP) architecture. The results of our study have the potential to contribute to the advancement of fire detection systems, leading to enhanced accuracy and efficiency. This, in turn, may mitigate the adverse impacts of fires on both society and the environment.  \nKeywords—IoT; CNN; MLP; image; machine learning.  \nManuscript received 10 Dec. 2022; revised 7 Jun. 2023; accepted 15 Aug. 2023. Date of publication 30 Nov. 2023.  \nInternational Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.  \nFigure 1 shows how fires can start in a variety of places and for a variety of reasons. As a result, the primary responsibility is to take preventive measures to reduce the occurrence of fires. Particular care should be taken in using and maintaining electrical appliances, ensuring their safe operation in both residential and workplace settings. A variety of fire detection methods have been developed over time. These methods make use of various sensors capable of detecting fire-related phenomena. Several systems have been developed that integrate these sensors to detect and report fires as soon as possible [4].  \nFigure 2 illustrates fire detection systems' various sensors and sensing methods. Notably, technological advancements have facilitated the integration of these fire sensors into smart systems. In the event ofa fire, these systems send notifications to servers or mobile devices in real-time, ensuring a prompt response. As documented in [5], wireless sensor networks (WSNs) have been used to connect fire sensors to ser","cbCaiq2MDcRB3Yli","https://ap.wps.com/l/cbCaiq2MDcRB3Yli","pdf",3663410,1,7,"English","en",105,"# Introduction\n## Fire detection systems and sensor challenges\n## Vision-sensor solutions and integration with ML\n# Methodology\n## Dataset and model selection\n## Classification models evaluated\n# Results and Discussion\n## Accuracy and ROC comparison\n## Learning curves and training dynamics\n## Scalability for real-world use\n# Conclusion","[{\"question\":\"How are dataset types organized in the reviewed fire detection studies?\",\"answer\":\"The studies are categorized into three dataset types: image-based datasets, data derived from Wireless Sensor Networks (WSNs), and hybrid combinations of both.\"},{\"question\":\"Which classification models are evaluated for fire detection?\",\"answer\":\"Support Vector Machines (SVMs), Decision Trees, Logistic Regression, and Multi-Layer Perceptron (MLP) are compared as the four classification models.\"},{\"question\":\"Why is MLP highlighted as the best-performing approach in the study?\",\"answer\":\"The results indicate MLP achieves the highest overall accuracy, reaching a score of 0.997, and it also shows strong ROC curve performance, supported by learning-curve analysis and scalability discussion.\"}]","Machine Learning Based Fire Detection - A Comprehensive Review and Evaluation of Classification Models | PDF",1785894165,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-fire-detection-a-comprehensive-review-and-evaluation-of-classification-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-fire-detection-a-comprehensive-review-and-evaluation-of-classification-models/124730/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How are dataset types organized in the reviewed fire detection studies?","Question",{"text":75,"@type":76},"The studies are categorized into three dataset types: image-based datasets, data derived from Wireless Sensor Networks (WSNs), and hybrid combinations of both.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which classification models are evaluated for fire detection?",{"text":80,"@type":76},"Support Vector Machines (SVMs), Decision Trees, Logistic Regression, and Multi-Layer Perceptron (MLP) are compared as the four classification models.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is MLP highlighted as the best-performing approach in the study?",{"text":84,"@type":76},"The results indicate MLP achieves the highest overall accuracy, reaching a score of 0.997, and it also shows strong ROC curve performance, supported by learning-curve analysis and scalability discussion.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]