Leveraging Large Language Models for Enhanced Classification and Analysis: Fire Incidents Case Study
Fire detection and analysis have been a central focus of numerous studies due to their importance in potentially reducing fire’s harmful impact. Fire detection and classification using artificial intelligence (AI) methods have drawn significant attention in the literature. These methods often tackle...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Fire |
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| Online Access: | https://www.mdpi.com/2571-6255/8/1/7 |
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| author | Eman H. Alkhammash |
| author_facet | Eman H. Alkhammash |
| author_sort | Eman H. Alkhammash |
| collection | DOAJ |
| description | Fire detection and analysis have been a central focus of numerous studies due to their importance in potentially reducing fire’s harmful impact. Fire detection and classification using artificial intelligence (AI) methods have drawn significant attention in the literature. These methods often tackle certain aspects of fire, such as classifying fire versus non-fire images or detecting smoke or flames. However, these studies lack emphasis on integrating the capabilities of large language models for fire classification. This study explores the potential of large language models, especially ChatGPT-4, in fire classification tasks. In particular, we utilize ChatGPT-4 for the first time to develop a classification approach for fire incidents. We evaluate this approach using two benchmark datasets: the Forest Fire dataset and the DFAN dataset. The results indicate that ChatGPT has significant potential for timely fire classification, making it a promising tool to complement existing fire detection technologies. Furthermore, it has the capability to provide users with more thorough information about the type of burning objects and risk level. By integrating ChatGPT, detection systems can benefit from the rapid analysis capabilities of ChatGPT to enhance response times and improve accuracy. Additionally, its ability to provide context-rich information can support better decision-making during fire episodes, making the system more effective overall. The study also examines the limitations of using ChatGPT for classification tasks. |
| format | Article |
| id | doaj-art-4190b649f7d4415987d40fc511ec190f |
| institution | Kabale University |
| issn | 2571-6255 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj-art-4190b649f7d4415987d40fc511ec190f2025-01-24T13:32:15ZengMDPI AGFire2571-62552024-12-0181710.3390/fire8010007Leveraging Large Language Models for Enhanced Classification and Analysis: Fire Incidents Case StudyEman H. Alkhammash0Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaFire detection and analysis have been a central focus of numerous studies due to their importance in potentially reducing fire’s harmful impact. Fire detection and classification using artificial intelligence (AI) methods have drawn significant attention in the literature. These methods often tackle certain aspects of fire, such as classifying fire versus non-fire images or detecting smoke or flames. However, these studies lack emphasis on integrating the capabilities of large language models for fire classification. This study explores the potential of large language models, especially ChatGPT-4, in fire classification tasks. In particular, we utilize ChatGPT-4 for the first time to develop a classification approach for fire incidents. We evaluate this approach using two benchmark datasets: the Forest Fire dataset and the DFAN dataset. The results indicate that ChatGPT has significant potential for timely fire classification, making it a promising tool to complement existing fire detection technologies. Furthermore, it has the capability to provide users with more thorough information about the type of burning objects and risk level. By integrating ChatGPT, detection systems can benefit from the rapid analysis capabilities of ChatGPT to enhance response times and improve accuracy. Additionally, its ability to provide context-rich information can support better decision-making during fire episodes, making the system more effective overall. The study also examines the limitations of using ChatGPT for classification tasks.https://www.mdpi.com/2571-6255/8/1/7large language modelsChatGPTfire classificationfire detectionfire incidents |
| spellingShingle | Eman H. Alkhammash Leveraging Large Language Models for Enhanced Classification and Analysis: Fire Incidents Case Study Fire large language models ChatGPT fire classification fire detection fire incidents |
| title | Leveraging Large Language Models for Enhanced Classification and Analysis: Fire Incidents Case Study |
| title_full | Leveraging Large Language Models for Enhanced Classification and Analysis: Fire Incidents Case Study |
| title_fullStr | Leveraging Large Language Models for Enhanced Classification and Analysis: Fire Incidents Case Study |
| title_full_unstemmed | Leveraging Large Language Models for Enhanced Classification and Analysis: Fire Incidents Case Study |
| title_short | Leveraging Large Language Models for Enhanced Classification and Analysis: Fire Incidents Case Study |
| title_sort | leveraging large language models for enhanced classification and analysis fire incidents case study |
| topic | large language models ChatGPT fire classification fire detection fire incidents |
| url | https://www.mdpi.com/2571-6255/8/1/7 |
| work_keys_str_mv | AT emanhalkhammash leveraginglargelanguagemodelsforenhancedclassificationandanalysisfireincidentscasestudy |