Fusing Innovation and Nature: Empowering Forest Fire Detection and Prediction Through IoT Enabled Edge Computing and Deep Learning-Reference-Cited by-同舟云学术

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Fusing Innovation and Nature: Empowering Forest Fire Detection and Prediction Through IoT Enabled Edge Computing and Deep Learning

Published:2023-12-13 Issue: Volume: Page:225-236
ISSN:1868-4238
Container-title:Transfer, Diffusion and Adoption of Next-Generation Digital Technologies
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Author:

Dhir Keshav,Singh Prabhsimran,Das Ronnie,Sawhney Ravinder Singh

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-50204-0_19

Reference22 articles.

1. Abdusalomov, A.B., Islam, B.M.S., Nasimov, R., Mukhiddinov, M., Whangbo, T.K.: An improved forest fire detection method based on the detectron2 model and a deep learning approach. Sensors 23(3), 1512 (2023)

2. Akyol, K. (2023). A comprehensive comparison study of traditional classifiers and deep neural networks for forest fire detection. Cluster Computing, 1–15

3. Alkhatib, R., Sahwan, W., Alkhatieb, A., Schütt, B.: A brief review of machine learning algorithms in forest fires science. Appl. Sci. 13(14), 8275 (2023)

4. Dwivedi, Y.K., Pandey, N., Currie, W., Micu, A.: Leveraging ChatGPT and other generative artificial intelligence (AI)-based applications in the hospitality and tourism industry: practices, challenges and research agenda. Int. J. Contemp. Hospitality Manag. (2023)

5. Chauhan, R., et al.: An IoT-based novel framework for early prediction of forest fire. In: 2023 International Conference on Disruptive Technologies (ICDT), pp. 727–732. IEEE (2023)

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