Open Access
| Issue |
ITM Web Conf.
Volume 74, 2025
International Conference on Contemporary Pervasive Computational Intelligence (ICCPCI-2024)
|
|
|---|---|---|
| Article Number | 01011 | |
| Number of page(s) | 11 | |
| Section | Artificial Intelligence and Machine Learning Applications | |
| DOI | https://doi.org/10.1051/itmconf/20257401011 | |
| Published online | 20 February 2025 | |
- Özel, B., Alam, M. S., Khan, M. U. (2024). Review of Modern Forest Fire Detection Techniques: Innovations in Image Processing and Deep Learning. Information, 15(9), 538. [CrossRef] [Google Scholar]
- Dost, S., Anwer, S., Saud, F., Shabbir, M. (2017, March). Outliers classification for mining evolutionary community using support vector machine and logistic regression on azure ml. In 2017 International Conference on Communication, Computing and Digital Systems (C-CODE) (pp. 216–221). IEEE. [CrossRef] [Google Scholar]
- Dua, M., Kumar, M., Charan, G. S., Ravi, P. S. (2020, February). An improved approach for fire detection using deep learning models. In 2020 International Conference on Industry 4.0 Technology (I4Tech) (pp. 171–175). IEEE. [CrossRef] [Google Scholar]
- Talaat, F. M., ZainEldin, H. (2023). An improved fire detection approach based on YOLO-v8 for smart cities. Neural Computing and Applications, 35(28), 20939–20954. [CrossRef] [Google Scholar]
- Md. Ashif Mahmud Joy, Ayesha Siddiqua, Md. Nurul Islam, Dr. Fuad Hasan Khan Chowdhury 2023 The 26th International Conference on Computer and Information Technology (ICCIT) 12 13-15 March, Cox’s Bazar, Bangladesh. Mission: Analysis, Challenges and Recommendations”. [Google Scholar]
- Shashidhara, K. S., Adithya, T., Deepak, C. (2024, April). Design and Development of Fire Fighting UAV for Urban Environments. In 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) (pp. 18). IEEE. [Google Scholar]
- Yanık et al. [27] (2021 or later) - This study presents a drone-based architecture for smoke and fire recognition using a lightweight deep learning model based on Mobile Net. [Google Scholar]
- Chandak, M. B. “Role of big-data in classification and novel class detection in data streams.” Journal Of Big Data 3.1 (2016): [CrossRef] [Google Scholar]
- A.S. Shamsoshoara, F. Afghah, A. Razi, L. Zheng, P.Z. Fulé, and E. Blasch, “Aerial imagery pile burn detection using deep learning: the flame dataset, vol. 193, 2021. [Google Scholar]
- de Venâncio et al. (2023) - Involves the use of YOLOv5 for fire detection with temporal analysis. [Google Scholar]
- Kala, C. P. (2023). Environmental and socioeconomic impacts of forest fires: A call for multilateral cooperation and management interventions. Natural Hazards Research, 3(2), 286–294. [CrossRef] [Google Scholar]
- Lu P., Zhao Y., Xu Y. (2021). A two-stream CNN model with adaptive adjustment of receptive field dedicated to flame region detection. Symmetry, 13(3), 397. [Google Scholar]
- Wahyono, A. Dharmawan, A. Harjoko, Chrystian, F.D. Adhinata, Region-Based Annotation Data of Fire Images for Intelligent Surveillance System, Zenodo, 2022, DOI: 10.5281/zenodo.5893854. [Google Scholar]
- Bahhar, C., Ksibi, A., Ayadi, M., Jamjoom, M.M., Ullah, Z., Soufiene, B.O., Sakli, H., 2023. Wildfire and smoke detection using staged YOLO model and ensemble CNN. Electronics 12, 228. [CrossRef] [Google Scholar]
- X. Song, S. Gao, C. Chen, A multispectral feature fusion network for robust pedestrian detection, Alexandria Eng. J. 60 (1) (2020) 73–85 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.