Real-Time Detection of Forest Fires Using FireNet-CNN and Explainable AI Techniques
This study presents FireNet-CNN, an advanced deep-learning model particularly designed for forest fire detection, which significantly surpasses existing methods in terms of reliability, efficiency, and interpretability. FireNet-CNN is compared to popular pre-trained models, including VGG16, VGG19, a...
Saved in:
| Main Authors: | Gazi Mohammad Imdadul Alam, Naima Tasnia, Tapu Biswas, Md. Jakir Hossen, Sharia Arfin Tanim, Md Saef Ullah Miah |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10930496/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method
by: Fa Li, et al.
Published: (2024-10-01) -
Predictive Understanding of Links Between Vegetation and Soil Burn Severities Using Physics‐Informed Machine Learning
by: Seyd Teymoor Seydi, et al.
Published: (2024-08-01) -
EA-CNN: Enhanced attention-CNN with explainable AI for fruit and vegetable classification
by: Zeshan Aslam Khan, et al.
Published: (2024-12-01) -
Innovative Approaches to Forest Fire Prevention: Integrating Technology and Ecological Strategies. A Comprehensive Review
by: Syed Shaheer Hassan, et al.
Published: (2025-01-01) -
Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications
by: Sayda Umma Hamida, et al.
Published: (2024-10-01)