A comprehensive comparison study of traditional classifiers and deep neural networks for forest fire detection
Yazarlar (1)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Cluster Computing (Q1) | ||
| Dergi ISSN | 1386-7857 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 04-2024 |
| Kabul Tarihi | 12-04-2023 | Yayınlanma Tarihi | 29-04-2023 |
| Cilt / Sayı / Sayfa | 27 / 2 / 1201–1215 | DOI | 10.1007/s10586-023-04003-z |
| Makale Linki | https://doi.org/10.1007/s10586-023-04003-z | ||
| Özet |
| Forest fires cause great harm to people, environment, and nature. Fire detection using forest landscape images can play a critical role in the design of expert systems required to solve the forest fire problem. The main aim of this study is to evaluate the classification accuracy of different classifier models for efficiently detecting forest fires and to present an effective and successful model. At this point, classification performances of traditional and deep neural networks (DNN) based classifiers were compared on landscape images dataset taken from the Mendeley repository within the frame of well-known metrics such as accuracy, sensitivity, specificity, precision and false negative rate. The DNN-3 classifier performed very well on the ResNet50 deep features extracted from images with 97.11% accuracy, 96.84% sensitivity, 3.16% false negative rate, 97.37% specificity, and 97.35% precision. This model (ResNet50+DNN … |
| Anahtar Kelimeler |
| Deep features | Deep neural networks | Fire detection | Forest fires |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 10 |
| Google Scholar | 14 |