Work place: North Carolina Agricultural and Techincal State University/Department of Computational Data Science and Engineering, Greensboro, NC, 27411, USA
E-mail: lowusu@aggies.ncat.edu
Website:
Research Interests: Deep Learning
Biography
Lawrence Owusu is a PhD Computational Data Science and Engineering student at North Carolina Agricultural and Technical State University. He holds BSc. Natural Resources Management from Kwame Nkrumah University of Science and Technology, Ghana, M.S. Agricultural and Environmental Systems and M.S. Data Science and Engineering from North Carolina A and T State University, Greensboro, USA. His research interests deep learning, data security, wildfire detection, and quantum computing.
By Lawrence Owusu Robert B Eshun Leila Hashemi-Beni Ali AlQahtani Masud R Rashel AKM K. Islam
DOI: https://doi.org/10.5815/ijisa.2024.05.05, Pub. Date: 8 Oct. 2024
Governments worldwide are increasingly prioritizing early wildfire detection to safeguard lives, property, and the environment. Although CNN-based models have demonstrated exceptional performance in various computer vision applications, the evolving nature of wildfire images poses significant challenges for a single CNN-based model in wildfire detection. In this study, we addressed this issue by integrating and weighting the differential learning capabilities of three individual transfer learning models: InceptionV3, ResNet50, and VGG16. Experimental results show that the ensemble deep learning models significantly outperformed all single classifiers across all performance metrics. Both the ensemble and weighted ensemble deep learning models achieved 99.7% accuracy, 99.5% precision, 100% recall, 99.8% F1-score, 0.5%false positive rate, 0.0% false negative rate and 0.3% error rate. Additionally, these models reduced the error rate by 98%, 91%, and 40% compared to the error rates of ResNet50, InceptionV3, and VGG16 respectively. A false negative rate of 0% indicates that our proposed ensemble deep learning models identified and predicted all the wildfire instances present in the test set correctly without a single misclassification. This positions our proposed ensemble deep learning models as superior choices for reducing misclassifications in wildfire detection.
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