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- W4308083187 abstract "Forest fires are a major environmental hazard, causing economic, ecological, and human harm. Forest fires are natural flames that occur in forests, shrubs, and grasslands. Wildfires are usually caused by lightning or human error and occur during dry hot summers. Predicting such an environmental issue is crucial to reducing the threat. WSN is one of the innovative technologies and approaches developed to anticipate and detect forest fires. The accuracy of such fire detection technologies ranges from 80% to 91%. In this work, deep learning identification systems based on Convolutional Neural Networks (CNN) namely VGG16 and Mobile Net via Teachable Machine are presented to automatically detect fire in its early phases. Fire, No Fire, Smoke, and Smoke Fire are the four classes of images of forest fires identified in this work. With 98.5% accuracy on the Fire Image dataset, Mobile Net via Teachable machine outperforms the literature when it comes to predicting the fire." @default.
- W4308083187 created "2022-11-08" @default.
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- W4308083187 date "2022-10-03" @default.
- W4308083187 modified "2023-10-02" @default.
- W4308083187 title "Development of A Surveillance System to Detect Forest Fire and Smoke Using Deep Neural Networks" @default.
- W4308083187 doi "https://doi.org/10.1109/khpiweek57572.2022.9916483" @default.
- W4308083187 hasPublicationYear "2022" @default.
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