Capsule neural network and adapted golden search optimizer based forest fire and smoke detection.

Forest fires represent a major risk to both ecosystems and human health that rising frequency of it exacerbates global warming. This study introduces an innovative methodology for detecting forest fires and smoke using an enhanced capsule neural network (CNN) together with an adapted golden search optimizer (AGSO). By using advanced deep learning and optimization strategies, the method effectively identifies complex patterns linked to wildfires. Testing this model on wildfire smoke imagery and the BowFire dataset reveals that the proposed methodology outperformed traditional feature selection and classification methods. The integration of the modified CNN and AGSO facilitated rapid response and mitigation efforts, enhancing the accuracy and dependability of forest fire identification. This research highlights the importance of advanced computational techniques in reducing risks, ensuring safety, and progressing automatic forest fire detection systems. The combination of capsule neural networks with the golden search optimizer illustrates the potential of merging cutting-edge technologies to tackle intricate environmental issues efficiently.