Princy Matlani1,*, Manish Shrivastava1
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 427-458, 2019, DOI:10.32604/cmes.2019.04985
Abstract Real-time wild smoke detection utilizing machine based identification method is not produced proper accuracy, and it is not suitable for accurate prediction. However, various video smoke detection approaches involve minimum lighting, and it is required for the cameras to identify the existence of smoke particles in a scene. To overcome such challenges, our proposed work introduces a novel concept like deep VGG-Net Convolutional Neural Network (CNN) for the classification of smoke particles. This Deep Feature Synthesis algorithm automatically generated the characteristics for relational datasets. Also hybrid ABC optimization rectifies the problem related to the slow convergence since complexity is reduced.… More >
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