A Deep Learning-Based Buffalo Optimizer based Squeeze and Excitation Network for Garbage Classification for a Sustainable Environment
Abstract:<p>A Squeeze and Excitation Network is a deep-learning architectural component designed to enhance networks. The "squeeze" step reduces the spatial dimensions of the input feature maps, and the "excitation" step adaptively recalibrates channel-wise feature responses. This allows the network to focus on more educational features and ignore less useful ones. Garbage classification is a crucial task for sustainable environmental management. It involves categorizing waste into recyclables, organics, and non-… Show more
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