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However, analysis of such high\u2010resolution images requires very high DL complexity. Several AI optimization techniques have been recently proposed that aim at reducing the complexity of deep neural networks and hence expedite their execution and eventually allow the use of low\u2010power, low\u2010cost computing devices with limited computation and memory resources. These methods include parameter pruning and sharing, quantization, knowledge distillation, low\u2010rank approximation, and resource efficient architectures. Rather than pruning network structures including filters, layers, and blocks of layers based on a manual selection of a significance metric such as\n                    <jats:italic>l<\/jats:italic>\n                    1\n                    <jats:italic>\u2010<\/jats:italic>\n                    norm and\n                    <jats:italic>l<\/jats:italic>\n                    2\n                    <jats:italic>\u2010<\/jats:italic>\n                    norm of the filter kernels, novel highly efficient AI\u2010driven DL optimization algorithms using variations of the squeeze and excitation in order to prune filters and layers of deep models such as VGG\u201016 as well as eliminate filters and blocks of residual networks such as ResNet\u201056 are introduced. The proposed techniques achieve significantly higher reduction in the number of learning parameters, the number of floating point operations, and memory space as compared to the\u2010state\u2010of\u2010the\u2010art methods.\n                  <\/jats:p>","DOI":"10.1002\/aisy.202400161","type":"journal-article","created":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T20:00:19Z","timestamp":1717704019000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Novel Attention\u2010Based Layer Pruning Approach for Low\u2010Complexity Convolutional Neural Networks"],"prefix":"10.1002","volume":"6","author":[{"given":"Md. 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