AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A deep learning model designed to automatically detect grape leaf diseases based on images, using a pretrained ResNet50 which is trained on ImageNet as feature extractor and a Convolutional Block Attention Module to boost its discriminative capacity is introduced.
This work introduces a hybrid residual U-Net-bidirectional long short-term memory with attention (ResUNet-BiLSTM-Attention) channel estimator that learns a nonlinear mapping from noisy pilot observations to MIMO channel coefficients and consistently outperforms both conventional and learning-based baselines.
Two hybrid architectures, namely HABSA and AAC-HABSA, centered on a novel Adaptive Aspect Conditioning Layer (AACL) that injects aspect information prior to transformer-based contextual encoding are proposed, providing a computationally efficient, mathematically coherent, and interpretable solution for fine-grained sen...
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