AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A computation-driven, multimodal hydrogel engineered to function as a programmable regulatory node is presented, demonstrating a promising data-driven, bottom-up rational design paradigm for advanced bioelectronic tissue repair materials.
The proposed deep learning model, particularly the Vision Transformer, demonstrated high diagnostic accuracy and robust performance across gingival biotype classes, and showed improved recall and F1-scores, and better class separability in the confusion matrix, confirming its robustness and statistical reliability.
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