AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The method reaches 31.7 AP on the AI-TOD-v2 validation split, including 23.4 AP on 2–8-pixel objects, and 36.2 AP on SODA-D, SODA-A, and AI-TOD-R, with 53.6 AP on tiny persons in TinyPerson.
AH-YOLOFlow provides an effective and computationally efficient framework for real-time crosswalk monitoring by integrating pedestrian detection, tracking, and spatial analytics and demonstrates enhanced tracking consistency following data augmentation.
A fusion-based artificial intelligence framework that integrates Quantization-Aware Training (QAT)-optimized PointNet++ models with machine-learning regression to predict a short-term dendrometer-derived stem-diameter response expressed in biomass-equivalent units is proposed.
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