AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
An AI-driven calorie burn prediction system that leverages machine learning to provide an accessible, low-cost alternative to conventional methods, with potential applications in personalized fitness analytics and digital health platforms.
The findings support the potential utility of machine learning-based calorie burn estimation in low resource regions with the CaloModel showing a strong potential for estimating calorie burn using easily obtainable physiological and activity-related variables.
This study proposes a regression-based SLE framework employing eight regression algorithms: linear regression (LR), k-nearest neighbors, support vector machine, decision tree, elastic network, random forest, histogram-based gradient boosting (HGB) regressor, and artificial neural network (ANN).
This paper presents a real-time food recognition and calorie estimation system that leverages the YOLOv8 nano object-detection architecture coupled with a curated nutritional knowledge base and outperforms prior approaches based on Faster R-CNN and SSD MobileNet on both speed and accuracy metrics.
Highlights What are the main findings? Explainable machine learning model, especially XGBoost, outperformed the conventional linear regression in predicting six-minute walk distance (6MWD), as it can better model complex and non-linear relationships between anthropometric parameters in children with obesity. Age is the...
The proposed ensemble-learning-based prediction framework for physical fitness assessment provides an engineering-oriented methodology for intelligent health monitoring, signal-based performance evaluation, and personalized training support in large-scale monitoring environments.
Results indicated that the newly developed models from Objective 1 produced significantly different estimates of time spent in SED, LPA, MVPA, and TPA compared to both previously published models and other existing METs-based models, highlighting the impact of different approaches to calculating METs.
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