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Fitness Movement Types and Completeness Detection Using a Transfer-Learning-Based Deep Neural Network.

Sensors (Basel, Switzerland)
Fitness is important in people's lives. Good fitness habits can improve cardiopulmonary capacity, increase concentration, prevent obesity, and effectively reduce the risk of death. Home fitness does not require large equipment but uses dumbbells, yog...

Explaining One-Dimensional Convolutional Models in Human Activity Recognition and Biometric Identification Tasks.

Sensors (Basel, Switzerland)
Due to wearables' popularity, human activity recognition (HAR) plays a significant role in people's routines. Many deep learning (DL) approaches have studied HAR to classify human activities. Previous studies employ two HAR validation approaches: sub...

Identification of Nonvolatile Migrates from Food Contact Materials Using Ion Mobility-High-Resolution Mass Spectrometry and in Silico Prediction Tools.

Journal of agricultural and food chemistry
The identification of migrates from food contact materials (FCMs) is challenging due to the complex matrices and limited availability of commercial standards. The use of machine-learning-based prediction tools can help in the identification of such c...

Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models.

Computational intelligence and neuroscience
Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressu...

Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network.

Artificial intelligence in medicine
Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice. However, most segmentation models do not perform well on scarce pediatric imaging data. We propose a new pre-trained regularized convolutiona...

Graph neural network modelling as a potentially effective method for predicting and analyzing procedures based on patients' diagnoses.

Artificial intelligence in medicine
BACKGROUND: Currently, the healthcare sector strives to improve the quality of patient care management and to enhance/increase its economic performance/efficiency (e.g., cost-effectiveness) by healthcare providers. The data stored in electronic healt...

Assessing the robustness of clinical trials by estimating Jadad's score using artificial intelligence approaches.

Computers in biology and medicine
BACKGROUND: Clinical trials are essential in medical science and are currently the most robust strategy for evaluating the effectiveness of a treatment. However, some of these studies are less reliable than others due to flaws in their design. Assess...

Classification of multi-lead ECG with deep residual convolutional neural networks.

Physiological measurement
. Automatic electrocardiogram (ECG) interpretation based on deep learning methods is attracting increasing attention. In this study, we propose a novel method to accurately classify multi-lead ECGs using deep residual neural networks.. ECG recordings...

A multi-birth metric learning framework based on binary constraints.

Neural networks : the official journal of the International Neural Network Society
Multi-metric learning plays a significant role in improving the generalization of algorithms related to distance metrics since using a single metric is sometimes insufficient to handle complex data. Metric learning can adjust automatically the distan...

Intelligent Face Recognition System Based on Universal Design Concept.

Computational intelligence and neuroscience
The rapid development of science and technology, i.e., integrated modules that are actuators and sensors, promotes the comprehensive popularization of intelligent products in people's life. More particularly, with the advent of the hybrid of the Inte...