AIMC Topic: Machine Learning

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Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units.

Biosensors
Gait phase recognition is of great importance in the development of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer durin...

Application of Machine Learning Methods in Nursing Home Research.

International journal of environmental research and public health
A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random...

Using Machine Learning to Train a Wearable Device for Measuring Students' Cognitive Load during Problem-Solving Activities Based on Electrodermal Activity, Body Temperature, and Heart Rate: Development of a Cognitive Load Tracker for Both Personal and Classroom Use.

Sensors (Basel, Switzerland)
Automated tracking of physical fitness has sparked a health revolution by allowing individuals to track their own physical activity and health in real time. This concept is beginning to be applied to tracking of cognitive load. It is well known that ...

A machine learning framework for genotyping the structural variations with copy number variant.

BMC medical genomics
BACKGROUND: Genotyping of structural variation is an important computational problem in next generation sequence data analysis. However, in cancer genomes, the copy number variant(CNV) often coexists with other types of structural variations which si...

A novel machine learning strategy for model selections - Stepwise Support Vector Machine (StepSVM).

PloS one
An essential aspect of medical research is the prediction for a health outcome and the scientific identification of important factors. As a result, numerous methods were developed for model selections in recent years. In the era of big data, machine ...

GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics.

Chemical research in toxicology
Predicting the structures of metabolites formed in humans can provide advantageous insights for the development of drugs and other compounds. Here we present GLORYx, which integrates machine learning-based site of metabolism (SoM) prediction with rea...

Importance of general adiposity, visceral adiposity and vital signs in predicting blood biomarkers using machine learning.

International journal of clinical practice
INTRODUCTION: Blood biomarkers are measured for their ability to characterise physiological and disease states. Much is known about linear relations between blood biomarker concentrations and individual vital signs or adiposity indexes (eg, BMI). Com...

White learning methodology: A case study of cancer-related disease factors analysis in real-time PACS environment.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Bayesian network is a probabilistic model of which the prediction accuracy may not be one of the highest in the machine learning family. Deep learning (DL) on the other hand possess of higher predictive power than many other...