AIMC Topic: Machine Learning

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Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification.

Sensors (Basel, Switzerland)
Physical activity has a strong influence on mental and physical health and is essential in healthy ageing and wellbeing for the ever-growing elderly population. Wearable sensors can provide a reliable and economical measure of activities of daily liv...

Machine Learning for Sensorless Temperature Estimation of a BLDC Motor.

Sensors (Basel, Switzerland)
In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed...

Comparing regression modeling strategies for predicting hometime.

BMC medical research methodology
BACKGROUND: Hometime, the total number of days a person is living in the community (not in a healthcare institution) in a defined period of time after a hospitalization, is a patient-centred outcome metric increasingly used in healthcare research. Ho...

Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study.

Frontiers in public health
South Africa (SA) has the highest incidence of colorectal cancer (CRC) in Sub-Saharan Africa (SSA). However, there is limited research on CRC recurrence and survival in SA. CRC recurrence and overall survival are highly variable across studies. Accu...

Transfer-RLS method and transfer-FORCE learning for simple and fast training of reservoir computing models.

Neural networks : the official journal of the International Neural Network Society
Reservoir computing is a machine learning framework derived from a special type of recurrent neural network. Following recent advances in physical reservoir computing, some reservoir computing devices are thought to be promising as energy-efficient m...

InpherNet accelerates monogenic disease diagnosis using patients' candidate genes' neighbors.

Genetics in medicine : official journal of the American College of Medical Genetics
PURPOSE: Roughly 70% of suspected Mendelian disease patients remain undiagnosed after genome sequencing, partly because knowledge about pathogenic genes is incomplete and constantly growing. Generating a novel pathogenic gene hypothesis from patient ...

Machine learning to advance the prediction, prevention and treatment of eating disorders.

European eating disorders review : the journal of the Eating Disorders Association
Machine learning approaches are just emerging in eating disorders research. Promising early results suggest that such approaches may be a particularly promising and fruitful future direction. However, there are several challenges related to the natur...

SAGES consensus recommendations on an annotation framework for surgical video.

Surgical endoscopy
BACKGROUND: The growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the annotatio...

The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions.

Sensors (Basel, Switzerland)
In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to feature vectors (time-series and discrete data), classifiers (ANN and SVM), and four different processing conditions (normalization, equalization, incr...

Multitask learning over shared subspaces.

PLoS computational biology
This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would b...