AIMC Topic: Machine Learning

Clear Filters Showing 14861 to 14870 of 34417 articles

The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms.

Scientific data
In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Clinical data are usually not openly available due to their confidential nature. This has hampered the ...

Early diagnosis of Parkinson's disease: A combined method using deep learning and neuro-fuzzy techniques.

Computational biology and chemistry
Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predi...

GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms.

Medical & biological engineering & computing
Selection of differentially expressed genes (DEGs) is a vital process to discover the causes of diseases. It has been shown that modelling of genomics data by considering relation among genes increases the predictive performance of methods compared t...

A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques.

Chemical biology & drug design
Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computation...

A Machine Learning Architecture Replacing Heavy Instrumented Laboratory Tests: In Application to the Pullout Capacity of Geosynthetic Reinforced Soils.

Sensors (Basel, Switzerland)
For economical and sustainable benefits, conventional retaining walls are being replaced by geosynthetic reinforced soil (GRS). However, for safety and quality assurance purposes, prior tests of pullout capacities of these materials need to be perfor...

Validating administrative data to identify complex surgical site infections following cardiac implantable electronic device implantation: a comparison of traditional methods and machine learning.

Antimicrobial resistance and infection control
BACKGROUND: Cardiac implantable electronic device (CIED) surgical site infections (SSIs) have been outpacing the increases in implantation of these devices. While traditional surveillance of these SSIs by infection prevention and control would likely...

Image local structure information learning for fine-grained visual classification.

Scientific reports
Learning discriminative visual patterns from image local salient regions is widely used for fine-grained visual classification (FGVC) tasks such as plant or animal species classification. A large number of complex networks have been designed for lear...

Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images.

Scientific reports
Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of co...

Using Machine Learning for Predicting the Effect of Mutations in the Initiation Codon.

IEEE journal of biomedical and health informatics
The effect of mutations has been traditionally predicted by studying what may happen due to the substitution of one amino acid for another one. This approach may be effective for mutations with impact in the function of the protein, but ineffective f...

A Connection Between Pattern Classification by Machine Learning and Statistical Inference With the General Linear Model.

IEEE journal of biomedical and health informatics
A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM parameters is expressed as a Linear Regression Model (LRM) of a...