AIMC Topic: Machine Learning

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A soft computing approach for diabetes disease classification.

Health informatics journal
As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noi...

EEG and fMRI agree: Mental arithmetic is the easiest form of imagery to detect.

Consciousness and cognition
fMRI and EEG during mental imagery provide alternative methods of detecting awareness in patients with disorders of consciousness (DOC) without reliance on behaviour. Because using fMRI in patients with DOC is difficult, studies increasingly employ E...

Mapping membrane activity in undiscovered peptide sequence space using machine learning.

Proceedings of the National Academy of Sciences of the United States of America
There are some ∼1,100 known antimicrobial peptides (AMPs), which permeabilize microbial membranes but have diverse sequences. Here, we develop a support vector machine (SVM)-based classifier to investigate ⍺-helical AMPs and the interrelated nature o...

SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing.

BMC genomics
BACKGROUND: Next-generation sequencing (NGS) allows unbiased, in-depth interrogation of cancer genomes. Many somatic variant callers have been developed yet accurate ascertainment of somatic variants remains a considerable challenge as evidenced by t...

Lung nodule classification using deep feature fusion in chest radiography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Lung nodules are small, round, or oval-shaped masses of tissue in the lung region. Early diagnosis and treatment of lung nodules can significantly improve the quality of patients' lives. Because of their small size and the interlaced nature of chest ...

Rectified-Linear-Unit-Based Deep Learning for Biomedical Multi-label Data.

Interdisciplinary sciences, computational life sciences
Disease diagnosis is one of the major data mining questions by the clinicians. The current diagnosis models usually have a strong assumption that one patient has only one disease, i.e. a single-label data mining problem. But the patients, especially ...

The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.

Journal of neuroscience methods
BACKGROUND: Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong back...

An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy.

PloS one
For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is o...

Machine Learning for Characterization of Insect Vector Feeding.

PLoS computational biology
Insects that feed by ingesting plant and animal fluids cause devastating damage to humans, livestock, and agriculture worldwide, primarily by transmitting pathogens of plants and animals. The feeding processes required for successful pathogen transmi...

Universal Feature Extraction for Traffic Identification of the Target Category.

PloS one
Traffic identification of the target category is currently a significant challenge for network monitoring and management. To identify the target category with pertinence, a feature extraction algorithm based on the subset with highest proportion is p...