AIMC Topic: Support Vector Machine

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Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

Journal of neuroscience methods
BACKGROUND: Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are prom...

ViralmiR: a support-vector-machine-based method for predicting viral microRNA precursors.

BMC bioinformatics
BACKGROUND: microRNAs (miRNAs) play a vital role in development, oncogenesis, and apoptosis by binding to mRNAs to regulate the posttranscriptional level of coding genes in mammals, plants, and insects. Recent studies have demonstrated that the expre...

Autonomous unobtrusive detection of mild cognitive impairment in older adults.

IEEE transactions on bio-medical engineering
The current diagnosis process of dementia is resulting in a high percentage of cases with delayed detection. To address this problem, in this paper, we explore the feasibility of autonomously detecting mild cognitive impairment (MCI) in the older adu...

A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine.

Physiological measurement
We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian ke...

Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data.

IEEE transactions on cybernetics
Automated recognition and classification of brain diseases are of tremendous value to society. Attention deficit hyperactivity disorder (ADHD) is a diverse spectrum disorder whose diagnosis is based on behavior and hence will benefit from classificat...

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

NeuroImage
Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional s...

Retinal vessel extraction using Lattice Neural Networks with Dendritic Processing.

Computers in biology and medicine
Retinal images can be used to detect and follow up several important chronic diseases. The classification of retinal images requires an experienced ophthalmologist. This has been a bottleneck to implement routine screenings performed by general physi...

Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores.

Artificial intelligence in medicine
INTRODUCTION: The length of stay of critically ill patients in the intensive care unit (ICU) is an indication of patient ICU resource usage and varies considerably. Planning of postoperative ICU admissions is important as ICUs often have no nonoccupi...

An improved poly(A) motifs recognition method based on decision level fusion.

Computational biology and chemistry
Polyadenylation is the process of addition of poly(A) tail to mRNA 3' ends. Identification of motifs controlling polyadenylation plays an essential role in improving genome annotation accuracy and better understanding of the mechanisms governing gene...

Link-topic model for biomedical abbreviation disambiguation.

Journal of biomedical informatics
INTRODUCTION: The ambiguity of biomedical abbreviations is one of the challenges in biomedical text mining systems. In particular, the handling of term variants and abbreviations without nearby definitions is a critical issue. In this study, we adopt...