AIMC Topic: Support Vector Machine

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A Realistic Seizure Prediction Study Based on Multiclass SVM.

International journal of neural systems
A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-...

QSPR studies for predicting polarity parameter of organic compounds in methanol using support vector machine and enhanced replacement method.

SAR and QSAR in environmental research
In the present work, enhanced replacement method (ERM) and support vector machine (SVM) were used for quantitative structure-property relationship (QSPR) studies of polarity parameter (p) of various organic compounds in methanol in reversed phase liq...

Sequence-Based Prediction of Protein-Carbohydrate Binding Sites Using Support Vector Machines.

Journal of chemical information and modeling
Carbohydrate-binding proteins play significant roles in many diseases including cancer. Here, we established a machine-learning-based method (called sequence-based prediction of residue-level interaction sites of carbohydrates, SPRINT-CBH) to predict...

Pornography classification: The hidden clues in video space-time.

Forensic science international
As web technologies and social networks become part of the general public's life, the problem of automatically detecting pornography is into every parent's mind - nobody feels completely safe when their children go online. In this paper, we focus on ...

LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical ...

Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates.

Proceedings of the National Academy of Sciences of the United States of America
Sequence analyses of pathogen genomes facilitate the tracking of disease outbreaks and allow relationships between strains to be reconstructed and virulence factors to be identified. However, these methods are generally used after an outbreak has hap...

Emotional states recognition, implementing a low computational complexity strategy.

Health informatics journal
This article describes a methodology to recognize emotional states through an electroencephalography signals analysis, developed with the premise of reducing the computational burden that is associated with it, implementing a strategy that reduces th...

Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets.

BioMed research international
In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detec...

A Novel Hybrid Feature Selection Model for Classification of Neuromuscular Dystrophies Using Bhattacharyya Coefficient, Genetic Algorithm and Radial Basis Function Based Support Vector Machine.

Interdisciplinary sciences, computational life sciences
An accurate classification of neuromuscular disorders is important in providing proper treatment facilities to the patients. Recently, the microarray technology is employed to monitor the level of activity or expression of large number of genes simul...

Predicting protein subcellular localization based on information content of gene ontology terms.

Computational biology and chemistry
Predicting the location where a protein resides within a cell is important in cell biology. Computational approaches to this issue have attracted more and more attentions from the community of biomedicine. Among the protein features used to predict t...