AIMC Topic: Support Vector Machine

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Individualized prediction of psychosis in subjects with an at-risk mental state.

Schizophrenia research
Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and ...

Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition.

Computational intelligence and neuroscience
This paper proposes two multimodal fusion methods between brain and peripheral signals for emotion recognition. The input signals are electroencephalogram and facial expression. The stimuli are based on a subset of movie clips that correspond to four...

A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States.

Environmental research
Fine ambient particulate matter has been widely associated with multiple health effects. Mitigation hinges on understanding which sources are contributing to its toxicity. Black Carbon (BC), an indicator of particles generated from traffic sources, h...

Deep learning approach to bacterial colony classification.

PloS one
In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for...

A Functional-Genetic Scheme for Seizure Forecasting in Canine Epilepsy.

IEEE transactions on bio-medical engineering
OBJECTIVE: The objective of this work is the development of an accurate seizure forecasting algorithm that considers brain's functional connectivity for electrode selection.

A Novel Energy-Efficient Approach for Human Activity Recognition.

Sensors (Basel, Switzerland)
In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy con...

A support vector machine classifier for the prediction of osteosarcoma metastasis with high accuracy.

International journal of molecular medicine
In this study, gene expression profiles of osteosarcoma (OS) were analyzed to identify critical genes associated with metastasis. Five gene expression datasets were screened and downloaded from Gene Expression Omnibus (GEO). Following assessment by M...

Head and Neck Cancer Tumor Segmentation Using Support Vector Machine in Dynamic Contrast-Enhanced MRI.

Contrast media & molecular imaging
OBJECTIVE: We aimed to propose an automatic method based on Support Vector Machine (SVM) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to segment the tumor lesions of head and neck cancer (HNC).

Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways.

PloS one
Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or pathways with these essential genes will further help ...

Fast Gaussian Naïve Bayes for searchlight classification analysis.

NeuroImage
The searchlight technique is a variant of multivariate pattern analysis (MVPA) that examines neural activity across large sets of small regions, exhaustively covering the whole brain. This usually involves application of classifier algorithms across ...