AIMC Topic: Support Vector Machine

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Functional connectivity-based classification of autism and control using SVM-RFECV on rs-fMRI data.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Considering the unsatisfactory classification accuracy of autism due to unsuitable features selected in current studies, a functional connectivity (FC)-based algorithm for classifying autism and control using support vector machine-recursive feature ...

Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships.

Journal of computer-aided molecular design
In this work, computational compound screening strategies on the basis of two- and three-dimensional (2D and 3D) molecular representations were investigated including similarity searching and support vector machine (SVM) ranking. Calculations based o...

Characterizing functional regional homogeneity (ReHo) as a B-SNIP psychosis biomarker using traditional and machine learning approaches.

Schizophrenia research
BACKGROUND: Recently, a biologically-driven psychosis classification (B-SNIP Biotypes) was derived using brain-based cognitive and electrophysiological markers. Here, we characterized a local functional-connectivity measure, regional homogeneity (ReH...

Flow regime identification for air valves failure evaluation in water pipelines using pressure data.

Water research
Air valve failure can cause air accumulation and result in a loss of carrying capacity, pipe vibration and even in some situations a catastrophic failure of water transmission pipelines. Air is most likely to accumulate in downward sloping pipes, lea...

Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies.

Seizure
Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medication or surgery does not effectively control seizure activity. A method that predicts the onset of a ...

SVR ensemble-based continuous blood pressure prediction using multi-channel photoplethysmogram.

Computers in biology and medicine
In this paper, a continuous non-occluding blood pressure (BP) prediction method is proposed using multiple photoplethysmogram (PPG) signals. In the new method, BP is predicted by a committee machine or ensemble learning framework comprising multiple ...

Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review.

Neurosurgical review
Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvem...

The Helitron family classification using SVM based on Fourier transform features applied on an unbalanced dataset.

Medical & biological engineering & computing
Helitrons are mobile sequences which belong to the class 2 of eukaryotic transposons. Their specificity resides in their mechanism of transposition: the rolling circle mechanism. They play an important role in remodeling proteomes due to their abilit...

mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

IEEE transactions on medical imaging
We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (AC) when reconstructing PET data on abdomen and pelvis. While MR does not intrinsically contain any ...

Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study.

Magnetic resonance imaging
BACKGROUND AND PURPOSE: Advanced imaging analysis for the prediction of tumor biology and modelling of clinically relevant parameters using computed imaging features is part of the emerging field of radiomics research. Here we test the hypothesis tha...