AIMC Topic: Support Vector Machine

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Applying a bagging ensemble machine learning approach to predict functional outcome of schizophrenia with clinical symptoms and cognitive functions.

Scientific reports
It has been suggested that the relationship between cognitive function and functional outcome in schizophrenia is mediated by clinical symptoms, while functional outcome is assessed by the Quality of Life Scale (QLS) and the Global Assessment of Func...

A machine learning-based survival prediction model of high grade glioma by integration of clinical and dose-volume histogram parameters.

Cancer medicine
PURPOSE: Glioma is the most common type of primary brain tumor in adults, and it causes significant morbidity and mortality, especially in high-grade glioma (HGG) patients. The accurate prognostic prediction of HGG is vital and helpful for clinicians...

Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm.

PloS one
The purposes are to meet the individual needs of leather production, improve the efficiency of leather cutting, and increase the product's competitiveness. According to the existing problems in current leather cutting systems, a Fault Diagnosis (FD) ...

Development of a portable oil type classifier using laser-induced fluorescence spectrometer coupled with chemometrics.

Journal of hazardous materials
Due to the recurrent small spills, oil pollution along coastal regions is still a major environmental issue. Standardized oil fingerprinting techniques are useful for oil spill identifications, but time- and resource-consuming. There have been ongoin...

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers.

Sensors (Basel, Switzerland)
Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed fr...

Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data.

Human brain mapping
Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such "brain age prediction" vary widely in terms of their...

QSAR Models for Active Substances against Using Disk-Diffusion Test Data.

Molecules (Basel, Switzerland)
is a Gram-negative bacillus included among the six "ESKAPE" microbial species with an outstanding ability to "escape" currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concent...

Predicting sensory evaluation of spinach freshness using machine learning model and digital images.

PloS one
The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processi...

Identification of focal epilepsy by diffusion tensor imaging using machine learning.

Acta neurologica Scandinavica
OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning based on diffusion tensor imaging (DTI) measures to distinguish patients with focal epilepsy versus healthy controls and antiseizure medication (ASM) responsiveness.

Transfer learning-based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data.

Medical & biological engineering & computing
The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). An early-stage detection of COVID-19 is...