AIMC Topic: Support Vector Machine

Clear Filters Showing 51 to 60 of 4975 articles

SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach.

Scientific reports
Sports injury prediction is crucial for university football player health, yet existing research predominantly focuses on professional athletes and lacks interpretability. Using the Kaggle "University Football Injury Prediction Dataset" (800 Chinese ...

Enhancing image based classification for crop disease detection using a multiclass SVM approach with kernel comparison.

Scientific reports
Agricultural production is still quite susceptible to plant diseases, despite the fact that it is essential to both economic growth and food security. Yellow rust can lower wheat yields by 20-30%, red rust by 5-10%, and anthracnose by up to 60% in cr...

Machine learning-based prediction model for omental metastasis in right-sided colon cancer patients: a retrospective multicenter study.

International journal of colorectal disease
PURPOSE: Current diagnostic modalities lack sufficient sensitivity for detecting omental metastasis (OM), often underestimating metastatic burden. Unlike traditional statistical model, machine learning (ML) model is designed to detect subtle variable...

Non-destructive defect detection in powder metallurgy automotive oil pump stators using acoustic signals and machine learning classification.

PloS one
Defects such as cracks and mass reduction frequently occur during the production of powder metallurgy (PM) automotive oil pump stators, making rigorous inspection essential for reliable operation. Conventional human visual inspection is threshold-bas...

Electroencephalography source-space functional connectivity reveals frequency-specific brain network dysfunctions in obsessive-compulsive disorder.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Obsessive-compulsive disorder (OCD) is characterized by disruptions in large-scale brain networks. However, the role of high-frequency neural synchrony in these abnormalities remains unclear. Elucidating frequency-specific alterations may...

A novel approach integrating topological deep learning from EEG Data in Alzheimer's disease.

Scientific reports
High-throughput analysis of EEG data has significantly contributed to understanding neural dynamics in Alzheimer's disease diagnosis. However, the complexity and high dimensionality of EEG signals pose challenges for traditional classification method...

Impact of image preprocessing methods on MRI radiomics feature variability and classification performance in Parkinson's disease motor subtype analysis.

Scientific reports
To evaluate the impact of various magnetic resonance imaging (MRI) preprocessing methods on radiomic feature reproducibility and classification performance in differentiating Parkinson's disease (PD) motor subtypes. We analyzed 210 T1-weighted MRI sc...

Enhancing Permeability Prediction of Heterobifunctional Degraders Using Machine Learning and Metadynamics-Informed 3D Molecular Descriptors.

Journal of chemical information and modeling
Heterobifunctional degraders, a class of targeted protein degraders (TPDs), often occupy beyond-rule-of-five (bRo5) chemical space, where traditional passive permeability models─calibrated on drug-like molecules or peptides and based on topological d...

Label-free histological identification of intraductal carcinoma of the prostate using texture analysis-based multimodal stimulated Raman scattering microscopy.

Scientific reports
Intraductal carcinoma of the prostate (IDC-P) is a very aggressive histopathological subtype of prostate cancer (PCa) that is strongly associated with poor clinical outcomes but for which no accurate biomarkers exist. Here, we demonstrate a novel app...

Using machine learning to predict student outcomes for early intervention and formative assessment.

Scientific reports
The increasing importance of early prediction of student performance has led to research into machine learning models that can be used to assess student outcomes more accurately.This study focused on developing a predictive model based on machine lea...