AIMC Topic: Principal Component Analysis

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OptiStack classifier: optimized stacking framework with ensemble feature engineering for enhanced cardiovascular risk prediction.

Inflammation research : official journal of the European Histamine Research Society ... [et al.] May 31, 2025
BACKGROUND: Cardiovascular diseases (CVD) are a leading cause of morbidity and mortality globally, highlighting the urgent need for accurate risk prediction to improve early intervention and management. Traditional models have difficulty capturing th...
Logistic Models Principal Component Analysis Bayes Theorem Cardiovascular Diseases Risk Assessment Humans Machine Learning Algorithms Risk Factors
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Comparative Analysis of Feature Extraction Methods and Machine Learning Models for Predicting Osteoporosis Prevalence.

Journal of medical systems May 29, 2025
This study systematically examined the impact of three feature selection techniques (Boruta, Extreme gradient boosting (XGBoost), and Lasso) for optimizing four machine learning models (Random forest (RF), XGBoost, Logistic regression (LR), and Suppo...
Bone Density Osteoporosis Female Humans Machine Learning Support Vector Machine Principal Component Analysis Prevalence ROC Curve Logistic Models
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Data augmentation using masked principal component representation for deep learning-based SSVEP-BCIs.

Journal of neural engineering May 28, 2025
Data augmentation has been demonstrated to improve the classification accuracy of deep learning models in steady-state visual evoked potential-based brain-computer interfaces (BCIs), particularly when dealing with limited electroencephalography (EEG)...
Principal Component Analysis Electroencephalography Brain-Computer Interfaces Evoked Potentials, Visual Photic Stimulation Adult Male Deep Learning Humans
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A robust and statistical analyzed predictive model for drug toxicity using machine learning.

Scientific reports May 23, 2025
Over the years, toxicity prediction has been a challenging task. Artificial intelligence and machine learning provide a platform to study toxicity prediction more accurately with a reduced time span. An optimized ensembled model is used to contrast t...
Drug-Related Side Effects and Adverse Reactions Humans Machine Learning Algorithms Deep Learning Models, Statistical Principal Component Analysis
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Self-supervised model-informed deep learning for low-SNR SS-OCT domain transformation.

Scientific reports May 22, 2025
This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (SDNet), for simultaneous denoising and super-resolution of swept-source optical coherence tomography (SS-OCT) images. The novelty of this work lies in ...
Humans Algorithms Principal Component Analysis Deep Learning Tomography, Optical Coherence Image Processing, Computer-Assisted Signal-To-Noise Ratio
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Metabolomics and machine learning identify urine metabolic characteristics and potential biomarkers for severe Mycoplasma pneumoniae pneumonia.

Scientific reports May 16, 2025
To study the differences in the urine metabolome between pediatric patients with severe Mycoplasma pneumoniae pneumonia (SMPP) and those with general Mycoplasma pneumoniae pneumonia (GMPP) via non-targeted metabolomics method, and potential biomarker...
Tandem Mass Spectrometry Pneumonia, Mycoplasma Mycoplasma pneumoniae Metabolome Principal Component Analysis Chromatography, High Pressure Liquid Child Metabolomics Female Male Child, Preschool Humans Machine Learning Biomarkers
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Machine Learning-Enhanced Dual-Band Plasmonic Sensing for Simultaneous Qualitative and Quantitative Detection of Biomolecules in the Mid-Infrared Region.

Sensors (Basel, Switzerland) May 15, 2025
Recently, sensing for biomolecules has become increasingly popular in the fields of environmental monitoring, personal health, and food safety. Plasmonic biosensors have been a powerful tool due to their high sensitivity and label-free operation. How...
Principal Component Analysis Surface Plasmon Resonance Infrared Rays Machine Learning Algorithms Biosensing Techniques
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Dynamic bone recognition for robotic vertebral plate cutting via unit energy consumption and SVM optimized by PSO.

Scientific reports May 11, 2025
Current orthopedic robots lack the ability to dynamically sense or accurately recognize bone layers during vertebral plate decompression surgery, limiting their ability to adjust actions in real time as skilled surgeons do. This study aims to improve...
Robotic Surgical Procedures Spine Animals Bone Plates Support Vector Machine Principal Component Analysis Swine
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Getting Started with Machine Learning for Experimental Biochemists and Other Molecular Scientists.

Current protocols Apr 1, 2025
Machine learning (ML) is rapidly gaining traction in many areas of experimental molecular science for elucidating relationships and patterns in large or complex data sets. Historically, ML was largely the preserve of those with specialized training i...
Least-Squares Analysis Software Discriminant Analysis Cluster Analysis Biochemistry Humans Principal Component Analysis Machine Learning
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Segmentation-based deep 2D-3D multibranch learning approach for effective hyperspectral image classification.

PloS one Jan 1, 2025
Deep learning has revolutionized the classification of land cover objects in hyperspectral images (HSIs), particularly by managing the complex 3D cube structure inherent in HSI data. Despite these advances, challenges such as data redundancy, computa...
Neural Networks, Computer Hyperspectral Imaging Imaging, Three-Dimensional Image Processing, Computer-Assisted Principal Component Analysis Algorithms Deep Learning
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