AIMC Topic: Principal Component Analysis

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Sparse discriminant PCA based on contrastive learning and class-specificity distribution.

Neural networks : the official journal of the International Neural Network Society
Much mathematical effort has been devoted to developing Principal Component Analysis (PCA), which is the most popular feature extraction method. To suppress the negative effect of noise on PCA performance, there have been extensive studies and applic...

Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning.

Applied neuropsychology. Adult
"Attention-Deficit Hyperactivity Disorder (ADHD)" is a neuro-developmental disorder in children under 12 years old. Learning deficits, anxiety, depression, sensory processing disorder, and oppositional defiant disorder are the most frequent comorbidi...

Advancing prostate cancer detection: a comparative analysis of PCLDA-SVM and PCLDA-KNN classifiers for enhanced diagnostic accuracy.

Scientific reports
This investigation aimed to assess the effectiveness of different classification models in diagnosing prostate cancer using a screening dataset obtained from the National Cancer Institute's Cancer Data Access System. The dataset was first reduced usi...

Fragments quantum descriptors in classification of bio-accumulative compounds.

Journal of molecular graphics & modelling
The aim of the following research is to assess the applicability of calculated quantum properties of molecular fragments as molecular descriptors in machine learning classification task. The research is based on bio-concentration and QM9-extended dat...

Using Feature Engineering and Principal Component Analysis for Monitoring Spindle Speed Change Based on Kullback-Leibler Divergence with a Gaussian Mixture Model.

Sensors (Basel, Switzerland)
Machining is a crucial constituent of the manufacturing industry, which has begun to transition from precision machinery to smart machinery. Particularly, the introduction of artificial intelligence into computer numerically controlled (CNC) machine ...

Spectrochemical analysis of blood combined with chemometric techniques for detecting osteosarcopenia.

Scientific reports
Among several complications related to physiotherapy, osteosarcopenia is one of the most frequent in elderly patients. This condition is limiting and quite harmful to the patient's health by disabling several basic musculoskeletal activities. Current...

Principal component analysis-multivariate adaptive regression splines (PCA-MARS) and back propagation-artificial neural network (BP-ANN) methods for predicting the efficiency of oxidative desulfurization systems using ATR-FTIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Oxidative desulfurization (ODS) of diesel fuels has received attention in recent years due to mild working conditions and effective removal of the aromatic sulfur compounds. There is a need for rapid, accurate, and reproducible analytical tools to mo...

Rapid detection of cholecystitis by serum fluorescence spectroscopy combined with machine learning.

Journal of biophotonics
While cholecystitis is a critical public health problem, the conventional diagnostic methods for its detection are time consuming, expensive and insufficiently sensitive. This study examined the possibility of using serum fluorescence spectroscopy an...

Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren's syndrome.

Scientific reports
The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS ...

Hybrid autoencoder with orthogonal latent space for robust population structure inference.

Scientific reports
Analysis of population structure and genomic ancestry remains an important topic in human genetics and bioinformatics. Commonly used methods require high-quality genotype data to ensure accurate inference. However, in practice, laboratory artifacts a...