AIMC Topic: Principal Component Analysis

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FTIR spectroscopy with machine learning: A new approach to animal DNA polymorphism screening.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Technological advances in recent decades, especially in molecular genetics, have enabled the detection of genetic DNA markers associated with productive characteristics in animals. However, the prospection of polymorphisms based on DNA sequencing is ...

Designing a hybrid dimension reduction for improving the performance of Amharic news document classification.

PloS one
The volume of Amharic digital documents has grown rapidly in recent years. As a result, automatic document categorization is highly essential. In this paper, we present a novel dimension reduction approach for improving classification accuracy by com...

Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN.

Scientific reports
Fecal samples can easily be collected and are representative of a person's current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits t...

Self-organising maps for the exploration and classification of thin-layer chromatograms.

Talanta
Thin-layer chromatography (TLC) allows the swift analysis of larger sample sets in almost any laboratory. The obtained chromatograms are patterns of coloured zones that are conveniently evaluated and classified by visual inspection. This manual appro...

Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC-MS-Based Untargeted Metabolomics.

Molecules (Basel, Switzerland)
Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC-MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discrimi...

Deriving accurate molecular indicators of protein synthesis through Raman-based sparse classification.

The Analyst
Raman spectroscopy has the ability to retrieve molecular information from live biological samples non-invasively through optical means. Coupled with machine learning, it is possible to use this large amount of information to create models that can pr...

Machine learning applied to near-infrared spectra for clinical pleural effusion classification.

Scientific reports
Lung cancer patients with malignant pleural effusions (MPE) have a particular poor prognosis. It is crucial to distinguish MPE from benign pleural effusion (BPE). The present study aims to develop a rapid, convenient and economical diagnostic method ...

Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach.

Journal of biological physics
The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet ...

Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN.

PloS one
Accurate prediction of cancer stage is important in that it enables more appropriate treatment for patients with cancer. Many measures or methods have been proposed for more accurate prediction of cancer stage, but recently, machine learning, especia...