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Principal Component Analysis

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An Intelligence Method for Recognizing Multiple Defects in Rail.

Sensors (Basel, Switzerland)
Ultrasonic guided waves are sensitive to many different types of defects and have been studied for defect recognition in rail. However, most fault recognition algorithms need to extract features from the time domain, frequency domain, or time-frequen...

Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain.

Scientific reports
Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data inform...

Non-invasive diagnosis of Crohn's disease based on SERS combined with PCA-SVM.

Analytical methods : advancing methods and applications
Crohn's disease (CD) is an idiopathic chronic inflammatory bowel disease without a cure. Most of the CD patients are firstly diagnosed by invasive endoscopy, and clinical and pathological examinations are further required to confirm the diagnosis. He...

Predictive Analysis and Evaluation Model of Chronic Liver Disease Based on BP Neural Network with Improved Ant Colony Algorithm.

Journal of healthcare engineering
Timely prediction of the mechanism and characteristics of chronic liver disease using next-generation information technology is an effective way to improve the diagnosis rate of chronic liver disease. In this paper, we have proposed a modified backpr...

Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data.

Computational and mathematical methods in medicine
The human health status can be assessed by the means of research and analysis of the human microbiome. Acne is a common skin disease whose morbidity increases year by year. The lipids which influence acne to a large extent are studied by metagenomic ...

Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening.

Computational intelligence and neuroscience
With the development of information technology, band expansion technology is gradually applied to college English listening teaching. This technology aims to recover broadband speech signals from narrowband speech signals with a limited frequency ban...

Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling.

Nature protocols
Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development ...

Target Recognition of SAR Images Based on SVM and KSRC.

Computational intelligence and neuroscience
A synthetic aperture radar (SAR) target recognition method combining linear and nonlinear feature extraction and classifiers is proposed. The principal component analysis (PCA) and kernel PCA (KPCA) are used to extract feature vectors of the original...

Towards ML-Based Diagnostics of Laser-Plasma Interactions.

Sensors (Basel, Switzerland)
The power of machine learning (ML) in feature identification can be harnessed for determining quantities in experiments that are difficult to measure directly. However, if an ML model is trained on simulated data, rather than experimental results, th...

A Method for Short-Term Prediction of the Metro Station's Individual Energy Consumption Item Based on G-ACO-BP Model.

Computational intelligence and neuroscience
This paper proposes a new method to make short-term predictions for the three kinds of primary energy consumption of power, lighting, and ventilated air conditioning in the metro station. First, the paper extracts the five main factors influencing me...