AIMC Topic: Principal Component Analysis

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Morphological characterization and machine learning-based hyperspectral identification of naturally pigmented traditional Chinese starches.

Food chemistry
As an intangible cultural heritage, food products derived from naturally pigmented traditional starches are facing a market trust crisis due to the adulteration of dyed starch. This study aimed to develop an integrated identification system to differ...

Predicting and Analyzing Nitrate Adsorption on High-Entropy Alloys Based on Pair Distribution Function Using a Hybrid Machine Learning Framework.

Journal of chemical information and modeling
Incorporating five or more metals into a single structure creates a new family of alloys, high-entropy alloys (HEAs), which hold several exceptional properties, such as outstanding stabilities and continuous electronic structures, making them promisi...

Evaluating machine learning models for clothing size prediction using anthropometric measurements from 3D body scanning.

Scientific reports
An analysis of a dataset comprising 677 participants revealed substantial discrepancies in size categorization. Only 63 individuals (9.15%) maintained consistency across bust, waist, and hip measurements, whereas 614 participants (90.84%) exhibited s...

Spatiotemporal evolution of water quality in long-distance water supply projects: an improved PSO-SVR model.

Environmental monitoring and assessment
The spatiotemporal evolution of water quality is fundamental for the management of water resources in long-distance water transfer projects (LWTPs). Due to the multidimensional and nonlinear characteristics of water quality monitoring data, a novel m...

Efficient Hybrid Hierarchical Clustering with Incremental Silhouette Score for Large, Noisy Datasets.

International journal of neural systems
This paper introduces a comprehensive framework for clustering analysis, centered on a novel incremental silhouette score calculation designed specifically for hierarchical clustering. This innovative method significantly reduces the computational co...

A rapid wine brand identification method based on the joint application of SERS and machine learning techniques.

Food chemistry
In this paper, an innovative approach is proposed to achieve no-preparation, rapid, and accurate identification of red wine brands by combining Surface-Enhanced Raman Scattering (SERS) spectroscopy with machine learning. SERS detects trace molecular ...

Multi-marker discovery for mild cognitive impairment in metabolomics using machine learning with a global surrogate model via partial least squares.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Dementia can be prevented through early intervention; hence, there is an urgent need for biomarkers to help diagnose mild cognitive impairment (MCI).

Advancing Medical Diagnostics: Rapid, Label-Free Detection and Differentiation of Shiga Toxin Variants in Human Serum Using a Cost-Effective PCA-Assisted SERS Platform.

ACS applied materials & interfaces
Shiga toxins-producing (STEC) are zoonotic pathogens causing severe diseases such as hemorrhagic colitis (HC) and hemolytic uremic syndrome (HUS). Infections caused by STEC represent a public health concern due to the severity of the possible outcom...

Machine learning based fault classification for improved induction motor performance.

PloS one
This study explores the design of an effective fault classification algorithm for 3 phase induction motor, an integral unit in many industrial systems. It is found that traditional fault detection methods and deep learning approaches are both effecti...

D-amphetamine alters the dynamic ECoG activity distribution patterns in the rat neocortex.

Scientific reports
Amphetamine has widespread effects on multiple neurotransmitter systems, potentially altering the physiological connectivity and network dynamics across various regions of the brain. In this study, we investigated the effects of D-amphetamine using o...