AIMC Topic: Principal Component Analysis

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Complementing subjective with objective data in analysing expertise: A machine-learning approach applied to badminton.

Journal of sports sciences
This study aimed to assess which combination of subjective and empirical data might help to identify the expertise level. A group of 10 expert coaches classified 40 participants in 5 different expertise groups based on the video footage of the rallie...

R-ELMNet: Regularized extreme learning machine network.

Neural networks : the official journal of the International Neural Network Society
Principal component analysis network (PCANet), as an unsupervised shallow network, demonstrates noticeable effectiveness on datasets of various volumes. It carries a two-layer convolution with PCA as filter learning method, followed by a block-wise h...

Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy.

The Analyst
Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis. However, machine learning methods generally require extra preprocessing o...

Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers.

Scientific reports
In this paper we develop statistical algorithms to infer possible cardiac pathologies, based on data collected from 24 h Holter recording over a sample of 2829 labelled patients; labels highlight whether a patient is suffering from cardiac pathologie...

Exploiting upper-limb functional principal components for human-like motion generation of anthropomorphic robots.

Journal of neuroengineering and rehabilitation
BACKGROUND: Human-likeliness of robot movements is a key component to enable a safe and effective human-robot interaction, since it contributes to increase acceptance and motion predictability of robots that have to closely interact with people, e.g....

Denoising of multi b-value diffusion-weighted MR images using deep image prior.

Physics in medicine and biology
The clinical value of multiple b-value diffusion-weighted (DW) magnetic resonance imaging (MRI) has been shown in many studies. However, DW-MRI often suffers from low signal-to-noise ratio, especially at high b-values. To address this limitation, we ...

Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification.

Analytical chemistry
Raman spectroscopy is a nondestructive, label-free, highly specific approach that provides the chemical information on materials. Thus, it is suitable to be used as an effective analytical tool to characterize biological samples. Here we introduce a ...

Detection and identification of Cannabis sativa L. using near infrared hyperspectral imaging and machine learning methods. A feasibility study.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Remote identification of illegal plantations of Cannabis sativa Linnaeus is an important task for the Brazilian Federal Police. The current analytical methodology is expensive and strongly dependent on the expertise of the forensic investigator. A fa...

Time-of-flight secondary ion mass spectrometry analysis of hair samples using unsupervised artificial neural network.

Biointerphases
Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is extensively employed for the structural analysis of the outermost surfaces of organic materials, including biological materials, because it provides detailed compositional information and e...

Rapid screening of hepatitis B using Raman spectroscopy and long short-term memory neural network.

Lasers in medical science
This study presents a rapid method to screen hepatitis B patients using serum Raman spectroscopy combined with long short-term memory neural network (LSTM). The serum samples taken from 435 hepatitis B patients and 699 non-hepatitis B people were mea...