AIMC Topic: Principal Component Analysis

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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...

Coupling of Trastuzumab chromatographic profiling with machine learning tools: A complementary approach for biosimilarity and stability assessment.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
Biosimilar products present a growing opportunity to improve the global healthcare systems. The amount of accepted variability during the comparative assessments of biosimilar products introduces a significant challenge for both the biosimilar develo...

Sensor Fault Diagnosis Method Based on -Grey Wolf Optimization-Support Vector Machine.

Computational intelligence and neuroscience
Aimed to address the low diagnostic accuracy caused by the similar data distribution of sensor partial faults, a sensor fault diagnosis method is proposed on the basis of Grey Wolf Optimization Support Vector Machine (-GWO-SVM) in this paper. Firstl...

A machine learning-based biological aging prediction and its associations with healthy lifestyles: the Dongfeng-Tongji cohort.

Annals of the New York Academy of Sciences
This study aims to establish a biological age (BA) predictor and to investigate the roles of lifestyles on biological aging. The 14,848 participants with the available information of multisystem measurements from the Dongfeng-Tongji cohort were used ...

Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data.

Diabetes & metabolic syndrome
AIMS: This research work presented a comparative study of machine learning (ML), including two objectives: (i) determination of the risk factors of diabetic nephropathy (DN) based on principal component analysis (PCA) via different cutoffs; (ii) pred...

Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling.

Molecules (Basel, Switzerland)
Wine aroma is an important quality trait in wine, influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, including the water status of grapevines, canopy management, an...