Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion by Jie-sheng Wang, Shuang Han, Na-na Shen

“…Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. …”
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Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models by Nhat-Duc Hoang

“…The Gabor filter supported by principal component analysis and k-means clustering is used for identifying the region of interest within an image sample. …”
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Empirical Analysis of Urban Community Social Vulnerability in Hilly Areas of Kajang by Nor Diana Mohd Idris, Zulkepli Nurul Atikah

“…Therefore, to determine the social vulnerability of the community living in the hilly areas of Kajang, the study assessed the social vulnerability index by using Principal Component Analysis (PCA). Seventeen variables were selected to determine the index including age, health, education, single-parent household, vehicle ownership, housing, income, unemployment, and elevation. …”
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A Region-Based Statistical Shape Modeling on the First Trapezoid-Metacarpal by Lin Fu, Kaczmarek Łukasz, Ci Jiang, Yaodong Gu

“…A training set of models was analyzed with principal component analysis, with both then- rSSM and rSSM. …”
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Recognition of Damage Degree of Tooth Root Crack based on PCA and Grey Relational by Jie Liu, Huanyu Li, Weiqiang Zhao

“…In order to realize quantitative detection of damage degree， the method of Principal Component Analysis （PCA） and Grey Relational Analysis （GRA） are combined to use. …”
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Comparative Analysis of Denoising Techniques for Optimizing EEG Signal Processing by I Putu Agus Eka Darma Udayana, Made Sudarma, I Ketut Gede Darma Putra, I Made Sukarsa, Minho Jo

“…This research aims to improve the quality of EEG signals related to concentration by comparing the effectiveness of two denoising methods, namely Independent Component Analysis (ICA) and Principal Component Analysis (PCA). Using commercial EEG headsets, this study recorded Alpha, Beta, Delta, and Theta signals from 20 participants while they performed tasks that required concentration. …”
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An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1 by Hai Guo, Jinghua Yin, Jingying Zhao, Yuanyuan Liu, Lei Yao, Xu Xia

“…The features of gray level cooccurrence matrix (GLCM) can be extracted from different types of surface morphology images of film; after that, the dimension reduction of film can be handled by principal component analysis (PCA). So it is possible to identify the element of film according to the Adaboost M1 algorithm of a strong classifier with ten decision tree classifiers. …”
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Use of Time- and Frequency-Domain Approaches for Damage Detection in Civil Engineering Structures by V. H. Nguyen, J. Mahowald, S. Maas, J.-C. Golinval

“…The methodology is based on Principal Component Analysis of the Hankel matrix built from output-only measurements and of Frequency Response Functions. …”
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Dinâmica espacial da produção de mandioca no Paraná, Brasil by Talita Pijus Ponce, Marina Ronchesel Ribeiro, Tiago Santos Telles

“…For this, the locational quotient (QL) methodology was applied, principal component analysis (ACP) and cluster analysis were carried out. …”
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Exploring chilling stress and recovery dynamics in C4 perennial grass of Miscanthus sinensis. by Karolina Sobańska, Monika Mokrzycka, Martyna Przewoźnik, Tomasz Pniewski, Katarzyna Głowacka

“…Various traits were measured, including growth and biomass yield, the rate of leaf elongation, and a variety of chlorophyll fluorescence parameters, as well as chlorophyll content estimated using the SPAD method. Principal Component Analysis revealed unique genotype responses to chilling stress, with distinct clusters emerging during the recovery phase. …”
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Selection of Machine Learning Models for Oil Price Forecasting: Based on the Dual Attributes of Oil by Lei Yan, Yuting Zhu, Haiyan Wang

“…First, we use principal component analysis (PCA), multidimensional scale (MDS), and locally linear embedding (LLE) methods to reduce the dimensions of the data. …”
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Adaptation, Validity and Reliability of the Body Sensations Questionnaire Turkish Version by Aysegül KART, M. Hakan TÜRKÇAPAR

“…Construct validity was assesed by factor analysis after Kaiser-Meyer-Olkin (KMO) and Bartlett tests applied. Principal component analysis and varimax rotation used for factor analysis. …”
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Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG Clustering by Tianjing He, Rongzhen Zhao, Yaochun Wu, Chao Yang

“…Finally, low-dimensional sensitive features obtained by principal component analysis (PCA) are fed into the adaptive GG clustering algorithm to perform fault identification. …”
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Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM Model by ZHANG Yajie, CUI Dongwen

“…To improve the accuracy of monthly runoff forecasts during dry seasons,this study proposes a forecasting method that combines the golden eagle optimization (GEO) algorithm and the relevance vector machine (RVM).On the basis of the runoff data of 67 a from a hydrological station in Yunnan Province,the monthly runoff with good correlation before the forecast month is selected as the influencing factor of forecasts,and the influencing factor is reduced in dimension by principal component analysis (PCA).The kernel width factor and hyperparameters of RVM are optimized by the GEO algorithm,and the GEO-RVM model is built to forecast the monthly runoff of the station during the dry season from November to April of the following year.Moreover,the forecast results are compared with those of the GEO-based support vector machine (SVM) model (GEO-SVM).The results demonstrate that the average relative errors of the GEO-RVM model for the monthly runoff forecasts from November to April of the following year are 8.59%,7.34%,5.97%,6.07%,5.99%,and 5.04%,respectively,which means the accuracy is better than that of the GEO-SVM model.The GEO algorithm can effectively optimize the kernel width factor and hyperparameters of RVM,and the GEO-RVM model has better forecast accuracy,which can be used for monthly runoff forecasting during dry seasons.…”
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A Terahertz Spectroscopy Nondestructive Identification Method for Rubber Based on CS-SVM by Xianhua Yin, Wei Mo, Qiang Wang, Binyi Qin

“…The SVM model optimized by the cuckoo search algorithm is abbreviated as CS-SVM. Principal component analysis (PCA) is applied to decrease the dimension of the spectral data. …”
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Fast Spectral Clustering via Efficient Multilayer Anchor Graph by Yiwei Wei, Chao Niu, Dejun Liu, Peinan Ren

“…First, FEMAG adopts superpixel principal component analysis (SuperPCA) to extract the low-dimensional features of HSIs. …”
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PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture by Kanokmon Rujirakul, Chakchai So-In, Banchar Arnonkijpanich

“…Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. …”
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Innovativeness of Enterprises in Poland in the Regional Context by Anna Golejewska

“…As research methods, the author uses selected methods of multdimensional comparatve analysis, principal component analysis and the hierarchical Ward’s method. …”
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Nondestructive Detection of Authenticity of Thai Jasmine Rice Using Multispectral Imaging by Wei Liu, Xue Xu, Changhong Liu, Lei Zheng

“…In this study, the multispectral imaging system (405–970 nm) was used for the detection of adulteration in Thai jasmine rice combined with chemometric methods including principal component analysis (PCA), partial least squares (PLS), least squares-support vector machines (LS-SVM), and backpropagation neural network (BPNN). …”
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Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles by Yu Sun, Shuhuai Qin, Yingli Li, Naimul Hasan, Yan Vivian Li, Jiangguo Liu

“…Experimental data collected from about 50 papers are analyzed by machine learning algorithms including linear regression, principal component analysis, Gaussian process regression, and artificial neural networks. …”
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