Infrared Thermal Image Gender Classifier Based on the Deep ResNet Model by Alyaa J. Jalil, Naglaa M. Reda

“…The proposed approach has been compared with convolutional neural network (CNN), principal component analysis (PCA), local binary pattern (LBP), and scale invariant feature transform (SIFT). …”
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Análise de dados aplicada às Cidades Inteligentes: reflexões sobre a Região Nordeste do Brasil by Jane Roberta de Assis Barbosa, Ignacio Sánchez-Gendriz

“…By combining the variables from the studied cities, principal component analysis and hierarchical clustering automatically identified similarities and differences between them. …”
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Lower bounds for quantum-inspired classical algorithms via communication complexity by Nikhil S. Mande, Changpeng Shao

“…We mainly focus on lower bounds for solving linear regressions, supervised clustering, principal component analysis, recommendation systems, and Hamiltonian simulations. …”
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An Empirical Study on Listed Company’s Value of Cash Holdings: An Information Asymmetry Perspective by Chuangxia Huang, Xin Ma, Qiujun Lan

“…Drawing on the market microstructure and the index of information asymmetry of managers and investors, this paper constructs a new proxy for information asymmetry based on the principal component analysis. We find that a company’s value of cash holdings decreases increasingly with its level of information asymmetry, and the relationship between information asymmetry and the value of cash holdings is nonlinear.…”
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Automated Stellar Spectra Classification with Ensemble Convolutional Neural Network by Zhuang Zhao, Jiyu Wei, Bin Jiang

“…The experimental result proved that our one-dimensional ECNN strategy could achieve 95.0% accuracy in the classification task of the stellar spectra, a level of accuracy that exceeds that of the classical principal component analysis and support vector machine model.…”
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Geometrical shape learning as basis for compact microstructure representations and microstructure-properties linkages by Rodrigo Iza Teran, Daniela Steffes-lai, Lukas Morand

“…For this purpose, typically, state-of-the-art feature extraction methods, such as principal component analysis, are used in combination with regression models, such as neural networks. …”
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Anomaly Detection in Moving Crowds through Spatiotemporal Autoencoding and Additional Attention by Biao Yang, Jinmeng Cao, Rongrong Ni, Ling Zou

“…Moving foregrounds are segmented from the input frames using robust principal component analysis decomposition. Comparisons with state-of-the-art approaches indicate the superiority of our approach in anomaly detection. …”
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Computational intelligence in the identification of Covid-19 patients  by using KNN-SVM Classifier by shaymaa adnan

“…We applied Principal Component Analysis (PCA) and Histogram of Gradients (HOG) as extract features. while we conducted a classification process using K nearest neighbors (KNN) and Support Vector Machine (SVM) algorithms .  …”
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Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques by Jaweria Kainat, Syed Sajid Ullah, Fahd S. Alharithi, Roobaea Alroobaea, Saddam Hussain, Shah Nazir

“…Through a principal component analysis approach, serial feature fusion is employed to provide a feature score. …”
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Developing a Model for Foot Anthropometric Descriptors for the Design of Prosthesis and Footwear in Nigeria by I.S. Monye, S.A. Omotehinse

“…The objective of this study is to develop a model of foot anthropometric descriptors for the design of prosthesis and footwear in Nigeria using craft questionnaires structured with Likert’s 5-point attitudinal scale which was administered to 100 respondents and the corresponding data analyzed with Kendall’s Coefficient of Concordance (KCC) and Principal Component Analysis (PCA). Thirteen judges ranked the 36 foot anthropometric descriptors in descending order of importance. …”
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Randomized SVD Methods in Hyperspectral Imaging by Jiani Zhang, Jennifer Erway, Xiaofei Hu, Qiang Zhang, Robert Plemmons

“…Approximation errors for the rSVD are evaluated on HSI, and comparisons are made to deterministic techniques and as well as to other randomized low-rank matrix approximation methods involving compressive principal component analysis. Numerical tests on real HSI data suggest that the method is promising and is particularly effective for HSI data interrogation.…”
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Illumination and expression robust face recognition using collaboration of double-dictionary's sparse representation-based classification by Fei GONG, Wei JIN, Keqing ZHU, Randi FU, Yan CAO

“…Firstly, the proposed method used principal component analysis (PCA) to achieve the fusion of three high-frequency detail sub-images which were generated by WT, and a integrated high-frequency detail image could be obtained; then, features extracted from the low-frequency images and high-frequency detail images by PCA were used to construct the low-frequency feature space and high-frequency detail space; and low-frequency dictionary and high-frequency dictionary could be constructed by samples' projection on two kinds of feature space. …”
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Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions by Hanbyul Lee, Junghyun Kim, Suyeon Kim, Jimin Yoo, Guang J. Choi, Young-Seob Jeong

“…In order to overcome the imbalance problem, our model performs a hybrid sampling which combines synthetic minority oversampling technique (SMOTE) algorithm with edited nearest neighbor (ENN) algorithm and reduces the dimensionality of the dataset using principal component analysis (PCA) algorithm during data preprocessing. …”
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PCA mix‐based Hotelling's T2 multivariate control charts for intrusion detection system by Mo Shaohui, Gulanbaier Tuerhong, Mairidan Wushouer, Tuergen Yibulayin

“…Hotelling's T2 multivariate control charts based on Principal Component Analysis mix (PCA mix) with bootstrap control limit were proposed, and applied to the network intrusion detection system. …”
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Quality Control of Olive Oils Using Machine Learning and Electronic Nose by Emre Ordukaya, Bekir Karlik

“…In the second, 32-input collected data are reduced to 8 inputs by Principal Component Analysis. These reduced data as 8 inputs are applied to the classifiers. …”
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Developing a Model for Foot Anthropometric Descriptors for the Design of Prosthesis and Footwear in Nigeria by I.S. Monye, S.A. Omotehinse

“…The objective of this study is to develop a model of foot anthropometric descriptors for the design of prosthesis and footwear in Nigeria using craft questionnaires structured with Likert’s 5-point attitudinal scale which was administered to 100 respondents and the corresponding data analyzed with Kendall’s Coefficient of Concordance (KCC) and Principal Component Analysis (PCA). Thirteen judges ranked the 36 foot anthropometric descriptors in descending order of importance. …”
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Multispectral Enhancement Method to Increase the Visual Differences of Tissue Structures in Stained Histopathology Images by Pinky A. Bautista, Yukako Yagi

“…On the other hand, the color of the tissue structure of interest is modified by modulating the transformed spectra with the sum of the pixel’s spectral residual-errors at specific bands weighted through an NxN weighting matrix; the spectral error is derived by taking the difference between the pixel’s original spectrum and its reconstructed spectrum using the first M dominant principal component vectors in principal component analysis. Promising results were obtained on the visualization of the collagen fiber and the non-collagen tissue structures, e.g., nuclei, cytoplasm and red blood cells (RBC), in a hematoxylin and eosin (H&E) stained image.…”
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Polycyclic Aromatic Hydrocarbons Bound to PM 2.5 in Urban Coimbatore, India with Emphasis on Source Apportionment by R. Mohanraj, S. Dhanakumar, G. Solaraj

“…PAH diagnostic ratios and principal component analysis results revealed vehicular emissions and diesel-powered generators as predominant sources of PAH in Coimbatore.…”
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Application of machine learning techniques for warfarin dosage prediction: a case study on the MIMIC-III dataset by Aasim Ayaz Wani, Fatima Abeer

“…By leveraging dimensionality reduction methods such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), and advanced imputation techniques including denoising autoencoders (DAE) and generative adversarial networks (GAN), we achieved significant improvements in predictive accuracy. …”
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A Multicomponent Magnetic Proxy for Solar Activity by Harry P. Warren, Linton E. Floyd, Lisa A. Upton

“…Since many of these time series are strongly correlated, we use principal component analysis to reduce them to a smaller number of uncorrelated time series. …”
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