Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction by Zhaoyang Zhang, Shijie Sun, Wei Wang

“…Matrix-based kernel principal component analysis (MKPCA) is a way for extracting matrix-based features. …”
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ODC: a method for online detecting ＆ classifying network-wide traffic anomalies by QIAN Ye-kui1, CHEN Ming1, HAO Qiang2, LIU Feng-rong2, SHANG Wen-zhong2

“…A method for online detecting ＆ classifying traffic anomalies（ODC for short） from a network-wide angle of view was put forward.This method constructed traffic matrix with a metric of traffic feature entropy incrementally,de-tected traffic anomalies online using incremental principal component analysis,and then classified traffic anomalies online using incremental k-means,from which network operators could benefit for taking corresponding countermeasures.Theoretical analysis and experiment analysis show that the method has lower storage and less computing time complexity,which could satisfy the requirements of real-time process.Analysis based on both measurement data from Abilene and simulation experiments demonstrate that the method has very good detection and classification performance.…”
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Time series generation model based on multi-discriminator generative adversarial network by Yanhui LU, Han LIU, Hang LI, Guangxu ZHU

“…Aiming at the problems of expensive collection cost and missing data due to the privacy and continuity of time series data set, a multi-discriminator generative adversarial network model based on recurrent neural network was proposed, which could synthesize time series dataset that were approximately distributed with real data of a small scale dataset.Multi-discriminator included four discriminators in time domain, frequency domain, time-frequency domain and autocorrelation.Different discriminators could effectively recognize the features of the time series in different domains.In the experiment, the convergence of loss function, principal component analysis and error analysis were performed to evaluate the performance of the model from qualitative and quantitative perspectives.The experimental results show that the proposed model has better performance than other reference models.…”
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Flower morphology of selected species of genera Kalanchoe and Bryophyllum in Nigeria by Tolulope Olutayo, Oluwabunmi Arogundade

“…Data gotten from quantitative attributes were subjected to least significance difference test (LSD) for mean separation and further subjected to single linkage cluster analysis (SLCA) and principal component analysis (PCA). Unifying attributes such as petals and sepals, four in number, were recorded. …”
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Using WPCA and EWMA Control Chart to Construct a Network Intrusion Detection Model by Ying-Ti Tsai, Chung-Ho Wang, Yung-Chia Chang, Lee-Ing Tong

“…This study develops a network intrusion detection model by integrating weighted principal component analysis into an exponentially weighted moving average control chart. …”
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Machine Learning–Based Risk Factor Analysis and Prediction Model Construction for the Occurrence of Chronic Heart Failure: Health Ecologic Study by Qian Xu, Xue Cai, Ruicong Yu, Yueyue Zheng, Guanjie Chen, Hui Sun, Tianyun Gao, Cuirong Xu, Jing Sun

“…Stringent data preprocessing procedures were implemented, which included meticulous management of missing values and the standardization of data. Principal component analysis and random forest (RF) were used as feature selection techniques. …”
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Evaluation of Juice Production Characteristics and Analysis of Characteristic Flavor Components of Phyllanthus emblica Fruit Varieties by Zifen ZHENG, Xiaowei CHEN, Bo ZOU, Yuanshan YU, Gengsheng XIAO, Lukai MA

“…Six principal components were extracted through principal component analysis, and the cumulative variance contribution rate reached 96.033%. …”
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Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method by Xiao-Yan Gao, Abdelmegeid Amin Ali, Hassan Shaban Hassan, Eman M. Anwar

“…Two features of extraction methods: linear discriminant analysis (LDA) and principal component analysis (PCA), are used to select essential features from the dataset. …”
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Fault Detection and Diagnosis in Process Data Using Support Vector Machines by Fang Wu, Shen Yin, Hamid Reza Karimi

“…In this paper, a combined measure of the original Support Vector Machine (SVM) and Principal Component Analysis (PCA) is provided to carry out the fault classification, and compare its result with what is based on SVM-RFE (Recursive Feature Elimination) method. …”
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Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning by Yugal Pande, Jyotismita Chaki

“…The second module employs Principal Component Analysis (PCA) for dimensionality reduction. …”
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Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network by Xiaochen Zhang, Hongli Gao, Haifeng Huang

“…Then the feature vectors can be obtained by principal component analysis (PCA). Second, the initialization parameters of the DFNN are optimized by means of QGA. …”
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Volatile Constituents of Some Selected Plant Species Traditionally Used as Tea in the Sharri Mountains (Kosovo) by Avni Hajdari, Nita Kelmendi, Genista Mustafa, Behxhet Mustafa, Dashnor Nebija

“…Monoterpenes, sesquiterpenes, diterpenes, and norisoprenoids were the most abundant volatile constituents. Principal component analysis (PCA) was deployed for data analysis and resulted in grouping these ten species in four principal clusters. …”
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Morphological Bias of Ancient Artifacts: A Case Study of Incense Burners in Ming and Qing Dynasties by Yu-Fu Chen, Jie Wei

“…According to the results of semantic principal component analysis, the perceptual semantic bias of the design group towards incense burners was concentrated, which is related to the style acceptance of incense burners. …”
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Kamu Hizmeti Motivasyonu Ölçeğinin Geliştirilmesi by İnayet AYDIN, Nihan DEMİRKASIMOĞLU, Tuğba GÜNER DEMİR, Özge ERDEMLİ

“…In order to determine the construct validity of the scale, exploratory factor analysis based on the principal component analysis was performed and then CFA was applied to confirm the factor structure. …”
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Predicting electrocatalytic urea synthesis using a two-dimensional descriptor by Amy Wuttke, Alexander Bagger

“…Firstly, we project high dimensional experimental data using principal component analysis (PCA) to lower dimensions, and thereby confirm that urea selectivity is correlated with the selectivity towards CO and NH3. …”
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Feature Selection Using Particle Swarm Optimization in Intrusion Detection by Iftikhar Ahmad

“…Latterly, principal component analysis (PCA) has been used for feature reduction and subset selection in which features are primarily projected into a principal space and then features are elected based on their eigenvalues, but the features with the highest eigenvalues may not have the guaranty to provide optimal sensitivity for the classifier. …”
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APPLICATION OF SVM METHOD IN FAULT DIAGNOSIS OF А MULTI-STAGE CENTRIFUGAL PUMP by LI YouGen, MA WenSheng, LI FangZhong, WANG QingFeng

“…The efficient fault classification was achieved by optimizing the quality of input samples with principal component analysis (PCA). In addition, by comparing the classification effects of SVM and back propagation (BP) neural network, it shows that the SVM model has better classification effect and high applicability in fault diagnosis of multi-stage centrifugal pump.…”
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L’usage de la télédétection pour l’évaluation économique des écosystèmes marins : application à l’aire marine protégée de Tristão en Guinée by Vincent Turmine, Thomas Binet, Pierre Failler

“…The remote sensing methods utilised in this study are innovative in that they stem from non supervised combined classifications and principal component analysis. These techniques offer a reliable method for the calculation of ecosystem surfaces, as well as estimation of their health status, both elements that are compulsory to the economic valuation exercises. …”
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Development and Validation of the Amharic Version of Self-Efficacy and Outcome Expectancy Measures on Intention to Take Preventive Actions on Noncommunicable Disease by Shumye Molla Legesse, Habtamu Wondimu

“…The intentions to take protective measures on NCDs’ self-efficacy and outcome expectancy scales were created in Amharic using a sequential nine-step process that included translation and contextualization of the items, content validity, pretesting of the questions, sampling, and survey administration. Principal component analysis was conducted on 829 university students which showed a one-factor solution for self-efficacy and a three-factor solution for outcome expectancy scales using split-half measures. …”
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A Method of Intrusion Detection Based on WOA-XGBoost Algorithm by Yan Song, Haowei Li, Panfeng Xu, Dan Liu

“…The collected network data are first preprocessed by the PCA (Principal Component Analysis) dimensionality reduction method, and then, the preprocessed data are imported into the WOA-XGBoost algorithm so that the overall model has better intrusion detection capabilities for data after training. …”
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