Development of Chemo-Selective Gas Sensors Based on Molecularly Imprinted Polymer-Quartz Crystal Microbalance for Detection of Bioactive Compounds in <i>Curcuma longa</i> by Fajar Hardoyono

“…The result of the principal component analysis showed that the QCM sensors performed well and could distinguish the turmeric samples at five combinations of the compounds. …”
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Nontarget Metabolites of Rhizomes of Edible Sacred Lotus Provide New Insights into Rhizome Browning by Chenyang Hao, Yuetong Yu, Xueting Zhang, Gangqiang Dong, Yan Liu, Sha Chen

“…Combined with the browning phenotype of 212 lotus rhizomes, (epi) catechin, norarmepavine, and N-feruloyl-3-methoxytyramine were used as predominant chemical markers to separate different degrees of lotus rhizome browning. p-Coumaroyltyramine and N-trans-feruloyltyramine were selected as predominant chemical markers to investigate the differential expression between tropical and temperate lotus using principal component analysis and orthogonal partial least squares discriminant analysis. …”
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Rapid Discrimination of Cheese Products Based on Probabilistic Neural Network and Raman Spectroscopy by Zheng-Yong Zhang

“…This paper further studied and established the analytical approach based on Raman spectroscopy, including wavelet denoising, normalization, principal component analysis, and probabilistic neural network discrimination. …”
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Klasifikasi dan Autentifikasi Tanaman Seurapoh (Chromolaena odorata Linn) Menggunakan Metode Kombinasi Spektroskopi Inframerah dan Kemometrik by Taufiq Karma Karma, Pasyamei Rembune Kala, Shofi Shofi, Zafirah Zafirah

“…Classification of Seurapoh leaf extracts was carried out using Principal Component Analysis (PCA), while authentication was conducted using Linear Discriminant Analysis (LDA). …”
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Masked and Unmasked Face Recognition Model Using Deep Learning Techniques. A case of Black Race. by Mabirizi, Vicent, Ampaire, Ray Brooks, Muhoza, Gloria B.

“…Machine learning techniques such as Principal Component Analysis, Geometric Feature Based Methods and double threshold techniques were used in the development phase while results were classified using CNN pre-trained models. …”
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Masked and unmasked Face Recognition Model Using Deep Learning Techniques. A case of Black Race. by Mabiriz,I, Vicent, Ampaire, Ray Brooks, Muhoza, B. Gloria

“…Machine learning techniques such as Principal Component Analysis, Geometric Feature-Based Methods, and double threshold techniques were used in the development phase while results were classified using CNN pre-trained models. …”
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Practices and pain points in personal records by Matt Balogh, William Billingsley, David Paul, Mary Anne Kennan, Melanie Robertson-Dean

“…Analysis was conducted using tabular analysis in SPSS, and Principal Component Analysis in R. Results. The research found that there is a statistical relationship between the practices that respondents adopted with their personal electronic records management and their level of satisfaction with that process. …”
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Risk factor analysis for stunting incidence using sparse categorical principal component logistic regression by Anna Islamiyati, Muhammad Nur, Abdul Salam, Wan Zuki Azman Wan Muhamad, Dwi Auliyah

“…. • The method applied is sparse logistic regression combined with categorical principal component analysis. • Analysis of risk factors for stunting in toddlers is based on the child's own condition, as well as parental factors, namely age, education, and intake of additional food and supplementary tablets during pregnancy.…”
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ALGORITHM OF PREPARATION OF THE TRAINING SAMPLE USING 3D-FACE MODELING by D. I. Samal, I. I. Frolov

“…The preparation and preliminary processing of images contains the following constituents like detection and localization of area of the person on the image, assessment of an angle of rotation and an inclination, extension of the range of brightness of pixels and an equalization of the histogram to smooth the brightness and contrast characteristics of the processed images, scaling of the localized and processed area of the person, creation of a vector of features of the scaled and processed image of the person by a Principal component analysis (algorithm NIPALS), training of the multiclass SVM-classifier.The provided algorithm of expansion of the training selection is oriented to be used in practice and allows to expand using 3D-models the processed range of 2D – photographs of persons that positively affects results of identification in system of face recognition. …”
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Characterization of Morphological Diversity of Jute Mallow (Corchorus spp.) by Munguatosha Ngomuo, Tsvetelina Stoilova, Tileye Feyissa, Patrick A. Ndakidemi

“…The traits that significantly correlated with biomass yield include plant height (r=0.448), petiole length (r=0.237), primary branches (r=0.319), and number of leaves per plant (r=0.333). Principal component analysis showed that the first five PCs with eigenvalues ≥1 explained 72.9% of the total variability in the accessions. …”
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Development and Validation of a Survey on Inclusive Judo: Judo Teachers’ Attitudes Towards Including Participants with Intellectual Developmental Disorders (J-TAID) by Gaston Descamps, Alain Massart, Terry Rizzo, Viktorija Pečnikar Oblak, Maria João Campos

“…The survey, translated into English, Portuguese, French, and Slovenian, was administered to 163 participants in order to assess its reliability and validity using Cronbach’s alpha, Principal Component Analysis (PCA), Confirmatory Factor Analysis (CFA), and Exploratory Factor Analysis (EFA). …”
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Biochemical Characterization of Seed Oil of Tunisian Sunflower (Helianthus annuus L.) Accessions with Special Reference to Its Fatty Acid Composition and Oil Content by Taoufik Hosni, Zouhaier Abbes, Leila Abaza, Sana Medimagh, Hamadi Ben Salah, Mohamed Kharrat

“…The first two components of the principal component analysis (PCA) contributed 45.7% of the total variability. …”
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Condition Monitoring of Sensors in a NPP Using Optimized PCA by Wei Li, Minjun Peng, Yongkuo Liu, Shouyu Cheng, Nan Jiang, Hang Wang

“…An optimized principal component analysis (PCA) framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP) in this paper. …”
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Spatial Modeling of Travel Demand Accounting for Multicollinearity and Different Sampling Strategies: A Stop-Level Case Study by Samuel de França Marques, Cira Souza Pitombo, J. Jaime Gómez-Hernández

“…To address limitations found in previous studies, this study proposes a novel approach based on Geographically Weighted Principal Component Analysis (GWPCA) and Ordinary Kriging to predict the stop-level boarding or alighting data along bus lines in São Paulo (Brazil), considering four different sampling methods. …”
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Cavitation Detection in Centrifugal Pump Based on Interior Flow-Borne Noise Using WPD-PCA-RBF by Liang Dong, Kan Wu, Jian-cheng Zhu, Cui Dai, Li-xin Zhang, Jin-nan Guo

“…In this work, to improve the accuracy and efficiency of identification, an approach combining wavelet packet decomposition (WPD) with principal component analysis (PCA) and radial basic function (RBF) neural network is introduced to detect the cavitation status for centrifugal pumps. …”
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The fundamentals of Indian personality: An investigation of the big five by Tanisha Shetty, Nitha Thomas, Ravindra Neelakanthappa Munoli

“…Statistical Analysis Used: Exploratory factor analysis using Principal Component Analysis with Varimax Rotation and Kaiser Normalization was carried out. …”
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Comparative analysis of Fenghuang Dancong, Tieguanyin, and Dahongpao teas using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and chemomet... by Zhangwei Li

“…In addition, chemometric methods such as hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used for distinguishing and classifying the three types of oolong teas on the basis of the similarities and differences in the volatile components. …”
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Optical coherence tomography characteristics in hydroxychloroquine retinopathy and the correlations with visual deterioration in Taiwanese by Shao-Kai He, Tso-Ting Lai, Yi-Ting Hsieh

“…We observed disruptions in the ellipsoid zone (EZ) and retinal pigment epithelium (RPE) across different retinal regions. Principal component analysis (PCA) was employed to identify the most significant factors associated with visual deterioration. …”
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Convolutional Neural Network-Based Fish Posture Classification by Xin Li, Anzi Ding, Shaojie Mei, Wenjin Wu, Wenguang Hou

“…Before training the network, all sample images are preprocessed to make the fish be horizontal on the image according to the principal component analysis. Meanwhile, the histogram equalization is used to make the grey distribution of different images be close. …”
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Human activity recognition system based on low-cost IoT chip ESP32 by Chao HU, Bangyan LU, Yanbing YANG, Zhe CHEN, Lei ZHANG, Liangyin CHEN

“…Human activity recognition widely exists in applications such as sports management and activity classification.The current human activity recognition applications are mainly divided into three types: camera-based, wearable device-based, and Wi-Fi awareness-based.Among them, the camera-based human activity recognition application has the risk of privacy leakage, and the wearable device-based human activity recognition application has problems such as short battery life and poor accuracy.Human activity recognition based on Wi-Fi sensing generally uses Wi-Fi network cards or software-defined radio devices to identify the rules of channel state information changes, so as to infer user activity.It does not have the problems of privacy leakage and short battery life.But Wi-Fi network cards need to rely on computers and software-defined radio platforms are expensive, which greatly limit the application scenarios of Wi-Fi sensing.Aiming at the above problems, a human activity recognition system based on the low-cost IoT chip ESP32 was proposed.Specifically, the Hampel filter and Gaussian filter were used to preprocess the channel state information obtained by ESP32.Then, the principal component analysis and discrete wavelet transform were utilized to reduce the dimension of the data.Finally, the K-nearest neighbor (KNN) algorithm was applied to classify data.The experimental results show that the system can achieve a recognition accuracy which close to the current mainstream Wi-Fi perception system (Intel 5300 network card) when only two ESP32 nodes are deployed, and the average accuracy rate for the six activities is 98.6%.…”
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