Dissolution profiles of perindopril and indapamide in their fixed-dose formulations by a new HPLC method and different mathematical approaches by Gumieniczek Anna, Mączka Paulina, Komsta Łukasz, Pietraś Rafał

“…Similarity of dissolution profiles was estimated using different model-independent and model-dependent methods and, additionally, by principal component analysis (PCA). Also, some kinetic models were checked for dissolved amounts of drugs as a function of time.…”
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PCA-Guided Routing Algorithm for Wireless Sensor Networks by Gong Chen, Liansheng Tan, Yanlin Gong, Wei Zhang

“…In this paper, we propose a routing algorithm termed as PCA-guided routing algorithm (PCA-RA) by exploring the principal component analysis (PCA) approach. Our algorithm remarkably reduces energy consumption and prolongs network lifetime by realizing the objective of minimizing the sum of distances between the nodes and the cluster centers in a WSN network. …”
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Short-term traffic flow prediction based on adaptive rank dynamic tensor analysis by Lingchao HE, Dong LIN, Xinxin FENG

“…Short-term traffic flow prediction in intelligent transportation system can provide data support in areas such as route planning,traffic management,public safety and so on.In order to improve the prediction accuracy with missing and abnormal data,a short-term traffic flow prediction method based on the adaptive rank dynamic tensor analysis was proposed.Firstly,a four dimensional tensor consisted of week,day,time and space was constructed,which could excavate the multimodal correlation of traffic flow data.Secondly,tensor flow data with dynamic structure was formed by using sliding window model.The principal component analysis (PCA) algorithm was extended to an offline tensor analysis algorithm that could accept tensor input.Then the adaptive rank and the forgetting factor were introduced to generate an adaptive rank dynamic tensor analysis algorithm.Finally,the tensor stream data was inputted into the adaptive rank dynamic tensor analysis algorithm to realize the short-term traffic flow prediction.The experimental results show that a good prediction can be achieved even with data missing.…”
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Research on Analyzing the Emotional Polarity of Malicious Swipe Comments on E-commerce Platforms Based on NPL by Ren Chaoyi

“…The methodology involves utilizing the Word2Vec model to vectorize the text data, followed by principal component analysis (PCA) for outlier detection to identify potential malicious reviews based on their unique characteristics. …”
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Age Discrimination of Chinese Baijiu Based on Midinfrared Spectroscopy and Chemometrics by Shiqi Hu, Le Wang

“…Next, the spectral date pretreatment methods are constructively introduced, and principal component analysis (PCA) and discriminant analysis (DA) are developed for data analyses. …”
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Research of Remaining Useful Life Prediction of Gear based on Exponential Smoothing and Improved Incremental SVR by Liu Zhenxiang, Chen Xiaohui

“…The new program utilizes principal component analysis（ PCA） to filtrate fusion index set,and then using DES to process fusion index set as the input of improved incremental SVR. …”
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A Preliminary Study on Accident Analysis of Portable Timber Sawmills Used in Mazamba Forest Plantation in Northern Malawi by Jameson T. T. Mzungu, Elisha S. Ngulube

“…However, a principal component analysis revealed that accidents were mainly caused by inappropriate equipment setup or operation and harsh weather conditions. …”
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FAULT DIAGNOSIS METHOD OF DIESEL ENGINE BASED ON PCA-EDT-CNN (MT) by BAI YunJie, JIA XiSheng, LIANG QingHai, MA YunFei, WEN Liang

“…Firstly, use Principal Component Analysis（PCA） to adaptively reduce the original data collected by the sensor, and construct a qualified Principal Component Eigenvector Matrix（PCEM）; secondly, perform Euclidean Distance Transformation（EDT） on PCEM, calculate the Euclidean distance between each row and construct the Euclidean Distance Matrix（EDM）; finally, flatten PCEM and EDM into one-dimensional vectors and synthesize a one-dimensional sample sequence, input into One-Dimensional Convolutional Neural Network（1 DCNN） to train and diagnosis the model. …”
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Dose-Dependent Differentiation of Gamma-Irradiated Hazelnut Samples by Mid-Infrared Spectroscopy Coupled with Chemometrics by Ayca Dogan, Mete Severcan, Feride Severcan

“…The objective of this work is to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy for the differentiation of γ-irradiated hazelnuts at higher doses (3 kGy and 10 kGy) from the lower (1.5 kGy) and nonirradiated ones using multivariate statistical analysis, namely, principal component analysis (PCA) and hierarchical cluster analysis (HCA). …”
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Detection of Adulteration in Canola Oil by Using GC-IMS and Chemometric Analysis by Tong Chen, Xinyu Chen, Daoli Lu, Bin Chen

“…In this work, 147 adulterated samples were detected by gas chromatography-ion mobility spectrometry (GC-IMS) and chemometric analysis, and two methods of feature extraction, histogram of oriented gradient (HOG) and multiway principal component analysis (MPCA), were combined to pretreat the data set. …”
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Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. by Yuan Wang, Shaolin Hu

“…The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)-SPE (Square Prediction Error)-CNN (Convolutional Neural Network) classifier. …”
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Time Effort Prediction Of Agile Software Development Using Machine Learning Techniques by Muchamad Bachram Shidiq, Windu Gata, Sigit Kurniawan, Dedi Dwi Saputra, Supriadi Panggabean

“…For this reason, this research aims to predict the time effort of agile software development using Machine Learning techniques, namely the Decision Tree, Random Forest, Gradient Boosting, and AdaBoost algorithms, as well as the use of feature selection in the form of RRelieff and Principal Component Analysis (PCA) to improve prediction accuracy. …”
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Compression Algorithm of Road Traffic Spatial Data Based on LZW Encoding by Dong-wei Xu, Yong-dong Wang, Li-min Jia, Gui-jun Zhang, Hai-feng Guo

“…First, the spatial correlation of road segments was analyzed by principal component analysis. Then, the road traffic spatial data compression based on LZW encoding is presented. …”
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Personal Satisfaction with Accessibility and Service Quality: Spatial Justice in Guangzhou’s Social Housing Communities by Ruixia Chao, Haolin Hong, Wei Wei

“…Subsequently, a principal component analysis revealed key factors influencing individuals’ satisfaction, including proximity to essential amenities, efficient service, high-quality education, affordability of commercial establishments, and access to healthcare services. …”
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Geographical Origin Identification of <italic>Panax notoginseng</italic> Using a Modified K-Nearest Neighbors Model With Near-Infrared Spectroscopy by Xuefeng Cheng, Min Liao, Juan Liu

“…The proposed model integrates distance and cosine similarity metrics to enhance classification performance, particularly for imbalanced datasets. Principal component analysis is employed to reduce dimensionality, significantly improving computational efficiency without sacrificing accuracy. …”
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Examining transaction-specific satisfaction and trust in Airbnb and hotels. An application of BERTopic and Zero-shot text classification by Manuel Rey-Moreno, Manuel Jesús Sánchez-Franco, María De la Sierra Rey-Tienda

“…Thirdly, we execute a Principal Component Analysis to investigate the sufficiency relationships between extracted topics, customer satisfaction, and trust-based labels. …”
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Analyzing Capacity Utilization and Travel Patterns of Chinese High-Speed Trains: An Exploratory Data Mining Approach by Fanxiao Liu, Zhanbo Sun, Peitong Zhang, Qiyuan Peng, Qingjie Qiao

“…This paper applies exploratory data mining techniques to a 3-month long real world train operation data of the Beijing-Shanghai High-Speed Railway. Principal component analysis (PCA) is conducted to find the principal components that can efficiently represent the collected data. …”
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Assessment of Genetic Variability for Yield and Yield-Contributing Traits in Groundnut (Arachis hypogaea L.) Genotypes by Abera Seboka Yami, Wosene Gebreselassie Abtew

“…The highest intercluster distance D2 was recorded between clusters I and III at 1690.78, followed by clusters II and III at 1198.27. Principal component analysis resulted in four principal components with eigenvalues greater than one. …”
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A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images by Varun Srivastava, Ravindra Kumar Purwar

“…This feature set is further reduced using probabilistic principal component analysis. The reduced set of features is then fed into either K nearest neighbor algorithm or feed-forward artificial neural network, to classify images. …”
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Fault Diagnosis of Crane Gearbox based on Variational Mode Decomposition and PSO-SVM by Wubang Yang, Bingpeng Gao, Fei Chen, Xinghe Zhang, Weidong Ma

“…Then the feature parameters of reconstructed signal are extracted to construct the feature vector， and kernel principal component analysis（KPCA） is used to realize the feature information fusion. …”
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