Efficient high‐speed framework for sparse representation‐based iris recognition by Michael Melek, Mohamed F. Abu‐Elyazeed, Ahmed Khattab

“…The proposed framework achieves a recognition rate of about 99.5% using two iris databases, with a significant improvement in speed over related frameworks.…”
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Integrated methodology for assessing the readiness of a territory for the creation of a transport cluster by K. B. Malysheva

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Diagnosis of Bearing Defects based on the Analysis of Vibration Images Using the RKEM SIFT Descriptor Method by Zohreh Hashempour, Hamed Agahi, Azar Mahmoodzadeh

“…In addition, for each descriptor, the best features are selected using the non-linear principal component analysis method. Finally, the selected features are combined and four classification methods are applied to achieve the best classification performance and after comparison, the best classification method is selected. …”
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Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection by Ghufran Basim Alghanimi, Hadeel Aljobouri, Khaleel Akeash Alshimmari, Rasha Massoud

“…However, in some cases related to the type or size of the dataset using machine or transfer deep learning methods alone is unable to achieve high accuracy and high specificity. Consequently, the addition of feature selection)FS) to the deep learning method enhances the results by reducing the high features and the time needed for training the dataset. …”
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PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things by Mutkule Prasad Raghunath, Shyam Deshmukh, Poonam Chaudhari, Sunil L. Bangare, Kishori Kasat, Mohan Awasthy, Batyrkhan Omarov, Rajesh R. Waghulde

“…Conversely, all numerical features are translated back into symbolic form at the conclusion of the procedure. Principal component analysis is employed to achieve the objective of attribute extraction. …”
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Groundwater quality and associated health risks in the Eastern Region of Ghana by Thomas Acquah, Miriam Appiah-Brempong, Geophrey Kwame Anornu

“…Multivariate statistical procedures, namely, principal component and correlation analyses were employed to define the major groundwater pollutants and their possible sources. …”
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Greenness, blueness and whiteness evaluated rapid Attenuated Total Reflection/ Fourier Transform Infrared Spectroscopy method for piper nigrum Samples Coupled with multivariate ana... by Rinkal N Patel, Kunjal L Vegad, Ruchik V Mistry, Nikunj D Patel

“…The greenness, evaluation of the developed method was achieved through Complementary Green Analytical Procedure Index (Complex GAPI) (85), Analytical Greenness Metric (AGREE) (0.95) revealing the method's favourable eco-friendly profile. …”
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2b-RAD genotyping for population genomic studies of Chagas disease vectors: Rhodnius ecuadoriensis in Ecuador. by Luis E Hernandez-Castro, Marta Paterno, Anita G Villacís, Björn Andersson, Jaime A Costales, Michele De Noia, Sofía Ocaña-Mayorga, Cesar A Yumiseva, Mario J Grijalva, Martin S Llewellyn

“…<h4>Background</h4>Rhodnius ecuadoriensis is the main triatomine vector of Chagas disease, American trypanosomiasis, in Southern Ecuador and Northern Peru. …”
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Cloud architecture data center network abnormal traffic filtering algorithm based on improved grey clustering algorithm by Xuefeng ZHOU, Qiang XU, Yanting TAN, Jiayi LANG, Hang JING, Zhiqiang ZHAO

“…To avoid abnormal traffic affecting the safe operation of the cloud architecture data center network, it was necessary to filter the abnormal traffic of the cloud architecture data center network.The difficulty of filtering abnormal traffic varies under different signal-to-noise ratios and channel conditions.In order to ensure the filtering effect of abnormal traffic under different filtering conditions, a cloud architecture data center network abnormal traffic filtering algorithm based on improved grey clustering algorithm was proposed.A network traffic transmission model was built for cloud architecture data centers through time-frequency analysis, and network traffic sequences were collected.Weighted generalized distance was introduced to improve the grey clustering algorithm, and the improved grey clustering algorithm was used to calculate the optimal clustering results of network traffic sequence features, achieving traffic sequence feature extraction.The principal component eigenvalues of traffic sequence features were obtained through principal component analysis, two subspaces were constructed, and traffic features were mapped in a matrix manner to the two subspaces.Abnormal traffic was filtered based on the square prediction error of the mapping period vector and the threshold calculation results.The experimental results show that this algorithm can achieve feature extraction of data center network traffic sequences through clustering, effectively filtering abnormal traffic under different signal-to-noise ratios and channel conditions.When the signal-to-noise ratio of the network was 25 dB and the traffic was transmitted in a Gaussian channel, the filtering effect of abnormal traffic was more prominent.…”
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Feature Extraction and Wear Damage Degree Identification of Planetary Gear based on PSO-FC Optimization KPCA by Yan He, Zongyan Wang

“…The results of example analysis show that the intelligently optimized KPCA based on PSO-FC have improve the structure distribution of data in the feature space and achieved the good scale clustering effect in planetary gear wear damage degree recognition. …”
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Quantification of mono-, di-, tri-chlorinated biphenyl in Ganga River: design of experiment, source analysis and spatial distribution by Sandeep Kumar, Triparna Das, Anshul Tiwari, Pranjal Yadav, Zain Ali, Jorge Paz-Ferreiro, Devendra Kumar Patel

“…Analysis of water samples from Kanpur (K1–K11) and Prayagraj (P1–P11) revealed significantly higher LC-PCB contamination in Kanpur. Principal component analysis (PCA) indicated that industrial activities were the primary contributors to the observed pollution, with the first principal component explaining 49% of the variance. …”
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Spatial delineation on marine environmental characteristics using fuzzy c-means clustering method by E.D. Lusiana, S. Astutik, N. Nurjannah, A.B. Sambah

“…It was expected that the areas within each group would have identical attributes and ecological processes.FINDINGS: The results suggested that the marine environmental factors in Lesser Sunda can be simplified using a principal component analysis technique: 6 principal component factors explained 81.06 percent of the overall raw data variability for the wet season, and 7 principal component variables explained 84.51 percent of the overall raw data variability for the dry season. …”
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Características clínico-epidemiológicas de la enfermedad de Chagas en comunidades del Chapare, Departamento Cochabamba, Bolivia by Orlando Cruz Martínez, Iris González Morales, María Caridad Fragoso Marchante, Dianelys Perla Sierra Martínez, Esteban Jesús Sánchez Martínez

“…<strong>Fundamento</strong>. La enfermedad de Chagas o tripanosomiasis americana existe en el continente americano desde antes de la colonización. …”
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IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target Recognition by George Karantaidis, Athanasios Pantsios, Ioannis Kompatsiaris, Symeon Papadopoulos

“…Deep learning techniques have achieved significant success in Synthetic Aperture Radar (SAR) target recognition using predefined datasets in static scenarios. …”
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Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis by Cevahir Yildirim, Alba M. Franco-Pereira, Rosa E. Lillo

“…Experimental results demonstrate that this approach achieves high classification accuracy using raw multi-sensor data, establishing multivariate FDA as a powerful tool for fault diagnosis in complex systems.…”
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FAULT DIAGNOSIS OF THE PLANETARY GEARBOX BASED ON SFLA-BP MODEL AND KPCA FEATURE EXTRACTION by HE Yan, WANG ZongYan

“…The diagnosis results show that the SFLA-BP model has a smaller overall output error compared with BP neural network,and the diagnostic accuracy increases by 12. 5 %,and has achieved a more accurate identification effect on different degrees damage faults.…”
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Plant Leaf Identification Using Feature Fusion of Wavelet Scattering Network and CNN With PCA Classifier by S. Gowthaman, Abhishek Das

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Analysis of Corn Deterioration Due to Molding Using Surface-Enhanced Raman Spectroscopy by Xiaoyan Wei, Guifang Wu, Pengcheng Qiu, Huihe Yang, Shubin Yan, Jianing Di, Xiangpeng Zhao, Feixu Zhang, Hongda Zhang

“…Spectral data were preprocessed using the Savitzky-Golay smoothing method, and principal component analysis (PCA) was applied to extract the top five components. …”
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Assessing Teachers’ Views on the Impact of Teacher Unions on Curriculum Development and Implementation by Modumedi Joseph Machaea, Berington Zanoxolo Gobingca

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Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks by Hussein A. M. Hussein, Sharafiz B. Abdul Rahim, Faizal B. Mustapha, Prajindra S. Krishnan, Nawal Aswan B. Abdul Jalil

“…The data underwent preprocessing, including the application of principal component analysis (PCA) for feature selection. …”
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