Possibilities of Language Technology in Lexical Analyses of Canonical Hungarian Bible Translations by Tibor M. PINTÉR, Katalin P. MÁRKUS

“…Statistics do not influence the work of the translators, and no far-reaching conclusions can be drawn from them. …”
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Detecting Botrytis Cinerea Control Efficacy via Deep Learning by Wenlong Yi, Xunsheng Zhang, Shiming Dai, Sergey Kuzmin, Igor Gerasimov, Xiangping Cheng

“…It aims to address the limitations of traditional statistical analysis methods in capturing non-linear relationships and multi-factor synergistic effects. …”
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Algorithm for the analysis of kinematic characteristics of running by N. S. Davydova, V. Е. Vasiuk, N. A. Paramonova, М. М. Mezhennaya, D. I. Guseinov

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Meta-Features Extracted from Use of kNN Regressor to Improve Sugarcane Crop Yield Prediction by Luiz Antonio Falaguasta Barbosa, Ivan Rizzo Guilherme, Daniel Carlos Guimarães Pedronette, Bruno Tisseyre

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Hybrid method for automatic initialization and segmentation of ventricular on large-scale cardiovascular magnetic resonance images by Ning Pan, Zhi Li, Cailu Xu, Junfeng Gao, Huaifei Hu

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Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery by Xuemei Han, Huichun Ye, Yue Zhang, Chaojia Nie, Fu Wen

“…These spectral features include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Water Index (NDWI), while texture features include contrast statistics in the near-infrared band (B4_CO) and the variance statistic, contrast statistic, heterogeneity statistic, and correlation statistic derived from NDVI images (NDVI_VA, NDVI_CO, NDVI_DI, and NDVI_COR). …”
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A LightGBM-Based Power Grid Frequency Prediction Method with Dynamic Significance–Correlation Feature Weighting by Jie Zhou, Xiangqian Tong, Shixian Bai, Jing Zhou

“…To verify the effectiveness of the D-SCW method, this study conducts comparative experiments on two actual grid operation datasets (including typical scenarios with wind/photovoltaic (PV) installations, accounting for 5–35% of the grid); additionally, the Spearman’s rank correlation coefficient method, mutual information (MI), Lasso regression, and the feature screening method of recursive feature elimination (RFE) are selected as the baseline control; root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are adopted as assessment indicators. …”
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Crude Oil and Hot-Rolled Coil Futures Price Prediction Based on Multi-Dimensional Fusion Feature Enhancement by Yongli Tang, Zhenlun Gao, Ya Li, Zhongqi Cai, Jinxia Yu, Panke Qin

“…Experimental results demonstrate that the MDFFE model excels in various metrics, including mean absolute error, root mean square error, mean absolute percentage error, coefficient of determination, and sum of squared errors. …”
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A novel motion key frame extraction and video stream classification based on reinforcement learning and feature fusion by Hongbo Cui, Tao Feng, Jinhui Zheng

“…Some fusion features are more effective than the original statistical features. …”
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Quantitative bias analysis for mismeasured variables in health research: a review of software tools by Codie J. C. Wood, Kate Tilling, Jonathan W. Bartlett, Rachael A. Hughes

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Investigating the correlation between smoking and blood pressure via photoplethysmography by Q. Qananwah, H. Quran, A. Dagamseh, V. Blazek, S. Leonhardt

“…The ML model demonstrated strong accuracy in estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) during and post-smoking, with a mean error of 0.01 ± 0.29 mmHg and a root mean square error (RMSE) of 0.2924 mmHg for DBP and RMSE of 0.0082 mmHg for SBP. …”
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Hematological features of mine-blast trauma, accompanied by acubarotrauma, among servicemen - participants in high-intensity combat operations by S. A. Husieva, G. V. Osyodlo, I. P. Goncharov, O. Ya. Antonyuk, Yu. Ya. Kotyk, A. V. Gusev, М. Е. Krol, I. V. Malysh, Ya. M. Klimenko, S. V. Ткаchenko

“…For each group of patients, the arithmetic mean (M), the mean square deviation (Ϭ), and the error of the arithmetic mean (m) were calculated. …”
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Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach by Gurman Gill, Matthew Toews, Reinhard R. Beichel

“…The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. …”
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A New Family of Odd Nakagami Exponential (NE-G) Distributions by Mathee Pongkitiwitoon, Obalowa Job, İbrahim Abdullahi

“…Quantile, hazard rate function, moments, incomplete moments, order statistics, and entropies are only a few of the statistical features that are investigated. …”
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Specific Features of Development of the Infectious Process Caused by Cultivable and Non Cultivable Bacteria in the Presence of Experimental Burn Injury by S. P. Sakharov, L. B. Kozlov

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Features of bioelectric brain activity in people with different level of situational anxiety when performing non-verbal tasks by S. I. Pavlenko, D. S. Gromova, O. A. Vedyasova, A. M. Kovalev, A. I. Budaev

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MultS-ORB: Multistage Oriented FAST and Rotated BRIEF by Shaojie Zhang, Yinghui Wang, Jiaxing Ma, Jinlong Yang, Liangyi Huang, Xiaojuan Ning

“…This multistage strategy yields more accurate and reliable feature matches. Experimental results demonstrate that for blurred images affected by illumination changes, the proposed method improves matching accuracy by an average of 75%, reduces average error by 33.06%, and decreases RMSE (Root Mean Square Error) by 35.86% compared to the traditional ORB algorithm.…”
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Feature Selection Using a Genetic Algorithms and Fuzzy logic in Anti-Human Immunodeficiency Virus Prediction for Drug Discovery by Houda Labjar, Mohammad Al-Sarem, Mohamed Kissi

“…The statistical parameters q2 (leave many out) is equal 0.59 and r (coefficient of correlation) is equal 0.98. …”
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Classification of Russian Texts by Genres Based on Modern Embeddings and Rhythm by Ksenia Vladimirovna Lagutina

“…Visualization and analysis of statistics for rhythm features made it possible to identify both the most diverse genres in terms of rhythm: novels and reviews, and the least ones: scientific articles. …”
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Feature-Driven Density Prediction of Maraging Steel Additively Manufactured Samples Using Pyrometer Sensor and Supervised Machine Learning by Rajesh Kumar Balaraman, Shaista Hussain, John Kgee Ong, Qing Yang Tan, Nagarajan Raghavan

“…The findings indicate that RS is the most effective HPO technique across all models and highlight the significance of MACH-S and PHYS-B features in improving model predictions over PYRO-D features, achieving an R2 score of 0.948 and a mean-squared error (MSE) of 0.007. …”
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