Features of estimation of left ventricular geometry in patients with arterial hypertension by V. V. Syvolap, Ye. V. Novikov, V. A. Lysenko, A. O. Bogun

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Alternative Nonparametric Test and Sample Size Procedures for the Comparison of Several Location Shifts by Show-Li Jan, Gwowen Shieh

“…Theoretical examination and empirical assessment are presented to justify the validity and usefulness of the proposed test in maintaining Type I error performance. The features of the suggested power and sample size calculations are also explicated for several important distributions. …”
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Specific features of cognitive skill development in athletes of situational sports by Vyacheslav Romanenko, Yrui Tropin, Leonid Podrigalo, Natalya Boychenko, Anatoly Abdula, Nataliia Sereda, Yaroslav Yatsiv

“…The results of the short-term visual memory test indicate that the differences between the study groups, as assessed by the Kruskal–Wallis test, were not statistically significant (p > 0.05). In the spatial perception test, statistically significant differences were observed only at the fourth stage, in the percentage of errors (p-value = 0.01). …”
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Statistical Evaluation of Smartphone-Based Automated Grading System for Ocular Redness Associated with Dry Eye Disease and Implications for Clinical Trials by Rodriguez JD, Hamm A, Bensinger E, Kerti SJ, Gomes PJ, Ousler III GW, Gupta P, De Moraes CG, Abelson MB

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Features of management strategy for the implementation of innovations aimed at improving the competitiveness of the enterprise by V. S. Alieva

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Integrating Multimodal Deep Learning with Multipoint Statistics for 3D Crustal Modeling: A Case Study of the South China Sea by Hengguang Liu, Shaohong Xia, Chaoyan Fan, Changrong Zhang

“…The proposed model is rigorously validated against existing methods such as Kriging interpolation and MPS alone, demonstrating superior performance in reconstructing both global and local spatial features of the crustal structure. The integration of diverse datasets significantly enhances the model’s accuracy, reducing errors and improving the alignment with known geological information. …”
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FEATURES OF THE INFLUENCE OF LOW LEVELS OF ALCOHOL INTOXICATION ON THE FUNCTIONS OF THE HUMAN BRAIN by Borys V. Mykhailychenko, Andrii M. Biliakov, Alina O. Pletenetska

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Assessing paleo channel probability for offshore wind farm ground modeling - comparison of multiple-point statistics and sequential indicator simulation by Lennart Siemann, Ramiro Relanez

“…Conventional 2D seismic data interpretation provides the best estimate of the position but lacks probabilistic assessment, specifically at unexplored locations. Multiple-point statistics (MPS) and sequential indicator simulation (SIS) are applied to quantify the probability of channel features from seismic data, away from seismic lines. …”
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A New Statistical-Based Correlation for the Rib Fin Effects on the Overall Heat Transfer Coefficient in a Rib-Roughened Cooling Channel by M. E. Taslim, V. Nezym

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Selection of Prediction Method of Basic Statistical Work Parameters of N.V. Sklifosovsky Research Institute for Emergency Medicine of the Moscow Healthcare Department by B. L. Kurilin, V. Y. Kisselevskaya-Babinina, N. A. Karasyov, I. V. Kisselevskaya-Babinina, E. V. Kislukhkina, V. A. Vasilyev

“…The choice of the optimal method for predicting the work of a medical institution, based on the identification of the main trends in the time series, taking most of the features in the modeling of random processes and events into account, allowed to reduce the relative forecast error.…”
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Supervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study by Kanika Godani, Vishma Prabhu, Priyanka Gandhi, Ayushi Choudhary, Shubham Darade, Rupal Kathare, Prathiba Hande, Ramesh Venkatesh

“…Abstract Purpose To evaluate the predictive accuracy of various machine learning (ML) statistical models in forecasting postoperative visual acuity (VA) outcomes following macular hole (MH) surgery using preoperative optical coherence tomography (OCT) parameters. …”
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Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. by Fatemeh Pouromran, Srinivasan Radhakrishnan, Sagar Kamarthi

“…We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. …”
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Improving Voice Spoofing Detection Through Extensive Analysis of Multicepstral Feature Reduction by Leonardo Mendes de Souza, Rodrigo Capobianco Guido, Rodrigo Colnago Contreras, Monique Simplicio Viana, Marcelo Adriano dos Santos Bongarti

“…Our framework involves extracting multicepstral features followed by the application of diverse dimensionality reduction methods, such as Principal Component Analysis (PCA), Truncated Singular Value Decomposition (SVD), statistical feature selection (ANOVA F-value, Mutual Information), Recursive Feature Elimination (RFE), regularization-based LASSO selection, Random Forest feature importance, and Permutation Importance techniques. …”
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A Hybrid Deep Learning Paradigm for Robust Feature Extraction and Classification for Cataracts by Akshay Bhuvaneswari Ramakrishnan, Mukunth Madavan, R. Manikandan, Amir H. Gandomi

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Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation by Luis Miguel Moreno Haro, Adaiton Oliveira-Filho, Bruno Agard, Antoine Tahan

“…The proposed approach transforms sensor data into image-based feature representations of statistics such as mean, variance, kurtosis, entropy, skewness, and correlation. …”
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Spatial correlation guided cross scale feature fusion for age and gender estimation by Shiyi Jiang, Qing Ji, Hukui Shi, Che Chen, Yang Xu

“…The method integrates multi-granularity semantic features through a Cross-Scale Combination (CSC) module, enhances local detail representation using a Local Feature Guided Fusion (LFGF) module, and designs a Spatial Correlation Composition Analysis (SCCA) module based on Getis-Ord Gi* statistics for feature reorganization, effectively resolving interference from non-informative regions. …”
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Crop row centerline extraction method based on regional feature point clustering by Baofeng Ji, Hang Wang, Chunhong Dong, Song Chen, Hongtao Chen, Fazhan Tao, Ji Zhang, Huitao Fan

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Evaluating the Performance of Satellite-Derived Soil Moisture Products Across South America Using Minimal Ground-Truth Assumptions in Spatiotemporal Statistical Analysis by B. G. Mousa, Alim Samat, Hong Shu

“…In arid, semi-arid, and moderate vegetation regions, the SMAP satellite outperforms SMOS and ASCAT, achieving better statistics values with GLDAS and ERA5 datasets, and achieving low error variance and high S/N in the TCM analysis. …”
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Forecasting Temperature Time Series Data Using Combined Statistical and Deep Learning Methods: A Case Study of Nairobi County Daily Temperature by John Kamwele Mutinda, Amos Kipkorir Langat, Samuel Musili Mwalili

“…Overall, this study underscores the importance of VMD in preprocessing data to enhance feature representation and forecasting accuracy. By combining statistical and deep learning methods, hybrid models incorporating VMD offer a comprehensive solution for accurate temperature prediction, with implications for climate modeling and environmental monitoring.…”
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Clinical, radiologic, and morphological diagnosis of hypersensitivity pneumonitis by A. L. Cherniaev, E. V. Kusraeva, M. V. Samsonova, S. N. Avdeev, N. V. Trushenko, E. L. Tumanova

“…We found that the clinical error rate in the diagnosis of HP was 84.5%, among pathologists – 92%. …”
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