Goal and shot prediction in ball possessions in FIFA Women’s World Cup 2023: a machine learning approach by Iyán Iván-Baragaño, Antonio Ardá, José L. Losada, Rubén Maneiro

“…The predictive capacity was tested using Random Forest and XGBoost and finally and SHAP values were calculated and visualized to understand the influence of the predictors.ResultsRandom Forest technique showed greater efficacy, with recall and sensitivity above 93% in the resampled dataset. …”
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Research on cloud dynamic public key information security based on elliptic curve and primitive Pythagoras by Zhenlong Man, Jianmeng Liu, Fan Zhang, Xiangfu Meng

“…In the scrambling algorithm, according to the parity of random number and the parity of image pixel value coordinates, elliptic curve is used to reset the plaintext image. …”
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Single-Pixel Compressive Digital Holographic Encryption System Based on Circular Harmonic Key and Parallel Phase Shifting Digital Holography by B. Lokesh Reddy, Anith Nelleri

“…An encryption system that combines compressive sensing (CS) and two-step parallel phase shifting digital holography (PPSDH) using double random phase encoding (DRPE) is presented in this paper. …”
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Evaluating the RELM Test Results by Michael K. Sachs, Ya-Ting Lee, Donald L. Turcotte, James R. Holliday, John B. Rundle

“…A perfect forecast would have λfi=1, and a random (no skill) forecast would have λfi=2.86×10-3. …”
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A simple tool to evaluate the effectiveness of HIV care for settings with gaps in data availability (ESTIHIV). by Dorthe Raben, Marie L Jakobsen, Jamina Trajanovska, Justyna Kowalska, Anna Vassilenko, Snezana Dragas, Arjan Harxhi, Gordana Dragovic, Nadine J Jaschinski, Bastian Neesgaard, Klaus Hjorth-Larsen, Harmony Garges, Joel Gallant, Jens D Lundgren, Andrew Philips, Valentina Cambiano, Yazdan Yazdanpanah, Amanda Mocroft

“…We developed an estimation-tool, ESTIHIV, and determined the minimal data required for a random sample, to produce representative estimates, with a specified level of precision, of people with HIV on ART and VS. …”
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Complexity Analysis of New Future Video Coding (FVC) Standard Technology by Soulef Bouaafia, Randa Khemiri, Seifeddine Messaoud, Fatma Elzahra Sayadi

“…First, we evaluate the FVC profiles under All Intra, Low-Delay P, and Random Access to determine which coding components consume the most time. …”
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Integrated estimation of parameters of radio transmitter power amplifier with automatic mode adjustment by two-frequency test signal by P. V. Sak

“…Comparative estimation of energy parameters of power amplifiers of single-band radio transmitters using automatic mode adjustment using a deterministic two-frequency test signal instead of a random single-band signal modulated by speech is investigated in the work. …”
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An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers. by Michael Owusu-Adjei, James Ben Hayfron-Acquah, Twum Frimpong, Abdul-Salaam Gaddafi

“…Prediction accuracy score of area under the curve obtained show Gradient Boosting classifier achieved 91% accuracy, Logistic Regression 93% and Random Forest 91%. Balanced accuracy score obtained for these techniques were; Gradient Boosting 82.73%, Logistic Regression 84.62% and Random Forest 83.02%. …”
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Adsorption on Heterogeneous Surfaces with Simple Topographies by P.M. Centres, F. Bulnes, J.L. Riccardo, A.J. Ramirez-Pastor, M.A. Perarnau

“…Patches were distributed spatially, either in a deterministic alternate way (ordered topography) or in a non-overlapping random way (random topography). The adsorption process was analyzed by following the behaviour of surface coverage versus chemical potential (adsorption isotherm) and the differential heat of adsorption as a function of the coverage. …”
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Assessing factors influencing adolescent sexual debut in Nigeria: a multi-cluster survival analysis approach by Fabio Mathias Correa, Peter Enesi Omaku, Joseph Odunayo Braimah, Joseph Odunayo Braimah

“…IntroductionEarly sexual debut is an area of concern in Nigeria with implications for reproductive health.MethodsThis study addresses this by proposing a more effective survival model—one that incorporates both independent and identically distributed (IID) and Besag intrinsically conditional auto-regressive (ICAR) random effect priors, using a generalised additive model that accounts for both individual and spatial influences on age at first sex. …”
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Analyzing Geospatial and Socioeconomic Disparities in Breast Cancer Screening Among Populations in the United States: Machine Learning Approach by Soheil Hashtarkhani, Yiwang Zhou, Fekede Asefa Kumsa, Shelley White-Means, David L Schwartz, Arash Shaban-Nejad

“…To evaluate the influence of these social determinants, we implemented a random forest model, with the aim of comparing its performance to linear regression and support vector machine models. …”
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Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models by Amir Hossein Sheikhshoaei, Ali Khoshsima, Davood Zabihzadeh

“…Our analysis indicates that the Random Forest and Deep Belief Network models outperform all other competing models. …”
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Automated Classification of Circulating Tumor Cells and the Impact of Interobsever Variability on Classifier Training and Performance by Carl-Magnus Svensson, Ron Hübler, Marc Thilo Figge

“…The random forest classifier turned out to be resilient to uncertainty in the training data while the support vector machine’s performance is highly dependent on the amount of uncertainty in the training data. …”
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Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes by Wei Song, Guang Hu, Liuqing OuYang, Zhenjie Zhu

“…In this paper, we propose a multitask multiview semisupervised learning model based on partial differential equation random field and Hilbert independent standard probability image genus attribute model, i.e., shared semantics. …”
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A Novel Two-Stage Spectrum-Based Approach for Dimensionality Reduction: A Case Study on the Recognition of Handwritten Numerals by Mohammad Amin Shayegan, Saeed Aghabozorgi, Ram Gopal Raj

“…Although there are different conventional approaches for feature selection, such as Principal Component Analysis, Random Projection, and Linear Discriminant Analysis, selecting optimal, effective, and robust features is usually a difficult task. …”
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Model Optimization of Shale Gas Reservoir Volume Fracturing Dissolved Gas Simulation Adsorbed Gas by Hao Dong, Yi Zhang, Zongwu Li, Chao Jiang, Jiaze Li, Tao Wu, Liting Wang, Chuangjiang Wang, Hao Wang, Fujing Li, Qian Ru

“…Accurate description of natural fractures: random distributed discrete fracture model is used as the basic model to describe natural fractures. …”
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Evolution characteristics and invulnerability simulation analysis of global zirconium ore trade network by Fanjie Luo, Wei Liu, Mao Xu, Qunyi Liu, Junbo Wang

“…This study constructs the international zircon ore trade network from 2013 to 2022, analyzes its structural evolution at both the network and node levels, and evaluates its robustness in 2022 using five attack strategies: random node removal, random edge removal, edge degradation, targeted removal based on node degree, and targeted removal based on node betweenness centrality. …”
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Automated classification of stress and relaxation responses in major depressive disorder, panic disorder, and healthy participants via heart rate variability by Sangwon Byun, Ah Young Kim, Min-Sup Shin, Hong Jin Jeon, Hong Jin Jeon, Chul-Hyun Cho, Chul-Hyun Cho

“…HRV data were collected during stress and relaxation tasks, with 20 HRV features extracted. Random forest and multilayer perceptron classifiers were applied to distinguish between the stress and relaxation tasks. …”
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Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospectiv... by Shingo Fukuma, Yukio Yuzawa, Daijo Inaguma, Hiroki Hayashi, Ryosuke Yanagiya, Akira Koseki, Toshiya Iwamori, Michiharu Kudo

“…The areas under the curve for classifying the extremely rapid eGFR declines in the G1, G2 and G3 groups were 0.69 (95% CI, 0.63 to 0.76), 0.71 (95% CI 0.69 to 0.74) and 0.79 (95% CI 0.75 to 0.83), respectively. The random forest model identified haemoglobin, albumin and C reactive protein as important characteristics.Conclusions The random forest model could be useful in identifying patients with extremely rapid eGFR decline.Trial registration UMIN 000037476; This study was registered with the UMIN Clinical Trials Registry.…”
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Predicting positive Clostridioides difficile test results using large-scale longitudinal data of demographics and medication history by Anh Pham, Robert El-Kareh, Frank Myers, Lucila Ohno-Machado, Tsung-Ting Kuo

“…Results: Logistic Regression, Random Forest, and Ensemble models yielded test AUROCs of 0.839, 0.851, and 0.866, respectively. …”
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