Enhancing Immersion in Virtual Reality–Based Advanced Life Support Training: Randomized Controlled Trial by Dilek Kitapcioglu, Mehmet Emin Aksoy, Arun Ekin Ozkan, Tuba Usseli, Dilan Cabuk Colak, Tugrul Torun

“…Following the pretest, participants were randomly divided into 2 groups: the voice command group (n=31) and the VR controller group (n=31). …”
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A random-forest-derived 35-year snow phenology record reveals climate trends in the Yukon River Basin by C. G. Pan, K. Lasko, S. P. Griffin, J. S. Kimball, J. Du, T. G. Meehan, P. B. Kirchner

“…<p>This study presents a 35-year snow phenology record for the Yukon River Basin (YRB), developed using a random forest (RF) model at a 3.125 km resolution, capturing detailed trends in snowmelt onset and snow-off. …”
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Application of bayesian additive regression trees in the development of credit scoring models in Brazil by Daniel Alves de Brito Filho, Rinaldo Artes

“…RF was superior to LRM only for the balanced sample. The best-adjusted BART model was superior to RF. …”
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Multi-Granularity User Anomalous Behavior Detection by Wenying Feng, Yu Cao, Yilu Chen, Ye Wang, Ning Hu, Yan Jia, Zhaoquan Gu

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Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir by Hendri Mahmud Nawawi, Agung Baitul Hikmah, Ali Mustopa, Ganda Wijaya

“…In this research, the application of the ML classification model is aimed at predicting career placement. From the data sample used of 215, this research evaluates the effectiveness of various ML models in the context of career placement. …”
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Using machine learning to unravel chemical and meteorological effects on ground-level ozone: Insights for ozone-climate control strategies by Zhiyuan Li, Yifan Wang, Junling Liu, Junrui Xian

“…The machine learning-based modelling framework developed in this study can be easily adapted to new sampling sites with minor modifications if necessary.…”
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Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning by Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović

“…We study how the variations of the temporal derivative thresholds of anisotropies and magnetic energies, and sampling strategies for simulation runs, affect classification. …”
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Current status of vocational delay of gratification among practicing nursing students and its influencing factors: a cross-sectional study by Tianyu Chu, Xian Chen, Qian Zhang, Juanjuan Yang, Hui Zhou, Yibo Wu, Jie Jiao

“…Data were collected using a general information questionnaire, job involvement scale, clinical communication ability scale, and vocational delay of gratification scale. Random forest, independent samples t-test, analysis of variance (ANOVA), and multiple linear regression analyses were applied to identify the key influencing factors. …”
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Hospitalization costs of oral cancer patients in Southeast of China: a quantile regression analysis by Xiaodan Bao, Weihai Huang, Yulan Lin, Fa Chen, Fengqiong Liu, Yu Qiu, Bin Shi, Lisong Lin, Jing Wang, Baochang He

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Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java by Khusnia Nurul Khikmah, Bagus Sartono, Budi Susetyo, Gerry Alfa Dito

“…The results show that the combination of the random-forest algorithm and the random-under sampling technique is the best classifier. …”
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A Random Parameter Logit Model of Immediate Red-Light Running Behavior of Pedestrians and Cyclists at Major-Major Intersections by Wencheng Wang, Zhenzhou Yuan, Yanting Liu, Xiaobao Yang, Yang Yang

“…This paper investigates three typical signalized major-major intersections in the center of Beijing, by collecting and analyzing 1368 samples of pedestrians and nonmotorized vehicles. …”
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A novel Compound-Pareto model with applications and reliability peaks above a random threshold value at risk analysis by Mohammad Abiad, M. M. Abd El-Raouf, Haitham M. Yousof, M. E. Bakr, Oluwafemi Samson Balogun, M. Yusuf, Getachew Tekle Mekiso, Yusra A. Tashkandy

“…The method of maximum likelihood is employed to estimate the unknown model parameters, and the performance of the estimators under finite samples is evaluated through a comprehensive simulation study. …”
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Stability prediction of circular sliding failure soil slopes based on a genetic algorithm optimization of random forest algorithm by Shengming Hu, Yongfei Lu, Xuanchi Liu, Cheng Huang, Zhou Wang, Lei Huang, Weihang Zhang, Xiaoyang Li

“…Here, we present an intelligent slope stability assessment method based on a genetic algorithm optimization of random forest algorithm (GA-RF algorithm). Based on 80 sets of typical slope samples, weight (γ), slope height (H), pore pressure value (P), cohesion force (C), internal friction angle (φ) and slope inclination angle (°) were selected as characteristic variables for slope stability evaluation. …”
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Development and pan-cancer validation of an epigenetics-based random survival forest model for prognosis prediction and drug response in OS by Chaoyi Yin, Kede Chi, Zhiqing Chen, Shabin Zhuang, Yongsheng Ye, Binshan Zhang, Cailiang Cai

“…BackgroundOsteosarcoma (OS) exhibits significant epigenetic heterogeneity, yet its systematic characterization and clinical implications remain largely unexplored.MethodsWe analyzed single-cell transcriptomes of five primary OS samples, identifying cell type-specific epigenetic features and their evolutionary trajectories. …”
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A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification by Wei Huang, Yao Huang, Zebin Wu, Junru Yin, Qiqiang Chen

“…However, these deep-learning methods not only take a lot of time in the pre-training phase, but also have relatively limited classification performance when there are fewer labeled samples. In order to improve classification performance while reducing costs, this article proposes a multikernel method based on a local binary pattern and random patches (LBPRP-MK), which integrates a local binary pattern (LBP) and deep learning into a multiple-kernel framework. …”
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A combined feature selection approach for malicious email detection based on a comprehensive email dataset by Han Zhang, Yong Shi, Ming Liu, Libo Chen, Songyang Wu, Zhi Xue

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Biochemical Oxygen Demand Prediction Based on Three-Dimensional Fluorescence Spectroscopy and Machine Learning by Xu Zhang, Yihao Zhang, Xuanyi Yang, Zhiyun Wang, Xianhua Liu

“…This avoids the complicated operation of DO determination, improves detection efficiency, and provides a convenient solution for analyzing large quantities of water samples and monitoring facile water quality.…”
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A Lightweight and High Yield Complementary Metal-Oxide Semiconductor True Random Number Generator with Lightweight Photon Post-Processing by Chi Trung Ngo, Hyun Woo Ko, Ji Woo Choi, Jae-Won Nam, Jong-Phil Hong

“…The measurements show that only a small number of measured TRNG samples passed the randomness NIST SP 800-22 tests, which is a common problem, not only with the proposed TRNG but also with other TRNG structures. …”
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Spatial autocorrelation in machine learning for modelling soil organic carbon by Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa

“…Spatial autocorrelation, the relationship between nearby samples of a spatial random variable, is often overlooked in machine learning models, leading to biased results. …”
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COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CLASSIFYING THE QUALITY OF PALU SHALLOTS by Desy Lusiyanti, Selvy Musdalifah, Agusman Sahari, Iman Al Fajri

“…The dataset consists of 1,500 samples of Palu shallots, each characterized by 10 key features, including size, color, texture, and moisture content. …”
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