ergm 4: New Features for Analyzing Exponential-Family Random Graph Models by Pavel N. Krivitsky, David R. Hunter, Martina Morris, Chad Klumb

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Optimizing public health management with predictive analytics: leveraging the power of random forest by Hongman Wang, Yifan Song, Yifan Song, Hua Bi

“…The study begins with comprehensive data collection from diverse health sources, followed by a systematic preprocessing stage, which includes resolving missing values, normalizing variables, and encoding categorical features. Using bootstrap sampling, multiple decision trees were trained on random subsets of health data, ensuring variability in the model learning. …”
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Classification and Recognition of Soybean Quality Based on Hyperspectral Imaging and Random Forest Methods by Man Chen, Zhichang Chang, Chengqian Jin, Gong Cheng, Shiguo Wang, Youliang Ni

“…Hyperspectral images of soybean samples were collected using the Pika L spectrometer, and spectral information was extracted from the regions of interest (ROI) in the images. …”
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Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest by Gullam Almuzadid, Egia Rosi Subhiyakto

“…This study proposes a stroke risk classification model using ensemble learning that combines Random Forest and XGBoost algorithms. A Kaggle dataset with 5110 samples (249 stroke, 4861 non-stroke) presented significant class imbalance. …”
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Attention mechanism augmented random forest model for multiple air pollutants estimation by Xinyu Yu, Man Sing Wong, Kwon-Ho Lee

“…Specifically, self-attention mechanism was incorporated with the multi-output random forest first to emphasize pertinent features in inputs during model training. …”
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Understanding overfitting in random forest for probability estimation: a visualization and simulation study by Lasai Barreñada, Paula Dhiman, Dirk Timmerman, Anne-Laure Boulesteix, Ben Van Calster

“…Median test slopes were higher with higher true AUC, higher minimum node size, and higher sample size. Conclusions Random forests learn local probability peaks that often yield near perfect training AUCs without strongly affecting AUCs on test data. …”
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Algorithm for Cloud Particle Phase Identification Based on Bayesian Random Forest Method by Fu Tao, Yang Zhipeng, Tao Fa, Hu Shuzhen, Lu Yuxiang, Fu Changqing

“…Statistical analysis shows that Bayesian Random Forest Method achieves a cloud particle phase state recognition accuracy of 96% under both rainy and non-rainy weather conditions. …”
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A new machine learning method for rainfall classification: temporal random tree by Kokten Ulas Birant, Bita Ghasemkhani, Özlem Varlıklar, Derya Birant

“…To address this issue, the article proposes a novel method, named temporal random tree (TRT), in which recent training samples have a greater impact on the model’s decision-making process. …”
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Predicting Short-Range Weather in Tropical Regions Using Random Forest Classifier by Sellappan Palaniappan, Rajasvaran Logeswaran, Anitha Velayutham, Bui Ngoc Dung

“…To address these challenges, we trained a Random Forest classifier on a synthetic (simulated) dataset comprising 1,500 samples, each representing a specific weather scenario. …”
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Facial Emotion Images Recognition Based On Binarized Genetic Algorithm-Random Forest by Murad Ibrahim Husin Alzawali, Yusliza Yusoff, Razana Alwee, Zuriahati Mohd Yunos, Mohamad Shukor Talib, Haswadi Hassan, Fahad Taha AL-Dhief, Musatafa Abbas Abbood Albadr, Majid Razaq Mohamed Alsemawi, Sharifah Zarith Rahmah Syed Ahmad

“…In addition, the Binarized Genetic Algorithm (BGA) is utilized as a features selection in order to select the most effective features of HOG. Random Forest (RF) functions as a classifier to categories facial emotions in people according to the image samples. …”
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RPEM: Randomized Monte Carlo parametric expectation maximization algorithm by Rong Chen, Alan Schumitzky, Alona Kryshchenko, Keith Nieforth, Michael Tomashevskiy, Shuhua Hu, Romain Garreau, Julian Otalvaro, Walter Yamada, Michael N. Neely

“…We compared RPEM with NONMEM's Importance Sampling Method (IMP), Monolix's Stochastic Approximation Expectation Maximization (SAEM), and Certara's Quasi‐Random Parametric Expectation Maximization (QRPEM) for a realistic two‐compartment voriconazole model with ordinary differential equations using simulated data. …”
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Mendelian randomization of serum micronutrients and osteoarthritis risk: focus on zinc by Wenxing Zeng, Enda Hong, Wei Ye, Luyao Ma, Dejun Cun, Feng Huang, Ziwei Jiang

“…Methods This study aimed to evaluate the potential causal relationships between 15 key micronutrients and the risk of OA using both two-sample and multivariate Mendelian randomization approaches. …”
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Designing Randomized Experiments to Predict Unit-Specific Treatment Effects by Elizabeth Tipton, Michalis Mamakos

“…We consider how different sampling processes and models affect the mean squared error of these predictions. …”
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Antibiotic prophylaxis in laparoscopic cholecystectomy: a randomized controlled trial. by Yoichi Matsui, Sohei Satoi, Masaki Kaibori, Hideyoshi Toyokawa, Hiroaki Yanagimoto, Kosuke Matsui, Morihiko Ishizaki, A-Hon Kwon

“…To evaluate the results of meta-analyses, we conducted a randomized controlled trial on the role of prophylactic antibiotics in low-risk laparoscopic cholecystectomy with an adequate sample size.…”
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Randomized Purifier Based on Low Adversarial Transferability for Adversarial Defense by Sangjin Park, Yoojin Jung, Byung Cheol Song

“…Recently, AP techniques using energy-based models or diffusion models have achieved meaningful robustness with a randomized defense based on a stochastic process. However, since they require a great number of diffusion steps or sampling steps in purifying attacked images, their computational cost is burdensome. …”
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The gut microbiome and ovarian cysts: a mendelian randomization study by Jiahui Qu, Liying Zhang

“…In this study, we conducted a two-sample Mendelian randomization (MR) analysis to investigate potential causal effects between gut microbial genera and ovarian cysts. …”
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Association of asthma and bronchiectasis: Mendelian randomization analyses and observational study by Rui Fan, Hao Qian, Jia-Yan Xu, Jia-Yi Wang, Yue Su, Jia-Wei Yang, Fang Jiang, Wei-Jun Cao, Jin-Fu Xu

“…Method All the necessary summarized information were obtained from publicly available genome-wide association study (GWAS). Two-sample Mendelian randomization (two-sample MR) was employed to explore the causal relationship between asthma and bronchiectasis, with an additional dataset used for validation. …”
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Mendelian randomization analysis of modifiable risk factors for breast cancer by Diabate Ousmane, Jie Liu, Ziyu Liu, Zongjiang Zhou, Liu Liu, Junpu Wang

“…Abstract This review explores the role of Mendelian randomization (MR) in the analysis of modifiable risk factors for breast cancer. …”
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Emotional dispositions and intracerebral hemorrhage: a Mendelian Randomization insight by Tian Hou, Yipeng Xu, Aili Buaijier, Xuetao Yu, Yuchen Guo, Di Zhang, Peng Liu

“…This study employs Mendelian Randomization (MR) to investigate the causal relationship between emotional traits of worry and anxiety and the incidence of ICH.MethodsWe used a two-sample MR approach, leveraging summary-level data from genome-wide association studies (GWAS) for emotional traits and ICH. …”
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Causality between ischemic stroke and epilepsy based on Mendelian randomization by SHU Yun, SHU Yun, YUAN Qing, WU Zhifeng

“…Methods Based on the summary data of genome-wide association study, Mendelian randomization (MR) analysis was performed with MR-Egger regression, weighted median and inverse-variance weighting. …”
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