Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019 by Gamal AbdElNasser Allam Abouzied, Guoqiang Tang, Simon Michael Papalexiou, Martyn P. Clark, Eleonora Aruffo, Piero Di Carlo

“…However, the machine learning strategy using random forest (ML3) has the most outstanding share in the final estimates among all other strategies. …”
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Prevalence of diabetic retinopathy and its associated factors among adults in East African countries: A systematic review and meta-analysis. by Habtamu Wagnew Abuhay, Tigabu Kidie Tesfie, Meron Asmamaw Alemayehu, Muluken Chanie Agimas, Getaneh Awoke Yismaw, Gebrie Getu Alemu, Nebiyu Mekonnen Derseh, Bantie Getnet Yirsaw

“…<h4>Methods</h4>We extensively searched PubMed, Embase, Scopus, Google Scholar, and Google for relevant studies. A forest plot was used to estimate the pooled prevalence of diabetic retinopathy using DerSimonian and Laird's random-effects model. …”
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Features of the biomorphological and geographic structure of segetal floras in a number of regions in Russia by O. G. Baranova, A. S. Tretyakova, N. N. Luneva, A. A. Zverev, P. V. Kondratkov, T. A. Terekhina, G. R. Khasanova, S. M. Yamalov, M. V. Lebedeva, N. A. Bagrikova

“…Depending on the zonal arrangement of segetal floras, the shares of boreal, forest-steppe and steppe species changed. The ratios among geographic elements in the alien fractions of the compared segetal floras were relatively stable. …”
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Zmienność runa leśnego w grądzie wysokim rezerwatu Las Bielański pod wpływem ruchu turystycznego by Anita Grutkowska

“…Las Bielański covers 150 hectares, it is a unique remnant of the ancient Mazowiecka Forest. At present 130 hectares out of 150 hectares are a nature reserve, where the influence of humans is limited only to tourist paths. …”
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Remotely sensed spectral indicators of bird taxonomic, functional and phylogenetic diversity across Afrotropical urban and non-urban habitats by Adewale G. Awoyemi, Tunrayo R. Alabi, Juan Diego Ibáñez-Álamo

“…To do so, we sampled birds at 400 points equally distributed across eight Nigerian areas, two vegetation zones (rainforest vs savannah), and two habitats (urban vs non-urban), and extracted 29 indicators (mean and SD) at 50-m radius of each point (exact area of bird censuses). Random Forest Regressions and Generalized Linear Mixed Effect Models were used to identify the topmost ranked indicator of each avian diversity component, and its variation between urban and non-urban areas. …”
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A novel framework to predict ADHD symptoms using irritability in adolescents and young adults with and without ADHD by Saeedeh Komijani, Dipak Ghosal, Manpreet K. Singh, Julie B. Schweitzer, Julie B. Schweitzer, Prerona Mukherjee, Prerona Mukherjee

“…Using a non-parametric-based approach, employing the Random Forest (RF) method, we found that among both adolescents and young adults, irritability in adolescent females significantly contributes to predicting impulsive symptoms in subsequent years, achieving a performance rate of 86%.ConclusionOur results corroborate and extend prior findings, allowing for an in-depth examination of longitudinal relations between irritability and ADHD symptoms, namely hyperactivity, impulsivity, and inattentiveness, and the unique association between irritability and ADHD symptoms in females.…”
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Practice and factors associated with sunlight exposure of infants among mothers in Ethiopia: a systematic review and meta-analysis by Shambel Dessale Asmamaw, Tibebu Habte Zewde, Abiel Teshome, Esayas Nigussie

“…The pooled prevalence with a 95% confidence interval (CI) of the meta-analysis utilizing the random effect model was displayed using forest plots, and adjusted odds ratio (AOR) was utilized to quantify the association. …”
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A Study on Canopy Volume Measurement Model for Fruit Tree Application Based on LiDAR Point Cloud by Na Guo, Ning Xu, Jianming Kang, Guohai Zhang, Qingshan Meng, Mengmeng Niu, Wenxuan Wu, Xingguo Zhang

“…For feature selection, a random forest-based recursive feature elimination method with cross-validation was employed to filter 10 features. …”
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Investigating the effects of hyperparameter sensitivity on machine learning algorithms for PV forecasting by Ehtsham Muhammad, Rotilio Marianna, Cucchiella Federica, Di Giovanni Gianni, Schettini Domenico

“…Four state-of-the-art ML models, namely Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) were investigated. …”
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Assessing the Impact of Traffic Emissions on Fine Particulate Matter and Carbon Monoxide Levels in Hanoi through COVID-19 Social Distancing Periods by Nhung H. Le, Bich-Thuy Ly, Phong K. Thai, Gia-Huy Pham, Ich-Hung Ngo, Van-Nguyet Do, Thuy T. Le, Luan V. Nhu, Ha Dang Son, Yen-Lien T. Nguyen, Duong H. Pham, Tuan V. Vu

“…To overcome this challenge, weather normalized concentrations of those pollutants were estimated using the random forest model, a machine learning technique. The normalized weather concentrations showed smaller reductions by 7–10% for PM2.5 and 5–11% for CO, indicating the presence of favorable weather conditions for better air quality during the social distancing period. …”
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Formulated citrus peel extract gold lotion improves cognitive and functional recovery from traumatic brain injury (TBI) in rats by Cheng Guang, Yue Zhiwei, Wang Liwen, Miyauchi Yutaka, Suzawa Michiko, Li Shiming, Ho Chi‐Tang, Zhao Hui, Chen Naiyao

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Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning by Qiqiang Liang, Xin Xu, Shuo Ding, Jin Wu, Man Huang

“…Next, we demonstrated that machine learning models, especially Random Forest and XGBoost, achieving an AUROC of 0.95. The XGBoost model exhibited superior accuracy, yielding an AUROC of 0.849.Conclusion High-risk factors for unsuccessful RRT weaning in severe AKI patients include prolonged RRT duration. …”
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Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury by Tianyun Gao, Zhiqiang Nong, Yuzhen Luo, Manqiu Mo, Zhaoyan Chen, Zhenhua Yang, Ling Pan

“…The AUC of the random forest (RF) model was the highest value both in the Ten-fold cross-validation and evaluation (AUC: 0.798, 95% CI: 0.774–0.821). …”
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A Hybrid Fuzzy Wavelet Neural Network Model with Self-Adapted Fuzzy c-Means Clustering and Genetic Algorithm for Water Quality Prediction in Rivers by Mingzhi Huang, Di Tian, Hongbin Liu, Chao Zhang, Xiaohui Yi, Jiannan Cai, Jujun Ruan, Tao Zhang, Shaofei Kong, Guangguo Ying

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Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm by Wenhao Xu, Xiaogang Liu, Jianhua Dong, Jiaqiao Tan, Xulei Wang, Xinle Wang, Lifeng Wu

“…Extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), gaussian process regression (GPR), and multiple stepwise regression (MSR) models were used to construct citrus fruit number and quality prediction models. …”
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Effect of Environmental Disturbance on the Population of Sandflies and Leishmania Transmission in an Endemic Area of Venezuela by Elsa Nieves, Luzmary Oraá, Yorfer Rondón, Mireya Sánchez, Yetsenia Sánchez, Masyelly Rojas, Maritza Rondón, Maria Rujano, Nestor González, Dalmiro Cazorla

“…Three agroecosystems with variable degrees of ecological disturbance, forest (conserved), cacao (fragmented), and orangery (disturbed), were selected. …”
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Effect of short inter-pregnancy interval on perinatal and maternal outcomes among pregnant women in SSA 2023: Systematic review and meta-analysis. by Fentahun Yenealem Beyene, Kihinetu Gelaye Wudineh, Simachew Animen Bantie, Azimeraw Arega Tesfu

“…The results are presented using texts, tables and forest plots with measures of effect and 95% confidence interval.…”
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Small mammal communities of Tuva Republic (Southern Siberia, Russia) in a changing climate by Sergey O. Ondar, Sergey N. Kirpotin, Andrey S. Babenko, Bogdan A. Mikhaleiko, Nikolay I. Putintsev, Aleksey V. Surov, Orlan Ch. Oidupaa, Andrey M. Samdan, Aivar V. Kuular, Bailak S. Mondush, Dayana S. Ondar, Pradip Kumar Kar, Aldynay O. Khovalyg, Anatoly F. Chuldum

“…The modifying influence of global and regional climate factors is shown, which leads to the exchange of communities within bioclimatic zones: the relative abundance and expansion of the range of humid territories increases; with the expansion of the pika's range, the ranges of specialized seed eaters also expand; an expansion of the ranges of forest species into the steppe zone and a reduction in the number of species in desert and dry steppe zones is recorded.…”
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Evaluating the Impact of Data Transformation Techniques on the Performance and Interpretability of Software Defect Prediction Models by Yu Zhao, Zhiqiu Huang, Lina Gong, Yi Zhu, Qiao Yu, Yuxiang Gao

“…Through empirical research on (i) six classification techniques (random forest, decision tree, logistic regression, Naive Bayes, K-nearest neighbors, and multilayer perceptron), (ii) six performance evaluation indicators (Accuracy, Precision, Recall, F1, MCC, and AUC), (iii) two interpretable methods (permutation and SHAP), (iv) two feature importance measures (Top-k feature rank overlap and difference), and (v) three datasets (Promise, Relink, and AEEEM), our results show that the data transformation methods can significantly improve the performance of the SDP models and greatly affect the variation of the most important features. …”
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Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS) by Saghar Tabib, Seyed Danial Alizadeh, Aref Andishgar, Babak Pezeshki, Omid Keshavarzian, Reza Tabrizi

“…Methods We analysed the data related to osteoporosis risk factors obtained from the Fasa Adults Cohort Study in eight ML methods, including logistic regression (LR), baseline LR, decision tree classifiers (DT), support vector classifiers (SVC), random forest classifiers (RF), linear discriminant analysis (LDA), K nearest neighbour classifiers (KNN) and extreme gradient boosting (XGB). …”
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