Study on Finger Gesture Interface Using One-Channel EMG by Hee-Yeong Yang, Young-Shin Han, Choon-Sung Nam

“…Four machine learning models were used: eXtreme Gradient Boost, Random Forest, k-Nearest Neighbors, and Logistic Regression. …”
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Molecular Identification of Fungal Communities in a Soil Cultivated with Vegetables and Soil Suppressiveness to Rhizoctonia solani by Silvana Pompéia Val-Moraes, Eliamar Aparecida Nascimbem Pedrinho, Eliana Gertrudes Macedo Lemos, Lucia Maria Carareto-Alves

“…For this purpose, total DNA was extracted from bulk soils cultivated with tomato (STC), vegetables (SHC), and native forest (SMS) from three sites of the Taquara Branca river basin in Sumaré County, São Paulo State, Brazil. …”
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Organophosphorus Pesticides Management Strategies: Prohibition and Restriction Multi-Category Multi-Class Models, Environmental Transformation Risks, and Special Attention List by Yingwei Wang, Lu Wang, Yufei Li

“…Among these, the random forest (RF) model demonstrated excellent predictive performance, as it was successfully validated and applied. …”
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Applying Sewage Sludge to Eucalyptus grandis Plantations: Effects on Biomass Production and Nutrient Cycling through Litterfall by Paulo Henrique Müller da Silva, Fabio Poggiani, Jean Paul Laclau

“…In most Brazilian cities sewage sludge is dumped into sanitary landfills, even though its use in forest plantations as a fertilizer and soil conditioner might be an interesting option. …”
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GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020 by Jian Zuo, Li Zhang, Jingfeng Xiao, Bowei Chen, Bo Zhang, Yingwen Hu, M. M. Abdullah Al Mamun, Yang Wang, Kaixin Li

“…The coastline classification was performed a hybrid transect classifier that integrates a random forest algorithm with stable training samples derived from multi-source geophysical data. …”
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Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study by Fisnik Dalipi, Sule Yildirim Yayilgan, Alemayehu Gebremedhin

“…The algorithms examined are Support Vector Regression (SVR), Partial Least Square (PLS), and random forest (RF). We use the data collected from buildings at several locations for a period of 29 weeks. …”
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Use of Plant Resources by Merosargus (Diptera, Stratiomyidae, Sarginae) Larvae by Julio C. R. Fontenelle, Flávia E. C. Viana-Silva, Rogério P. Martins

“…This study identified substrate types used as a resource by Merosargus larvae and investigated the degree of specialization and overlap in resource use by different species at an Atlantic forest remnant in Minas Gerais, Brazil. Every potential resource, especially those with adults in the vicinity, was collected opportunistically from October 2001 to October 2004. …”
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Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble Algorithm by Nana Zhang, Qi An, Shuai Zhang, Huanhuan Ma

“…The prediction performance of the Light gradient boosting machine model is evaluated by comparing it against multiple benchmark models (ARIMA, decision tree, random forest, support vector machine, XGBoost, and artificial neural network) in terms of accuracy, generalizability, and robustness on different datasets and under different time windows. …”
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A Comparison of Classification Algorithms for Predicting Dis-tinctive Characteristics in Fine Aroma Cocoa Flowers Using WE-KA Modeler by Daniel Tineo, Yuriko S. Murillo, Mercedes Marín, Darwin Gomez, Victor H. Taboada, Malluri Goñas, Lenin Quiñones Huatangari

“…Three attribute evaluators (InfoGainAttributeEval, CorrelationAttributeEval and GainRatioAttributeEval), and six algorithms (Naive Bayes, Multinomial Logistic Regression, J48, Random Forest, LTM and Simple Logistic) were employed in this study. …”
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Energy Budget on Various Land Use Areas Using Reanalysis Data in Florida by Chi-Han Cheng, Fidelia Nnadi, Yuei-An Liou

“…Therefore, in this study, North American regional reanalysis (NARR) data set from 1992 to 2002 were employed to investigate the energy budget on various land uses (lake, wetland, agriculture, forest, and urban) at a regional scale in Florida. …”
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Decision Tree Algorithm-Based Model and Computer Simulation for Evaluating the Effectiveness of Physical Education in Universities by Zhifei Zhang, Zijian Zhao, Doo-Seoung Yeom

“…In this paper, the forest algorithm and the decision tree algorithm are mainly used to analyze students’ physical education information, course exam results, and student learning data and relevant feature attributes from the online teaching platform. …”
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Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients by Cheng Qu, Lin Gao, Xian-qiang Yu, Mei Wei, Guo-quan Fang, Jianing He, Long-xiang Cao, Lu Ke, Zhi-hui Tong, Wei-qin Li

“…With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). …”
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Studying Summer Season Drought in Western Russia by Anthony R. Lupo, Igor I. Mokhov, Yury G. Chendev, Maria G. Lebedeva, Mirseid Akperov, Jason A. Hubbart

“…The record heat, high humidity, dry weather, and smoke from forest fires caused increased human mortality rates in the Moscow region during the summer. …”
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Multidimensional patterns of bird diversity and its driving forces in the Yangtze River Basin of China by Wei Liu, Tong Mu, Sijia Yuan, Jianfeng Yi, Dandan Yu, Jiaqi Li, Fangzhou Ma, Yaqiong Wan, Jing Chen, Riquan Zhang, David S. Wilcove, Haigen Xu

“…Here, we constructed an optimized living planet index (LPIO) by combining Partial Least Squares Structural Equation Modeling and Random Forest Modeling. Using data from a monitoring network of 536 sites, we observed an increasing trend in terrestrial bird diversity and functional complexity across the entire watershed from 2011 to 2020. …”
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Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning by Xiaoqing Liu, Miaoran Wang, Rui Wen, Haoyue Zhu, Ying Xiao, Qian He, Yangdi Shi, Zhe Hong, Bing Xu

“…An 80/20 train-test split was implemented for model development and validation, employing various machine learning classifiers, including artificial neural networks (ANN), random forest (RF), XGBoost (XGB), and LASSO regression. …”
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Enhanced analysis of tabular data through Multi-representation DeepInsight by Alok Sharma, Yosvany López, Shangru Jia, Artem Lysenko, Keith A. Boroevich, Tatsuhiko Tsunoda

“…We demonstrate the effectiveness of MRep-DeepInsight on single-cell datasets, Alzheimer's data, and artificial data, showcasing an improved accuracy over the original DeepInsight approach and machine learning methods like random forest, XGBoost, LightGBM, FT-Transformer and L2-regularized logistic regression. …”
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Agricultural Technology for Phytophage and Phytopathogen Resistant Planting Material by Evgenia A. Dyukova, Ekaterina G. Ulyanova, Maria A. Osintseva, Victoria A. Kryuk

“…The technology was able to improve the survival rate of pine seedlings in forest container nurseries in the harsh climate of West Siberia.…”
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Metasurface-enabled multifunctional single-frequency sensors without external power by Masaya Tashiro, Kosuke Ide, Kosei Asano, Satoshi Ishii, Yuta Sugiura, Akira Uchiyama, Hiroki Wakatsuchi

“…Moreover, we provide a method for predicting physical parameters via the machine learning-based approach of random forest regression. The sensing performance was confirmed by estimating the temperature and light intensity, and excellent determination coefficients larger than 0.96 were achieved. …”
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Modeling of winter wheat yield prediction based on solar-induced chlorophyll fluorescence by machine learning methods by Minxue Zheng, Han Hu, Yue Niu, Qiu Shen, Feng Jia, Xiaolei Geng

“…However, in the Extreme Gradient Boosting (XGB) model, SIF’s predictive performance was better than that of the combination of SIF and NIRv, indicating that combining SIF and NIRv could not completely enhance SIF’s predictive performance. (b) Random Forest (RF) and XGB models were significantly better than the other models in yield prediction; specifically, the RF model had high stability. …”
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Database of soil properties incorporating organic content from roots and soil organisms for regional slope stabilisation by Yangyang Li, Saranya Rangarajan, Harianto Rahardjo, Yuanjie Shen, Abdul Halim Hamdany, Alfrendo Satyanaga, Eng Choon Leong, Swee Khian Wong, Chien Looi Wang, Huiling Kew, Tint Htoo Naing, Choon Hock Poh, Subhadip Ghosh

“…Hence, this study pioneers the construction of an extensive soil database using random forest machine learning and ordinary kriging methods, focusing on the influence of plant roots on the saturated and unsaturated properties of residual soils. …”
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