Landscape dynamics and its related factors in the Citarum River Basin: a comparison of three algorithms with multivariate analysis by Moh. Dede, Sunardi Sunardi, Kuok-Choy Lam, Susanti Withaningsih, Hendarmawan Hendarmawan, Teguh Husodo

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Variance Reduction Optimization Algorithm Based on Random Sampling by GUO Zhenhua, YAN Ruidong, QIU Zhiyong, ZHAO Yaqian, LI Rengang

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Random Oversampling-Based Diabetes Classification via Machine Learning Algorithms by G. R. Ashisha, X. Anitha Mary, E. Grace Mary Kanaga, J. Andrew, R. Jennifer Eunice

“…Comparative analysis of this model suggests that the random forest algorithm outperforms all the remaining classifiers, with the greatest accuracy of 92% on the BRFSS diabetes dataset and 94% accuracy on the PIDD dataset, which is greater than the 3% accuracy reported in existing research. …”
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Machine learning classification algorithm screening for the main controlling factors of heavy oil CO2 huff and puff by Peng-xiang Diwu, Beichen Zhao, Hangxiangpan Wang, Chao Wen, Siwei Nie, Wenjing Wei, A-qiao Li, Jingjie Xu, Fengyuan Zhang

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Fluid Identification of Deep Low-Contrast Gas Reservoirs Based on Random Forest Algorithm by CAO Yuan, ZHAO Yuanliang, YUAN Xuehua, YUAN Long, RONG Junqing, ZHAO Pan, BIE Kang

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Hybrid Methods Random Forest and FOX-Inspired Optimization Algorithm for Selecting Features in Cervical Cancer Data by Afidatul Masbakhah, Umu Sa'adah, Mohamad Muslikh

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Coverage Optimization Algorithm Based on Sampling for 3D Underwater Sensor Networks by Du Xiaoyu, Sun Lijuan, Liu Linfeng

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Algorithm for Calculating Noise Immunity of Cognitive Dynamic Systems in the State Space by A. A. Solodov, T. G. Trembach, K. E. Zhovnovatiy

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Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering. by Xini Fang

“…To enhance the accuracy and response speed of the risk early warning system, this study develops a novel early warning system that combines the Fuzzy C-Means (FCM) clustering algorithm and the Random Forest (RF) model. Firstly, based on operational risk theory, market risk, research and development risk, financial risk, and human resource risk are selected as the primary indicators for enterprise risk assessment. …”
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Predicting autoimmune thyroiditis in primary Sjogren’s syndrome patients using a random forest classifier: a retrospective study by Jia-yun Wu, Jing-yu Zhang, Wen-qi Xia, Yue-ning Kang, Ru-yi Liao, Yu-ling Chen, Xiao-min Li, Ya Wen, Fan-xuan Meng, Li-ling Xu, Sheng-hui Wen, Hui-fen Liu, Yuan-qing Li, Jie-ruo Gu, Qing Lv, Yong Ren

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Urban growth simulation using cellular automata model and machine learning algorithms (case study: Tabriz metropolis) by Omid Ashkriz, Babak Mirbagheri, Ali Akbar Matkan, Alireza Shakiba

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Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach by Zeyue Li, Jianzhao Bi, Yang Liu, Xuefei Hu

“…In this research, we adopted a forecasting model that integrates the random forest algorithm with NASA’s Goddard Earth Observing System “Composing Forecasting” (GEOS-CF) product. …”
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Development of a prediction model for acute respiratory distress syndrome in ICU patients with acute pancreatitis based on machine learning algorithms by REN Xia*，LIU Luojie，ZHA Junjie，YE Ye，XU Xiaodan，YE Hongwei，ZHANG Yan

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SIGLEC1 has the potential to be an immune-related prognostic indicator in colon adenocarcinoma: a study based on transcriptomic data and Mendelian randomization analysis by Jue Gu, Yaxuan Wang, Hui Zhang, Haijuan Gu, Haixia Zhu

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Evaluating Feature Selection Methods for Accurate Diagnosis of Diabetic Kidney Disease by Valeria Maeda-Gutiérrez, Carlos E. Galván-Tejada, Jorge I. Galván-Tejada, Miguel Cruz, José M. Celaya-Padilla, Hamurabi Gamboa-Rosales, Alejandra García-Hernández, Huizilopoztli Luna-García, Klinge Orlando Villalba-Condori

“…After selecting suitable features detected by the methodologies, they are included in the random forest classifier, obtaining four models. <b>Results</b>: Galgo with Random Forest achieved the best performance with only three predictors, “creatinine”, “urea”, and “lipids treatment”. …”
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SGLT2 inhibitors and kidneys: mechanisms and main effects in diabetes mellitus patients by Vladimir V Salukhov, Yurii Sh. Khalimov, Sergey B. Shustov, Sergey I. Popov

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Effectiveness of a clinical decision support algorithm (ePOCT+) in improving quality of care for sick children in primary health facilities in Tanzania (DYNAMIC project): results f... by Dr Caroline Enos, Godfrey A. Kavishe, Alexandra V. Kulinkina, Sabine Renggli, Chacha D. Mangu, Lameck Luwanda, Peter Agrea, Humphrey Mhagama, Margaret Joram, Ibrahim Mtebene, Geofrey Isdory Ashery, Marie-Annick Le Pogam, Honorati Masanja, Nyanda E. Ntinginya, Valérie D'Acremont, Rainer Tan

“…Methods: 18 health facilities (9 intervention and 9 control, randomized 1:1) were sampled from the main cluster randomized trial. …”
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Proposing a framework for body mass prediction with point clouds: A study applied in typical swine pen environments by Gabriel Pagin, Luciane Silva Martello, Rubens André Tabile, Rafael Vieira de Sousa

“…Challenges persist in implementing these techniques in pens with a large number of animals, especially in extracting physical body characteristics from images in a production environment. In this context, the main objective of this research is to investigate a novel framework comprising effective algorithms for feature extraction, attribute selection, hyperparameter optimization, and prediction modelling, using point clouds collected from production animals (growing and finishing pigs). …”
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Assessing the spatiotemporal dynamics of seasonal and perennial surface water resources across Lesotho’s agroecological zones by Kunwar K. Singh, Sayedeh Sara Sayedi, Ariel BenYishay, Tšepiso A. Rantšo

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Research on Ship Heave Motion Compensation Control Under Complex Sea State Environment Based on Improved Reinforcement Learning by ZHANG Qin, ZHOU Jingyi, WANG Xingyue, HU Xiong

“…In these diversified test scenarios, the improved TD3 algorithm exhibits remarkable adaptability and stability. …”
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