Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data by Vahid Nourani, Ramin Ahmadi, Yongqiang Zhang, Dominika Dąbrowska

“…This study applies several machine learning (ML) models—including a backpropagation neural network (BPNN), an adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and long short-term memory (LSTM)—to simulate PML-V2 ET in the Ahar Chay basin, Northwestern Iran. The Seto mixed forest site in Japan, characterized by a contrasting ecosystem, served as a cross-validation site to further validate the methodology. …”
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HMOX1 as a potential drug target for upper and lower airway diseases: insights from multi-omics analysis by Enhao Wang, Shazhou Li, Yang Li, Tao Zhou

“…Candidate genes were further screened using Gene Set Enrichment Analysis (GSEA) and Random Forest (RF) algorithms. Causal inference between candidate genes and upper and lower airway diseases (CRSwNP, allergic rhinitis (AR), and asthma (AS)) was conducted using bidirectional two-sample Mendelian randomization (TwoSampleMR) analysis. …”
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Integrating testing and modeling methods to examine the feasibility of blended waste materials for the compressive strength of rubberized mortar by Amin Muhammad Nasir, Nassar Roz-Ud-Din, Khan Kaffayatullah, Ul Arifeen Siyab, Khan Mubasher, Qadir Muhammad Tahir

“…Two individual techniques, support vector machine and random forest, generate R 2 values of 0.943 and 0.983, respectively.…”
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Pretreatment attrition after rifampicin-resistant tuberculosis diagnosis with Xpert MTB/RIF or ultra in high TB burden countries: a systematic review and meta-analysis by Tom Decroo, Palmer Masumbe Netongo, Tinne Gils, Christelle Geneviève Jouego

“…The pooled proportion of pretreatment attrition and reasons were assessed using random-effects meta-analysis. Forest plots were generated using R software.Results Thirty eligible studies from 21 countries were identified after full-text screening and included in the meta-analysis. …”
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Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach by Shaikh Afnan Birahim, Avijit Paul, Fahmida Rahman, Yamina Islam, Tonmoy Roy, Mohammad Asif Hasan, Fariha Haque, Muhammad E. H. Chowdhury

“…This paper proposes a novel Intrusion Detection System (IDS) leveraging Particle Swarm Optimization (PSO) and an ensemble machine learning approach combining Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN) models to enhance the accuracy and reliability of intrusion detection in WSNs. …”
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Quality of life disparities among Mexican people with systemic lupus erythematosus. by Ana Laura Hernández-Ledesma, Domingo Martínez, Elizabeth Fajardo-Brigido, Talía V Román-López, Karen J Nuñez-Reza, Andrea Y Tapia-Atilano, Sandra V Vera Del Valle, Donají Domínguez-Zúñiga, Lizbet Tinajero-Nieto, Angélica Peña-Ayala, Estefania Torres-Valdez, Gabriel Frontana-Vázquez, María Gutiérrez-Arcelus, Florencia Rosetti, Sarael Alcauter, Miguel E Rentería, Alejandra E Ruiz-Contreras, Deshiré Alpízar-Rodríguez, Alejandra Medina-Rivera

“…Data was analyzed as a cross-sectional study. A random forest model was implemented to assess potential predictive variables. …”
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Development of a respiratory virus risk model with environmental data based on interpretable machine learning methods by Shuting Shi, Haowen Lin, Leiming Jiang, Zhiqi Zeng, ChuiXu Lin, Pei Li, Yinghua Li, Zifeng Yang

“…We utilized the CRFC algorithm, a random forest-based method for multi-label classification, to predict the presence of various respiratory viruses. …”
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Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditi... by Seda Günaydın, Necati Çetin, Cevdet Sağlam, Kamil Sacilik, Ahmad Jahanbakhshi

“…Also, the MR was predicted by the MC, and the drying rate (DR), drying times, and final thickness were predicted using the multi-layer perceptron (MLP), gaussian process (GP), k-nearest neighbors (KNN), random forest (RF), and support vector regression (SVR) algorithms. …”
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Amazon Journeys and Poetic Re-Discoveries in Jan Conn’s 'Jaguar Rain' and Malu de Martino’s 'Margaret Mee e a Flor da Lua' by Magali Sperling Beck, Anelise R. Corseuil

“…Originally from Britain, Mee lived in Brazil for more than thirty years, embarking on fifteen expeditions to the Amazon region between 1956 and 1988, painting while travelling in dugout canoes deep in the forest and becoming a fervent activist for the protection of the environment. …”
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Characterization of differences in volatile compounds and metabolites of six varieties of potato with different processing properties by Wenyuan Zhang, Liang Li, Yaqi Zhao, Haixia Yang, Xuejie Zhang, Zhanquan Zhang, Xue Wang, Zhenzhen Xu, Wanxing Wang, Jianjun Deng

“…Moreover, the different expressed metabolites were involved in the metabolism of amino acids, flavone and flavanol biosynthesis, and tryptophan metabolism. The Random forest showed that the fresh eating and processing type potatoes could be distinguished by the content of amino acids and phenols.…”
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Spatiotemporal Relationships between Air Quality and Multiple Meteorological Parameters in 221 Chinese Cities by Mengyi Ji, Yuying Jiang, Xiping Han, Luo Liu, Xinliang Xu, Zhi Qiao, Wei Sun

“…This study used air quality monitoring data, namely, the air pollution index (API) and air quality index (AQI) between 2005 and 2018, together with meteorological data and identified key meteorological factors that affected the spatial and temporal variation of air quality using a random forest algorithm. The spatial and temporal differences in the threshold values of different meteorological factors affecting the concentrations of PM2.5, PM10, SO2, CO, NO2, and O3 were identified. …”
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Predicting cadmium enrichment in crops/vegetables and identifying the effects of soil factors based on transfer learning methods by Rui Chen, Zean Liu, Jingyan Yang, Tiantian Ma, Aihong Guo, Rongguang Shi

“…The results show that the best accuracy of the random forest probability model in the rice dataset is 0.89. …”
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A Machine Learning Based Framework for a Stage-Wise Classification of Date Palm White Scale Disease by Abdelaaziz Hessane, Ahmed El Youssefi, Yousef Farhaoui, Badraddine Aghoutane, Fatima Amounas

“…To classify the WSD into its four classes (healthy, low infestation degree, medium infestation degree, and high infestation degree), two types of ML algorithms were tested; classical machine learning methods, namely, support vector machine (SVM) and k-nearest neighbors (KNN), and ensemble learning methods such as random forest (RF) and light gradient boosting machine (LightGBM). …”
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Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis by Jaydeo K. Dharpure, Ian M. Howat, Saurabh Kaushik, Bryan G. Mark

“…Unlike previous studies, we use a combination of Machine Learning (ML) methods—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Deep Neural Network (DNN), and Stacked Long-Short Term Memory (SLSTM)—to identify and efficiently bridge the gap between GRACE and GFO by using the best-performing ML model to estimate TWSA at each grid cell. …”
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A Landscape-Clustering Zoning Strategy to Map Multi-Crops in Fragmented Cropland Regions Using Sentinel-2 and Sentinel-1 Imagery with Feature Selection by Guanru Fang, Chen Wang, Taifeng Dong, Ziming Wang, Cheng Cai, Jiaqi Chen, Mengyu Liu, Huanxue Zhang

“…These schemes are then optimized for each CHZ using a random forest classifier. The results demonstrate that the landscape-clustering zoning strategy achieves an overall accuracy of 93.52% and a kappa coefficient of 92.67%, outperforming the no-zoning method by 2.9% and 3.82%, respectively. …”
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Weighted Gene Coexpression Network Analysis Identifies TBC1D10C as a New Prognostic Biomarker for Breast Cancer by Huiying Qiao, Rong Lv, Yongkui Pang, Zhibing Yao, Xi Zhou, Wei Zhu, Wenqing Zhou

“…TME-related green and black modules were selected by WGCNA to further screen hub genes. Random forest and univariate and multivariate Cox regressions were applied to screen hub genes (MYO1G, TBC1D10C, SELPLG, and LRRC15) and construct a nomogram to predict the survival of BRCA patients. …”
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Metabarcoding the night sky: Monitoring landscape-scale insect diversity through bat diet by Cynthia Tobisch, Svenja Dege, Bernd Panassiti, Julian Treffler, Christoph Moning

“…Species composition in the diet showed high variation in space and time, but was also associated with edge density and the proportion of grassland within 2 km radius of the roosts. Moreover, forest and grassland percentages within 2-km buffers around the roosts significantly increased species richness within the diet. …”
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Structural equation modeling for social capital empowerment in supporting mangrove rehabilitation by I. Listiana, D. Ariyanto

“…The districts were deliberately chosen due to the fact that they are designated mangrove forest rehabilitation areas. Data collection took place from July to December 2023. …”
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Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards by Tünde Takáts, László Pásztor, Mátyás Árvai, Gáspár Albert, János Mészáros

“…Soil loss was formerly modeled by USLE, thus providing non-observation-based reference datasets for the calibration of parcel-specific prediction models using various ML methods (Random Forest, eXtreme Gradient Boosting, Regularized Support Vector Machine with Linear Kernel), which is a well-established approach in digital soil mapping (DSM). …”
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The interphase period “germination–heading” of 8x and 6x triticale with different dominant Vrn genes by P. I. Stepochkin, A. I. Stasyuk

“…The results of studying the interphase period “germination–heading” of spring octaploid and hexaploid forms of triticale created for use in research and breeding programs under the conditions of forest-steppe of Western Siberia are given in this article. …”
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