A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain at... by Magboul M. Sulieman, Fuat Kaya, Abdullah S. Al-Farraj, Eric C. Brevik

“…The proposed methodology combined measurements of the target HMs and fifty-two environmental covariates (ECOVs) derived from 2017 to 2021 Landsat 8/9 OLI and Shuttle Radar Topography Mission (SRTM)-derived terrain attributes. Random forest and stepwise multiple linear regression models were further used to digitally map the studied HMs. …”
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Soil organic carbon contents and their major influencing factors in mangrove tidal flats: a comparison between estuarine and non-estuarine areas by Ting Wu, Jia Guo, Gang Li, Yu Jin, Wei Zhao, Guangxuan Lin, Fang-Li Luo, Yaojun Zhu, Yifei Jia, Li Wen

“…We compared the SOC and soil physicochemical properties between estuarine and non-estuarine mangrove tidal flats. The Random Forest algorithm was employed to identify the main influencing factors affecting SOC. …”
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An investigation of machine learning methods applied to genomic prediction in yellow-feathered broilers by Bogong Liu, Huichao Liu, Junhao Tu, Jian Xiao, Jie Yang, Xi He, Haihan Zhang

“…In this study, seven different ML methods—support vector regression (SVR), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), kernel ridge regression (KRR) and multilayer perceptron (MLP) were employed to predict the genomic breeding values of laying traits, growth and carcass traits in a yellow-feathered broiler breeding population. …”
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Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective... by Seok Jun Park, Seungwon Yang, Suhyun Lee, Sung Hwan Joo, Taemin Park, Dong Hyun Kim, Hyeonji Kim, Soyun Park, Jung-Tae Kim, Won Gun Kwack, Sung Wook Kang, Yun-Kyoung Song, Jae Myung Cha, Sang Youl Rhee, Eun Kyoung Chung

“…Among the tested ML algorithms, random forest achieved the highest accuracy (area under the receiver operating characteristic curve [AUROC] 0.88 for both cohorts). …”
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The INFLUENCE 3.0 model: Updated predictions of locoregional recurrence and contralateral breast cancer, now also suitable for patients treated with neoadjuvant systemic therapy by M.C. Van Maaren, T.A. Hueting, D.J.P. van Uden, M. van Hezewijk, L. de Munck, M.A.M. Mureau, P.A. Seegers, Q.J.M. Voorham, M.K. Schmidt, G.S. Sonke, C.G.M. Groothuis-Oudshoorn, S. Siesling

“…Cox regression with restricted cubic splines was compared to Random Survival Forest (RSF) to predict five-year LRR and CBC risks. …”
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First Measurements of Ambient PM2.5 in Kinshasa, Democratic Republic of Congo and Brazzaville, Republic of Congo Using Field-calibrated Low-cost Sensors by Celeste McFarlane, Paulson Kasereka Isevulambire, Raymond Sinsi Lumbuenamo, Arnold Murphy Elouma Ndinga, Ranil Dhammapala, Xiaomeng Jin, V. Faye McNeill, Carl Malings, R. Subramanian, Daniel M. Westervelt

“…Employing two calibration models, namely, multiple linear regression and random forests, decreased the MAE to 3.4 µg m−3 and increased R2 to 0.96. …”
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Evaluation of Machine Learning Models for Stress Symptom Classification of Cucumber Seedlings Grown in a Controlled Environment by Kyu-Ho Lee, Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Shahriar Ahmed, Yeon Jin Cho, Dong Hee Noh, Sun-Ok Chung

“…Four ML classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF), were trained to detect stress symptoms based on selected features, highlighting that stress symptoms were detectable after day 4. …”
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Analysis of ecological network evolution in an ecological restoration area with the MSPA-MCR model: A case study from Ningwu County, China by Ziyan Guo, Chuxin Zhu, Xiang Fan, Muye Li, Nuo Xu, Yuan Yuan, Yanjun Guan, Chunjuan Lyu, Zhongke Bai

“…Further analysis suggests that the substantial increase of ecological source area was due to the ecosystem service enhancement on existing ecological land and the emergence of new planted forest land. And implications for future ecological restoration were given based on the ecological network structure.…”
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Efficacy of Antimicrobial Photodynamic Therapy for Treating Moderate to Deep Periodontal Pockets in Individuals with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis by João Victor Soares Rodrigues, Mariella Boaretti Deroide, Wilton Mitsunari Takeshita, Valdir Gouveia Garcia, Rafael Scaf de Molon, Leticia Helena Theodoro

“…The principal periodontal parameters assessed included PPD, clinical attachment level (CAL), plaque index (PI), and bleeding on probing (BOP). Forest plots for PD, BOP, PI, and CAL at baseline, three months, and six months revealed no statistically significant differences between the SI+aPDT group and the SI-only group. …”
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Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model by Qingqing Lin, Qingqing Lin, Wenxiang Zhao, Wenxiang Zhao, Hailin Zhang, Hailin Zhang, Wenhao Chen, Sheng Lian, Qinyun Ruan, Qinyun Ruan, Zhaoyang Qu, Zhaoyang Qu, Yimin Lin, Yimin Lin, Dajun Chai, Dajun Chai, Dajun Chai, Dajun Chai, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin

“…For developing a predictive model for HF risk in AMI patients, the least absolute shrinkage and selection operator (LASSO) Regression was used to feature selection, and four ML algorithms including Random Forest (RF), Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR) were employed to develop the model on the training set. …”
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Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders by Muhammad Waleed Iqbal, Muhammad Shahab, Zakir ullah, Guojun Zheng, Irfan Anjum, Gamal A. Shazly, Atrsaw Asrat Mengistie, Xinxiao Sun, Qipeng Yuan

“…Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds. …”
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Prediction of digestible energy requirement in growing finishing stage of pigs using machine learning models by Nibas Chandra Deb, Jayanta Kumar Basak, Sijan Karki, Elanchezhian Arulmozhi, Dae Yeong Kang, Niraj Tamrakar, Eun Wan Seo, Junghoo Kook, Myeong Yong Kang, Hyeon Tae Kim

“…Therefore, this study sought to predict the digestible energy requirement (DER) in the growing-finishing phase of pigs, where four machine learning (ML) models: multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), and multilayer perceptron (MLP) were applied across four datasets, with the input parameters including body weight of pigs (BW), inside temperature (IT), inside relative humidity (IRH), and inside CO2 concentration (ICO2) of pig barns. …”
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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…To enhance predictive accuracy, advanced machine learning models, including Random Forest, Gradient Boosting, Multi-Layer Perceptron, and Categorical Boosting, were applied. …”
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A comparative analysis of five land surface temperature downscaling methods in plateau mountainous areas by Ju Wang, Ju Wang, Ju Wang, Bo-Hui Tang, Bo-Hui Tang, Bo-Hui Tang, Bo-Hui Tang, Xinming Zhu, Xinming Zhu, Xinming Zhu, Dong Fan, Dong Fan, Dong Fan, Menghua Li, Menghua Li, Menghua Li, Junyi Chen, Junyi Chen, Junyi Chen

“…Three machine learning models, including Back Propagation (BP) Neural Network, random forest (RF), and extreme gradient boosting (XGBoost), and two classic single-factor linear regression models (DisTrad and TsHARP) were compared. …”
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Modeling Climate‐Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean Basin by Marco Bianchini, Mohamed Tarhouni, Matteo Francioni, Marco Fiorentini, Chiara Rivosecchi, Jamila Msadek, Abderrazak Tlili, Farah Chouikhi, Marina Allegrezza, Giulio Tesei, Paola Antonia Deligios, Roberto Orsini, Luigi Ledda, Maria Karatassiou, Athanasios Ragkos, Paride D'Ottavio

“…A set of 33 environmental variables (topography, soil, and bioclimatic) was screened using Pearson correlation matrices, and predictive models were built using four algorithms: MaxEnt, Random Forest, XG Boost, and Neural Network. Results revealed increasing temperatures and declining precipitation in both regions, confirming Mediterranean climate trends. …”
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Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments by Jianqiang He, Yonglin Jia, Yi Li, Asim Biswas, Hao Feng, Qiang Yu, Shufang Wu, Guang Yang, Kadambot.H.M. Siddique

“…Six advanced machine learning methods, including Random Forest (RF), were used alongside the ratio mean method to effectively upscale soil water and salt content models from the field to the regional level. …”
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Insights into the contribution of multiple factors on Ixodes ricinus abundance across Europe spanning 20 years using different machine learning algorithms by Samantha Lansdell, Abin Zorto, Misaki Seto, Edessa Negera, Saeed Sharif, Sally Cutler

“…Furthermore, using a Random Forest (RF) model across three clustering methods, we determined which features most significantly impacted upon I. ricinus abundance. …”
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Effect of tropical climates on the quality of commonly used antibiotics: the protocol for a systematic review and meta-analysis by Johnstone Thitiri, Moses Ngari, Christina W Obiero, James A Berkley, Tsegaye Melaku, Sultan Suleman, Gemmechu Hasen, Sileshi Belew, Jimmy Shangala

“…The degree of heterogeneity will be evaluated by the inverse of variance (I2). Forest plots will be used to present the meta-analysis data.Ethics and dissemination Ethical approval is not required as the study is a systematic review. …”
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Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial Infarction by Yu-Hang Wang, Chang-Ping Li, Jing-Xian Wang, Zhuang Cui, Yu Zhou, An-Ran Jing, Miao-Miao Liang, Yin Liu, Jing Gao

“…Subsequently, Lasso–logistic, random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to establish prediction models based on the training set. …”
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Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging by Jessica Reyes-Rojas, Julien Guigue, Daniel Žížala, Vít Penížek, Tomáš Hrdlička, Petra Vokurková, Aleš Vaněk, Tereza Zádorová

“…A variety of nonlinear machine learning techniques such as cubist regression tree (Cubist), random forest (RF), support vector machine regression (SVMR) and one linear technique partial least square regression (PLSR) were compared to determine the most suitable model for the prediction of SOC and CaCO3 content in each profile. …”
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