The probability of detecting host-specific microbial source tracking markers in surface waters was strongly associated with method and season by Claire M. Murphy, Daniel L. Weller, Tanzy M. T. Love, Michelle D. Danyluk, Laura K. Strawn

“…Variance partitioning analysis was used to quantify the variance in host-specific MST marker detection attributable to non-methodological and methodological factors. Conditional forest and regression analysis were utilized to assess the association between detection and select non-methodological and methodological factors. …”
Get full text

Variabilidad intraespecífica en la calidad de semillas de especies forestales nativas en bosques montanos en el sur del Ecuador: Implicaciones para la restauración de bosques... by Ximena Palomeque, Andrea Maza, Juan Pablo Iñamagua, Sven Günter, Patrick Hildebrandt, Michael Weber, Bernd Stimm

Get full text

Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models by Imran Said, Vasit Sagan, Kyle T. Peterson, Haireti Alifu, Abuduwanli Maiwulanjiang, Abby Stylianou, Omar Al Akkad, Supria Sarkar, Noor Al Shakarji

“…Convolutional neural networks (CNNs) with attention mechanisms were proposed along with traditional machine learning models based on feature engineering including Random Forest (RF) and Support Vector Machine (SVM) regression for comparative analysis. …”
Get full text

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. …”
Get full text

Novel Indices of Glucose Homeostasis Derived from Principal Component Analysis: Application for Metabolic Assessment in Pregnancy by Tina Stopp, Michael Feichtinger, Ingo Rosicky, Gülen Yerlikaya-Schatten, Johannes Ott, Hans Christian Egarter, Christian Schatten, Wolfgang Eppel, Peter Husslein, Martina Mittlböck, Andrea Tura, Christian S. Göbl

“…PCS1 to 3 assessed at early pregnancy were also associated with development of GDM, whereby random forest analysis revealed the highest variable importance for PCS1. …”
Get full text

Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates by Siham Acharki, Ali Raza, Dinesh Kumar Vishwakarma, Mina Amharref, Abdes Samed Bernoussi, Sudhir Kumar Singh, Nadhir Al-Ansari, Ahmed Z. Dewidar, Ahmed A. Al-Othman, Mohamed A. Mattar

“…The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. …”
Get full text

Construction of a prognostic prediction model for colorectal cancer based on 5-year clinical follow-up data by Boao Xiao, Min Yang, Yao Meng, Weimin Wang, Yuan Chen, Chenglong Yu, Longlong Bai, Lishun Xiao, Yansu Chen

“…Decision tree, random forest, support vector machine, and extreme gradient boosting (XGBoost) models were selected for modeling based on the features identified through recursive feature elimination (RFE). …”
Get full text

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. …”
Get full text

Effects of poultry by-product meal and complete replacement of fish oil with alternative oils on growth performance and gut health of rainbow trout (Oncorhynchus mykiss): a FEEDNET... by Imam Hasan, Simona Rimoldi, Biagina Chiofalo, Marianna Oteri, Micaela Antonini, Rosangela Armone, Violeta Kalemi, Laura Gasco, Genciana Terova

Get full text

Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

“…To address this gap, we applied two machine learning algorithms—random forest (RF), and artificial neural networks (ANN) to predict benthic chl-a concentrations by incorporating these specific P fractions as separate variables. …”
Get full text

Unveiling new therapeutic horizons in rheumatoid arthritis: an In-depth exploration of circular RNAs derived from plasma exosomes by Guoqing Li, Hongyi Chen, Jiacheng Shen, Yimin Ding, Jingqiong Chen, Yongbin Zhang, Mingrui Tang, Nan Xu, Yuxuan Fang

“…A diagnostic xgboost model was developed using common hub genes identified by random forest and least absolute shrinkage and selection operator (LASSO), with intersection genes derived from overlapping machine learning-selected genes. …”
Get full text

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. …”
Get full text

A novel method for detecting intracranial pressure changes by monitoring cerebral perfusion via electrical impedance tomography by Ming-xu Zhu, Jun-yao Li, Zhan-xiu Cai, Yu Wang, Wei-ce Wang, Yi-tong Guo, Guo-bin Gao, Qing-dong Guo, Xue-tao Shi, Wei-chen Li

“…Under both circumstances, ROC curve analysis showed that the comprehensive model of perfusion parameters based on the random forest algorithm had a sensitivity and specificity of more than 90% and an area under the curve (AUC) of more than 0.9 for detecting ICP increments of both 5 and 10 mmHg. …”
Get full text

Distribution Characteristics and Coupling Relationship Between Soil Erosion and Hydrologic and Sediment Connectivity in Changchong River Basin by LI Jianing, ZHANG Hongli, TIAN Changyuan, ZHANG Yi, ZHA Tonggang

“…[Results] (1) The average soil erosion modulus in the Changchong River Basin was 380 t/(hm2·a), and the soil erosion intensity was mainly slight erosion, which gradually intensified from north to south. (2) The high hydrological and sediment connectivity is mainly distributed in cultivated land, and the opposite is true in forest and grassland land. The higher value is mainly located in the low-lying flat area with low slope and easy water accumulation, while the lower value is mainly in the steep mountainous area. (3) Topographic factors and land use types significantly affected soil erosion and hydrological and sediment connectivity (p<0.01). …”
Get full text

Evaluation of the impact of the environment on the genetic improvement of the buffalo species by Rafael Emilio Rincón-Márquez, Néstor Simón Montiel-Urdaneta, José Raúl Pérez-González

“…On 5 May 2004, a buffalo farm was started in Finca, Florida, in Zulia state’s arid tropical forest zone (DTFZ). Furthermore, on 9 May 2012, the herd was transferred to Finca Miraflores, located in a premontane rainforest zone (PRZ) in Mérida state. …”
Get full text

A Data-Driven Approach to Engineering Instruction: Exploring Learning Styles, Study Habits, and Machine Learning by Lauren Genith Isaza Dominguez, Antonio Robles-Gomez, Rafael Pastor-Vargas

“…Several machine learning models, including Random Forest and Voting Ensemble, were tested to predict academic performance using study behavior data. …”
Get full text

Mapping Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi&#x0027;an, China by Chen Chen, Mimi Peng, Mahdi Motagh, Xinxin Guo, Mengdao Xing, Yinghui Quan

“…In this study, four machine learning models are compared to determine the optimal model, and found that the Random Forest (RF) performs the best in predicting InSAR-derived spatial deformation (Root Mean Square Error = 3.53 mm) and susceptibility (Area Under the Curve = 0.97). …”
Get full text

Glossina pallidipes Density and Trypanosome Infection Rate in Arba Minch Zuria District of Gamo Zone, Southern Ethiopia by Ephrem Tora, Wasihun Seyoum, Firew Lejebo

“…Relatively higher Glossina pallidipes and biting flies, respectively, were caught in a wood-grass land (15.87 F/T/D and 3.69 F/T/D) and riverine forest (15.13 F/T/D and 3.42 F/T/D) than bush land vegetation types (13.87 F/T/D and 1.76 F/T/D). …”
Get full text

<i>Broussonetia papyrifera</i> Extract Can Be Used as a Raw Material Source for a Sterility Agent for <i>Microtus fortis</i> by Shuangye Wang, Tian Lan, Yunlin Zhao, Wanfu Liu, Tian Huang, Meiwen Zhang, Zhiyuan Hu, Zhenggang Xu

Get full text

Ethnobotanical survey of plants locally used in the control of termite pests among rural communities in northern Uganda by Betty C. Okori, Christine Oryema, Robert Opiro, Acur Amos, Gilbert I. Obici, Karlmax Rutaro, Geoffrey M. Malinga, Eric Sande

“…Abstract Background Termites are the most destructive pests in many agricultural and forest plantations in Uganda. Current control of termites mostly relies on chemical pesticides. …”
Get full text