“…We applied MetaHipMer2, a distributed metagenome assembler that runs on supercomputing clusters, to coassemble 3.4 terabases (Tbp) of metagenome data from a tropical soil in the Luquillo Experimental Forest (LEF), Puerto Rico. The resulting coassembly yielded 39 high-quality (>90% complete, <5% contaminated, with predicted 23S, 16S, and 5S rRNA genes and ≥18 tRNAs) metagenome-assembled genomes (MAGs), including two from the candidate phylum Eremiobacterota. …”
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“…About 200,000 hectors of forest were destroyed by the fire, which reached into Saskatchewan. …”
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“…It was decided to approach the problem from a vector formulation (through 9 nutrient dimensions) that led to proposals for classifiers such as Spherical K-Means (SKM), and by developing this idea, it was possible to smooth the limits of the classifier with the help of a Multilayer Perceptron (MLP) which were compared with two other algorithms of machine learning, these being Random Forest and XGBoost. RESULTS: The algorithm proposed in this study could classify and calculate the equivalent portion of a single or a list of foods. …”
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“…The first phase is based on a multi-classification technique using Random Forest Trees (RFT), k-Nearest Neighbor (K-NN), J48, AdaBoost, and Bagging. …”
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“…The region, dominated by native forests and a burgeoning salmon farming industry, has few inventories, so the database presented here adds significantly to local botanical knowledge. …”
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“…Least absolute shrinkage and selection operator (LASSO), RandomForest (RF) and support vector machine-recursive feature elimination (SVM-RFE) machine learning methods were conducted to detect gene signatures. …”
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“…Machine learning classifiers [eXtreme Gradient Boosting (XGBoost), random forest (RF), logistic regression, and support vector machine (SVM) classifier] were generated to predict severe total parenting stress and its subscales (parental distress, parent-child dysfunctional interaction, and difficult child). …”
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“…A funnel plot and Egger’s regression test were used to test publication bias. A forest plot was used to present the pooled prevalence and odds ratio with a 95% confidence interval (CI) using the random effect model. …”
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“…A logistic regression model was built based on seven key genes identified through Random Forests and Support Vector Machines - Recursive Feature Elimination (SVM-RFE). …”
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“…Exposure to particles with an aerodynamic diameter ≤2.5 micron (μm) (PM(2.5)), particles with an aerodynamic diameter ≤10 μm (PM(10)) and particles with an aerodynamic diameter between 2.5 and 10 μm (PM(c)) was estimated using satellite-based random forest models. Linear regression and logistic regression models were used to assess the associations between PM and the above malnutrition indicators. …”
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“…We applied 4 machine learning algorithms (logistic regression, decision tree, random forest, and extreme gradient boost) to identify important factors and then applied LCA to categorize the risk groups of metabolic syndromes in single-person households. …”
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“…For three models that only used baseline characteristics (linear regression, random forest, artificial neural network) to predict maximum postoperative lactate concentration, the prediction error ranged from a median of 2.52 mmol/L (IQR 2.46, 2.56) to 2.58 mmol/L (IQR 2.54, 2.60). …”
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“…Relevant posts were further labelled for mentions of hot flashes, cognition, reports of adverse mood, sleep problems and reported age. A random forest model for identifying relevant posts from words and bigrams of the posts was trained using the labelled samples and its performance evaluated using 10-fold cross-validation. …”
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“…The loitering score of each trajectory is calculated with the parameters, and the Isolation Forest algorithm is employed to establish a threshold and rank. …”
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“…(M.) interpunctus in the Southeastern Brazil, corresponding to the Atlantic Forest. Minimum temperature of the coldest month and mean temperature of coldest quarter were the variables that most influenced the development of the model. …”
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“…The reduced dataset for [Formula: see text]-SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. …”
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“…This comparison was applied as part of a larger study of arthropod species richness in silver fir (Abies alba Mill., 1759) stands across a range of climate-induced tree dieback levels and forest management strategies. RESULTS: Of the 53 H(2)O-MPG samples from WFTs, 16 produced no metabarcoding results, while the remaining 37 samples yielded 77 arthropod MOTUs in total, of which none matched any of the 343 beetle species morphologically identified from the same traps. …”
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“…Fourteen features ( [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , qNet, qInward) could be generated from the simulation and used as input to several machine learning models, including k-nearest neighbor (KNN), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). …”
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“…The neural network (NN) algorithm outperformed and showed a 0.961 accuracy, 0.961 F1 score, 0.961 precision, 0.961 recall, and 0.972 area under the curve (AUC) for classification of hyperhidrosis type. The random forest (RF) model outperformed showed a 0.852 accuracy, 0.853 F1 score, 0.856 precision, 0.852 recall, and 0.914 AUC for prediction of the degree of CH. …”
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