“…We summarized the proportion of birth registration among children in SSA using a forest plot. We utilized a multilevel binary logistic regression analysis to examine the predictors of birth registration. …”
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“…We then used this data set to train several ML algorithms, including deep neural network (DNN), random forest, and several gradient boosting variants (i.e., XGBoost, LightGBM, CatBoost) and compared their performance. …”
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“…Various ML algorithms were utilized to construct predictive models, encompassing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perceptron (MLP). …”
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“…From the data set, we extracted 28 features and evaluated their significances using an extra tree classifier predictor. A random forest model was trained using the 6 most important features identified by the predictor. …”
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“…Five machine-learning algorithms including decision-tree (DT), extreme-boost (EB), support vector machine (SVM), random forest (RF), and linear model (LM) were used to build five models using the image features extracted from the first cohort of patients. …”
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“…Six classifiers, including Gauss naive Bayes (GNB), K-nearest neighbors (KNN), Random forest (RF), Adaptive boosting (AB), and Support vector machine (SVM) with linear kernel and multilayer perceptron (MLP), were used to build the prediction models, and the prediction performance of the six classifiers was evaluated by fivefold cross-validation. …”
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“…The main findings were similar by random forest analysis and in the Chronic Renal Insufficiency Cohort study. …”
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“…We assessed the performance of 6 representative ML models, including random survival forest (RSF), gradient boosting machine (GBM), DeepSurv, DeepHit, neural net-extended time-dependent Cox (or Cox-Time), and neural multitask logistic regression (N-MTLR) in predicting CRC-specific survival. …”
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“…One of the main alternatives to combat the effects of climate change is the recovery of natural areas, be they forests, pastures, wetlands, or mountains. For that, in addition to factors such as soil preparation and post-planting management, the selection of species is fundamental to guarantee the success of the restoration. …”
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“…Supervised classification of LANDSAT imagery permitted good separation between Paddy, Forest, and Water land-use classes. The immature collections of Anopheles sinensis were significantly correlated with land-use as determined in the land-use classification in both Ganghwa Island and Paju District. …”
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“…It primarily inhabits tundra, forest edge habitats and sub-alpine vegetation. Willow grouse are hunted throughout its range, and regionally it is a game bird of great cultural and economical importance. …”
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“…Descriptive appraisals of variation in rates, including tables and forest plots, and where suitable, random-effects meta-analyses and meta-regressions to test specific hypotheses; rate heterogeneity was assessed by the I(2)-statistic. …”
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“…BACKGROUND: Carbon isotope data from conifer trees play an important role in research on the boreal forest carbon reservoir in the global carbon cycle. …”
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“…Sensitivities and specificities were calculated on a per-sample basis, stratified by sample type and smear microscopy status and summarised using forest plots. Pooled estimates were calculated for groups with sufficient data. …”
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“…The meta-effect is presented in Forest plots with 95 % confidence Study appraisal and synthesis methods: intervals and random weights assigned to each trial. …”
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“…METHODS: The methods applied were as follows: forest walks and semi-structured interviews with adult inhabitants of Shiri village, participant and non-participant observation. …”
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“…Factors that were independently associated with overweight and obesity included: having overweight mother (adjusted odds ratio (aOR) = 1.51; 95% CI 1.15 to 1.97) and obese mother (aOR = 2.19; 95% CI = 155 to 3.07), compared to having normal weight mother; high birth weight (aOR = 1.69; 95% CI 1.24 to 2.28) compared to normal birth weight; male gender (aOR = 1.56; 95% CI 1.24 to 1.95); low birth rank (aOR = 1.35; 95% CI 1.06 to 1.72); being aged between 13–24 months (aOR = 1.81; 95% CI = 1.21 to 2.66) and 25–36 months (aOR = 2.79; 95% CI 1.93 to 4.13) compared to being aged 45 to 49 months; living in the grassfield area (aOR = 2.65; 95% CI = 1.87 to 3.79) compared to living in Forest area. Muslim appeared as a protective factor (aOR = 0.67; 95% CI 0.46 to 0.95).compared to Christian religion. …”
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“…The richest HUC8 drainages occurred in northeastern, south-central, and southern regions of the state where drainages were heavily forested, had the highest slopes, and were contained within or adjacent to the unglaciated Allegheny and Appalachian Plateaus. …”
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“…There is also the danger of over-exploitation of plant species as most of them are obtained from the wild, mainly Kakamega forest. Therefore, there is need for determining the economically and medicinally important plants in this community and planning for their preservation.…”
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