Materias dentro de su búsqueda.
Mostrando 37,641 - 37,660 Resultados de 37,890 Para Buscar '"forestal"', tiempo de consulta: 0.35s Limitar resultados
  1. 37641
    “…Feature selection methods were used to select for subsets of transcripts to be used in the selected classification approaches: support vector machine, logistic regression, decision trees, random forest, and extremely randomized decision trees (extra-trees). …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis
  2. 37642
    “…Second, to address the problems of many types of ambient air quality parameters in sheep barns and possible redundancy or overlapping information, we used a random forests algorithm (RF) to screen and rank the features affecting CO(2) mass concentration and selected the top four features (light intensity, air relative humidity, air temperature, and PM2.5 mass concentration) as the input of the model to eliminate redundant information among the variables. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    A Method to Predict CO(2) Mass Concentration in Sheep Barns Based on the RF-PSO-LSTM Model
  3. 37643
    “…Their preferred topics were animals, beaches, swimming, and forests. They also recommended adding soft sounds related to the session topic. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Children’s and Caregivers’ Review of a Guided Imagery Therapy Mobile App Designed to Treat Children With Functional Abdominal Pain Disorders: Leveraging a Mixed Methods Approach With User-Centered Design
  4. 37644
    “…In the discovery set, the ICS was constructed using a random forest algorithm and confirmed in the validation set to predict overall survival (OS) and event-free survival (EFS). …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Cross-cohort analysis identified an immune checkpoint-based signature to predict the clinical outcomes of neuroblastoma
  5. 37645
    “…We measured antigen-specific immunoglobulin responses against antigens using a customised Luminex assay and used conditional random forest models to examine which baseline biomarkers were most important for classifying individuals who went on to develop infection versus those who remained uninfected or asymptomatic. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Predicting Vibrio cholerae infection and symptomatic disease: a systems serology study
  6. 37646
    “…It was also used to conduct machine learning exercises such as random forest and regression to identify the best candidate for immune-related central genes. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Predictive model, miRNA-TF network, related subgroup identification and drug prediction of ischemic stroke complicated with mental disorders based on genes related to gut microbiome
  7. 37647
    “…To set up benchmarking ML models to predict LBW, we applied 7 classic ML models (ie, logistic regression, naive Bayes, random forest, extreme gradient boosting, adaptive boosting, multilayer perceptron, and sequential artificial neural network) while using 4 different data rebalancing methods: random undersampling, random oversampling, synthetic minority oversampling technique, and weight rebalancing. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies
  8. 37648
    “…In the training set, gradient boosting decision tree (GBDT), extremely random trees (ET), random forest, logistic regression and extreme gradient boosting (XGBoost) obtained AUROC values > 0.90 and AUPRC > 0.87. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population
  9. 37649
    “…Data included year of birth, sex, height, weight, motivation to join the program, use statistics (eg, weight entries, entries into the food diary, views of the menu, and program content), program type, and weight loss. Random forest, extreme gradient boosting, and logistic regression with L1 regularization models were developed and validated using a 10-fold cross-validation approach. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Predicting Disengagement to Better Support Outcomes in a Web-Based Weight Loss Program Using Machine Learning Models: Cross-Sectional Study
  10. 37650
    “…The results were synthesized through information extraction and presented in tables and forest plots. RESULTS: In total, 5 RCTs were included in this systematic review, with 3 (60%) providing information for the meta-analysis. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Virtual Reality for Upper Limb Rehabilitation in Patients With Obstetric Brachial Palsy: Systematic Review and Meta-Analysis of Randomized Controlled Trials
  11. 37651
    “…Along with conventional ML models such as logistic regression (LR), random forest (RF), and gradient boosting (GB), the DNN model to discern recurrences was trained using a dataset of 778 consecutive patients with primary head and neck cancers who received CCRT. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Detection of Cancer Recurrence Using Systemic Inflammatory Markers and Machine Learning after Concurrent Chemoradiotherapy for Head and Neck Cancers
  12. 37652
    “…METHODS: We trained an ML model using the random forest (RF) algorithm and data from a 6-month weight loss intervention (N=419). …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study
  13. 37653
    “…METHODS: Our study included 5,420,640 participants with fatty liver from Meinian Health Care Center. We used random forest, elastic net (EN), and extreme gradient boosting ML algorithms to select important features from potential predictors. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Combinatorial Use of Machine Learning and Logistic Regression for Predicting Carotid Plaque Risk Among 5.4 Million Adults With Fatty Liver Disease Receiving Health Check-Ups: Population-Based Cross-Sectional Study
  14. 37654
    “…Classes were balanced using a synthetic minority oversampling technique and Boruta, a feature selection algorithm, was used to refine gene lists. We performed random forest and calculated “out of box” (OOB) area under the curve (AUC) and OOB error rate (ER), measures of predictive accuracy and robustness, respectively. …”
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    OR21-01 Pre-treatment Blood Transcriptome Predicts First-year Growth And IGF-I Response To Somapacitan Treatment In Children With GH Deficiency
  15. 37655
    por Hofman, P., Calabrese, F., Kern, I., Adam, J., Alarcão, A., Alborelli, I., Anton, N.T., Arndt, A., Avdalyan, A., Barberis, M., Bégueret, H., Bisig, B., Blons, H., Boström, P., Brcic, L., Bubanovic, G., Buisson, A., Caliò, A., Cannone, M., Carvalho, L., Caumont, C., Cayre, A., Chalabreysse, L., Chenard, M.P., Conde, E., Copin, M.C., Côté, J.F., D’Haene, N., Dai, H.Y., de Leval, L., Delongova, P., Denčić-Fekete, M., Fabre, A., Ferenc, F., Forest, F., de Fraipont, F., Garcia-Martos, M., Gauchotte, G., Geraghty, R., Guerin, E., Guerrero, D., Hernandez, S., Hurník, P., Jean-Jacques, B., Kashofer, K., Kazdal, D., Lantuejoul, S., Leonce, C., Lupo, A., Malapelle, U., Matej, R., Merlin, J.L., Mertz, K.D., Morel, A., Mutka, A., Normanno, N., Ovidiu, P., Panizo, A., Papotti, M.G., Parobkova, E., Pasello, G., Pauwels, P., Pelosi, G., Penault-Llorca, F., Picot, T., Piton, N., Pittaro, A., Planchard, G., Poté, N., Radonic, T., Rapa, I., Rappa, A., Roma, C., Rot, M., Sabourin, J.C., Salmon, I., Prince, S. Savic, Scarpa, A., Schuuring, E., Serre, I., Siozopoulou, V., Sizaret, D., Smojver-Ježek, S., Solassol, J., Steinestel, K., Stojšić, J., Syrykh, C., Timofeev, S., Troncone, G., Uguen, A., Valmary-Degano, S., Vigier, A., Volante, M., Wahl, S.G.F., Stenzinger, A., Ilié, M.
    Publicado 2023
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Real-world EGFR testing practices for non-small-cell lung cancer by thoracic pathology laboratories across Europe
  16. 37656
    “…Three ML algorithms—a regression model with elastic net regularization (glmnet), a random survival forest (RSF), and a gradient tree-boosting technique (xgboost)—were evaluated for 5 combinations of clinical data, tumor radiomics, and whole-liver features. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    A Comprehensive Machine Learning Benchmark Study for Radiomics-Based Survival Analysis of CT Imaging Data in Patients With Hepatic Metastases of CRC
  17. 37657
    “…RESULTS: We developed a random forest classifier over features derived from Gene Ontology annotations and genetic context scores provided by STRING database for predicting Mtb and CD interactions independently. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Mycobacterium tuberculosis and Clostridium difficille interactomes: demonstration of rapid development of computational system for bacterial interactome prediction
  18. 37658
    “…Reported estimates of effect on the probability of surgery from analyses adjusting for confounders were summarised in narrative form and synthesised in odds ratio (OR) forest plots for individual determinants. RESULTS: The review included 26 quantitative studies−23 on individuals’ decisions or views on having the operation and three about health professionals’ opinions-and 10 qualitative studies. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Determinants of demand for total hip and knee arthroplasty: a systematic literature review
  19. 37659
    “…BACKGROUND: Pine moths (Lepidoptera; Bombycoidea; Lasiocampidae: Dendrolimus spp.) are among the most serious insect pests of forests, especially in southern China. Although COI barcodes (a standardized portion of the mitochondrial cytochrome c oxidase subunit I gene) can distinguish some members of this genus, the evolutionary relationships of the three morphospecies Dendrolimus punctatus, D. tabulaeformis and D. spectabilis have remained largely unresolved. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Mitochondrial phylogenomics and genetic relationships of closely related pine moth (Lasiocampidae: Dendrolimus) species in China, using whole mitochondrial genomes
  20. 37660
    “…Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild
Herramientas de búsqueda: RSS