8.473 hasil untuk machine learning
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Correction: Using machine learning to understand age and gender classification based on infant temperament
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Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study
…chronic diseases. Methods: In this study, we present the use of a machine learning approach to build cost-effective multimorbidity prediction models. Based on predictors easily obtainable in clinical practice (sociodemographic, clinical, family disease history and lifestyle), we build and compared…
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Using machine learning to study the effect of medication adherence in Opioid Use Disorder
…Opioid Use Disorder (MOUD) is effective in the treatment of OUD. We use machine learning to investigate the association between patient’s adherence to prescribed MOUD along with other risk factors in patients diagnosed with OUD and potential OD following the treatment. Methods: We used longitudinal…
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Forecasting under-five stunting in Ethiopia using classical and machine learning time series models
Background: The prevalence of under-five stunting in Ethiopia remains above 30%, which, according to World Health Organization (WHO), constitutes a major public health concern. Stunting has long-term consequences for child growth, development, and overall health. Accurate forecasting of its…
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A robust multi-location evaluation of a machine learning framework for wind power forecasting
…has become essential for ensuring consistent energy production. Machine learning (ML) significantly advances wind power forecasting, improving the reliability and efficiency of wind power systems. This study presents an analysis of ML algorithms applied to four datasets from different geographical…
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Disentangling key cloud properties for precipitation retrievals from geostationary satellite data using machine learning
…satellite data collected in the year 2023, in conjunction with machine learning and Shapley additive explanations. In the East Asian mid-latitudes, light-to-moderate precipitation is associated with the ice water path and the cloud particle growth characteristics, as indicated by the spectral…
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Integrating biological and machine learning models for rainbow trout growth: Balancing accuracy and interpretability
…interactions. We evaluated hybrid frameworks integrating biological and machine learning models for rainbow trout (Oncorhynchus mykiss) growth in the Lower Colorado River using ten years of tag–recapture data and environmental covariates, comparing traditional and Bayesian von Bertalanffy (VBGM)…
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A machine learning classifier to identify and prioritise genes associated with murine cardiac development
…be distinguished by their attributes. We therefore developed a supervised machine learning (ML) method to identify Mus musculus genes with a high probability of being involved in cardiac development. These genes, when mutated, are candidates for causing human CHD. Our classifier showed a…
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Machine learning–assisted optimization of a terahertz photonic metamaterial absorber for blood cancer detection
…was also developed and validated against the simulated results. Several machine learning models were also employed for design prediction, with Gradient Boosting demonstrating excellent performance and enabling up to a 60% reduction in optimization time. The combination of a multi-band…
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Risk assessment in cardiac surgery: Exploring machine learning and laboratory indices as adjunctive tools
…While the specific reasons are often multifactorial and complex, certain machine learning methods are promising ways to both estimate mortality after surgery and elucidate important factors linked with mortality. Methods: Using the MIMIC-IV database, we identified 11,261 patients on the…
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A topological map of the genetic components of grapevine—Admixture meets SOMmelier machine learning
…multilocus population data relate to clustering approaches that leverage machine learning, specifically Self-Organizing Maps (SOMs)? We implemented this approach through our custom SOM-based tool, SOMmelier, which enables the portrayal of genetic structure by identifying modules of co-mutated SNPs…
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Accurate characterization of mix plastic waste using ATR-FTIR spectroscopy and machine learning methods
…Transform Infrared (ATR-FTIR) spectroscopy combined with optimized machine learning to overcome these key limitations. Two models were established. Model 1 focused on the high-accuracy identification of 10 common plastic types, demonstrating 97.1% accuracy on an independent test set that included…
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Enhancing reliability in electrical grids: A hybrid machine learning approach for electrical faults classification
…model. A comprehensive investigation is conducted, employing a variety of machine learning (ML) algorithms, including Decision Tree (DT), Random Forests (RF), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and, AdaBoost, for fault classification. Additionally…
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An interpretable machine learning framework for adverse drug reaction prediction from drug-target interactions
…understood. Leveraging drug-target interaction data with interpretable machine learning offers a promising approach to anticipate ADRs and understand their underlying mechanisms. Objective: This study proposes an interpretable machine learning (ML) framework to predict significant ADRs using…
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Understanding drivers of neonatal mortality in Zimbabwe: A machine learning approach using survey data
…other child mortality indicators have improved, NMR has increased. Using machine learning, we aimed to identify the key predictors of neonatal mortality in Zimbabwe. Pooled secondary data analysis of three rounds of the Zimbabwe Demographic Health Survey (ZDHS) from 2005 to 2015 was done. The study…
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Developing a machine learning model to map new-build gentrification: A mixed-methods approach
…maps and projections of gentrification trends. Recent advancements in machine learning (ML), specifically computer vision models that apply neural network “deep mapping” algorithms, have found application in the research for their ability to track changes in urban streetscapes. In our research, we…
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Interpretable machine learning for cardiovascular risk prediction: Insights from NHANES dietary and health data
Background: Cardiovascular diseases (CVD) are one of the leading global causes of death, which requires an accurate early prediction. This study aimed to develop transparent machine learning (ML) models using National Health and Nutrition Examination Survey (NHANES) data from 2017–2023 to predict…
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Predicting and improving diagnosis of tuberculosis outcomes in South Africa using machine learning techniques
…study aimed to predict and improve TB diagnosis in South Africa using machine learning techniques. Data from the National Income Dynamics Survey, conducted by the Southern African Labour and Development Research Units, were analyzed. The dataset underwent a 70:30 train-test split for Random Forest…
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Benchmarking machine learning for bowel sound pattern classification – From tabular features to pretrained models
…BS patterns. This dataset is used to evaluate the performance of machine learning models to detect and/or classify BS patterns. The selection of considered models covers models using tabular features, convolutional neural networks based on spectrograms and models pre-trained on large audio…
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Predicting grain growth kinetic in steels using machine learning and XAI for mechanical properties
…elements and processing parameters. In this study, we introduce a novel machine learning (ML) based framework that predicts austenitic grain growth behaviour directly from chemical composition and process conditions, utilizing a comprehensive dataset of 1039 experimentally validated samples. Among…
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