8.473 hasil untuk machine learning
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Personalizing the empiric treatment of gonorrhea using machine learning models
…Surveillance Project collected between 2000–2010 to train and validate machine learning models to identify resistance to ciprofloxacin (CIP), one of the recommended first-line antibiotics until 2007. We used these models to personalize empiric treatments based on sexual behavior and geographic…
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Temperature dependence of mosquitoes: Comparing mechanistic and machine learning approaches
…and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial…
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Metabolic syndrome predictive modelling in Bangladesh applying machine learning approach
…Bangladeshi population. Subsequently, we intend to construct predictive machine learning (ML) models and ultimately, assess the accuracy and reliability of these models. In this particular study, we utilized the ATP III criteria as the basis for evaluating various health parameters from a dataset…
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Predicting early cessation of exclusive breastfeeding using machine learning techniques
Background: Identification of mother-infant pairs predisposed to early cessation of exclusive breastfeeding is important for delivering targeted support. Machine learning techniques enable development of transparent prediction models that enhance clinical applicability. We aimed to develop and…
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Eight quick tips for biologically and medically informed machine learning
Machine learning has become a powerful tool for computational analysis in the biomedical sciences, with its effectiveness significantly enhanced by integrating domain-specific knowledge. This integration has give rise to informed machine learning, in contrast to studies that lack domain knowledge…
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Predicting noncontact injuries of professional football players using machine learning
…an automated injury identification and prediction approach using machine learning, leveraging GPS data and player-specific parameters. A sample of 34 male professional players from a Portuguese first-division team was analyzed, combining GPS data from Catapult receivers with descriptive variables…
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Machine learning–based new classification for immune infiltration of gliomas
…characteristics of glioma and predict different immune subtypes using machine learning methods, providing guidance for immune therapy in glioma. Methods: We first performed unsupervised cluster analysis on the genes and arrays of 693 gliomas in CGGA database and 702 gliomas in TCGA database. Then…
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Comparing machine learning approaches for estimating soil saturated hydraulic conductivity
…in other areas of the field. In this study some of the most appropriate machine learning approaches, including variants of artificial neural networks (ANNs) were used for predicting Kfs by some easily measurable soil attributes. The analyses were performed using 100 measurements in Bajgah…
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Evaluating machine learning algorithms to predict lameness in dairy cattle
…other precision technologies. The objective was to evaluate the use of machine learning algorithms for the identification of lameness conditions in dairy cattle. In this study, 310 multiparous Holstein dairy cows from a herd in Northern Colorado were affixed with a leg-based accelerometer…
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Mental health analysis of international students using machine learning techniques
…students’ mental health, and the scarcity of studies on the use of machine learning algorithms in this area, this study aimed to analyse data to understand the demographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed…
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Predicting adverse pregnancy outcome in Rwanda using machine learning techniques
…This study aimed to predict these outcomes in Rwanda using supervised machine learning algorithms. Methods: This cross-sectional study utilized data from the Rwanda Demographic and Health Survey (RDHS, 2019–2020) involving 14,634 women. K-fold cross-validation (k = 10) and synthetic minority…
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Flight delay prediction: Evaluating machine learning algorithms for enhanced accuracy
…This study systematically evaluates the predictive performance of six machine learning classifiers—Decision Tree, Random Forest, Support Vector Classifier (SVC), Logistic Regression, K-Nearest Neighbors (KNN), and Naive Bayes—on a comprehensive flight dataset, with particular attention to the…
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Retraction: Interpretable machine learning framework for predicting Urban air quality
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Image-based machine learning models for customized soil moisture management
…10 cm, and 15 cm) to capture vertical moisture distribution. Several deep learning models were evaluated for predicting surface moisture, with DenseNet121 showing the highest accuracy (R² = 97.3%, RMSE = 4.14). For deeper soil layers, the random forest regression model achieved the best performance…
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Machine learning based fault classification for improved induction motor performance
…systems. It is found that traditional fault detection methods and deep learning approaches are both effective; however, current techniques can either be computationally exhaustive, or suffer from low accuracy, thus making them inapplicable in many real-world settings. To address these limitations…
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Unbiased inference for echocardiogram urgency prediction using double machine learning
…variables extracted from Electronic Health Record data. We use double machine learning techniques to analyze these variables and improve our predictions of patient urgency. Traditional methods for estimating variable effects have limitations, particularly in our research context, where clinical and…
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Dental age prediction from panoramic radiographs using machine learning techniques
Dental age (DA) estimation is a key diagnostic tool in pediatric dentistry, particularly when birth records are unavailable or unreliable. It guides decisions on growth assessment, orthodontic planning, and timing of interventions such as space maintenance or extractions. Unlike skeletal maturity…
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RETRACTED: Interpretable machine learning framework for predicting Urban air quality
Urban air pollution remains a critical challenge for public health and environmental sustainability. This study investigates the predictive capabilities of five machine learning (ML) models: Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and…
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Predicting malaria outbreak in The Gambia using machine learning techniques
…To achieve this objective, we employ and compare the performance of eight machine learning algorithms, including C5.0 decision trees, artificial neural networks, k-nearest neighbors, support vector machines with linear and radial kernels, logistic regression, extreme gradient boosting, and random…
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Machine learning in epidemiology: Neural networks forecasting of monkeypox cases
This study integrates advanced machine learning techniques, namely Artificial Neural Networks, Long Short-Term Memory, and Gated Recurrent Unit models, to forecast monkeypox outbreaks in Canada, Spain, the USA, and Portugal. The research focuses on the effectiveness of these models in predicting…
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