8.473 hasil untuk machine learning
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Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures
Machine learning was shown to be effective at identifying distinctive genomic signatures among viral sequences. These signatures are defined as pervasive motifs in the viral genome that allow discrimination between species or variants. In the context of SARS-CoV-2, the identification of these…
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An interpretable machine learning framework for opioid overdose surveillance from emergency medical services records
The goal of this study is to develop and validate a lightweight, interpretable machine learning (ML) classifier to identify opioid overdoses in emergency medical services (EMS) records. We conducted a comparative assessment of three feature engineering approaches designed for use with unstructured…
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Applicability of machine learning algorithm to predict the therapeutic intervention success in Brazilian smokers
…to the success of therapeutic intervention (STI) in Brazilian smokers by machine learning (ML) algorithms. To address this gap in the literature, we evaluated the ability of eight ML algorithms to correctly predict the STI in Brazilian smokers who were treated at a smoking cessation program in…
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Clarifying the relationship between mental illness and recidivism using machine learning: A retrospective study
Objective: There is currently inconclusive evidence regarding the relationship between recidivism and mental illness. This retrospective study aimed to use rigorous machine learning methods to understand the unique predictive utility of mental illness for recidivism in a general population (i.e…
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Multi-classification of national fitness test grades based on statistical analysis and machine learning
…breaks group restrictions, studies national groups, and hopes to use machine learning models to provide helpful health suggestions for citizens to measure their physical status. We analyzed the significance of the selected indicators through nonparametric tests and exploratory statistical analysis…
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A machine learning approach to assess Sustainable Development Goals food performances: The Italian case
…and their understanding of SDGs. Furthermore, we employed an unsupervised machine learning approach in our research to conduct a comprehensive evaluation of SDGs across European countries and position Italy relative to the others. Additionally, we conducted a detailed analysis of the responses to a…
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A machine learning approach to graduate admissions and the role of letters of recommendation
…on admissions decisions. This study addresses these concerns by building machine learning models to predict admissions decisions for two STEM graduate programs, with a focus on examining the contribution of LORs in the decision-making process. We train our predictive models leveraging information…
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Analyzing drama metadata through machine learning to gain insights into social information dissemination patterns
…patterns of social information about dramas, this study used machine learning to predict drama ratings and the contribution of various drama metadata, including broadcast year, broadcast season, TV stations, day of the week, broadcast time slot, genre, screenwriters, status as an original work or…
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Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data
Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does not adequately resemble data from the real world…
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Impact of mobile connectivity on students’ wellbeing: Detecting learners’ depression using machine learning algorithms
…further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of…
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Iterative rolling difference-Z-score and machine learning imputation for wind turbine foundation monitoring
…iterative rolling difference-Z-score method for anomaly detection and a machine learning-based imputation framework combining linear interpolation with LightGBM. Experimental results show that the iterative rolling difference-Z-score method detects single-point and clustered anomalies with a…
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Multi-model machine learning for automated identification of rice diseases using leaf image data
…sustainable food production. This paper presents a novel hybrid deep-learning and machine-learning framework for automatically identifying rice plant diseases from leaf photos. We extracted deep features from rice leaf images using pre-trained CNN models—MobileNetV2, DarkNet19, and ResNet18. These…
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Identification of plasma proteins associated with seizures in epilepsy: A consensus machine learning approach
…expressed proteins (CDH15, PAEP, LTBP3, PHOSPHO1). Using two machine-learning techniques, we identified ten consensus candidate protein biomarkers (CDH15, PAEP, LTBP3, PHOSPHO1, NEFL, SFRP1, TDGF1, DUSP3, WWP2 and DSG3) that contributed to the classification of patients as being seizure-free or…
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Modeling of injury severity of distracted driving accident using statistical and machine learning models
…Logistic Regression (BLR) method, and at the same, to select the best machine learning (ML) model in predicting the injury severity of the DDA. The selection of the best ML model was based on the optimum combination of accuracy, F1 score, and area under curve metrics. Ten years of DDA data…
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Morphological traits and machine learning for genetic lineage prediction of two reef-building corals
…of molecular and morphological trait analysis can be improved using machine learning. Morphological traits were documented for Porites and Pocillopora coral species that were collected and genotyped through genome-wide, genetical hierarchical clustering, and coalescence analyses for the Tara…
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Finding the original mass: A machine learning model and its deployment for lithic scrapers
…mass, height of retouch, maximum thickness and the GIUR index). Four machine learning models were trained using these variables in order to estimate the mass of the flake prior to any retouch. A Random Forest model provided the best results with an r2 value of 0.97 when predicting original flake…
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Baltic dry index forecast using financial market data: Machine learning methods and SHAP explanations
…marine trading regions—the U.S., EU, and Hong Kong. We employ advanced machine learning methods, such as Extremely Randomized Trees, Categorical Boosting (CatBoost), and Random Forest, to achieve superior forecasting accuracy. Additionally, we utilize the Shapley Additive Explanations (SHAP)…
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Hybrid feature selection framework for enhanced credit card fraud detection using machine learning models
…hybrid feature selection framework designed to enhance the performance of machine learning models in credit card fraud detection. Our framework integrates three complementary feature selection techniques: Pearson correlation, information gain (IG), and random forest importance (RFI), each optimized…
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Ensemble machine learning prediction model for clinical refraction using partial interferometry measurements in childhood
Purpose: To develop an ensemble machine learning prediction model for clinical refraction in childhood using partial interferometry measurements. Methods: Age, sex, cycloplegic refraction, and partial interferometry data collected within one month were obtained from patients aged 5–16 years…
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