359.866 hasil untuk machine learning
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Quantifying Unknown Multiqubit Entanglement Using Machine Learning
…data produced by locally measuring target quantum states. By leveraging machine learning techniques to handle non-linear relations between outcome statistics and entanglement measurement SE, we achieve high-precision quantification of unknown multipartite entanglement states with a linear number of…
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Machine Learning Predictors for Min-Entropy Estimation
This study investigates the application of machine learning predictors for the estimation of min-entropy in random number generators (RNGs), a key component in cryptographic applications where accurate entropy assessment is essential for cybersecurity. Our research indicates that these predictors…
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Subphenotyping prone position responders with machine learning
…subphenotypes of ARDS patients undergoing prone positioning using machine learning and assess their association with mortality and response to prone positioning. METHODS: In this retrospective observational study, we enrolled 353 mechanically ventilated ARDS patients who underwent at least one…
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Evaluation of hydraulic fracturing using machine learning
…and geological parameters. This study presents a comprehensive machine learning (ML)-based framework to address this challenge by predicting HF efficiency using three widely used algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN). The novelty of this research…
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Classification of musculoskeletal pain using machine learning
Musculoskeletal pain is a significant health concern affecting individuals across various demographics and professions, often leading to reduced productivity and impaired quality of life. This study proposes a framework leveraging Particle Swarm Optimization (PSO) to evaluate and assess…
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Machine learning in dentistry: a scoping review
Artificial intelligence (AI), specifically machine learning (ML), is increasingly applied in decision-making for dental diagnosis, prognosis, and treatment. However, the methodological completeness of published models has not been rigorously assessed. We performed a scoping review of PubMed-indexed…
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Applying machine learning to safe vascular anastomosis
BACKGROUND: Machine-learning technology is currently being introduced into the medical field and has been shown to aid diagnostic imaging, patient examinations, patient-data analysis, various surgical aspects, and medical education. Recent advances in exoscopes and monitors are prompting a shift…
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Machine learning techniques for lipid nanoparticle formulation
…With advancements in computer science, we argue that the use of machine learning can accelerate the design of ionizable lipids systematically. Assuming researchers in lipid nanoparticle synthesis may come from various backgrounds, an entry-level guide is needed to outline and summarize the general…
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Machine learning fusion for glioma tumor detection
…gliomas are extracted and classified as independent components. A deep learning model is then employed to categorize these gliomas. The proposed model classifies gliomas into three primary categories: meningioma, pituitary, and glioma. Performance evaluation demonstrates a high level of accuracy…
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Using machine learning to identify pediatric ophthalmologists
This cross-sectional study used data from the American Academy of Ophthalmology IRIS Registry (Intelligent Research in Sight) and machine learning algorithms to identify pediatric ophthalmologists based on physician coding patterns. A random forest model achieved an area under the receiver…
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Prediction of biological age using machine learning
…of study. This research utilizes health examination data to propose a machine learning-based biological age prediction model that quantifies physiological age through residual life estimation. The model leverages LightGBM, which shows an 11.40% improvement in predictive performance (R-squared)…
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Interpretable machine learning for precision cognitive aging
INTRODUCTION: Machine performance has surpassed human capabilities in various tasks, yet the opacity of complex models limits their adoption in critical fields such as healthcare. Explainable AI (XAI) has emerged to address this by enhancing transparency and trust in AI decision-making. However, a…
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Machine Learning to Diagnose Complications of Diabetes
Machine learning (ML) uses computer systems to develop statistical algorithms and statistical models that can draw inferences from demographic data, structured behavioral data, continuous glucose monitor (CGM) tracings, laboratory data, cardiovascular and neurological physiology measurements, and…
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Automated spectrometer alignment via machine learning. Corrigendum
Corrigendum to the article by Feuer-Forson et al. [(2024). J. Synchrotron Rad.31, 698–705].
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Machine learning for personalized antimicrobial susceptibility breakpoints
…predict diagnosis when specimens are received. Here, we assess whether machine learning could facilitate personalized antimicrobial susceptibility breakpoint reporting by predicting urinary tract infection (UTI) diagnoses. METHODS: XGBoost models were trained using open-source electronic healthcare…
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Machine Learning in Transforming the Food Industry
…setting the futuristic trends in the food sector. The use of different Machine Learning (ML)-based models has demonstrated promising applications in the food processing industry. Processing operations such as drying, frying, cooking, heating, and baking are complex and challenged by multifaceted…
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Machine learning-based prediction of glioma grading
…by subjectivity and poor reproducibility. This study aimed to develop a machine learning–based prediction model that integrates clinical and molecular characteristics to improve early glioma grading, thereby enhancing diagnostic accuracy and supporting individualized treatment strategies. METHODS…
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Koina: Democratizing machine learning for proteomics research
Recent developments in machine learning (ML) and deep learning have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML models are regularly published, the rate at which…
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Machine Learning for Protein Science and Engineering
Recent years have seen significant breakthroughs at the intersection of machine learning and protein science. Tools such as AlphaFold have revolutionized protein structure prediction. They are also enabling variant effect prediction and functional annotation of proteins, as well as opening up new…
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Machine learning-based penetrance of genetic variants
Accurate variant penetrance estimation is crucial for precision medicine. We constructed machine learning (ML) models for 10 diseases using 1,347,298 participants with electronic health records (EHRs), then applied them to an independent cohort with linked exome data. Resulting probabilities were…
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