8.473 hasil untuk machine learning
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Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity
Objective: In this work, we explore and develop a method that uses Raman spectroscopy to measure and differentiate radiation induced toxicity in murine lungs with the goal of setting the foundation for a predictive disease model. Methods: Analysis of Raman tissue data is achieved through a…
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Interpretable machine learning for automated left ventricular scar quantification in hypertrophic cardiomyopathy patients
…of scar burden in predicting clinical outcomes. We aimed to develop a machine learning (ML) model that contours left ventricular (LV) endo- and epicardial borders and quantifies CMR LGE images from HCM patients.We retrospectively studied 2557 unprocessed images from 307 HCM patients followed at the…
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Using machine learning to understand microgeographic determinants of the Zika vector, Aedes aegypti
…from 172 traps deployed around Miami-Dade County. Using random forest, a machine learning method, predictive models of spatiotemporal dynamics of Ae. aegypti in response to meteorological conditions and neighborhood-specific socio-demographic and physical characteristics, such as land-use and…
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Using machine learning pipeline to predict entry into the attack zone in football
Sports sciences are increasingly data-intensive nowadays since computational tools can extract information from large amounts of data and derive insights from athlete performances during the competition. This paper addresses a performance prediction problem in soccer, a popular collective sport…
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Blood pressure monitoring during anesthesia induction using PPG morphology features and machine learning
…by Lasso regression, and a feature relevance analysis is conducted. Three machine learning (ML) methods are then investigated to translate this subset of features into systolic BP (SBP) and diastolic BP (DBP) estimates; namely Lasso regression, support vector regression and Gaussian process…
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Using machine learning to improve our understanding of COVID-19 infection in children
…under the age of 18 years were considered for this study. An ensemble learning method, Random Forest Classifier (RFC), was used for classification of patients suffering from infection. Results: Five RFC models were implemented with incremental features and the best model achieved an F1-score of…
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Evaluating spatially enabled machine learning approaches to depth to bedrock mapping, Alberta, Canada
…the elevation in rugged or incised terrain. Here, we describe a machine learning spatial prediction approach that uses information from traditional digital elevation model derived estimates of terrain morphometry and satellite imagery, augmented with spatial feature engineering techniques to…
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A machine-learning method for biobank-scale genetic prediction of blood group antigens
A key element for successful blood transfusion is compatibility of the patient and donor red blood cell (RBC) antigens. Precise antigen matching reduces the risk for immunization and other adverse transfusion outcomes. RBC antigens are encoded by specific genes, which allows developing…
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Essential elements of physical fitness analysis in male adolescent athletes using machine learning
…Previous approaches to analyzing PF have relied on statistical or machine learning algorithms that focus on predicting athlete injury or performance. In this study, six machine learning algorithms were used to analyze the PF of 1,489 male adolescent athletes across five sports, including track &…
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AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning
Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions between patient covariates in a data-driven manner…
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Topic prediction for tobacco control based on COP9 tweets using machine learning techniques
…categorized tweets related to harm reduction. Subsequently, various machine learning techniques were employed to predict these topics, achieving a prediction accuracy of 91.87% using the Random Forest algorithm. Additionally, the study explored correlations between retweets and sentiment scores. It…
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A machine learning model for prediction of sarcopenia in patients with Parkinson’s Disease
…if gait parameters could be utilized to predict sarcopenia based on machine learning. Methods: Clinical and sarcopenia profiles were collected from patients with PD at Hoehn and Yahr (HY) stage ≤ 2. Sarcopenia was defined based on the updated criteria of the Asian Working Group for Sarcopenia. The…
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Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning
…kriging, regression-kriging and geographically weighted regression) and machine learning algorithms to produce high resolution digital maps of topsoil properties in Romania. Six continuous predictors were considered in our study (digital elevation model, topographic wetness index, normalized…
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Machine learning-based spatial data development for optimizing astronomical observatory sites in Indonesia
…integrating the physical and atmospheric observatory suitability indexes, machine learning models, and long-term climate models. Subsequently, potential sites are equalized based on longitude and latitude zonal divisions considering air pollution disturbance risks. The study novelty comes from the…
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A webcam-based machine learning approach for three-dimensional range of motion evaluation
…Objective: The current study presents and evaluates an alternative machine learning-based ROM evaluation method that could be remotely accessed via a webcam. Methods: To evaluate its reliability, the ROM measurements for a diverse set of joints (neck, spine, and upper and lower extremities) derived…
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Diagnostic suspicion bias and machine learning: Breaking the awareness deadlock for sepsis detection
Many early warning algorithms are downstream of clinical evaluation and diagnostic testing, which means that they may not be useful when clinicians fail to suspect illness and fail to order appropriate tests. Depending on how such algorithms handle missing data, they could even indicate “low risk”…
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Machine learning with optimization to create medicine intake schedules for Parkinson’s disease patients
…patient’s response to medication. Firstly, it demonstrates the use of machine learning models to predict the patient’s medicine response based on their state evaluation acquired during examination with biomedical sensors. Two architectures, a multilayer perceptron and a deep neural network with…
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Implementation and empirical evaluation of a quantum machine learning pipeline for local classification
In the current era, quantum resources are extremely limited, and this makes difficult the usage of quantum machine learning (QML) models. Concerning the supervised tasks, a viable approach is the introduction of a quantum locality technique, which allows the models to focus only on the neighborhood…
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Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis
…regeneration and biotechnology applications. To address this issue, machine learning (ML) can be considered a powerful tool to analyze callogenesis data, extract key parameters, and predict optimal conditions for petunia callogenesis, facilitating more controlled and productive tissue culture…
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Evaluation of functional tests performance using a camera-based and machine learning approach
The objective of this study is to evaluate the performance of functional tests using a camera-based system and machine learning techniques. Specifically, we investigate whether OpenPose and any standard camera can be used to assess the quality of the Single Leg Squat Test and Step Down Test…
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