8.473 hasil untuk machine learning
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Defining heatwave thresholds using an inductive machine learning approach
Establishing appropriate heatwave thresholds is important in reducing adverse human health consequences as it enables a more effective heatwave warning system and response plan. This paper defined such thresholds by focusing on the non-linear relationship between heatwave outcomes and…
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Machine learning framework for assessment of microbial factory performance
…metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an…
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Distinguishing Discoid and Centripetal Levallois methods through machine learning
In this paper, we apply Machine Learning (ML) algorithms to study the differences between Discoid and Centripetal Levallois methods. For this purpose, we have used experimentally knapped flint flakes, measuring several parameters that have been analyzed by seven ML algorithms. From these analyses…
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Interactive machine learning for fast and robust cell profiling
…biases and dependent on user experience. Here, we use interactive machine learning to identify the optimum cell profiling configuration that maximises quality of the cell profiling outcome. The process is guided by the user, from whom a rating of the quality of a cell profiling configuration is…
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Machine learning detection of Atrial Fibrillation using wearable technology
…detect AF but do not generally incorporate diagnostic algorithms. Machine learning algorithms have the potential to improve patient outcomes particularly where diagnoses are made from large volumes or complex patterns of data such as in AF. Methods: We designed a novel AF detection algorithm using…
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Predicting atrial fibrillation in primary care using machine learning
…sought to develop and evaluate novel and conventional statistical and machine learning models for risk-predication of AF. This was a retrospective, cohort study of adults (aged ≥30 years) without a history of AF, listed on the Clinical Practice Research Datalink, from January 2006 to December 2016…
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Downscaling satellite soil moisture using geomorphometry and machine learning
…This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991–2016). We…
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Gait can reveal sleep quality with machine learning models
…data. After data preprocessing, gait features were extracted for training machine learning models that predicted sleep quality scores based on the data. The t-test indicated that the following joints had stronger weightings in the prediction: the Head, Spine Shoulder, Wrist Left, Hand Right, Thumb…
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Machine learning for syndromic surveillance using veterinary necropsy reports
…zoonotic diseases, or bioterrorism threats. In this study, we evaluate machine learning methods for conducting syndromic surveillance using free-text veterinary necropsy reports. We train a system to detect if a necropsy report from the Wisconsin Veterinary Diagnostic Laboratory contains evidence…
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Machine learning algorithm validation with a limited sample size
…and conducting feasibility and pilot work, however it can lead to biased machine learning (ML) performance estimates. Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification…
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An exploration of automated narrative analysis via machine learning
The accuracy of four machine learning methods in predicting narrative macrostructure scores was compared to scores obtained by human raters utilizing a criterion-referenced progress monitoring rubric. The machine learning methods that were explored covered methods that utilized hand-engineered…
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Forecasting severe grape downy mildew attacks using machine learning
…observations to develop and compare several generalized linear models and machine learning algorithms predicting the probability of high incidence and severity in the Bordeaux region. The algorithms tested use the date of disease onset and/or average monthly temperatures and precipitation as input…
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Alternate fluency in Parkinson’s disease: A machine learning analysis
…the new alternate phonemic/semantic fluency test. Method: We used machine learning (ML) in order to develop high accuracy classification between PD patients with high and low scores in the alternate fluency test. Results: The models developed resulted to be accurate in such classification in a…
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Predicting malaria epidemics in Burkina Faso with machine learning
Accurately forecasting the case rate of malaria would enable key decision makers to intervene months before the onset of any outbreak, potentially saving lives. Until now, methods that forecast malaria have involved complicated numerical simulations that model transmission through a community. Here…
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Topology identification in distribution system via machine learning algorithms
…topology identification (TI) of distribution systems based on supervised machine learning (SML) algorithms. This methodology is capable of analyzing the feeder’s voltage profile without requiring the utilization of sensors or any other extraneous measurement device. We show that machine learning…
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Deciphering small business community disaster support using machine learning
…business’ active involvement in community resilience using random forest machine learning, and find that adding social capital predictors greatly increases model performance (F1 score of 0.88, Matthews Correlation Coefficient of 0.67).
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A new machine learning method for cancer mutation analysis
…genes, especially in the context of networks. Here we propose a machine learning method to study the functionality of mutually exclusive genes in the networks derived from mutation associations, gene-gene interactions, and graph clustering. These networks have indicated critical biological…
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Bandgap prediction of two-dimensional materials using machine learning
…high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of…
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Comparing machine learning methods for predicting land development intensity
…applied four algorithms, XGBoost, random forest model, support vector machine, and decision tree, to simulate and predict the land development intensity, and then compared the prediction accuracy of the four algorithms, and also carried out hyperparameter adjustment and prediction accuracy…
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Predicting self-intercepted medication ordering errors using machine learning
…to newer approaches. In this paper, we update that analysis using machine learning (ML) models to predict erroneous medication orders and identify its contributing factors. We retrieved patient demographics (race/ethnicity, sex, age), clinician characteristics, type of medication order (inpatient…
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