8,473 results for machine learning
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Machine learning models for net photosynthetic rate prediction using poplar leaf phenotype data
…correlated. The photosynthetic rate data are nonnormal and skewed. Many machine learning algorithms can produce reasonably accurate predictions despite these data issues. Influential outliers are removed to allow an accurate and precise prediction, and cluster analysis is implemented as part of a…
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Election forensics: Using machine learning and synthetic data for possible election anomaly detection
…for determining the integrity of a democratic election. Here we present a machine learning methodology for identifying polling places at risk of election fraud and estimating the extent of potential electoral manipulation, using synthetic training data. We apply this methodology to mesa-level data…
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Computer-aided diagnosis of external and middle ear conditions: A machine learning approach
…tackle this problem, we propose and test a computer-aided system based on machine learning models and image processing techniques for otoscopic examination, as a support for a more accurate diagnosis of ear conditions at primary care before specialist referral; in particular, for myringosclerosis…
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Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty
Background: Predictive models could help clinicians identify risk factors that cause adverse events after total knee arthroplasty (TKA), allowing for appropriate preoperative preventive interventions and allocation of resources. Methods: The National Inpatient Sample datasets from 2010–2014 were…
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Garlic ( Allium sativum ) feature-specific nutrient dosage based on using machine learning models
Brazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the…
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Designing transformer oil immersion cooling servers for machine learning and first principle calculations
A transfomer oil immersion cooling server is designed and constructed for machine learning applications and first principle calculations that are carried out for materials-related research. CPU, motherboard, random access memory, hard disk drive, solid state drive, graphic card, and the power…
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Machine learning-based modeling of acute respiratory failure following emergency general surgery operations
…utilized and rely on traditional statistical methods, application of machine learning (ML) in prediction of PRF following EGS remains unexplored. Objective: The present study aimed to develop ML-based prediction models for respiratory failure following EGS and compare their performance to…
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A machine learning and clustering-based approach for county-level COVID-19 analysis
COVID-19 is a global pandemic threatening the lives and livelihood of millions of people across the world. Due to its novelty and quick spread, scientists have had difficulty in creating accurate forecasts for this disease. In part, this is due to variation in human behavior and environmental…
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Machine learning predicts translation initiation sites in neurologic diseases with nucleotide repeat expansions
…to the initiation site, are believed to enhance translation initiation. A machine learning model has been published to help identify ATG and near-cognate translation initiation sites; however, this model has diminished predictive power due to its extensive feature selection and limited training…
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Cross-linguistic patterns of speech prosodic differences in autism: A machine learning study
…demonstrate cross-linguistic variability in prosody. Using a supervised machine-learning analytic approach, we examined acoustic features relevant to rhythmic and intonational aspects of prosody derived from narrative samples elicited in English and Cantonese, two typologically and prosodically…
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Integrating additional factors into the TNM staging for cutaneous melanoma by machine learning
…for patients with cutaneous melanoma. In the present study, we introduce machine learning as a novel tool that incorporates additional prognostic factors to improve the current TNM staging system. Methods and findings: Cancer-specific survival data for cutaneous melanoma with at least a 5 years…
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Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction
With the great advancements in experimental data, computational power and learning algorithms, artificial intelligence (AI) based drug design has begun to gain momentum recently. AI-based drug design has great promise to revolutionize pharmaceutical industries by significantly reducing the time and…
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Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome
Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated…
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Using machine learning to understand age and gender classification based on infant temperament
Age and gender differences are prominent in the temperament literature, with the former particularly salient in infancy and the latter noted as early as the first year of life. This study represents a meta-analysis utilizing Infant Behavior Questionnaire-Revised (IBQ-R) data collected across…
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An unsupervised machine learning approach to evaluate sports facilities condition in primary school
…uncertain task. This paper describes the utilization of an unsupervised machine learning method to objectively evaluate the condition of sports facilities in primary school (PSSFC). The statistical data of 845 samples with nine PSSFC indicators (indoor and outdoor included) were collected from the…
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Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches
…Specifically, I analyze the comorbidity associated with coronavirus using Machine Learning. I have implemented two classifiers: I use the first classifier to predict the type of care procedure that a person diagnosed with coronavirus presenting chronic diseases will obtain (i.e. outpatient or…
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Machine learning-based prediction of critical illness in children visiting the emergency department
…outcomes. Therefore, studies have attempted to augment this process with machine learning models, showing advantages in predicting critical conditions and hospitalization outcomes. The aim of this study was to utilize nationwide registry data to develop a machine learning-based classification model…
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Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences
Machine learning with multi-layered artificial neural networks, also known as “deep learning,” is effective for making biological predictions. However, model interpretation is challenging, especially for sequential input data used with recurrent neural network architectures. Here, we introduce a…
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Patient journey through cases of depression from claims database using machine learning algorithms
Health insurance and acute hospital-based claims have recently become available as real-world data after marketing in Japan and, thus, classification and prediction using the machine learning approach can be applied to them. However, the methodology used for the analysis of real-world data has been…
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Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms
…operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing…
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