8,473 results for machine learning
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Prediction of preterm birth in nulliparous women using logistic regression and machine learning
Objective: To predict preterm birth in nulliparous women using logistic regression and machine learning. Design: Population-based retrospective cohort. Participants: Nulliparous women (N = 112,963) with a singleton gestation who gave birth between 20–42 weeks gestation in Ontario hospitals from…
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Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms
…analysis. By constructing the LASSO regression model, Support vector machine (SVM-REF) and Gaussian mixture model (GMMs) algorithms are used to screen, to identify early diagnostic genes. We have obtained a total of 171 DEGs through WGCNA analysis and differentially expressed genes (DEGs)…
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Modeling vehicle ownership with machine learning techniques in the Greater Tamale Area, Ghana
…advancement in computing power of computers and Artificial Intelligence, Machine Learning (ML) algorithms are becoming an alternative or a complement to the statistical models in modeling the transportation planning processes. Although the application of ML algorithms to the transportation planning…
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Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation
…including Logistic Regression (LR) and Naïve Bayes (NB), and 2) standard machine learning techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB) or Support Vector Machines (SVM), among others. The methods were compared with standard scores, MELD, SOFT and BAR…
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Machine learning and statistics to qualify environments through multi-traits in Coffea arabica
…biological material. This study was developed to evaluate statistical and machine learning models that would better discriminate environments through multi-traits of coffee genotypes and identify the main agronomic and beverage quality traits responsible for the variation of the environments. For…
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Predictors of the rate of cognitive decline in older adults using machine learning
…to identify the most significant predictors of cognitive changes using machine learning techniques. Method: In total, data of 2733 participants aged 50–85 years from the English Longitudinal Study of Ageing are included. Two categories of cognitive changes were determined including minor cognitive…
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Predicting functional effects of ion channel variants using new phenotypic machine learning methods
…characterization presents a bottleneck in translational medicine. Machine learning models may be able to rapidly generate supporting evidence by predicting variant functional effects. Here, we describe a multi-task multi-kernel learning framework capable of harmonizing functional results and…
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Machine learning for the identification of respiratory viral attachment machinery from sequences data
At the outset of an emergent viral respiratory pandemic, sequence data is among the first molecular information available. As viral attachment machinery is a key target for therapeutic and prophylactic interventions, rapid identification of viral “spike” proteins from sequence can significantly…
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Assessing incentives to increase digital payment acceptance and usage: A machine learning approach
An important step to achieve greater financial inclusion is to increase the acceptance and usage of digital payments. Although consumer adoption of digital payments has improved dramatically globally, the acceptance and usage of digital payments for micro, small, and medium-sized retailers (MSMRs)…
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Fe-based superconducting transition temperature modeling by machine learning: A computer science method
…in measuring transition temperature, in this paper, we adopt a machine learning method to build a model based on the lattice parameters to predict the transition temperature of Fe-based superconductors. The model results are in accordance with available transition temperatures, showing 91.181%…
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A fast machine-learning-guided primer design pipeline for selective whole genome amplification
…primer sets. Unlike previous methods, swga2.0 incorporates active and machine learning methods to evaluate the amplification efficacy of individual primers and primer sets. Additionally, swga2.0 optimizes primer set search and evaluation strategies, including parallelization at each stage of the…
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A novel approach to dry weight adjustments for dialysis patients using machine learning
…study, we tried to predict the clinically proper dry weight (DWCP) using machine learning for patient’s clinical information including BIS. We then analyze the factors that influence the prediction of the clinical dry weight. Methods: As a retrospective, single center study, data of 1672…
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A hybrid trust computing approach for IoT using social similarity and machine learning
…approach that combines a distributed computation technique and a global machine learning approach. The approach considers social similarity while assessing other users’ ratings and utilize a cloud-based architecture. Further, we propose a dynamic way to aggregate the different computed trust…
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Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling
Many individuals visit rural telemedicine centres to obtain safe and effective health remedies for their physical and emotional illnesses. This study investigates the antecedents of patients’ satisfaction relating to telemedicine adoption in rural public hospitals settings in Bangladesh through the…
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Predictors of perceived success in quitting smoking by vaping: A machine learning approach
…aimed to identify and rank the importance of these characteristics using machine learning. During July and August 2019, an online survey was administered to a convenience sample of 889 adult smokers (age ≥ 20) in Ontario, Canada who tried vaping to quit smoking in the past 12 months. Fifty-one…
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Using machine learning to improve risk prediction in durable left ventricular assist devices
Risk models have historically displayed only moderate predictive performance in estimating mortality risk in left ventricular assist device therapy. This study evaluated whether machine learning can improve risk prediction for left ventricular assist devices. Primary durable left ventricular assist…
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Predicting sensory evaluation of spinach freshness using machine learning model and digital images
…feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative…
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Machine learning predicts and provides insights into milk acidification rates of Lactococcus lactis
…properties, which affect the final product. Here, we explored the use of machine learning to create systematic, high-throughput screening methods for these properties. Fast acidification of milk is such a strain-dependent property. To predict the maximum hourly acidification rate (Vmax), we trained…
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Classification of QRS complexes to detect Premature Ventricular Contraction using machine learning techniques
…pathologies. This work introduces an approach to identify PVCs using machine learning techniques without feature extraction and cross-validation techniques. In particular, a group of six classifiers has been used: Decision Tree, Random Forest, Long-Short Term Memory (LSTM), Bidirectional LSTM…
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Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness
…this study, we developed a workflow using image-based cell profiling and machine learning to examine morphological, contextual and molecular states of individual breast carcinoma cells (MDA-MB-231). MDA-MB-231 heterogeneous response to the host organ microenvironment was modelled by substrates with…
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