113,116 results for machine learning
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Factor Retention Using Machine Learning With Ordinal Data
…A new method called the Factor Forest that combines data simulation and machine learning has been developed recently. This method based on simulated data reached very high accuracy for multivariate normal data, but it has not yet been tested with ordinal data. Hence, in this simulation study, we…
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Machine Learning Approaches for Developing Land Cover Mapping
…to be used in the classification process. However, it is well known in machine learning literature that some of the extracted features are irrelevant to the classification process with a negative or no effect on its accuracy. In this work, a genetic algorithm-based feature selection approach is…
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Machine Learning for Renal Pathologies: An Updated Survey
Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our…
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Machine Learning for Discovery of New ADORA Modulators
…public data for A(1)AR agonists we generated and validated a Bayesian machine learning model (Receiver Operator Characteristic of 0.87) that we used to identify molecules for testing. Three selected molecules, crisaborole, febuxostat and paroxetine, showed initial activity in vitro using the HEK293…
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Machine learning applications for COVID-19 outbreak management
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management…
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Early Prediction of Cardiogenic Shock Using Machine Learning
…proper management. We therefore developed and evaluated retrospectively a machine learning model based on the XGBoost (XGB) algorithm which runs automatically on patient data from the electronic health record (EHR). The algorithm was trained on 8 years of de-identified data (from 2010 to 2017)…
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Protein–Ligand Docking in the Machine-Learning Era
…VS and discuss the assessment and development of classical and machine learning protein–ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general…
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Machine learning: A modern approach to pediatric asthma
Among modern methods of statistical and computational analysis, the application of machine learning (ML) to healthcare data has been gaining recognition in helping us understand the heterogeneity of asthma and predicting its progression. In pediatric research, ML approaches may provide rapid…
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Materials Discovery With Machine Learning and Knowledge Discovery
Machine learning and other artificial intelligence methods are gaining increasing prominence in chemistry and materials sciences, especially for materials design and discovery, and in data analysis of results generated by sensors and biosensors. In this paper, we present a perspective on this…
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Product pricing solutions using hybrid machine learning algorithm
E-commerce platforms have been around for over two decades now, and their popularity among buyers and sellers alike has been increasing. With the COVID-19 pandemic, there has been a boom in online shopping, with many sellers moving their businesses towards e-commerce platforms. Product pricing is…
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Unemployment in Rural Europe: A Machine Learning Perspective
…related to unemployment in those areas through the use of a set of machine learning techniques. Unemployment status is predicted using tree-based classification models: namely, classification tree, bootstrap aggregation, random forest, gradient boosting, and stochastic gradient boosting. The…
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Personalized antibiograms for machine learning driven antibiotic selection
…targeting only the most likely pathogens. We investigate the utility of machine learning-based clinical decision support for antibiotic prescribing stewardship. METHODS: In this retrospective multi-site study, we developed machine learning models that predict antibiotic susceptibility patterns…
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Artificial Intelligence and Machine Learning in Cardiac Electrophysiology
…that have been used to extract information from patient-specific data, machine learning offers a new set of advanced tools capable of revealing previously unknown data patterns and features. This new tool set can substantially improve the speed and level of confidence with which…
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Population pharmacokinetic model selection assisted by machine learning
…complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic…
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Machine learning-based analysis of adolescent gambling factors
…environments and its serious effects on adolescent lives. We proposed a machine learning-based analysis method for predicting the degree of problem gambling. METHODS: Of the 17,520 respondents in the 2018 National Survey on Youth Gambling Problems dataset (collected by the Korea Center on Gambling…
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Machine-Learning-Based Disease Diagnosis: A Comprehensive Review
…in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to…
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Security intrusion detection using quantum machine learning techniques
Conventional machine learning approaches applied for the security intrusion detection degrades in case of big data input ([Formula: see text] and more samples in a dataset). Model training and computing by traditional machine learning executed on big data at a common computing environment may…
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Bioink Formulation and Machine Learning-Empowered Bioprinting Optimization
…with multiple cell types and heterogeneous spatial material properties. Machine learning (ML) has been applied to accelerate these processes. It is a new paradigm for bioprinting. In this review, we explore current trends in bioink formulation and how ML has been used to accelerate optimization and…
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