214.423 hasil untuk machine learning
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Machine Learning in Human Olfactory Research
…and data science, with its currently most popular implementation as machine learning, facilitate complex data-driven research approaches. The use of machine learning in human olfactory research included major approaches comprising 1) the study of the physiology of pattern-based odor detection and…
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Machine learning plastic deformation of crystals
…purely random or to some extent predictable. Here we show, by employing machine learning techniques such as regression neural networks and support vector machines that deformation predictability evolves with strain and crystal size. Using data from discrete dislocations dynamics simulations, the…
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Machine learning bandgaps of double perovskites
…impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double…
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Detecting Bacterial Vaginosis Using Machine Learning
Bacterial Vaginosis (BV) is the most common of vaginal infections diagnosed among women during the years where they can bear children. Yet, there is very little insight as to how it occurs. There are a vast number of criteria that can be taken into consideration to determine the presence of BV. The…
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A Developmental Approach to Machine Learning?
Visual learning depends on both the algorithms and the training material. This essay considers the natural statistics of infant- and toddler-egocentric vision. These natural training sets for human visual object recognition are very different from the training data fed into machine vision systems…
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Machine Learning Approaches in Cardiovascular Imaging
Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine…
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Enabling Machine Learning in Critical Care
Critical care units are home to some of the most sophisticated patient technology within hospitals. In parallel, the field of machine learning is advancing rapidly and increasingly touching our lives. To facilitate the adoption of machine learning approaches in critical care, we must become better…
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Machine Learning for Fetal Growth Prediction
…using the correct fetal weight standard, which was no different from the machine learning-based predicted standards, but higher than the regression-based predicted standards. Machine learning algorithms show promise in recovering missing fetal weight information.
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Classifying smoking urges via machine learning
BACKGROUND AND OBJECTIVE: Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning…
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Morphological Neuron Classification Using Machine Learning
…to classification and (ii) to assess and compare the accuracy of machine learning algorithms to classify neuron morphologies. The algorithms were trained on 430 digitally reconstructed neurons subjectively classified into layers and/or m-types using young and/or adult development state population…
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Machine Learning for Biomedical Literature Triage
This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three…
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Machine learning and big scientific data
…Laser Facility. Increasingly, scientists are now required to use advanced machine learning and other AI technologies both to automate parts of the data pipeline and to help find new scientific discoveries in the analysis of their data. For commercially important applications, such as object…
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Machine-Learning Studies on Spin Models
With the recent developments in machine learning, Carrasquilla and Melko have proposed a paradigm that is complementary to the conventional approach for the study of spin models. As an alternative to investigating the thermal average of macroscopic physical quantities, they have used the spin…
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Talker age estimation using machine learning
…parameters were extracted. These parameters were fitted to a supervised learning model to predict chronological age and perceived age. Differences between the chronological age and perceived age models as well as the usefulness of the various acoustic parameters will be discussed.
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Estimating treatment effects with machine learning
OBJECTIVE: To demonstrate the performance of methodologies that include machine learning (ML) algorithms to estimate average treatment effects under the assumption of exogeneity (selection on observables). DATA SOURCES: Simulated data and observational data on hospitalized adults. STUDY DESIGN: We…
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Machine Learning to Quantitate Neutrophil NETosis
We introduce machine learning (ML) to perform classification and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate neutrophil NETosis, a recently discovered…
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Machine learning and glioma imaging biomarkers
AIM: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. MATERIALS AND METHODS: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant…
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How Machine Learning will Transform Biomedicine
This Perspective explores the application of machine learning toward improved diagnosis and treatment. We outline a vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health monitoring, where the goal is to maintain health…
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