214.423 hasil untuk machine learning
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Automated spectrometer alignment via machine learning
During beam time at a research facility, alignment and optimization of instrumentation, such as spectrometers, is a time-intensive task and often needs to be performed multiple times throughout the operation of an experiment. Despite the motorization of individual components, automated alignment…
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Advancing Rheumatology Care Through Machine Learning
…support, heralding a shift towards patient-centered, decentralized care. Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data, promising to enhance treatment quality and patient well-being. Convolutional neural networks (CNN) are particularly…
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Machine learning-based test smell detection
…design and experiment with a novel test smell detection approach based on machine learning to detect four test smells. First, we develop the largest dataset of manually-validated test smells to enable experimentation. Afterward, we train six machine learners and assess their capabilities in within-…
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Machine learning and acute stroke imaging
BACKGROUND: In recent years, machine learning (ML) has had notable success in providing automated analyses of neuroimaging studies, and its role is likely to increase in the future. Thus, it is paramount for clinicians to understand these approaches, gain facility with interpreting ML results, and…
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Machine learning assisted vector atomic magnetometry
…a paradigm of indirect encoding for vector atomic magnetometry based on machine learning. We encode the three-dimensional magnetic-field information in the set of four simultaneously acquired signals associated with the optical rotation of a laser beam traversing the atomic sample. The map between…
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Explainable machine learning for diffraction patterns
…data in serial crystallography experiments. Therefore, existing deep learning methods, including CNNs classifying serial crystallography data, are like a ‘black box’. To this end, presented here is a qualitative study to unpack the internal workings of CNNs with the aim of visualizing information…
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Machine learning of cellular metabolic rewiring
…interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry (GC/MS) to predict abundance changes in…
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Machine Learning Methods for Systematic Reviews:
OBJECTIVE: At the forefront of machine learning research since its inception has been natural language processing, also known as text mining, referring to a wide range of statistical processes for analyzing textual data and retrieving information. In medical fields, text mining has made valuable…
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Solving musculoskeletal biomechanics with machine learning
Deep learning is a relatively new computational technique for the description of the musculoskeletal dynamics. The experimental relationships of muscle geometry in different postures are the high-dimensional spatial transformations that can be approximated by relatively simple functions, which…
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Decoding Optical Data with Machine Learning
…technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a…
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Machine Learning Methods in Drug Discovery
…fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to…
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Machine learning–accelerated computational fluid dynamics
…trade-offs between accuracy and tractability. Here we use end-to-end deep learning to improve approximations inside computational fluid dynamics for modeling two-dimensional turbulent flows. For both direct numerical simulation of turbulence and large-eddy simulation, our results are as accurate as…
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Machine Learning for Cancer Drug Combination
When treating multiple complex diseases, such as cancer, polytherapy may demonstrate efficiency than monotherapy. However, due to the multiplicative relationship between the number of drugs and cell lines vs. the number of combinations, it is impractical to test all drug combinations using…
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Machine Learning Identification of Piezoelectric Properties
…for manufacturers and end users. In this paper, we present the use of a machine learning approach to determine the parameters in the model. In this first work, the main sensitive parameters, c(11), c(13), c(33), c(44) and e(33) were predicted using a neural network numerically trained by using…
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Regulatory responses to medical machine learning
Companies and healthcare providers are developing and implementing new applications of medical artificial intelligence, including the artificial intelligence sub-type of medical machine learning (MML). MML is based on the application of machine learning (ML) algorithms to automatically identify…
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Predicting Kidney Discard using Machine Learning
BACKGROUND. Despite kidney supply shortage, 18%−20% deceased donor kidneys are discarded annually in the US. In 2018, 3,569 kidneys were discarded. METHODS. We compared Machine Learning (ML) techniques to identify kidneys at risk of discard at the time of match-run, and after biopsy and machine…
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Machine learning applications in radiation oncology
Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for…
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