359.866 hasil untuk machine learning
Menampilkan 581–600 · 1,59 detik · RSS
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Artikel Jurnal Teks lengkap
Machine learning prediction of academic collaboration networks
…examined both network and non-network attributes. Using four machine learning predictive algorithms (LASSO, Neural Network, Gradient Boosting, and Random Forest) our results show that, over various model specifications: (i) best model link formation accuracy is larger than 80%, (ii) among the…
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Multi-omic integration by machine learning (MIMaL)
…in different omic layers were discovered through a combination of machine learning and model interpretation. Discovered connections reflected protein control (ProC) over metabolites. Proteins discovered to control citrate were mapped onto known genetic and metabolic networks, revealing that these…
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Machine learning magnetism classifiers from atomic coordinates
…is still largely limited to collinear magnetism. Here, we present a machine learning model that aims to classify the magnetic structure by inputting atomic coordinates containing transition metal and rare earth elements. By building a Euclidean equivariant neural network that preserves the…
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Objectification of intracochlear electrocochleography using machine learning
…different methods: correlation analysis, Hotelling's T(2) test, and deep learning. We benchmarked these methods against the visual analysis of three ECochG experts. RESULTS: For the visual analysis of ECochG recordings, the Fleiss' kappa value demonstrated a substantial to almost perfect agreement…
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Enabling Fairness in Healthcare Through Machine Learning
The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their…
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Detection of trachoma using machine learning approaches
BACKGROUND: Though significant progress in disease elimination has been made over the past decades, trachoma is the leading infectious cause of blindness globally. Further efforts in trachoma elimination are paradoxically being limited by the relative rarity of the disease, which makes clinical…
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Machine-Learning-Guided Discovery of Electrochemical Reactions
…the exploration of emerging areas of organic chemistry by devising a machine-learning-guided workflow for reaction discovery. Specifically, this study uses machine learning to predict competent electrochemical reactions. To this end, we first develop a molecular representation that enables the…
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Nuclear Cardiology Data Analyzed Using Machine Learning
Machine learning has become popular in clinical practice, and the amount of research that uses artificial intelligence is rapidly increasing. In contrast to conventional statistical and rule-based methods, machine learning creates algorithms based only on combinations of input and output databases…
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Machine Learning Informs RNA-Binding Chemical Space
…largest fully public, experimentally derived library of its kind to date. Machine learning was used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA…
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Neonatal Disease Prediction Using Machine Learning Techniques
…This can make the diagnosis inconclusive and may lead to a misdiagnosis. Machine learning has great potential for early prediction if relevant historical data is available. We have applied a classification stacking model for the following four main neonatal diseases: sepsis, birth asphyxia…
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Machine Learning Magnetic Parameters from Spin Configurations
…a protocol for Hamiltonian parameters estimation from images based on a machine learning (ML) architecture is provided. It consists in learning a mapping between spin configurations and Hamiltonian parameters from a small amount of simulated images, applying the trained ML model to a single…
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Prediction of Promiscuity Cliffs Using Machine Learning
…promiscuity differences). To address this prediction task, different machine learning models were generated and the results were compared with single compound predictions. PCs encoding promiscuity differences were found to contain more structure‐promiscuity relationship information than sets of…
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Machine learning identifies stroke features between species
…are often based on single parameters, or in the case of complex machine learning methods, require vast training datasets and are prone to a lack of model interpretation. Methods: We induced IS using the middle cerebral artery occlusion model on two rat cohorts. We acquired apparent diffusion…
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Mitigating bias in machine learning for medicine
Several sources of bias can affect the performance of machine learning systems used in medicine and potentially impact clinical care. Here, we discuss solutions to mitigate bias across the different development steps of machine learning-based systems for medical applications.
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Cyberbullying severity detection: A machine learning approach
…information technique. Based on these features, we developed a supervised machine learning solution for cyberbullying detection and multi-class categorization of its severity in Twitter. In the study we applied Embedding, Sentiment, and Lexicon features along with PMI-semantic orientation…
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Machine learning in neurosurgery: a global survey
BACKGROUND: Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance of and attitudes toward ML in neurosurgical…
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Software Requirements Classification Using Machine Learning Algorithms
…shows a comparison among the text feature extraction techniques, and machine learning algorithms to the problem of requirements engineer classification to answer the two major questions “Which works best (Bag of Words (BoW) vs. Term Frequency–Inverse Document Frequency (TF-IDF) vs. Chi Squared…
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Machine Learning for Discovery of GSK3β Inhibitors
…amounts of published data on targets for this disease, we proposed that machine learning software could be used to find novel small-molecule treatments that can supplement the AD drugs currently on the market. In order to do this, we used publicly available data in ChEMBL to build and validate…
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Artikel Jurnal Teks lengkap
Machine learning glass transition temperature of polymers
As an important thermophysical property, polymers' glass transition temperature, Tg, could sometimes be difficult to determine experimentally. Modeling methods, particularly data-driven approaches, are promising alternatives to predictions of Tg in a fast and robust way. The molecular traceless…
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