214.423 hasil untuk machine learning
Menampilkan 481–500 · 0,63 detik · RSS
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Machine learning in knee osteoarthritis: A review
OBJECTIVE: The purpose of present review paper is to introduce the reader to key directions of Machine Learning techniques on the diagnosis and predictions of knee osteoarthritis. DESIGN: This survey was based on research articles published between 2006 and 2019. The articles were divided into four…
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Prediction of antifreeze proteins using machine learning
…Scoring Matrix Tri-Slicing (PseTS-PSSM). Keeping the benefits of ensemble learning, these feature sets are concatenated into different combinations. The best feature set is selected by Extremely Randomized Tree-Recursive Feature Elimination (ERT-RFE). The models are trained by Light eXtreme…
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Machine-Learning-Inspired Workflow for Camera Calibration
…and characterizing the calibration outcomes that is inspired by typical machine learning workflows and practical requirements of camera-based measurements. Combining standard calibration tools and the technique of active targets with phase-shifted cosine patterns, we demonstrate that the imaging…
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Coding for Large-Scale Distributed Machine Learning
This article aims to give a comprehensive and rigorous review of the principles and recent development of coding for large-scale distributed machine learning (DML). With increasing data volumes and the pervasive deployment of sensors and computing machines, machine learning has become more…
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Unsupervised Machine Learning to Identify Depressive Subtypes
OBJECTIVES: This study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data. METHODS: Data from 18,314 depressed patients were used to create LDA models. The outcomes included future emergency…
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Mitigating urinary incontinence condition using machine learning
…consequences. OBJECTIVE: This paper proposes a framework based on machine learning for predicting urination time, which can benefit people with various degrees of UI. METHOD: A total of 850 data points were self-recorded by 51 participants to investigate how different factors impact urination time…
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Autism screening: an unsupervised machine learning approach
Early screening of autism spectrum disorders (ASD) is a key area of research in healthcare. Currently artificial intelligence (AI)-driven approaches are used to improve the process of autism diagnosis using computer-aided diagnosis (CAD) systems. One of the issues related to autism diagnosis and…
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Machine Learning on Early Diagnosis of Depression
To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were “depression” (title) and “random forest” (abstract). The eligibility criteria were the…
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Machine Learning-Based Analytical Systems: Food Forensics
…by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine…
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Vickers hardness prediction from machine learning methods
…or require coding knowledge. In this investigation, we built a successful machine learning model that implements Gradient Boosting Regressor (GBR) to predict hardness and uses the mechanical properties of a solid (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) as input…
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PAMMELA: Policy Administration Methodology using Machine Learning
…this paper, we propose PAMMELA, a Policy Administration Methodology using Machine Learning to assist system administrators in creating new ABAC policies as well as augmenting existing policies. PAMMELA can generate a new policy for an organization by learning the rules of a policy currently…
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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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