214.423 hasil untuk machine learning
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Koina: Democratizing machine learning for proteomics research
Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and…
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Interactive Machine Learning for Laboratory Data Integration
…to a target clinical concept. In this work, we develop an interactive machine learning tool to “extend the reach” of expert laboratory test adjudicators. Our tool provides access to automatic laboratory classification in a user-facing front end that covers all steps in an adjudication workflow, in…
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Detection of honey adulteration using machine learning
…adulterated honey. This research presents a comparative study of machine learning algorithms for detecting adulteration in honey. The study uses hyperspectral imaging, a promising tool for food quality assurance, to classify and predict adulteration in honey. The proposed model relies on…
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Machine learning makes magnificent macromolecules for medicine
At the University of Minnesota, scientists explore the application of machine learning to screen a multiparametric library of polymers to investigate the relationship between polymer attributes, payload type, and biological outcomes to optimize polymeric vector development for delivery of nucleic…
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Classification of Malicious URLs Using Machine Learning
…could be compromised. This study aims to develop models based on machine learning algorithms for the efficient identification and classification of malicious URLs, contributing to enhanced cybersecurity. Within this context, this study leverages support vector machines (SVMs), random forests (RFs)…
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Identifying risk of stillbirth using machine learning
BACKGROUND: Prior predictive models using logistic regression for stillbirth do not leverage the advanced and nuanced techniques involved in sophisticated machine learning methods, such as modeling non-linear relationships between outcomes. OBJECTIVE: To create and refine machine learning models…
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Machine Learning for Human Motion Intention Detection
…sensors for future mobile applications. Furthermore, we tested using two machine learning networks: a linearfFeedforward neural network and a long short-term memory network. The gait data are from five subjects for training and testing. The study results show that: 1. The network can successfully…
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Viral Immunogenicity Prediction by Machine Learning Methods
…of 1588 known viral immunogens and 468 viral non-immunogens, we apply machine learning algorithms to develop models for the prediction of protective immunogens of viral origin. The datasets are split into training and test sets in a 4:1 ratio. The protein structures are encoded by E-descriptors and…
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Unravelling individual rhythmic abilities using machine learning
…fingerprints of both musicians and non-musicians. Here we harnessed machine learning to extract a parsimonious model of rhythmic abilities, based on behavioral testing (with perceptual and motor tasks) of individuals with and without formal musical training (n = 79). We demonstrate that variability…
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Open-Source Machine Learning in Computational Chemistry
[Image: see text] The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to…
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Power of data in quantum machine learning
The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems…
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QUBO formulations for training machine learning models
Training machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum…
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Predicting ventilator-associated pneumonia with machine learning
…their use may delay antimicrobial therapy. VAP diagnostics derived from machine learning (ML) methods that utilize electronic health record (EHR) data have not yet been explored. The objective of this study is to compare the performance of a variety of ML models trained to predict whether VAP will…
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A Survey of Topological Machine Learning Methods
…to be effective in supporting, enhancing, and augmenting both classical machine learning and deep learning models. In this paper, we review the state of the art of a nascent field we refer to as “topological machine learning,” i.e., the successful symbiosis of topology-based methods and machine…
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COVID-19 detection using federated machine learning
The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient’s private data…
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Advancing agricultural research using machine learning algorithms
Rising global population and climate change realities dictate that agricultural productivity must be accelerated. Results from current traditional research approaches are difficult to extrapolate to all possible fields because they are dependent on specific soil types, weather conditions, and…
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