359.866 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
MiPepid: MicroPeptide identification tool using machine learning
BACKGROUND: Micropeptides are small proteins with length
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Artificial Intelligence and Machine Learning in Anesthesiology
Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis and self-learning. Progress had historically languished in these areas…
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Pediatric Severe Sepsis Prediction Using Machine Learning
…methods are needed to facilitate this early detection. Objective: Can a machine-learning based prediction algorithm using electronic healthcare record (EHR) data predict severe sepsis onset in pediatric populations? Methods: EHR data were collected from a retrospective set of de-identified…
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Pullout Strength Predictor: A Machine Learning Approach
…feeling of insertion torque. The objective of the paper was to develop a machine learning based model using density of foam, insertion angle, insertion depth, and reinsertion to predict the pullout strength of pedicle screw. METHODS: To predict the pullout strength of pedicle screw, an experimental…
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Machine Learning Approach for Prescriptive Plant Breeding
We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row…
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Machine learning in resting-state fMRI analysis
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning…
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Machine Learning in Psychometrics and Psychological Research
…complementing the analytical workflow of psychological experiments with Machine Learning-based analysis will both maximize accuracy and minimize replicability issues. As compared to statistical inference, ML analysis of experimental data is model agnostic and primarily focused on prediction rather…
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Attention in Psychology, Neuroscience, and Machine Learning
…including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This…
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Preparing Medical Imaging Data for Machine Learning
Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical…
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Machine learning predicts new anti-CRISPR proteins
…identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method to aid direct identification of new potential anti-CRISPRs using only protein sequence information. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking. We then…
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Choosing the right molecular machine learning potential
…level and yield such important observables as reaction rates and spectra. Machine learning potentials promise to significantly reduce the computational cost and hence enable otherwise unfeasible simulations. However, the surging number of such potentials begs the question of which one to choose or…
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GDP Forecasting: Machine Learning, Linear or Autoregression?
This paper compares the predictive power of different models to forecast the real U.S. GDP. Using quarterly data from 1976 to 2020, we find that the machine learning K-Nearest Neighbour (KNN) model captures the self-predictive ability of the U.S. GDP and performs better than traditional time series…
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Employing Multimodal Machine Learning for Stress Detection
…are identified in the literature for this purpose; however, very few machine learning-based methods are proposed for such multimodal fusion tasks. In this work, a multimodal AI-based framework is proposed to monitor a person's working behavior and stress levels. We propose a methodology for…
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Machine learning in patient flow: a review
This work is a review of the ways in which machine learning has been used in order to plan, improve or aid the problem of moving patients through healthcare services. We decompose the patient flow problem into four subcategories: prediction of demand on a healthcare institution, prediction of the…
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Machine learning approaches for drug combination therapies
…and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized…
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Machine learning in optical coherence tomography angiography
…vascular changes. The feasibility of using quantitative OCTA features for machine learning classification of different retinopathies has been demonstrated. Deep learning-based applications have also been explored for automatic OCTA image analysis and disease classification. In this article, we…
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Artificial intelligence: machine learning for chemical sciences
Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/ Machine Learning (ML) methods, especially artificial neural networks, few decades ago. However, we see a major resurgence in the use of modern ML methods in scientific research during the last few years…
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Protein pK(a) Prediction with Machine Learning
…between protein structure and function. In this work, we introduce a deep learning-based protein pK(a) predictor DeepKa, which is trained and validated with the pK(a) values derived from continuous constant-pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here, the CpHMD…
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Machine Learning Predictions of Cancer Driver Mutations
A method to predict the activation status of kinase domain mutations in cancer is presented. This method, which makes use of the machine learning technique support vector machines (SVM), has applications to cancer treatment, as well as numerous other diseases that involve kinase misregulation.
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