8.473 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Correction: Modeling wind erosion susceptibility of Eastern Iran using machine learning
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Artikel Jurnal Teks lengkap
Machine learning-based temperature mapping in Sydney: Accuracy, transferability, and scalability
…sparse and unevenly distributed. To address this data gap, we developed a machine learning (ML) framework that creates high-resolution, gridded Ta maps using crowdsourced observations and diverse geospatial datasets describing the city, and evaluated its accuracy, transferability, and performance…
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A novel machine learning strategy for model selections - Stepwise Support Vector Machine (StepSVM)
…developed for model selections in recent years. In the era of big data, machine learning has been broadly adopted for data analysis. In particular, the Support Vector Machine (SVM) has an excellent performance in classifications and predictions with the high-dimensional data. In this research, a…
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Perceptual metrics for odorants: Learning from non-expert similarity feedback using machine learning
…space. We introduce data-driven approaches to perceptual metrics learning (PMeL) based on two key insights: a) by combining physicochemical features with the user’s perceptual feedback, we can leverage both structural and perceptual attributes of odors to define dissimilarity, and b) instead of…
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Independent evaluation of machine learning and deep learning models for breast cancer detection
…and accurate detection. In this study, we provide an overview of several machine learning (ML) and deep learning (DL) algorithms used for breast cancer detection with structured clinical data and imaging data. We applied ML models to structured clinical data which included Wisconsin Breast Cancer…
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Comparing machine learning, deep learning, and reinforcement learning performance in Culex pipiens predictive modeling
Several machine learning (ML) and deep learning (DL) methods have been used to predict the presence of species in classification problems. Another set of methods, called reinforcement learning (RL), has been used in training agents to perform various tasks, but not in predicting species…
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Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme
…of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training…
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A Data-Driven Predictive Approach for Drug Delivery Using Machine Learning Techniques
…In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and…
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Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery
…these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological…
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ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction
We developed a composite machine-learning based algorithm, called ANGLOR, to predict real-value protein backbone torsion angles from amino acid sequences. The input features of ANGLOR include sequence profiles, predicted secondary structure and solvent accessibility. In a large-scale benchmarking…
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Genome-Scale Identification of Legionella pneumophila Effectors Using a Machine Learning Approach
…data, genomic organization, similarity to eukaryotic proteomes and more. Machine learning algorithms utilizing these features were then applied to classify all the ORFs within the L. pneumophila genome. Using this approach we were able to predict and experimentally validate 40 new effectors…
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Rare Variants Detection with Kernel Machine Learning Based on Likelihood Ratio Test
This paper mainly utilizes likelihood-based tests to detect rare variants associated with a continuous phenotype under the framework of kernel machine learning. Both the likelihood ratio test (LRT) and the restricted likelihood ratio test (ReLRT) are investigated. The relationship between the…
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"What is relevant in a text document?": An interpretable machine learning approach
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate very large text collections, more than could be…
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Applying machine learning to identify autistic adults using imitation: An exploratory study
…diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was…
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A machine learning approach to triaging patients with chronic obstructive pulmonary disease
…effective, on-demand decision support tools. In this study, we present a machine learning-based strategy for early detection of exacerbations and subsequent triage. Our application uses physician opinion in a statistically and clinically comprehensive set of patient cases to train a supervised…
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Ensemble machine learning and forecasting can achieve 99% uptime for rural handpumps
…in western Kenya to demonstrate how sensors and supervised ensemble machine learning could be used to increase total fleet uptime from a best-practices baseline of about 70% to >99%. We accomplish this increase in uptime by forecasting pump failures and identifying existing failures very quickly…
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Driver behavior profiling: An investigation with different smartphone sensors and machine learning
Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate…
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Genomics and Machine Learning for Taxonomy Consensus: The Mycobacterium tuberculosis Complex Paradigm
…isolates from the database were subsequently implemented into an ensemble learning approach based on Machine Learning tool Weka to derive a classification scheme. All assignations were reproduced with very good sensibilities and specificities. When applied to independent datasets, it was able to…
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Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights
…features presents new challenges, and validated computational tools for learning tasks are lacking. Moreover, classification rules have scarcely been validated in independent studies, posing questions about the generality and generalization of disease-predictive models across cohorts. In this…
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Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes
Although reinforcement learning (RL) is suitable for highly uncertain systems, the applicability of this class of algorithms to medical treatment may be limited by the patient variability which dictates individualised tuning for their usually multiple algorithmic parameters. This study explores the…
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