8.473 hasil untuk machine learning
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Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds
…of bees become relevant. Hence, we aim to verify the capacity of Machine Learning (ML) algorithms in recognizing the taxonomic identity of visiting bees to tomato flowers based on the characteristics of their buzzing sounds. We compared the performance of the ML algorithms combined with the Mel…
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Predicting a diagnosis of ankylosing spondylitis using primary care health records–A machine learning approach
…confirm from symptom onset (via x-rays). The aim of this study was to use machine learning methods to develop a profile of the characteristics of people who are likely to be given a diagnosis of AS in future. The Secure Anonymised Information Linkage databank was used. Patients with ankylosing…
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Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning
…and altered fidelity. We applied molecular dynamics simulation (MD) and machine learning (ML) techniques to characterize TL mutations in the Saccharomyces cerevisiae RNA Polymerase II (Pol II) system. We did so to determine relationships between individual mutations and phenotypes and to associate…
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A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis
Theoretically-driven models of suicide have long guided suicidology; however, an approach employing machine learning models has recently emerged in the field. Some have suggested that machine learning models yield improved prediction as compared to theoretical approaches, but to date, this has not…
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Impact of labor characteristics on maternal and neonatal outcomes of labor: A machine-learning model
…intrapartum prediction models of unfavorable labor outcomes using machine-learning algorithms. Materials and methods: Consortium on Safe Labor is a large database consisting of pregnancy and labor characteristics from 12 medical centers in the United States. Outcomes, including maternal and…
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Identifying neuroanatomical and behavioral features for autism spectrum disorder diagnosis in children using machine learning
…as well as behavioral features associated with theory of mind. A separate machine learning classifier (i.e., k-nearest neighbors algorithm) was used to validate the CCEA feature selection and for ASD prediction. Study findings demonstrate how machine learning tools might help move the needle on…
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Modeling CRISPR gene drives for suppression of invasive rodents using a supervised machine learning framework
…accompany our population model with a meta-model that utilizes supervised machine learning to approximate the outcome space of the underlying model with a high degree of accuracy. This enables us to conduct an exhaustive inquiry of the population model, including variance-based sensitivity analyses…
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Integrating structure-based machine learning and co-evolution to investigate specificity in plant sesquiterpene synthases
…STSs have a strong selectivity towards one precursor cation. We use a machine learning approach combining sequence and structure information to accurately predict precursor cation specificity for STSs across all plant species. We combine this with a co-evolutionary analysis on the wealth of…
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Identification of drug combinations on the basis of machine learning to maximize anti-aging effects
…agents. We utilized an aging-related gene expression pattern-trained machine learning system that can implement reversible changes in aging by linking combinatory drugs. In silico gene expression pattern-based drug repositioning strategies, such as connectivity map, have been developed as a method…
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Machine learning-based estimation of riverine nutrient concentrations and associated uncertainties caused by sampling frequencies
Accurate and sufficient water quality data is essential for watershed management and sustainability. Machine learning models have shown great potentials for estimating water quality with the development of online sensors. However, accurate estimation is challenging because of uncertainties related…
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Prenatal stress assessment using heart rate variability and salivary cortisol: A machine learning-based approach
Objective: To develop a machine learning algorithm utilizing heart rate variability (HRV) and salivary cortisol to detect the presence of acute stress among pregnant women that may be applied to future clinical research. Methods: ECG signals and salivary cortisol were analyzed from 29 pregnant…
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Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease
…estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods…
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Dementia risks identified by vocal features via telephone conversations: A novel machine learning prediction model
…is associated with AD progression. Thus, we have developed a novel machine learning prediction model to identify AD risk by using the rich voice data collected from daily conversations, and evaluated its predictive performance in comparison with a classification method based on the Japanese version…
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A community-powered search of machine learning strategy space to find NMR property prediction models
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of…
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Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling
…may require log ratio (ilr) transformation to avoid numerical biases. Machine learning can relate numerous independent variables that may impact on NIR spectra to assess particle-size distribution. Our objective was to reach high IRS prediction accuracy across a large range of PSD methods and soil…
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Study on microwave ablation temperature prediction model based on grayscale ultrasound texture and machine learning
…and temperature data from multiple sampling points were collected. The machine learning method of random forests was used to train the selected texture features, obtaining temperature prediction models for sampling points and the entire ultrasound imaging area. The accuracy of the algorithm was…
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Development and validation of a cardiovascular risk prediction model for Sri Lankans using machine learning
…to develop a CV risk prediction model specific for Sri Lankans using machine learning (ML) of data of a population-based, randomly selected cohort of Sri Lankans followed up for 10 years and to validate it in an external cohort. Material and methods: The cohort consisted of 2596 individuals between…
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Study on design optimization of GFRP tubular column composite structure based on machine learning method
…under different conditions on the two kinds of GFRP columns, automatic machine learning along with four other commonly used machine learning methods were employed for modeling to analyze how the column parameters (cross section shape, concrete strength, height of GFRP column, wound GFRP wall…
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A computational and machine learning approach to identify GPR40-targeting agonists for neurodegenerative disease treatment
…new agonist compounds for the GPR40 protein through the utilization of machine learning and pharmacophore-based screening techniques, in conjunction with other computational methodologies such as docking, molecular dynamics simulations, free energy calculations, and investigations of the free…
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A machine-learning based model for automated recommendation of individualized treatment of rifampicin-resistant tuberculosis
…and 14 treatment regimen features were identified and quantified. Using machine learning, a model was developed to predict the optimal treatment regimen based on a training set of 3895 treatment regimen-expert feedback pairs. The acceptability of the treatment recommender CDSS was assessed as part…
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