8.473 hasil untuk machine learning
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Pre-Impact Fall Detection: Optimal Sensor Positioning Based on a Machine Learning Paradigm
…perturbations during walking. Whole-body 3D kinematics was recorded and a machine learning algorithm was developed to detect perturbation events. In particular, the linear acceleration of all the body segments was parsed by Independent Component Analysis and a Neural Network was used to classify…
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miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences
…novel algorithm for predicting miRNAs named miRLocator, which isbased on machine learning techniques and sequence and structural features extracted from miRNA:miRNA* duplexes. To address the class imbalance problem (few real miRNAs and a large number of pseudo miRNAs), the prediction models in…
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Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery
…Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC50 < 10μM. We progressed five compounds to an in…
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Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction
Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for…
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Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning
A key challenge in contemporary ecology and conservation is the accurate tracking of the spatial distribution of various human impacts, such as fishing. While coastal fisheries in national waters are closely monitored in some countries, existing maps of fishing effort elsewhere are fraught with…
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Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators
…For each segment 30 features were computed and fed to state of the art machine learning (ML) algorithms. ML-algorithms with built-in feature selection capabilities were used to determine the optimal feature subsets for both databases. Patient-wise bootstrap techniques were used to evaluate…
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Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem
…The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set…
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Feature Extraction and Machine Learning for the Classification of Brazilian Savannah Pollen Grains
…computer performance, a combination of three feature extractors and four machine learning techniques has been implemented, fine tuned and tested. The results of these tests are also presented in this paper.
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Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees
…rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity…
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Application of machine learning algorithms for multiparametric MRI-based evaluation of murine colitis
…mice, and evaluate the diagnostic benefit of parameter combinations using machine learning. This study relied on colitis induction by Dextran Sodium Sulfate (DSS) and investigated the colon of mice in vivo as well as ex vivo. Receiver Operating Characteristics were used to calculate sensitivity…
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Managing the 1920s’ Chilean educational crisis: A historical view combined with machine learning
…Education, and the Statistical Yearbooks between 1895 and 1930, we apply machine learning techniques (clustering and decision trees) to assess the impact of this law on the Chilean schooling process between 1920 and 1930. We conclude that the law had a positive impact on the schooling indicators in…
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Using machine-learning to optimize phase contrast in a low-cost cellphone microscope
…the quantitative phase using the derived qDPC approach [3]. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast…
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A machine learning based framework to identify and classify long terminal repeat retrotransposons
…role in genome evolution. We introduce TE-Learner, a framework based on machine learning that automatically identifies TEs in a given genome and assigns a classification to them. We present an implementation of our framework towards LTR retrotransposons, a particular type of TEs characterized by…
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Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica
…sequence lead to differences in disease is limited. Here, we have used machine learning to detect an enrichment in functionally significant mutations in genes associated with a shift in pathogenic niche. This approach captures convergence in functional outcomes that does not necessarily result in a…
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Classification of red blood cell shapes in flow using outlier tolerant machine learning
The manual evaluation, classification and counting of biological objects demands for an enormous expenditure of time and subjective human input may be a source of error. Investigating the shape of red blood cells (RBCs) in microcapillary Poiseuille flow, we overcome this drawback by introducing a…
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Vineyard water status assessment using on-the-go thermal imaging and machine learning
…for the estimation of vineyard water status using thermal imaging and machine learning. The experiments were conducted during seven different weeks from July to September in season 2016. A thermal camera was embedded on an all-terrain vehicle moving at 5 km/h to take on-the-go thermal images of the…
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Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder
…and timeliness of the surveillance system. We developed and evaluated a machine learning approach to classify case status in ADDM using words and phrases contained in children’s developmental evaluations. We trained a random forest classifier using data from the 2008 Georgia ADDM site which…
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Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models
…Damage Index (SDI). We applied Recurrent Neural Networks (RNNs) as a machine-learning model to predict the risk of chronic damage. The clinical data sequences registered for each patient during the follow-up were used for building and testing the RNNs. Results: At the first visit in the Lupus…
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Using machine learning and an ensemble of methods to predict kidney transplant survival
We used an ensemble of statistical methods to build a model that predicts kidney transplant survival and identifies important predictive variables. The proposed model achieved better performance, measured by Harrell’s concordance index, than the Estimated Post Transplant Survival model used in the…
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Biomarkers of erosive arthritis in systemic lupus erythematosus: Application of machine learning models
…proteins antibodies (anti-CarP). Here, we considered the application of machine learning models to identify relevant factors in the development of ultrasonography (US)-detected erosive damage in a large cohort of SLE patients with joint involvement. Methods: We enrolled consecutive SLE patients…
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