AIMC Topic: Pattern Recognition, Automated

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Monitoring of total positive end-expiratory pressure during mechanical ventilation by artificial neural networks.

Journal of clinical monitoring and computing Apr 11, 2016
Ventilation treatment of acute lung injury (ALI) requires the application of positive airway pressure at the end of expiration (PEEP) to avoid lung collapse. However, the total pressure exerted on the alveolar walls (PEEP) is the sum of PEEP and intr...
Reproducibility of Results Sensitivity and Specificity Animals Respiration, Artificial Neural Networks, Computer Positive-Pressure Respiration Pattern Recognition, Automated Therapy, Computer-Assisted Manometry Acute Lung Injury Swine Diagnosis, Computer-Assisted Treatment Outcome Monitoring, Physiologic
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Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

NeuroImage Apr 11, 2016
Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the succes...
Brain Adult Functional Neuroimaging Magnetic Resonance Imaging Pattern Recognition, Automated Humans Neural Networks, Computer Perception Motor Activity
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A Computational Framework for Realistic Retina Modeling.

International journal of neural systems Apr 7, 2016
Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different v...
Macaca Nonlinear Dynamics Linear Models Pattern Recognition, Automated Vision, Ocular Neural Pathways Adaptation, Physiological Feedback, Physiological Membrane Potentials Animals Neuronal Plasticity Models, Neurological Retina Neural Networks, Computer Software Algorithms Computer Simulation
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Improve Glioblastoma Multiforme Prognosis Prediction by Using Feature Selection and Multiple Kernel Learning.

IEEE/ACM transactions on computational biology and bioinformatics Apr 7, 2016
Glioblastoma multiforme (GBM) is a highly aggressive type of brain cancer with very low median survival. In order to predict the patient's prognosis, researchers have proposed rules to classify different glioma cancer cell subtypes. However, survival...
Glioblastoma Sensitivity and Specificity Survival Analysis Brain Neoplasms Pattern Recognition, Automated Male Middle Aged Algorithms Risk Assessment Humans Prognosis Prevalence Machine Learning Female Diagnosis, Computer-Assisted Reproducibility of Results Decision Support Systems, Clinical
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Pattern recognition for electroencephalographic signals based on continuous neural networks.

Neural networks : the official journal of the International Neural Network Society Apr 6, 2016
This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs). The training method fo...
Databases, Factual Electroencephalography Pattern Recognition, Automated Evoked Potentials, Visual Humans Neural Networks, Computer Algorithms Photic Stimulation Pattern Recognition, Visual
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Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease.

International journal of neural systems Apr 4, 2016
Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the constr...
Humans Unsupervised Machine Learning Area Under Curve Alzheimer Disease Brain Early Diagnosis Disease Progression Positron-Emission Tomography Female Pattern Recognition, Automated Cognitive Dysfunction Image Interpretation, Computer-Assisted Aged Gray Matter ROC Curve Datasets as Topic Male Imaging, Three-Dimensional
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Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images.

Magma (New York, N.Y.) Mar 30, 2016
OBJECTIVES: To develop and validate a machine learning based automated segmentation method that jointly analyzes the four contrasts provided by Dixon MRI technique for improved thigh composition segmentation accuracy.
Image Interpretation, Computer-Assisted Pattern Recognition, Automated Subcutaneous Fat Image Processing, Computer-Assisted Magnetic Resonance Imaging Machine Learning Thigh Algorithms Humans Body Composition Female Aged, 80 and over Aged Male Middle Aged Imaging, Three-Dimensional
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Texture- and deformability-based surface recognition by tactile image analysis.

Medical & biological engineering & computing Mar 23, 2016
Deformability and texture are two unique object characteristics which are essential for appropriate surface recognition by tactile exploration. Tactile sensation is required to be incorporated in artificial arms for rehabilitative and other human-com...
Robotics Arm Image Interpretation, Computer-Assisted Pattern Recognition, Automated Surface Properties Touch Nontherapeutic Human Experimentation Young Adult Adult Male Support Vector Machine Humans Female
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Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Mar 18, 2016
Electroencephalography (EEG) headsets are the most commonly used sensing devices for brain-computer interface. In real-world applications, there are advantages to extrapolating data from one user session to another. However, these advantages are limi...
Equipment Failure Analysis Humans Online Systems Electroencephalography Machine Learning Electrodes Equipment Design Algorithms Pattern Recognition, Automated Reproducibility of Results Brain-Computer Interfaces Sensitivity and Specificity
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A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

PloS one Mar 17, 2016
Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of...
Support Vector Machine Neural Networks, Computer Pattern Recognition, Automated Music Machine Learning Algorithms Computer Simulation
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