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A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction.

IEEE transactions on medical imaging
Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate ...

Development of a partial least squares-artificial neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green tea beverages.

Food research international (Ottawa, Ont.)
In order to develop products that would be preferred by consumers, the effects of the chemical compositions of ready-to-drink green tea beverages on consumer liking were studied through regression analyses. Green tea model systems were prepared by do...

Classification of the trabecular bone structure of osteoporotic patients using machine vision.

Computers in biology and medicine
Osteoporosis is a common bone disease which often leads to fractures. Clinically, the major challenge for the automatic diagnosis of osteoporosis is the complex architecture of bones. The clinical diagnosis of osteoporosis is conventionally done usin...

Pattern of improvement in upper limb pointing task kinematics after a 3-month training program with robotic assistance in stroke.

Journal of neuroengineering and rehabilitation
BACKGROUND: When exploring changes in upper limb kinematics and motor impairment associated with motor recovery in subacute post stroke during intensive therapies involving robot-assisted training, it is not known whether trained joints improve befor...

A pilot study for robot appearance preferences among high-functioning individuals with autism spectrum disorder: Implications for therapeutic use.

PloS one
Recent rapid technological advances have enabled robots to fulfill a variety of human-like functions, leading researchers to propose the use of such technology for the development and subsequent validation of interventions for individuals with autism...
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A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network.

IEEE transactions on neural networks and learning systems
This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced perfor...
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Design of a Lightweight Soft Robotic Arm Using Pneumatic Artificial Muscles and Inflatable Sleeves.

Soft robotics
As robots begin to interact with humans and operate in human environments, safety becomes a major concern. Conventional robots, although reliable and consistent, can cause injury to anyone within its range of motion. Soft robotics, wherein systems ar...

Poor vitamin D status increases the risk of anemia in school children: National Food and Nutrition Surveillance.

Nutrition (Burbank, Los Angeles County, Calif.)
OBJECTIVES: This study aimed to investigate the association between 25-hydroxyvitamin D (25[OH]D) concentrations and the risk of anemia in a large cohort of children with consideration for the effects of sex, body mass index (BMI), serum intact parat...

Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks.

International journal of neural systems
One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated m...

Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI.

NeuroImage. Clinical
Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group...