IEEE transactions on neural networks and learning systems
Apr 12, 2018
Practical time-varying formation tracking problems for second-order nonlinear multiagent systems with multiple leaders are investigated using adaptive neural networks (NNs), where the time-varying formation tracking error caused by time-varying exter...
Neural networks : the official journal of the International Neural Network Society
Apr 12, 2018
Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more co...
Nanomedicine : nanotechnology, biology, and medicine
Apr 12, 2018
In this study, a new approach to the implementation of optical imaging of fluorescent nanoparticles in a biological medium using artificial neural networks is proposed. The studies were carried out using new synthesized nanocomposites - nanometer gra...
Explicit structural inference is one key point to improve the accuracy of scene parsing. Meanwhile, adversarial training method is able to reinforce spatial contiguity in output segmentations. To take both advantages of the structural learning and ad...
International journal of biometeorology
Apr 11, 2018
The present study deals with the development and application of artificial neural network models (ANNs) to estimate the values of a complex human thermal comfort-discomfort index associated with urban heat and cool island conditions inside various ur...
In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vul...
IEEE transactions on neural networks and learning systems
Apr 10, 2018
In this paper, we present an approach for minimizing the computational complexity of the trained convolutional neural networks (ConvNets). The idea is to approximate all elements of a given ConvNet and replace the original convolutional filters and p...
Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the develo...
Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-base...
Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN frame...
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