The aim of this paper is to investigate various information-theoretic measures, including entropy, mutual information, and some systematic measures that are based on mutual information, for a class of structured spiking neuronal networks. In order to...
Neural networks : the official journal of the International Neural Network Society
May 19, 2020
This paper mainly studies quasi-bipartite synchronization (QBPS) of signed delayed neural networks (SDNNs) under impulsive effects, in which the nodes have cooperative as well as antagonistic interactions. It is assumed that disturbance occurs in the...
IEEE transactions on biomedical circuits and systems
May 19, 2020
This paper proposes novel methods for making embryonic bio-inspired hardware efficient against faults through self-healing, fault prediction, and fault-prediction assisted self-healing. The proposed self-healing recovers a faulty embryonic cell throu...
Neural networks : the official journal of the International Neural Network Society
May 19, 2020
In this paper, we proposed nested encoder-decoder architecture named T-Net. T-Net consists of several small encoder-decoders for each block constituting convolutional network. T-Net overcomes the limitation that U-Net can only have a single set of th...
Functional identification of cancer stem-like cells (CSCs) is an established method to identify and study this cancer subpopulation critical for cancer progression and metastasis. The method is based on the unique capability of single CSCs to survive...
Human action recognition is an important research area in the field of computer vision that can be applied in surveillance, assisted living, and robotic systems interacting with people. Although various approaches have been widely used, recent studie...
In this paper, response surface methodology (RSM) designs and an artificial neural network (ANN) are used to obtain the optimal conditions for the oxy-combustion of a corn-rape blend. The ignition temperature () and burnout index () were selected as ...
The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying...
Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocardiogram (ECG) and can generally provide high-output diagnostic accuracy if subjected to robust train...
Fibroglandular tissue (FGT) segmentation is a crucial step for quantitative analysis of background parenchymal enhancement (BPE) in magnetic resonance imaging (MRI), which is useful for breast cancer risk assessment. In this study, we develop an auto...
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