Neural networks : the official journal of the International Neural Network Society
Feb 8, 2018
This paper considers the delay-dependent stability of memristive complex-valued neural networks (MCVNNs). A novel linear mapping function is presented to transform the complex-valued system into the real-valued system. Under such mapping function, bo...
This paper describes Quantitative Structure-Activity Relationships (QSAR) studies, molecular docking and in vitro antibacterial activity of several potent imidazolium-based ionic liquids (ILs) against S. aureus ATCC 25923 and its clinical isolate. Sm...
BACKGROUND AND PURPOSE: Convolutional neural networks (CNNs) are commonly used for segmentation of brain tumors. In this work, we assess the effect of cross-institutional training on the performance of CNNs.
Myocardial infarction is one of the leading causes of death worldwide. As it is life threatening, it requires an immediate and precise treatment. Due to this, a growing number of research and innovations in the field of biomedical signal processing i...
In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's v...
Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used in the area of time-series prediction and control. Recently, Evolutionary Wavelet Neural Networks have been employed to develop cancer prediction mode...
Neural networks : the official journal of the International Neural Network Society
Feb 7, 2018
This paper investigates the problem of globally exponential synchronization of coupled neural networks with hybrid impulses. Two new concepts on average impulsive interval and average impulsive gain are proposed to deal with the difficulties coming f...
Neural networks : the official journal of the International Neural Network Society
Feb 7, 2018
This paper presents a new theoretical design of nonlinear optimal control on achieving chaotic synchronization for coupled stochastic neural networks. To obtain an optimal control law, the proposed approach is developed rigorously by using Hamilton-J...
Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input...
Computer methods and programs in biomedicine
Feb 6, 2018
BACKGROUND AND OBJECTIVE: Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy...
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