Computational intelligence and neuroscience
Apr 27, 2016
Artificial Neural Network (ANN) is a widely used algorithm in pattern recognition, classification, and prediction fields. Among a number of neural networks, backpropagation neural network (BPNN) has become the most famous one due to its remarkable fu...
Medical & biological engineering & computing
Apr 22, 2016
The segmentation of liver tumours in CT images is useful for the diagnosis and treatment of liver cancer. Furthermore, an accurate assessment of tumour volume aids in the diagnosis and evaluation of treatment response. Currently, segmentation is perf...
Medical & biological engineering & computing
Apr 22, 2016
Crohn's disease (CD) diagnosis is a tremendously serious health problem due to its ultimately effect on the gastrointestinal tract that leads to the need of complex medical assistance. In this study, the backpropagation neural network fuzzy classifie...
The most important part of the early prevention of atherosclerosis and cardiovascular diseases is the estimation of the cardiometabolic risk (CMR). The CMR estimation can be divided into two phases. The first phase is called primary estimation of CMR...
Synchronization is an important and prevalent phenomenon in natural and engineered systems. In many dynamical networks, the coupling is balanced or adjusted to admit global synchronization, a condition called Laplacian coupling. Many networks exhibit...
The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. CAC is clinically quantified in cardiac calcium scoring CT (CSCT), but it has been shown that cardiac CT angiography (CCTA) may also be ...
Screening tests are used in forensic science for field testing and directing laboratory analysis of physical evidence. These tests are often binary in that the data produced is interpreted as yes/no or present/absent. The utility of screening assays ...
Neural networks : the official journal of the International Neural Network Society
Apr 20, 2016
We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the q...
Neural networks : the official journal of the International Neural Network Society
Apr 20, 2016
This paper considers the problem of exponential stability for switched memristive neural networks (MNNs) with time-varying delays. Different from most of the existing papers, we model a memristor as a continuous system, and view switched MNNs as swit...
Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmac...
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