Neural network approach to assessing cybersecurity risks in large-scale dynamic networks

2020 ◽  
Author(s):  
Vasiliy Krundyshev
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Dynamic Networks ◽  
Network Approach ◽  
Neural Network Approach

Neural network approach to a colorimetric value transform based on a large-scale spectral dataset

2016 ◽  
Vol 133 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Qiang Liu ◽  
Xiaoxia Wan ◽  
Jinxing Liang ◽  
Zhen Liu ◽  
Dehong Xie ◽  
...  
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging

NeuroImage ◽  
2020 ◽  
Vol 218 ◽  
pp. 116993
Author(s):  
Bo Li ◽  
Marius de Groot ◽  
Rebecca M.E. Steketee ◽  
Rozanna Meijboom ◽  
Marion Smits ◽  
...  
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Diffusion Imaging ◽  
Population Based ◽  
Network Approach ◽  
Neural Network Approach ◽  
Scale Population

Neural-Symbolic Processing in Business Applications

2011 ◽  
pp. 270-314
Author(s):  
Nick F Ryman-Tubb
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Black Box ◽  
Network Approach ◽  
Neural Network Approach ◽  
New Approach ◽  
Business Applications ◽  
The Neural Network ◽  
Black Boxes ◽  
Over Time

Neural networks are mathematical models, inspired by biological processes in the human brain and are able to give computers more “human-like” abilities. Perhaps by examining the way in which the biological brain operates, at both the large-scale and the lower level anatomical level, approaches can be devised that can embody some of these remarkable abilities for use in real-world business applications. One criticism of the neural network approach by business is that they are “black boxes”; they cannot be easily understood. To open this black box an outline of neural-symbolic rule extraction is described and its application to fraud-detection is given. Current practice is to build a Fraud Management System (FMS) based on rules created by fraud experts which is an expensive and time-consuming task and fails to address the problem where the data and relationships change over time. By using a neural network to learn to detect fraud and then extracting its’ knowledge, a new approach is presented.

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