Recent trends in neural networks for multimedia processing

Author(s):  
Z. Bojkovic ◽  
D. Milovanovic
Author(s):  
Shuo Zhang ◽  
YangQuan Chen ◽  
Yongguang Yu

In this paper, the literature of fractional-order neural networks is categorized and discussed, which includes a general introduction and overview of fractional-order neural networks. Various application areas of fractional-order neural networks have been found or used, and will be surveyed and summarized such as neuroscience, computational science, control and optimization. Recent trends in dynamics of fractional-order neural networks are presented and discussed. The results, especially the stability analysis of fractional-order neural networks, are reviewed and different analysis methods are compared. Furthermore, the challenges and conclusions of fractional-order neural networks are given.


Author(s):  
Siddhartha Bhattacharyya

These networks generally operate in two different modes, viz., supervised and unsupervised modes. The supervised mode of operation requires a supervisor to train the network with a training set of data. Networks operating in unsupervised mode apply topology preservation techniques so as to learn inputs. Representative examples of networks following either of these two modes are presented with reference to their topologies, configurations, types of input-output data and functional characteristics. Recent trends in this computing paradigm are also reported with due regards to the application perspectives.


1997 ◽  
Vol 14 (4) ◽  
pp. 44-45 ◽  
Author(s):  
S.Y. Kung ◽  
Jenq-Neng Hwang

Author(s):  
Steven Walczak

Artificial neural networks (ANNs) have proven to be efficacious for modeling decision problems in medicine, including diagnosis, prognosis, resource allocation, and cost reduction problems. Research using ANNs to solve medical domain problems has been increasing regularly and is continuing to grow dramatically. This chapter examines recent trends and advances in ANNs and provides references to a large portion of recent research, as well as looking at the future direction of research for ANN in medicine.


2022 ◽  
pp. 1491-1509
Author(s):  
Steven Walczak

Artificial neural networks (ANNs) have proven to be efficacious for modeling decision problems in medicine, including diagnosis, prognosis, resource allocation, and cost reduction problems. Research using ANNs to solve medical domain problems has been increasing regularly and is continuing to grow dramatically. This chapter examines recent trends and advances in ANNs and provides references to a large portion of recent research, as well as looking at the future direction of research for ANN in medicine.


1998 ◽  
Vol 86 (6) ◽  
pp. 1244-1272 ◽  
Author(s):  
Sun-Yuan Kung ◽  
Jenq-Neng Hwang

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