scholarly journals Correction to: Artificial neural network based high dimensional data visualization technique for interactive data exploration in E-commerce

2021 ◽  
Author(s):  
Cong Wu ◽  
Hongxin Li ◽  
Jiajia Ren ◽  
K. Marimuthu ◽  
Priyan Malarvizhi Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Visualization ◽  
High Dimensional Data ◽  
Data Exploration ◽  
High Dimensional ◽  
Visualization Technique ◽  
Interactive Data ◽  
Artificial Neural

scholarly journals A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data

2020 ◽  
Vol 17 (7) ◽  
pp. 2586 ◽  
Author(s):  
Xi Cheng ◽  
Clément Henry ◽  
Francesco P. Andriulli ◽  
Christian Person ◽  
Joe Wiart
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Uncertainty Quantification ◽  
Network Architecture ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Attractive Alternative ◽  
Neural Network Architecture ◽  
Radiation Modelling ◽  
Artificial Neural

This paper focuses on quantifying the uncertainty in the specific absorption rate values of the brain induced by the uncertain positions of the electroencephalography electrodes placed on the patient’s scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.

scholarly journals Swarm Intelligence for Self-Organized Clustering (Extended Abstract)

2020 ◽  
Author(s):  
Michael C. Thrun ◽  
Alfred Ultsch
Keyword(s):  
Swarm Intelligence ◽  
Data Visualization ◽  
High Dimensional Data ◽  
Self Organization ◽  
High Dimensional ◽  
Topographic Map ◽  
Visualization Technique ◽  
Robust Clustering ◽  
Related Data ◽  
Self Organized

The Databionic swarm (DBS) is a flexible and robust clustering framework that consists of three independent modules: swarm based projection, high-dimensional data visualization and representation guided clustering. The first module is the parameter-free projection method Pswarm, which exploits concepts of self-organization and emergence, game theory, and swarm intelligence. The second module is a parameter-free high-dimensional data visualization technique called topographic map. It uses the generalized U-matrix, which enables to estimate first, if any cluster tendency exists and second, the estimation of the number of clusters. The third module offers a clustering method which can be verified by the visualization and vice versa. Benchmarking w.r.t. conventional algorithms demonstrated that DBS can outperform them. Several applications showed that cluster structures provided by DBS are meaningful. Exemplary, a clustering of worldwide country-related data w.r.t the COVID-19 pandemic is presented here. Code and data is made available via open source.

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