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《Journal of Global Information Management》特刊 Call for papers: Deep Learning for the Applications of Global Information Management

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Description


Deep learning techniques (e.g. neural network (NN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM) network, gate recurrent unit (GRU) network, etc.) have been popularly applied to data analysis and management. For instance, CNN and auto-encoder can be used to analyze the pattern recognition and extract the features of data in various applications (e.g. regression, classification, image recognition, etc.). Furthermore, the RNN, LSTM network and GRU network can be used to perform the time-series inference for time-series oriented data (e.g. speech data, weather data, transportation data, stock market data, etc.). In the case of global telecommunications and data security, LSTM networks could be applied to analyze the sequence of network signals and extract the patterns of cyberattacks for intrusion detection. However, how to enhance the performance and efficiency of these deep learning techniques is one of the biggest challenges for implementing these real-time applications.
Furthermore, several optimization techniques (e.g. stochastic gradient descent (SGD), adaptive moment estimation (Adam), Nesterov-accelerated adaptive moment estimation (Nadam) algorithms, etc.) have been proposed to support deep learning algorithms for faster solution searching, e.g., the gradient descent method is a popular optimization technique to quickly seek the optimized weight sets and filters of CNN for image recognition. The hybrid approaches typical of mathematics for engineering and computer science such as the deep learning and optimization techniques can be investigated and developed to support a variety of data analysis and information management.


Submission deadline


Submission Due Date 6/30/2022


Submission URL


https://www.igi-global.com/calls-for-papers-special/journal-global-information-management/1070



Editors


Guest Editors


Chi-Hua Chen, Fuzhou University, chihua0826@gmail.com
Feng-Jang Hwang, University of Technology Sydney, Feng-Jang.Hwang@uts.edu.au
Chunjia Han, University of Greenwich, c.han@gre.ac.uk
Jiayuan Xin, Newcastle University, jiayuan.xin@ncl.ac.uk