Description
The IEICE Transactions on Information and Systems announces that it will publish a special section entitled “Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications” in May 2023. 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 analyses 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 chronology oriented data (e.g. speech data, weather data, transportation data, stock market data, etc.). In the application in transportation, the advanced driver assistance systems and autonomous cars have been developed based on deep learning techniques, which perform the forward collision warning, blind spot monitoring, lane departure warning, traffic sign recognition, traffic safety, infrastructure management and congestion, and so on. 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 in engineering and computer science such as the deep learning and optimization techniques can be investigated and developed to support a variety of data analyses and management.
Topics of interest
This special section aims at timely dissemination of research in these areas. Possible topics include but are not limited to:
- Deep learning for data analyses
- Deep learning for data managements
- Deep learning for transportation
- Deep learning for geographical information systems
- Deep learning for financial technology
- Deep learning for bio-informatics
- Deep learning for business intelligence
- Deep learning for e-business, m-commerce, and social-commerce
- Deep learning for enterprise systems and supply chain integration
- Deep learning for Internet of things
Submission deadline
Submission deadline of the manuscript is 01 April, 2022.
Publishing date
Special section entitled “Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications” will be published in May 2023.
Submission URL
Submit the manuscript through the IEICE Web site (https://review.ieice.org/regist/regist_baseinfo_e.aspx).
Choose “[Special-DL] Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications” in the menu of “Journal/Section” in the submission page. Do not choose "[Regular-ED] Information and Systems" or other special sections.
Editors
Guest Editor-in-Chief:
Chi-Hua Chen, Fuzhou University, China
Guest Associate Editor-in-Chief:
Wenzhong Guo, Fuzhou University, China
Yi-Bing Lin, National Yang Ming Chiao Tung University, Taiwan
Haishuai Wang, Fairfield University & Harvard University, USA
Kuo-Ming Chao, Coventry University, UK
Feng-Jang Hwang, University of Technology Sydney, Australia
Cheng Shi, Xi'an University of Technology, China
Guest Associate Editors:
Shaoen Wu, Illinois State University, USA
Xianbiao Hu, Missouri University of Science and Technology, USA
Xiao-Guang Yue, European University Cyprus, Cyprus
Hanhua Chen, Huazhong University of Science and Technology, China
Yuh-Shyan Chen, National Taipei University, Taiwan
Shui Yu, University of Technology Sydney, Australia
Ting Bi, Maynooth University, Ireland Tianxi Dong, Trinity University, USA
Chunjia Han, University of Greenwich, UK
Mu-Yen Chen, National Cheng Kung University, Taiwan
Xiongbiao Luo, Xiamen University, China
Nianyin Zeng, Xiamen University, China
Wen-Kang Jia, Fujian Normal University, China
Doris Xin, Newcastle University, UK
Usman Tariq, Prince Sattam bin Abdulaziz University, Saudi Arabia
Moayad Aloqaily, Gnowit Inc., Ottawa, Canada
K. Shankar, Alagappa University, India
Paula Fraga-Lamas, Universidade da Coruña, Spain
Meng-Hsun Tsai, National Cheng Kung University, Taiwan
Fuquan Zhang, Minjiang University, China
Chih-Min Yu, Yango University, China
Chin-Ling Chen, Chaoyang University of Technology, Taiwan
Hsu-Yang Kung, National Pingtung University of Science and Technology, Taiwan
Hao-Chun Lu, Chang Gung University, Taiwan
Yao-Huei Huang, Fu-Jen Catholic University, Taiwan
Hao-Hsiang Ku, National Taiwan Ocean University, Taiwan
Mingyang Pan, Dalian Maritime University, China
Fangying Song, Fuzhou University, China
Genggeng Liu, Fuzhou University, China
Yu-Chih Wei, National Taipei University of Technology, Taiwan
Hsiao-Ting Tseng, National Central University, Taiwan
Chia-Yu Lin, Yuan Ze University, Taiwan