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Special Session 1: Big Data and Deep Learning Applications

Special Session 1: Big Data and Deep Learning Applications


Big Data and 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 time-series oriented data (e.g. speech data, weather data, transportation data, stock market data, etc.). In the case of transportation, advanced driver assistance systems and autonomous cars have been developed based on the 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 for 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.

 

This special session named “Big Data and Deep Learning Applications” in ICCCBDA 2021 will solicit papers on various disciplines, including but not limited to:

  • Big data and deep learning for Internet of things
  • Big data and deep learning for fog computing
  • Big data and deep learning for cloud computing
  • Big data and deep learning for semantic web of things
  • Big data and deep learning for security
  • Big data and deep learning for agronomy
  • Big data and deep learning for industry
  • Big data and deep learning for transportation
  • Big data and deep learning for biomedical informatics
  • Big data and deep learning for healthcare
  • Big data and deep learning for medical imaging
  • Big data and deep learning for management
  • Big data and deep learning for the applications of multimedia streaming
  • Big data and deep learning for education
  • Optimization for big data and deep learning

 

Important dates:

  1. Submission of Papers of Special Sessions: March 10, 2021
  2. Notification of Review Result of Papers from Special Sessions: March 25, 2021
  3. Conference: 24-26 April, 2021

Organizers

    • Cheng Shi, Xi'an University of Technology, China
    • Chi-Hua Chen, Fuzhou University, China
    • K. Shankar, Alagappa University, India
    • Hao-Chun Lu, Chang Gung University, Taiwan
    • Feng-Jang Hwang, University of Technology Sydney, Australia
    • Yao-Huei Huang, Fu Jen Catholic University, Taiwan

Submission Link

http://www.easychair.org/conferences/?conf=icccbda2021
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