The 3rd IEEE International Workshop on Deep Analysis of Data-Driven Applications (DADA 2021)

Call for Papers


DADA 2021 aims to identify challenging and novel applications of deep learning algorithms to address scientific and engineering problems. The workshop fosters deep learning techniques to modeling and analyzing data-driven applications. It creates a venue for researchers from academia and industry to share their intellectual ideas and experiences with respect to challenges, novel problems, innovative applications, and creative solutions regarding the applications of deep learning to scientific and industrial problems driven by data.

AI-based application systems are becoming the mainstream for software industry. The recent advancement in big data generation and management has created an avenue for decision makers to utilize these huge data collected from many application domains for different purposes. Application developers and data scientists have utilized conventional machine learning techniques for a long time. However, with the advancement of deep learning paradigm, developers and decision makers are able to learn more about their data and then explore and model hidden features for prediction and analysis purposes. The new trends of practices in developing data driven application systems and decision making algorithms seek adaptation of deep learning algorithms and techniques in many application domains including AI-based software systems and applications and variety of scientific domains. An interesting aspect of adaptation of deep learning algorithms to such problems is that new challenging problems can be identified and deep learning algorithm can be innovatively adapted to address the discovered problems are explored.

Researchers and practitioners all over the world, from both academia, research institute, and industry, working in the area of data analysis and data-driven application domains using deep learning approaches are invited to discuss the state of the art solutions, novel issues, recent developments, applications, methodologies, techniques, experience reports, and tools for the development and use of deep learning in their application domains. Topics of interest include, but are not limited to, the following applications of deep learning to:

  • Intelligent data analysis
  • AI-based software development and analysis
  • Smart businesses and intelligent financial systems and applications
  • Time series modeling and prediction
  • Generative adversarial modeling of problems
  • Natural language processing
  • Security and privacy
  • Attention-based networks
  • Transfer learning

Important Dates


Workshop papers due: 21 April 2021

Workshop paper notifications: 15 May 2021

Camera-ready and registration due: 31 May 2021


Authors are invited to submit original, unpublished research work, as well as industrial practice reports. Simultaneous submission to other publication venues is not permitted.  In accordance with IEEE policy, submitted manuscripts will be checked for plagiarism. Instances of alleged misconduct will be handled according to the IEEE Publication Services and Product Board Operations Manual.

Please note that in order to ensure the fairness of the review process, COMPSAC follows the double-blind review procedure. Therefore we kindly ask authors to remove their names, affiliations and contacts from the header of their papers in the review version. Please also redact all references to authors’ names, affiliations or prior works from the paper when submitting papers for review. Once accepted, authors can then include their names, affiliations and contacts in the camera-ready revision of the paper, and put the references to their prior works back.

Formatting


Workshop papers are limited to 6 pages. Page limits are inclusive of tables, figures, appendices, and references. Workshop papers can add an additional 2 pages with additional page charges ($250USD/page).

Paper Templates


IEEE Paper templates are available in MS Word 2003 and LaTex. All submissions must use US 8.5×11 letter page format.



Workshop Organizers


Akbar Namin, Texas Tech University
Email: akbar.namin@ttu.edu

Fang Jin, George Washington University
Email: fangjin@gwu.edu

Sima Siami-Namini, Mississippi State University
Email: ss4625@msstate.edu

Program Committee


Arindam Pal, CSIRO, Australia

Bhuvan Unhelkar, University of South Florida, USA

Helei Cui, Northwestern Polytechnical University, China

Jacob Biros, Chura Data in Okinawa, Japan

Juan A. Álvarez-García, University of Seville, Spain

Keng Siau, Missouri University of Science and Technology, USA

Kenichi Yoshida, Tsukuba University, Japan

Kozo Ohara, Aoyama Gakuin University, Japan

Krishnadas Nanath, Middlesex University Dubai, Dubai

Marco Conoscenti, Politecnico di Torino, Italy

Mohan K. Bavirisetty, CISCO, USA

Rajesh Subramanian, Siemens, USA

Sunil Mithas, University of South Florida, USA

Tad Gonsalves, Sophia University, Japan

Takeshi Morita, Keio University, Japan

Tania Cerquitelli, Politecnico di Torino, Italy

Thomas Deserno, Peter L. Reichertz Institute for Medical Informatics, Germany

Uttam Ghosh, Vanderbilt University, USA

Xiaolong Zheng, Institute of Automation Chinese Academy of Sciences, China