DADA 2019: The 1st IEEE International Workshop on Deep Analysis of Data-Driven Applications
Call for Papers
The DADA workshop aims to foster deep learning approaches to data-driven applications. DADA creates a venue for resaerchers from academia and industry to share their intellectual ideas with respect to challenges, novel problems, innovative applications, and creative solutions regarding the application of deep learning to scientific and industrial problems
Theme and Scope of the Workshop
AI-based application systems are becoming the mainstream for the software industry. The recent advancement in big data generation and management has created an avenue for decision makers to utilize these huge data for different purposes. Application developers have utilized traditional machine learning techniques for a long time. However, with the advancement of deep learning algorithms, developers and decision makers are able to explore and learn more data and their hidden features. 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 software systems. An interesting aspect of deep learning algorithms to such problems is that new challenging problems and how deep learning algorithms can be refined to address the discovered problems are explored.
Researchers and practitioners all over the world, from both academia and industry, working in the area of data-driven application domains using deep learning approaches are invited to discuss state of the art solutions, novel issues, recent developments, applications, methodologies, techniques, experience reports, and tools for the development and use of deep learning. Topics of interest include, but are not limited to the following:
- Intelligent software development
- Smart business and intelligent financial systems and applications
- Time series modeling using LSTM
- Generative adversarial modeling of problems
- Natural language processing
- Security and privacy
Submission deadlines are given on the Important Dates page.
Paper templates and additional information for authors is available on the Information for Authors page.
All papers submitted to symposia and workshops must be original, previously unpublished work. Interested in presenting previously IEEE journal published work at COMPSAC? Please see our page on Journal First/Conference Second (J1C2) publishing opportunities!
Akbar Siami Namin, Department of Computer Science, Texas Tech University, USA
Chihiro Shibata, Tokyo Institute of Technology, Japan
Tommy Dang, Texas Tech University, USA
Jin Fang, Texas Tech University, USA
Md. Karim, Southern Arkansas University, USA
Abdul Serwadda, Texas Tech University, USA
Victor Shengli, University of Central Arkansas, USA
Sima Siami-Namini, Texas Tech University, USA
Neda Tavakoli, Georgia Institute of Technology, USA