DADA 2024: The 6th IEEE International Workshop on Deep Analysis of Data-Driven Applications

Call for Papers

The DADA workshop 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.

Workshop theme:

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.

Scope of the workshop:

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

Paper Templates

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

IEEE Conference Publishing Policies

All submissions must adhere to IEEE Conference Publishing Policies.

IEEE Cross Check

All submission will be screened for plagiarized material through the IEEE Cross Check portal.

Important Dates – Workshops

Workshop, Fast Abstract, SRS, JC  papers due
April 15, 2024  Extended to April 21

May 1, 2024

Camera Ready & Registration due
May 25, 2024

Workshop Chair

Akbar Namin
Texas Tech University, USA
Email: akbar.namin[at]

Program Committee

Yulei Pang, Southern Connecticut State University, USA

Sima Siami-Namini, Johns Hopkics University, USA

Faranak Abri, San Jose State University, USA

Luis Felipe Gutierrez Espinoza, Texas Tech University, USA

Prerit Datta, Coastal Carolina University, USA

Long Nguyen, Meharry Medical College, USA

Vinh T. Nguyen, University of Information and Communication Technology, Vietnam

Vijayan Sugumaran, Oakland University, USA

Vito Walter Anelli, Information Systems Research Group, Department of Electrical & Informative Engineering, polytechnic University of Bari, Italy

Yiltan Bitirim, eastern Mediterranean University, North Cyprus

Zeki Bayram, eastern Mediterranean University, North Campus