DADA 2023: The 5th IEEE International Workshop on Deep Analysis of Data-Driven Applications

Technical Program

*= in-person presentation in Torino

The Application of the BERT Transformer Model for Phishing Email Classification
Denish Omondi Otieno and Akbar Siami Namin

Content based Image Retrieval using Fine-tuned Deep Features with Transfer Learning
Zakariya Oraibi and Safaa Albasri

A Survey on Blockchain-Based Federated Learning and Data Privacy
Bipin Chhetri, Saroj Gopali, Rukayat Olapojoye, Samin Dehbashi and Akbar Siami Namin

Machine Learning For Text Anomaly Detection : A Systematic Review*
Karima Boutalbi, Faiza Loukil, Hervé Verjus, David Telisson and Kavé Salamatian

Using Graph Neural Network to Detect Java Annotation Misuse
Jingbo Yang, Wenjun Wu and Jian Ren

Goal of the workshop:

The 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.

Important Dates

Main Conference/Symposium
Main conference/symposium papers due: 15 January 2023
Extended to 15 February 2023
Notification: 7 April 2023
Camera-ready and registration due: 7 May 2023 Updated: 18 May 2023

Journal then Conference Submissions
Due date: April 7, 2023
Notifications: April 30, 2023

Workshops, Fast Abstract, SRS Programs
EXTENDED: Workshop papers due: 21 April 2023
UPDATED: Notifications: 7 May 2023
UPDATED: Camera-ready and registration due: Updated: 18 May 2023

Submission Link

Please submit your paper on EasyChair

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.

Workshop Organizer

Akbar Namin
Texas Tech University, USA

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