DADA 2019: The 1st IEEE International Workshop on Deep Analysis of Data-Driven Applications

DADA 2019 Program

DADA 1: The 1st IEEE International Workshop on Deep Analysis of Data-Driven Applications
LSTM Modeling
Monday July 15, 8:00 – 9:30
Location: Ballroom D
Session Chair: Akbar Namin, Texas Tech University, USA

LiRUL: A Lightweight LSTM based Model for Remaining Useful Life Estimation at the Edge
Olumide Kayode and Ali Tosun

Modeling Genome Data Using Bidirectional LSTM
Neda Tavakoli

A Scalable Framework for Multilevel Streaming Data Analytics using Deep Learning
Shihao Ge, Haruna Isah, Farhana Zulkernine and Shahzad Khan

DADA 2: The 1st IEEE International Workshop on Deep Analysis of Data-Driven Applications
NLP
Monday July 15, 10:00 – 12:00
Location: Ballroom D
Session Chair: Aerambamoorthy Thavaneswaran, University of Manitoba, Canada

Social Media and Forecasting Stock Price Change
Joseph Coelho, Dawson d’Almeida, Scott Coyne, Katelyn Mills, Nathan Gilkerson and Praveen Madiraju
TALS: A Framework for Text Analysis, Fine-Grained Annotation, Localisation and Semantic Segmentation
Shatha Jaradat, Nima Dokoohaki, Ummal Wara, Mallu Goswami, Kim Hammar and Mihhail Matskin

Twitter vs News: Concern Analysis of the 2018 California Wildfire Event
Hanxiang Du, Hoang Long Nguyen, Zhou Yang, Hashim Abu-Gellban, Xingyu Zhou, Wanli Xing and Fang Jin

DADA 3: The 1st IEEE International Workshop on Deep Analysis of Data-Driven Applications
Software, Risk & Security
Monday July 15, 1:00 – 2:30
Location: Ballroom D
Session Chair: Mikio Aoyama, Nanzan University, Japan

A Speech Data-Driven Stakeholder Analysis Methodology
Yuta Shirasaki, Yuya Kobayashi and Mikio Aoyama

Fuzzy Value-at-Risk Forecasts Using a Novel Data-Driven Neuro Volatility Predictive Model
Aerambamoorthy Thavaneswaran, Ruppa Thulasiram, Zimo Zhu, Md. Erfanul Hoque and Nalini Ravishanker

Detecting Phishing Websites through Deep Reinforcement Learning
Moitrayee Chatterjee and Akbar-Siami Namin

DADA 4: The 1st IEEE International Workshop on Deep Analysis of Data-Driven Applications
Visualization
Monday July 15, 3:00 – 4:15
Location: Ballroom D
Session Chair: Tommy Dang, Texas Tech University, USA

COMEX: Identifying Mislabeled Human Behavioral Context Data Using Visual Analytics
Hamid Mansoor, Walter Gerych, Luke Buquicchio, Kavin Chandrasekaran, Elke Rundensteiner and Emmanuel Agu

SpacePhaser: Phase Space Embedding Visual Analytics
Ngan Nguyen and Tommy Dang

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