Special Issue on Artificial Intelligence and Data Science

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Special Issue on Artificial Intelligence and Data Science

 

Innovations in artificial intelligence (AI) and a paradigm shift to data-driven approaches owing to the growing trend in data drive new research opportunities in a variety of areas, such as social networks, bioinformatics, healthcare, manufacturing business, beyond 5G (6G) communications, Internet of Things (IoT), and so forth.  In the aforementioned areas, system-generated information such as smart devices, sensors, agents, and meters as well as human-generated information such as texts, photos, and videos lead to a tremendous amount of data while new levels of security, performance, and reliability are required. In this context, equipping the relevant functionality with AI or data mining-based algorithms, including regression models, Bayesian learning, clustering, neural networks, decision trees, information retrieval, decision processes, multi-armed bandits, reinforcement learning, generative models, and graphical models, has received a substantial attention both in academia as well as in industrial communities. Recently developed AI or data mining approaches will provide promising solutions to many challenging problems through learning and decision making in terms of significantly improving user experience and service quality.

 

The “Artificial Intelligence and Data Science” special issue aims at bringing this perspective to AI and data science, and focuses on the latest research, algorithm design, analysis, and implementation for various applications. This special issue will address a comprehensive overview of how to enable autonomous and intelligent services/applications though collecting, processing, learning, and controlling a vast amount of information across various domains. The topics of interest include, but are not limited to:

 

Network mining and graph mining

Deep learning and neural network-based approach

Social network analysis

Reinforcement learning and multi-armed bandits 

Knowledge representation and reasoning

Anomaly and fake content detection

Information retrieval

Recommendation and ranking engines

Machine learning in medicine and healthcare informatics 

Big data analytics for beyond 5G or 6G

Edge/fog computing using machine learning

IoT data analytics

Data-driven services and applications

 

Interested authors need to submit their papers according to the following schedule: 

February 29, 2020: Paper submission deadline. PDF format with MS word or Latex source to ICT Express website

May 31, 2020: Reviews returned to authors

June 15, 2020: Final revised manuscript due

June 30, 2020: Final Decision Due

September 30, 2020: Publication date

Prof. Won-Yong Shin, Yonsei University, Republic of Korea, wy.shin@yonsei.ac.kr 

Prof. Jangyoung Kim, University of Suwon, Republic of Korea, jykim77@suwon.ac.kr 

Prof. Cheol Jeong, Sejong University, Republic of Korea, cjeong@sejong.ac.kr 

Prof. Esma Yildirim, Queensborough Community College, USA, eyildirim@qcc.cuny.edu 

Prof. Kiho Lim, William Paterson University, USA, limk2@wpunj.edu 

 

Electronic submissions should be made through the Elsevier’s ICT Express website at http://www.journals.elsevier.com/ict-express. ICT Express is a high-quality quarterly archival journal published by KICS and hosted by Elsevier. ICT Express invites short length (up to 4 pages in double columns) high-quality, original articles. Papers published in the ICT Express are indexed in Emerging Sources Citation Index (ESCI), Directory of Open Access Journals (DOAJ), and Science Direct. Learn more about this journal and click here for submission tips. Please, direct inquiries and correspondence regarding intent to submit to the Lead Guest Editor, Prof. Won-Yong Shin (wy.shin@yonsei.ac.kr).