Call for Papers: Role of Deep Learning Models & Analytics in an Industrial Multimedia Environment

Guest Editors

Dr. Nawab Muhammad Faseeh Qureshi (faseeh@skku.edu)

Dr. Ali Kashif Bashir (dr.alikashif.b@ieee.org)

Dr. Varun G Menon (varunmenon@ieee.org)

Dr. Shahid Mumtaz (smumtaz@av.it.pt)

Dr. Irfan Mehmood (I.Mehmood4@bradford.ac.uk)

Scope 

Analytics has evolved much in processing distributed multimedia-based Information and Communication Technologies (ICT) as well as financial computing aspects in the distributed computing environment. With this expansion, it is increasing many new elements within itself, such as business analysis, discrete assets analysis, model generation analysis, services analysis, ICT components analysis, multimedia content analysis, and convergence analytics among cloud, big data and IoT environments. This Combinatio-nova of ICT, along with financial perspectives, is known as Business Application analytics.

Deep learning is a recent paradigm, that is functionally supporting all the modern intelligence modules by providing layer-2-layer technique of analysis. This technique is no longer leaving old statistics dataset values aside, but also providing newer functions to involve non-matching statistical methods for in-depth data analytics. The resultant impact of this analytics is vast and benefiting society for modern-day multimedia analytics related to users using android apps, financial software, online data managers, and market data statistical figure analyzers. The more significant challenge to witness in today’s data science environment would be observing the role of such deep learning techniques in modern IoT-enabled data analysis and review the outcomes of multimedia-enabled industrial environment’s response. This special issue is seeking conceptual, empirical, or technological papers that will offer new insights into the following topics, but is not limited to them:

  • Deep Learning aspects in multimedia tools
  • The role of deep learning in predictive analysis
  • Modern deep learning platforms to analyze datasets
  • Machine learning algorithms to process large-scale datasets
  • Financial analytics aspects through Deep learning environment
  • Prescriptive analysis in business intelligence
  • IoT (Internet of Things) dataset processing in Big data environment
  • Cloud data processing in Deep Learning
  • Medical Image data processing in business analytics
  • Smart environment dataset processing in a business intelligence environment
  • Pattern analysis in the deep learning environment
  • Modeling and evaluation of large-scale datasets in the deep learning environment
  • Social data analysis in the deep learning platform
  • High availability perspectives in deep learning platforms
  • Heterogeneous storage computing in the business analytics environment
  • Replica management in socio-economic-aware business intelligence platform
  • Security issues in data sciences related to business intelligence
  • Functional issues of business analytics convergence
  • Novel deep learning platform propositions in business intelligence perspectives

Paper submission guidelines

Submitted papers should present original, unpublished work, relevant to one of the topics of the special issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process.


Important dates

First submission deadline: 15/10/2020

Notification of first decision: 30/12/2020

First revision submission deadline: 30/01/2021

Notification of final decision: 30/3/2021

Final manuscript (camera ready) submission deadline: 30/4/2021

Publication of issue: 2021