Call for Papers: Special Issue on Cognitive Computing Methods and Applications of Converged IT environments

Cognitive computing has broad horizons, which cover different characteristics of cognition. The field is highly trans-disciplinary in nature, combining ideas, principles and methods of psychology, computer and Internet technologies, linguistics, philosophy, neuroscience, etc. This special issue explores domain knowledge and reasoning of data science technologies and cognitive methods over the Converged IT environments. The main focus is design of best cognitive embedded data technologies to process and analyze the large amount of data collected through various sources and help for good decision-making.

This special issue is to gather new soft computing and deep learning trends and methodological recent advances on a wide range of problems arising in different fields to handle practical data of converged environment. Many advanced computational methods have been successfully applied to a range of optimization and classification problems in soft computing and deep learning, but there are still many practical problems tackled by traditional methods that are generally difficult to solve experimentally in practical converged data. More specifically, many computational problems arising in fields of scientific programming have been addressed in AI, HPC, large-scale and data mining that handles practical converged data.

This special issue aims to advance our understanding of the emerging or already more mature research challenges at the cross point of the different areas mentioned. Such understanding will help academics and practitioners to explore new directions and generate knowledge and solutions towards cognitive computing methods and applications of converged IT environments. Topics appropriate for this special issue include, but are not necessarily limited to:

  • Artificial Intelligence and Machine Learning enhance cognitive computing environments
  • Big data analytics & data science for real world applications
  • Cognitive computing models and prediction analytics for converged IT environments
  • Cognitive, reactive and proactive systems for converged IT environments
  • Distributed and parallel algorithms for Cognitive computing models
  • Grid and scalable computing for Cognitive computing models
  • Novel feature representation using deep learning, dictionary learning for real world applications
  • Security and trust management for Cognitive computing models
  • Theoretical results on representation of cognitive computing architectures for converged IT environments
  • Visual analytics to identify patterns and processes for mining large real world datasets

Manuscripts (which should be original and not previously published either in full or in part or presented even in a more or less similar form at any other forum) covering the latest developments, trends, industrial applications and management challenges for cognitive computing technologies are invited. The role of cognitive computing should be significant and clearly demonstrated in the manuscript. Absolutely no cut and pastes from prior publications (of text and/or figures or tables or other illustrations) will be permitted. All such reproduced material should be excluded by generous use of citations to the relevant prior publications wherever necessary within the text of the Journal submission. The manuscript will be judged solely on the basis of new contributions excluding the contributions made in earlier publications. Contributions should be described in sufficient detail to be reproducible on the basis of the material presented in the paper and the references cited therein. Manuscripts should be submitted electronically online at choose 'SI: Cognitive Computing Methods and Applications of Converged IT environments ' when specifying the Article Type.

Important dates:

Deadline for Submission: May 31, 2021

Decision Notification: August 31, 2021

Final Manuscript Submission: October 31, 2021

Guest Editors:Ruey-Shun Chen, Professor, Department of Information Management, National Chiao Tung University, Taiwan. E-mail:

Pedro Peris L√≥pez, Professor, Department of Computer Science, Universidad Carlos III de Madrid, Spain. E-Mail: