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ISCApad Archive  »  2019  »  ISCApad #251  »  Journals  »  IT=Indformation Technology, Special Issue: Affective Computing, Deep Learning & Health

ISCApad #251

Sunday, May 12, 2019 by Chris Wellekens

7-13 IT=Indformation Technology, Special Issue: Affective Computing, Deep Learning & Health
  

IT-Information Technology

Call for papers

Special Issue:

Affective Computing, Deep Learning & Health


Scope of the Journal: IT - Information Technology is a strictly peer-reviewed scientific journal. It is the oldest German journal in the field of information technology. Today, the major aim of IT - Information Technology is highlighting issues on ongoing newsworthy areas in information technology and informatics and their application. It aims at presenting the topics with a holistic view It addresses scientists, graduate students, and experts in industrial research and development.

Aim of the Special Issue:
Analysis of human behaviours and emotions based on affective computing techniques have received considerable attention in the relevant literature in recent years. The main aim of this interest is to endow computers with the human traits of adequately recognising and responding to emotion or affect. One particularly interesting field of applying affective computing technologies is in healthcare scenarios. In clinical psychology and psychotherapy settings, affective computing can be used to provide objective diagnostic information, accurately track changes in patients? mood or emotion regulations in therapy, or enable Virtual Therapists to have the ability to empathise and appropriately respond to their patients' needs. As in most areas based heavily on Artificial Intelligence, deep learning solutions are the pre-eminent approach in many affective computing applications.

This special issue aims to solicit papers which contribute ideas, methods and case studies for how affective computing technologies can aid healthcare. In particular, these include, but are not limited to, solutions utilising:

  • State-of-the-art deep learning techniques
  • Adversarial training paradigms
  • Attention models
  • End-to-end learning
  • Explainable AI
  • Multitask learning
  • Reinforcement learning
  • Longer-term user adaptation
  • Transfer learning

Authors are asked to kindly submit their manuscript online at: http://www.editorialmanager.com/itit/.

The style guide for preparing the manuscript (Word or Latex) is listed there. A step by step guide through the submission process will be provided after registration.

Language: Publication language is English.

Length: The length of a contribution to the special issue should be at most eight printed pages
 
Important Dates:
  • First Submission: May 31st, 2019
  • First Notification: July 12th, 2019
  • Second Submission: August 9th, 2019
  • Second Notification: September 6th, 2019
  • Camera-ready Version of Papers: September 20th, 2019
Special Issue Editors
  • Björn Schuller, Imperial College London
  • Nicholas Cummins, University of Augsbug

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