Special Issue on Bioinspired and Evolutionary Computation Approaches
towards Coping with Complexity in Human-Machine Interaction (BECA)
Aim and Scope:
Intelligent systems for human-robot interaction are not only expected to
automatically acquire and manage knowledge through a variety of sensors but
also expected to learn, adapt, and optimize their behaviour over time.
Motivated by exciting and successful advances, biologically inspired models
are becoming the choice in machine learning and computational intelligence
to solve complex problems in a variety of applications. It goes from
extraction of middle- and high-level abstract features, recognition tasks,
optimization problems, and more.
This Special Issue will focus attention on approaches based on complex adaptive
systems in nature such as artificial neural networks; evolutionary
algorithms; game theory; adaptive programming; and chaos theory towards
coping with complexity in Human-Machine Interaction (HMI). Examples include
human behaviour, emotional state, and other biosignals analysis and
recognition, which can be used to learn and monitor normal and anomalous
actions/activities and also health related issues (e.g. physical and
emotional problems during human-machine interaction). Automated human
behaviour and emotional state analysis has been, and still remains, a
challenging problem in socially assistive robotics. Thus, this Special Issue
aims to attract high quality original research articles related to how
biologically inspired methods and evolutionary computation can help coping
with complexity in HMI-based applications, such as health care,
surveillance, and Human-Robot Interaction (HRI). Novel and innovative
contributions including reviews related to bioinspired and adaptive
approaches are also welcome.
Potential topics include but are not limited to the following:
- Complex adaptive systems for Human-Machine Interaction (HMI)
- Complex evolutionary computation for behaviour
- Bioinspired approaches for complex human activity recognition
- Bioinspired approaches for socially assistive robotics in complex
- Deep learning for advanced affective computing
- Bioinspired approaches for Brain Computer Interfaces (BCI) and
complex biosignal processing
- Biologically inspired methods for artificial perception in
Human-Robot Interaction (HRI) in complex environments
Deadline: Friday, 14 September 2018.
Publication: February, 2019.
** Papers are published upon acceptance, regardless of the Special
How to Submit:
Authors can submit their manuscripts through the Manuscript Tracking System
Lead Guest Editor
Dr Kamrad K. Roudposhti, Islamic Azad University, Lahijan, Iran,
Dr Diego R. Faria, Aston University, Birmingham, UK
Dr Hadi A. Akbarpour, University of Missouri, Columbia, USA
Dr Luis J. Manso, University of Extremadura, Badajoz, Spain
About Complexity (Hindawi):
- The most recent Impact Factor for Complexity is 1.829 according to the
Journal Citation Reports released by Clarivate Analytics in 2017.
- Indexing Databases: ACM Digital Library; Biological Abstracts; BIOSIS
Previews; CompuMath Citation Index; Computer and Communications Security
Abstracts (CCSA); Computer and Information Systems Abstracts; Computer
Science Index; Current Index to Statistics (CIS); DBLP Computer Science
Bibliography; Directory of Open Access Journals (DOAJ); Elsevier BIOBASE;
Current Awareness in Biological Sciences (CABS); INSPEC; Journal Citation
Reports - Science Edition; Mathematical Reviews (MathSciNet); PSYNDEX;
Science Citation Index (SCI); Science Citation Index Expanded; Scopus;
Zentralblatt MATH Database (zbMATH).
Kamrad Khoshhal Roudposhti, PhD.
Director of Intelligent System Laberatory
Islamic Azad University, Lahijan Branch.
Lahijan, Guilan, Iran.
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