Special Issue on Bioinspired and Evolutionary Computation Approaches towards Coping with Complexity in Human-Machine Interaction
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 innovat
ive 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) applications
- Complex evolutionary computation for behaviour analysis/recognition
- Bioinspired approaches for complex human activity recognition
- Bioinspired approaches for socially assistive robotics in complex scenarios
- 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 Issue publication date.
How to Submit:
Authors can submit their manuscripts through the Manuscript Tracking System at:
Lead Guest Editor
Dr Kamrad K. Roudposhti, Islamic Azad University, Lahijan, Iran, [hidden email]
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 4.621 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).
Dr Diego Resende Faria
Lecturer (Assistant Professor) in Computer Science
School of Engineering & Applied Science
Aston University, Birmingham, B4 7ET, UK
Room MB 211-D, Tel: +44 (0) 1212 044 868
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