Journal Information
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
https://www.computer.org/csdl/journal/tb
Impact Factor:
2.896
Publisher:
IEEE/ACM
ISSN:
1545-5963
Viewed:
14028
Tracked:
15
Call For Papers
Affective computing is the field of study concerned with understanding, recognizing and utilizing human emotions in the design of computational systems. Research in the area is motivated by the fact that emotion pervades human life – emotions motivate human behavior, they promote social bonds between people and between people and artifacts, and emotional cues play an important role in forecasting human mental state and future actions. Technology is less efficient if it perturbs human emotions; more efficient if it engages with them productively; more attractive if it appeals to human emotions; and often it is primarily concerned with enabling humans to experience particular emotions (notably happiness). Since the coining of the term by Picard in 1997, affective computing has emerged as a cohesive sub-discipline in computer science with its own international conference (the International Conference on Affective Computing and Intelligent Interaction) and professional society (the HUMAINE Association).

IEEE Transactions on Affective Computing is intended to be a cross disciplinary and international archive journal aimed at disseminating results of research on the design of systems that can recognize, interpret, and simulate human emotions and related affective phenomena. The journal will publish original research on the principles and theories explaining why and how affective factors condition interaction between humans and technology, on how affective sensing and simulation techniques can inform our understanding of human affective processes, and on the design, implementation and evaluation of systems that carefully consider affect among the factors that influence their usability. Surveys of existing work will be considered for publication when they propose a new viewpoint on the history and the perspective on this domain. The journal covers but is not limited to the following topics:

Sensing and analysis

    Algorithms and features for the recognition of affective state from face and body gestures
    Analysis of text and spoken language for emotion recognition
    Analysis of prosody and voice quality of affective speech
    Recognition of auditory and visual affect bursts
    Recognition of affective state from central (e.g. fMRI, EEG) and peripheral (e.g. GSR) physiological measures
    Methods for multi-modal recognition of affective state
    Recognition of group emotion
    Methods of data collection with respect to psychological issues as mood induction and elicitation or technical methodology as motion capturing
    Tools and methods of annotation for provision of emotional corpora

(Cyber)Psychology and behavior

    Clarification of concepts related to ‘affective computing' (e.g., emotion, mood, personality, attitude) in ways that facilitate their use in computing.
    Computational models of human emotion processes (e.g., decision-making models that account for the influence of emotion; predictive models of user emotional state)
    Studies on cross-cultural, group and cross-language differences in emotional expression
    Contributions to standards and markup language for affective computing

Behavior generation and user interaction

    Computational models of visual, acoustic and textual emotional expression for synthetic and robotic agents
    Models of verbal and nonverbal expression of various forms of affect that facilitate machine implementation
    Methods to adapt interaction with technology to the affective state of users
    Computational methods for influencing the emotional state of people
    New methods for defining and evaluating the usability of affective systems and the role of affect in usability
    Methods of emotional profiling and adaptation in mid- to long-term interaction
    Application of affective computing including education, health care, entertainment, customer service, design, vehicle operation, social agents/robotics, affective ambient intelligence, customer experience measurement, multimedia retrieval, surveillance systems, biometrics, music retrieval and generation
Last updated by Dou Sun in 2020-05-07
Special Issues
Special Issue on Deep Learning-Empowered Big Data Analytics in Biomedical Applications and Digital Healthcare
Submission Date: 2021-12-30

Deep learning and big data analysis are among the most important research topics in the fields of biomedical applications and digital healthcare. With the fast development of AI and IoT technologies, deep learning for big data analytics, including affective learning, reinforcement learning, and transfer learning, are widely applied to sense, learn, and interact with human health. Examples of biomedical application include smart biomaterials, biomedical imaging, heartbeat/blood pressure measurement, and eye tracking. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. With an enormous amount of historical data, deep learning and big data analysis technologies are able to identify potential linkage between features and possible risks, raise important decision for medical diagnosis, and provide precious advice for better healthcare treatment and lifestyle. Although significant progress has been made with AI, deep learning, and big data analysis technologies for medical and healthcare research, there remain gaps between the computer-aided treatment design and real-world healthcare demands. In addition, there are unexplored areas in the fields of healthcare and biomedical applications with cutting-edge AI and deep learning technologies. Therefore, exploring the possibility of deep learning and big data analysis technology in the fields of biomedical applications and healthcare is in high demand. This special issue invites a wide range of researchers, both from the computer science community and the biomedical research groups, to submit up-to-date results in cutting-edge deep learning and big data analysis technologies in biomedical and healthcare applications. With the emergence of novel methods and techniques in AI, machine learning, and deep learning, research results from both AI-based and traditional methods will be closely connected, bringing significant impacts on data mining, machine learning, computer vision, biomedical research, healthcare engineering, etc. Topics of interest include (but are not limited to): Deep learning in medicine, human biology, and healthcare Deep learning-based clinical decision making Deep learning in biomedical applications Deep learning in medical and healthcare education Deep learning-based computer vision on medical images Big data with smart computing in bioinformatics and biomechanics Big data analytics for human biology and healthcare services Big data with intelligent IoT for smart healthcare Big data analytics in biomedical services Knowledge-based or agent-based models for biological systems Distributed systems in medical and healthcare services Intelligent devices and instruments for medical and healthcare services Intelligent and process-aware information systems in human biology, healthcare, and medicine Important Dates Paper Submission Deadline: December 30, 2021 First Round of Reviews Deadline: March 30, 2022 Submission of Revision Deadline: May 30, 2022 Second Round of Reviews Deadline: July 30, 2022 Decision of Acceptance Deadline: August 30, 2022 Contact Information Dr. Zhou (zhou@biwako.shiga-u.ac.jp) Guest Editors Xiaokang Zhou, Shiga University, Japan Carson Leung, University of Manitoba, Canada Kevin Wang, The University of Auckland, New Zealand Giancarlo Fortino, University of Calabria, Italy
Last updated by Dou Sun in 2021-04-09
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