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SPECIAL ISSUE ON MULTI-SOURCE INFORMATION FUSION FOR SMART HEALTH WITH AI

Information Fusion, Elsevier

https://www.journals.elsevier.com/information-fusion/call-for-papers/multi-source-information-fusion
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#Overview
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Information fusion is the process of integrating multiple information sources to obtain more complex, reliable, consistent and accurate information for decision-making support. To achieve the goal, inference is essential, which comes from combination of data from multiple sources and transformation of multi-source information into discrete, actionable format for analysis. Artificial Intelligence with newly developed techniques in pattern recognition and image / natural language processing, such as deep learning, have largely improved the performance of processing massive data from multiple resources and leveraged the power of references because of more accurate and meaningful patterns being discovered.

Smart Health, deemed as the revolutionary form of traditional health, is promised by recent advancement of science and technology, such as Internet of Things (IoT), Wisdom Web of Things (W2T), Brain Informatics, Big Data, Artificial Intelligence, and mobile Internet like 5G. Smart Health uses wearable devices, IoT, and mobile Internet to dynamically access information, connect people, materials and infrastructures related to healthcare, and then manages and responds to medical ecosystem demands actively and intelligently. Specifically, the core of Smart Health lies on the concept of P4 (predictive, preventive, personalized and participatory) medicine, which will make healthcare and medical systems to be evidence-based instead of traditional experience-based. Information Fusion has the potential technology and methodology to answer the demand, especially with the super accessibility to data and knowledge, as a power granted from the recent advances of Artificial Intelligence.

While the whole world is suffering from the COVID-19 pandemic, the Special Issue will discuss the theories, methodologies and possible breakthroughs designed and adopted information fusion for smart health adopting recent Artificial Intelligence advances (e.g., learning models, representations, reasoning and metrics). How to achieve and realize human-level intelligence reflected in Smart Health systems and services by developing intelligent technologies using collective and fused information from multi-sources?

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.

#Topics
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Topics appropriate for this special issue include (but are not necessarily limited to):
- New AI techniques, models, algorithms for multi-source health and medical data fusion systems
- Deep learning models for multi-source health and medical data processing
- Feature fusion for intelligent health and medical systems
- Shared multi-source health and medical model learning
- Improved algorithms for multi-source health and medical data fusion systems
- Analysis on big health and medical data fusion
- Hierarchical intelligent systems for multi-source health and medical data fusion
- Multi-source data fusion applications for smart health and P4 medicine
- Computational issues in fusion methods for real-time bio-signal analysis
- Heterogeneous information fusion in big health and medical data context
- Tensor methods and constraint techniques for health and medical data fusion

# Important Dates
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- Deadline for Submission: October 31, 2020

# Instructions for Authors
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Please prepare your paper along with all the supplementary materials for your submission. The papers submitted to this special issue must be original. Besides that, they must not be published, “under review”, or even be submitted in any other journal, conference, or workshop. Papers will be peer-reviewed by at least three independent reviewers and will be chosen based on contributions including their originality, scientific quality as well as their suitability to this special issue. The journal editors will make the final decision on which papers will be accepted.

Authors must ensure that you carefully read the guide for authors before submitting your papers. The guide for authors and link for online submission is available on the Information Fusion homepage at: https://www.journals.elsevier.com/information-fusion. Please select “SI: MIF-SHAI” when you reach the “Article Type” step when submitting your papers. For any inquiry or question regarding this special issue, authors may contact directly via email to Xiaohui Tao at xiaohui.tao@usq.edu.au.

# Guest Editors
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* Juan D. Velásquez, University of Chile, Chile
Email: jvelasqu@dii.uchile.cl
* Xiaohui Tao, University of Southern Queensland, Australia
Email: xiaohui.tao@usq.edu.au