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Call for Book Chapters - Multimodal Analytics for Next-Generation Big Data Technologies and Applications

jasmineseng123 - Posted on June 4, 2017 at 9:04 pm.

  • Links: http://csusap.csu.edu.au/~kseng/Call%20For%20Book%20Chapter.pdf
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  • Call for Book Chapters

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    Multimodal Analytics for Next-Generation Big Data Technologies and Applications

    Editors

    Prof. Jasmine Seng K. P., School of Computing & Mathematics, Charles Sturt University

    Dr Kenneth Li Minn Ang, School of Computing & Mathematics, Charles Sturt University

    A/Prof. Alan Liew, School of Information & Communication Technology, Griffith University.

    Prof. Junbin Gao, The University of Sydney Business School, University of Sydney. 

    Prof. Chang Tsun-Li, School of Computing & Mathematics, Charles Sturt University

     

    Introduction

    The first generation of big data systems was mostly concerned with the processing of text-based data for applications such as website analytics, social media analytics, credit card fraud, etc. It is anticipated that the next generation of big data systems will focus on the processing of myriad forms of multimodal-based data such as speech, gestures, facial expressions, location-based data, group dynamics, etc. While the use of multimodal-based data gives increased information-rich content for information and knowledge processing, they lead to a number of additional challenges in terms of scalability, decision-making, data fusion, distributed architectures, predictive analytics, etc. Addressing these challenges require new approaches for its collection, transmission, storage and processing (analytics). The objective of this book is to collect together quality research works on multimodal analytics for big data to serve as a comprehensive source of reference and to play an influential role in setting the scopes and directions of this emerging field of research. The prospective audience would be researchers, professionals and students in engineering and computer science that engage with speech/audio processing, image/visual processing, multimodal information processing, data science, artificial intelligence, etc. The book will serve as a source of reference for technologies and applications for single modality to multimodality data analytics in big data environments.

    Recommended Topics

    The recommended topics include, but are not limited to the following:

    ·      Information and knowledge processing for multimodal big data

    ·      Machine learning and computational intelligence approaches for multimodal big data

    ·      Decision making and fusion in multimodal big data

    ·      Optimization techniques for multimodal big data

    ·      Classification and regression methods for multimodal big data

    ·      Divide-and-conquer multimodal signal processing

    ·      Extraction of multimodal data from big data

    ·      Feature extraction techniques for multimodal big data

    ·      Advanced models and architectures for multimodal big data

    ·      Distributed multimodal processing on big data

    ·      Feature extraction techniques based on divide and conquer architectures

    ·      Distributed audio visual signal processing based on divide and conquer architectures

    ·      Large scale learning for multimodal big data

    ·      Distributed and scalable techniques for multimodal big data

    ·      Real-time and streaming multimodal analytics in big data

    ·      Predictive and prescriptive multimodal analytics in big data

    ·      Multimodal big data analytics systems and architectures

    ·      Visualization of multimodal big data

    ·      Security and encryption of multimodal big data

    ·      Redundancy elimination for multimodal big data

    ·      Multimodal big databases and evaluations.

    ·      Event detection and analytics in multimodal big data

    ·      Human behaviour analytics in multimodal big data

    ·      Summarization (video, audio, etc.) for multimodal big data

    ·      Fusion techniques for multimodal big data

    ·      Advanced applications and future trends for multimodal big data (e.g. sensor network, biometrics, surveillance, customer relation management, health care, social networks, business intelligence, security, web intelligence, location-based services, biomedicine, transportation, mobility applications, etc.)

    Submission Procedure:

    Researchers and practitioners are invited to submit their chapter proposals for this edited book ‘Multimodal Analytics for Next-Generation Big Data Technologies and Applications’ by 1st July, 2017. All submissions must be original and should not be under review by another publication. All submitted book chapter will be reviewed on a double-blind peer review basis.

    Important Dates:

    Chapter Proposal / Extended Abstract (1-2 pages):   1st    July 2017

    Full Chapter Submission:                                            15th September 2017

    Notification to Authors:                                                15th  November 2017

    Submission of the Revised Chapter:                           15th  December 2017

    Final Acceptance Notification:                                     5th    January 2018

    Final Book Chapter Submission:                                 31st  January 2018

    Manuscript Preparation:

    ·       Please prepare the book chapter according to the following guideline: https://www.springer.com/gp/authors-editors/book-authors-editors/manuscript-preparation/5636%23c3324

    ·       Each book chapter should be 20-30 pages.

    ·       Please submit the proposal of your chapter(s) via email: jasmine.seng@gmail.com and cc to kseng@csu.edu.au

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