Submission Deadline: 01 July 2018
IEEE Access invites manuscript submissions in the area of Big Data Learning and Discovery.
We are now witnessing a dramatic growth of heterogeneous data, consisting of a complex set of cross-media content, such as text, images, videos, audio, graphics, spatio-temporal data, multivariate time series, and so on. The inception of informatics has offered very robust and hi-tech solutions for data and information analysis, collection, storage and organization, as well as product and service delivery to the customers. Recently, technological advancements, particularly in the form of Big Data, have resulted in the storage of enormous amounts of potentially valuable data in a wide variety of formats. This situation is creating new challenges for the development of effective algorithms and frameworks to meet the requirements of big data representation and analysis, knowledge understanding and discovery. Computer vision, for example, has a huge potential in many aspects for automated understanding of big data and has been used successfully to speed up and improve applications such as large-scale image segmentation, object detection, object tracking, event modeling, scene parsing, 3D reconstruction, image classification and retrieval and so on. Moreover, deep learning has revolutionized diverse key areas, such as speech recognition, object detection, image classification, and machine translation, with the data-driven representation learning. Therefore, it is now necessary to explore advanced theories and techniques for heterogeneous big data learning and discovery. This includes theory related to: data acquisition, feature representation, time series analysis, knowledge understanding, data-based modeling, dimension reduction, semantic modeling and the novel and promising big data analytic research direction, e.g. image/video captioning, affection computing, multimedia storytelling, Internet commerce, healthcare, earth system, communications, augmented/virtual reality and elsewhere.
This Special Section in IEEE Access invites contributions from diverse research fields, such as deep learning, feature extraction and fusion, big data indexing and retrieval, complex network analysis of time series (big data), brain-computer interface and EEG data analysis, healthcare big data analysis, ocean observing data mining, etc…
The topics of interest include, but are not limited to:
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
Associate Editor: Zhong-Ke Gao, Tianjin University, China
Relevant IEEE Access Special Sections:
IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland
Paper submission: Contact Associate Editor and submit manuscript to:
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