Foundation of Data Science and Machine Learning

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (21 May 2021) | Viewed by 1057

Special Issue Editors

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Guest Editor
School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia
Interests: big data; data management; AI; predictive analytics; and health informatics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Information Technology, Deakin University, Southport, Australia
Interests: information retrieval; machine learning; deep learning

Special Issue Information

Dear Colleagues,

Data science has a long, rich history and is currently a very active topic with an extensive scope in machine learning, both in terms of theory and application. Recent advancement in data science has revolutionized the face of industrial development in the past decade. Data science applications in the real world environment provide important challenges that can often be addressed only with advanced machine learning methods. Research is therefore needed to understand and improve the potential and suitability of data analytics and machine learning in real-world applications. This will provide a deeper understanding and better decision making based on largely available data. This Special Issue focuses on the latest developments of novel machine learning methods in data science, as well as the synergy between data science and machine learning. We welcome new developments in data analytics regarding machine learning methods that are relevant for data science from a machine learning perspective.

This Special Issue will respond to research challenges by encouraging researchers in the computing world to present novel techniques, combinations of tools, and so forth to build effective ways to handle, retriev, and make use of data.

Topics of interest include, but are not limited to, the following:

  • Emerging data-driven methods
  • Deep learning
  • Data analytics in healthcare
  • Risk analysis
  • Causality and learning casual models
  • Green data science
  • Privacy preserving, ethics, and transparency
  • Multiple inputs and outputs: multi-instance, multi-label, and multi-target
  • Semi-supervised and weakly supervised learning
  • Data streaming and online learning
  • Reinforcement learning
  • AutoML
  • IoT data analytics and big data
  • Social data analytics
  • Information retrieval

Dr. Imran Razzak
Dr. Md. Saiful Islam
Dr. Mohamed Reda Bouadjenek
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers

There is no accepted submissions to this special issue at this moment.
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