Profile

Mohamed Gaber Profile
Title: Dr
First Name: Mohamed
Surname: Gaber
Position: Reader, Data Science Course Leader
Telephone: 01224 262718
Email:
ORCID: ORCID Icon http://orcid.org/0000-0003-0339-4474

Duties and Responsibilities

  • Supervisor of PhD students
  • Teaching undergraduate and postgraduate modules
  • Course Leader: MSc Data Science

Academic Background

PhD in Artificial Intelligence (Monash University), MSc in Computer Science (University of Louisville)

Research Interests

Big Data Analytics, Mining Data Streams, Mobile Data Mining and Sentiment Analysis

Professional Memberships

Fellow of the Higher Education Academy

Publications

View a complete list of publications on Google Scholar

Authored Books

  • Gaber M. M., Stahl F., and Gomes J., Pocket Data Mining: Big Data on Small Devices, Studies in Big Data Series, Volume 2, Springer Verlag, ISBN 978-3-319-02711-1, 2014.
  • Edwards K. J., and Gaber M. M., Astronomy and Big Data: A Data Clustering Approach to Identifying Uncertain Galaxy Morphology, Studies in Big Data Series, Volume 6, Springer Verlag, ISBN 978-3-319-06599-1, 2014.

Edited Books

  • Gaber M. M., Cocea M., Wiratunga N., and Goker A. (Eds.), Advances in Social Media Analysis, Studies in Computational Intelligence, Vol. 602, Springer Verlag, ISBN 978-3-319-18457-9, 2015.
  • Sakr S., and Gaber M. M. (Eds.), Large Scale and Big Data: Processing and Management, Auerbach Publications, CRC Press, ISBN-10: 1466581506, ISBN-13: 978-1466581500, 2014.
  • Gaber M. M. (Ed.), Journeys to Data Mining: Experiences from 15 Renowned Researchers, a book published by Springer Verlag, ISBN 978-3-642-28046-7, 2012.
  • Gaber M. M. (Ed.), Scientific Data Mining and Knowledge Discovery: Principles and Foundations, a book published by Springer Verlag, ISBN 978-3-642-02787-1, 2009.
  • Ganguly A., Gama J., Omitaomu O., Gaber M. M., and Vatsavai R. R. (Eds.), Knowledge Discovery from Sensor Data, a book published by CRC Press, ISBN 1420082329, 9781420082326, 2008.
  • Gama J., and Gaber M. M. (Eds.), Learning from Data Streams: Processing Techniques in Sensor Networks, a book published by Springer Verlag, ISBN 978-3-540-73678-3, 2007.

Selected Journal Articles

  • Elyan E., and Gaber M. M., A Fine-Grained Random Forests using Class Decomposition: An Application to Medical Diagnosis, Neural Computing and Applications, Springer (in press).
  • Abdelsamea M. M., Gnecco G., Gaber M. M., and Elyan E., On the Relationship between Variational Level Set-based and SOM-based Active Contours, Computational Intelligence and Neuroscience, vol. 2015, Article ID 109029.
  • Abdallah Z. S., Gaber M. M., Srinivasan B., and Krishnaswamy S., Adaptive Mobile Activity Recognition System with Evolving Data Stream, Neurocomputing, Volume 150, Part A, 20 February 2015, pp. 304-317, Elsevier.
  • Abdelsamea M., Gnecco G., and Gaber M. M., An Efficient Self Organizing Active Contour Model for Image Segmentation, Neurocomputing, Volume 149, Part B, 3 February 2015, pp. 820-835,Elsevier.
  • Stahl F., May D., Mills H., Bramer M., and Gaber M. M., A Scalable Expressive Ensemble Learning using Random Prism: A MapReduce Approach, Transactions on Large-Scale Data- and Knowledge-Centered Systems XX,  LNCS 9070, pp. 90-107, Springer-Verlag, 2015.
  • Tran D., Gaber M. M., and Sattler K., Change Detection in Streaming Data in the Era of Big Data: Models and Issues, ACM SIGKDD Explorations Newsletter, Volume 16, Issue 1, June 2014, pp. 30-38, ACM press
  • Gaber, M. M., Gama J., Krishnaswamy S., Gomes J. B., and Stahl F., Data Stream Mining in Ubiquitous Environments: State?of?the?art and Current Directions, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(2),  pp. 116-138, March/April 2014.
  • Gomes J. B., Gaber M. M., Menasalvas E., and Sousa P., Mining Recurring Concepts in a Dynamic Feature Space, IEEE Transactions on Neural Networks and Learning Systems, Volume 25, Issue 1, pp. 95-110, January 2014.
  • Gaber M. M., Krishnaswamy S., Gillick B., AlTaiar H., Nicoloudis N., Liono J., and Zaslavsky A., Interactive Self-Adaptive Clutter-Aware Visualisation for Mobile Data Mining, Journal of Computer and System Sciences, Volume 79 Issue 3, May 2013, pp. 369-382. Elsevier.
  • ┼Żliobait? I., Bifet, A., Gaber, M. M., Gabrys B., Gama J., Minku L. and Musial K., Next Challenges for Adaptive Learning Systems, ACM SIGKDD Explorations Newsletter, Volume 14 Issue 1, June 2012, pp. 48-55, ACM press.

Chong S. K., Gaber M. M., Krishnaswamy S., and Loke S. W., Energy Conservation in Wireless Sensor Networks: A Rule-based Approach, Knowledge and Information Systems (KAIS

Media Coverage

Dr Gaber's Website

Grants while working at Monash University, Australia

  • Australian Research Council (ARC) Linkage project with IBM Research Labs (India) (2010-2013) - A$ 162,383: Wattzup - A Context-Aware Residential Demand-Response System for Smart Energy Management. [Role: Chief Investigator]
  • Australian Research Council (ARC) Discovery Project (2008-2011) – A$ 324,000: Adaptive data stream processing in heterogeneous distributed computing environments using real-time context. [Role: Named Australian Postdoctoral Fellow (2008 – 2010) – Principal Investigator: (2010 – 2011)]
  • Group of Eight Australia – Germany Joint Research Co-operation Scheme (2009-2010) – A$ 20,000: Context-Aware Smart Robots for Ambient Assisted Living Environments. [Role: Member of the Monash University Team]
  • Australian Research Council (ARC) EII Network Taskforce in Context-aware Computing (January 2007 – December 2007) – A$ 47,000 coordinated by Dr. Shonali Krishnaswamy, Monash University. [Role: A taskforce named member]

Citation Indices

Citation Indices

Citation count: 2,300+

h-index: 23

g-index: 45

i10-index: 45