Mohamed Gaber

Professor in Data Analytics

School of Computing and Digital Technology - CDT

Email:: mohamed.gaber@bcu.ac.uk
Phone:: 0121 331 4203

Mohamed Gaber is a Professor in Big Data Analytics at our School of Computing and Digital Technology.

He received his PhD from Monash University in Australia, and then held appointments with the University of Sydney and the Commonwealth Scientific and Industrial Research Organisation (CSIRO). Prior to joining Birmingham City University, Mohamed held academic positions at Robert Gordon University and the University of Portsmouth.

Mohamed has been listed in the world’s top two per cent of best scientists in his field of research and in the world’s top one per cent in artificial intelligence and image processing (Elsevier BV and Stanford University, 2021).

He has published over 150 papers, co-authored two monograph-style books, and edited/co-edited six books on data mining and knowledge discovery. Mohamed has served in the programme committees of several major conferences related to data mining, including the International Conference on Data Mining, the Pacific-Asia Conference on Knowledge Discovery and Data Mining, and the International Conference on Machine Learning. In 2007, he was awarded the CSIRO Teamwork award.

Current Activity

Areas of Expertise

Qualifications

Teaching

Research

Postgraduate Supervision

Publications

Gaber, M. (2017). Expressive modelling for trusted big data analytics: techniques and applications in sentiment analysis

Gaber, M. (2016). A rule dynamics approach to event detection in Twitter with its application to sports and politics

Gaber, M. (2016). An Outlier Ranking Tree Selection Approach to Extreme Pruning of Random Forests, Engineering Applications of Neural Networks: 17th International Conference, Aberdeen, Scotland

Gaber, M. (2016). AnyNovel: detection of novel concepts in evolving data streams

Gaber, M. (2016). Clustering-Based Spatio-Temporal Analysis of Big Atmospheric Data, International Conference on Internet of Things and Cloud Computing, Birmingham, UK

Gaber, M. (2015). Spatio-temporal analysis of greenhouse gas data via clustering techniques, IEEE International Conference on Computer Supported Cooperative Work in Design CSCWD

Gaber, M. (2015). Unsupervised learning techniques to diversifying and pruning random firest, RWTH Aachen University

Gaber, M. (2015). A scalable expressive ensemble learning using random prism: A MapReduce approach

Gaber, M. (2015). Advances in Social Media Analysis, Springer, New York, USA.

Gaber, M. (2015). An efficient self-organising active contour model for image segmentation

Gaber, M. (2015). An outlier detection-based tree selection approach to extreme pruning of random forests

Gaber, M. (2015). Autonomic Discovery of News Evolvement in Twitter

Gaber, M. (2015). Distributed Classification of Data Streams: An Adaptive Technique

Gaber, M. (2015). Mobile data stream mining (advances), Big Data Meets Machine Learning 2015 Summer Training Workshop

Gaber, M. (2015). Research and Development in Intelligent Systems XXXII: Incorporating Applications and Innovations in Intelligent Systems

Full lists of publications are available at:

Selected publications

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 Genetic Algorithm Approach to Optimising Random Forests Applied to Class Engineered Data, Information Sciences, Elsevier (in press).
Abdallah Z. S., Gaber M. M., Srinivasan B., and Krishnaswamy S., AnyNovel: Detection of Novel Concepts in Evolving Data Streams, Evolving Systems, June 2016, Volume 7, Issue 2, pp. 73-93, Springer.
Adedoyin-Olowe M., Gaber M. M., Martin-Dancausa C., Stahl F., and Gomes J. B., A Rule Dynamics Approach to Event Detection in Twitter with Its Application to Sports and Politics, Expert Systems with Applications, Volume 55, 15 August 2016, pp. 351–360, Elsevier.
Abdelsamea M., Gnecco G., and Gaber M. M., A SOM-based Chan-Vese Model for Unsupervised Image Segmentation, Soft Computing, Springer (in press).
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).
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.
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.
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) Journal, Volume 28, Number 3, pp. 579-614, 2011, Springer London.