Mohamed is the Centre Lead of the Centre of Enterprise Systems and the leader of the Big Data Analytics Group.
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 forests, 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.