Biography

Mohamed is a Professor in Data Analytics at the School of Computing and Digital Technology, Birmingham City University. Mohamed received his PhD from Monash University, Australia. He then held appointments with the University of Sydney, CSIRO, and Monash University, all in Australia. Prior to joining Birmingham City University, Mohamed worked for the Robert Gordon University as a Reader in Computer Science and at the University of Portsmouth as a Senior Lecturer in Computer Science, both in the UK. He has published over 200 papers, co-authored 3 monograph-style books, and edited/co-edited 6 books on data mining and knowledge discovery. His work has attracted well over six thousand citations, with an h-index of 41. Mohamed has served in the program committees of major conferences related to data mining, including ICDM, PAKDD, ECML/PKDD and ICML. He has also co-chaired numerous scientific events on various data mining topics. Professor Gaber is recognised as a Fellow of the British Higher Education Academy (HEA). He is also a member of the International Panel of Expert Advisers for the Australasian Data Mining Conferences. In 2007, he was awarded the CSIRO teamwork award. 

A more interesting biography

Picture
I was born a few miles away from the Pyramids in Cairo, Egypt. I received my PhD from Monsah University in Melbourne, the first capital (temporarily) of Australia.  During my PhD, I did an internship at IBM Thomas J. Watson, where the relational database model was invented by Edgar F. Codd. After my PhD, I held research positions at the University of Sydney, the oldest/first Australian University which is located close to where Captain James Cook first landed in Australia; CSIRO in Hobart, the capital of Tasmania: the only Australian state out of the Australian mainland; and back to Monash University when awarded the prestigious Australian Postdoctoral Fellowship. I worked as a senior lecturer in computer science at the University of Portsmouth, where Charles Dickens was born. I then moved to the oil Capital of Europe and the Granite City, Aberdeen, to take up the position of Reader in computer science at Robert Gordon University. Most recently, in Birmingham, the first manufacturing town in the world, I became a Full Professor in data analytics at Birmingham City University.
By the words of the great poet, Hafez Ibrahim, the legend Umm Kulthum sang: "..and the builders of the pyramids in the ancient age sufficed me the talk when challenged". Listen to great music and song

Research Contribution Map

Publications

10 Recent/Representative Journal Articles

  1. Abbas A., Abdelsamea M., and Gaber M. M., 4S-DT: Self Supervised Super Sample Decomposition for Transfer Learning with Application to COVID-19 Detection, IEEE Transactions on Neural Networks and Learning Systems, 2021. [DOI]
  2. Hettiarachchi H., Adedoyin-Olowe M., Bhogal J., and Gaber M. M., Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media, Machine Learning, 2021, Springer. [DOI]
  3. Hatwell J., Gaber M. M., and Azad R. M. A., Ada-WHIPS: Explaining AdaBoost Classification with Applications in the Health Sciences, BMC Medical Informatics and Decision Making,  Volume 20, Issue 1: 250, 2020, Springer Nature. [DOI]
  4. Abbas A., Abdelsamea M., and Gaber M. M., Classification of COVID-19 in Chest X-ray Images using DeTraC Deep Convolutional Neural Network, Applied Intelligence, 2020, Springer.  [DOI]
  5. Hatwell J., Gaber M. M., and Azad R. M. A., CHIRPS: Explaining Random Forest Classification, Artificial Intelligence Review, 2020, Springer. [DOI]
  6. Abdallah Z. S., and Gaber M. M., Co-eye: A Multi-resolution Symbolic Representation to Time Series Diversified Ensemble Classification, Machine Learning, 2020, Springer. [DOI]
  7. Abbas  A., Abdelsamea M., and Gaber M. M., DeTraC:  Transfer  Learning  of  Class  Decomposed  Medical Images in Convolutional Neural Networks, IEEE  Access, 2020, Volume 8, pp. 74901-74913, IEEE press.  [DOI]
  8. ​Ghomeshi H., Gaber M. M., and Kovalchuk Y., A Non-Canonical Hybrid Metaheuristic Approach to Adaptive Data Stream Classification, Future Generation Computer Systems, Volume 102, January 2020, pp. 127-139, Elsevier.  [DOI]
  9. Ghomeshi H., Gaber M. M., and Kovalchuk Y., EACD: Evolutionary Adaptation to Concept Drifts in Data Streams, Data Mining and Knowledge Discovery, May 2019, Volume 33, Issue 3, pp 663–694. Springer-Verlag. [DOI]
  10. 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. [DOI]

10 Selected Survey/Review Articles

  1. Dridi A., Gaber M. M., Azad R. M. A., and Bhogal J., Scholarly Data Mining: A Systematic Review of its Applications, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Wiley press. [h5-index = 29, ranked 15th in top publications of Data Mining & Analysis, Impact Factor = 4.476].
  2. Gaber M. M., Aneiba A., Basurra S., Batty O., Elmisery A., Kovalchuk Y., and Habib ur Rehman M. Internet of Things and Data Mining: From Applications to Techniques and Systems, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 9, Issue 3, May/June 2019, e12922019. [h5-index = 29, ranked 15th in top publications of Data Mining & Analysis, Impact Factor = 4.476].
  3. Abdallah Z. S., Gaber M. M., Srinivasan B., and Krishnaswamy S., Activity Recognition with Evolving Data Streams: A Review, ACM Computing Surveys (CSUR), Volume 51 Issue 4, July 2018, ACM press. [h5-index = 90, ranked 9th in top publications of Engineering & Computer Science (general), Impact Factor = 5.55].
  4.  Hussein A., Gaber M. M., Elyan E., and Jayne C., Imitation Learning: A Survey of Learning Methods, ACM Computing Surveys (CSUR), Volume 50 Issue 2, April 2017, ACM press. [h5-index = 90, ranked 9th in top publications of Engineering & Computer Science (general), Impact Factor = 5.55, contributed to h5-index [2015-2020] ranked the 41st most cited paper]
  5. Fawagreh K., Gaber M. M., and Elyan E., Random Forests: From Early Developments to Recent Advancements, Systems Science & Control Engineering, Volume 2, Issue 1, 2014, pp. 602-609, Taylor & Francis. [h5-index = 17, contributed to h5-index [2014-2019], ranked the 4th most cited paper]
  6. Gaber M. M., Gama J., Krishnaswamy S., Gomes J., and Stahl F., Data Stream Mining in Ubiquitous Environments: State-of-the-art and Current Directions, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 4, Issue 2, pp. 116-138, March/April 2014. [h5-index = 29, ranked 15th in top publications of Data Mining & Analysis, Impact Factor = 4.476, contributed to h5-index [2014-2019], ranked the 35th most cited paper].
  7. Stahl F., Gabrys B., Gaber M. M., and Berendsen M., An Overview on Interactive Visual Data Mining Techniques for Knowledge Discovery, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 3(4), pp. 239-256, 2013. [h5-index = 29, ranked 15th in top publications of Data Mining & Analysis, Impact Factor = 4.476].
  8. Gaber M. M., Advances in Data Stream Mining, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 2 (1), pp. 79-85, 2012. [h5-index = 29, ranked 15th in top publications of Data Mining & Analysis, Impact Factor = 4.476].
  9. Chong S. K., Gaber M. M., Krishnaswamy S., and Loke S. W., Energy-aware Data Processing Techniques for Wireless Sensor Networks: A Review, Transactions on Large-Scale Data- and Knowledge-Centered Systems III, Lecture Notes in Computer Science Volume 6790, pp. 117{137 Springer-Verlag, 2011.
  10.  Gaber, M, M., Zaslavsky, A., and Krishnaswamy, S., Mining Data Streams: A Review, ACM SIGMOD Record, Vol. 34, No. 1, pp. 18-26, June 2005, ISSN: 0163-5808. [h5-index = 22, the 2nd most cited paper published in the journal over the decade post publication [2005-2015] (total of 348 papers)]

List of Publications (different sources)

Books

According to Google Scholar in June 2021

Citations
6,361
h-index
41
g-index
74
i10-index
102
ResearchGate Research Interest
3K+ (top 2% in all areas, and top 2% in AI)
The above graph is generated using the R package "scholar" in November 2013. The calculations are based on the following paper: 
D E Acuna, S Allesina, K P Kording (2012) Future impact: Predicting scientific success. Nature 489, pp. 201-202. 
Since March, 2014
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