Professor Brijesh Verma Technology / School of Engineering and Technology
Brijesh Verma is a Professor and the Director of the Centre for Intelligent Systems (CIS) in the School of Engineering and Technology (SET) at Central Queensland University (CQUni) in Brisbane, Australia. He was a co-founder, a co-leader and the Director of the Centre for Intelligent and Networked Systems (CINS) that has been recently re-structured and re-named as CIS. He was the Chair of the IEEE Computational Intelligence Society's Queensland Chapter and under his leadership the Chapter won Outstanding Chapter Award. He has recently won best overall paper award at 2015 IEEE Congress on Evolutionary Computation in Sendai, Japan.
His main research interests include Computational Intelligence and Pattern Recognition. He has authored/co-authored/co-edited 13 books (most recent book: Pattern Recognition Technologies and Applications: Recent Advances), 9 book chapters and over 150 papers [Download Papers via Google Scholar, Download Papers via CQU's Acquire Database] in areas such as neural networks, evolutionary algorithms, pattern recognition, computer vision, image processing, data mining, digital mammography and web information retrieval. He has developed a number of novel techniques for segmentation and classification of images, training of neural networks, creation of ensemble classifiers, optimisation using multi-objective evolutionary algorithm, segmentation of cursive handwriting, facial feature selection, detection and classification of microcalcification and web search. His publications and techniques have been widely cited (2228 Citations in Google Scholar, i10-index: 61, h-index: 26).
He is an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (Tier A* Journal in ERA 2010) and an Editor in Chief of International Journal of Computational Intelligence and Applications (IJCIA) (Tier A Journal in ERA 2010). He was also an Associate Editor of IEEE Transaction on Biomedicine in Information Technology (2004-2007) - Tier A* Journal. He is an editorial board member of 5 other international journals (Tier B/Tier C Journals). He is a Co-Chair of Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition at IEEE SSCI 2016 and the Chair of Special Session on Machine Learning for Computer Vision at IEEE WCCI 2016. He was the Chair of Special Session on Machine Learning for Computer Vision at IEEE WCCI 2014. He is/was a Program Committee Member of over 90 national and international conferences (15 conferences in 2015) including IEEE Joint International Conference on Neural Networks (IJCNN 2015) and 30th International Conference on Image and Vision Computing New Zealand (IVCNZ 2015).
He has received many competitive research grants including 4 ARC (Australian Research Council) grants, collaborative industry grants, CQU merit grant, GU infrastructure grant, GURD grant, GU campus grant and Batory foundation grant. His most recent grants are ARC Discovery Project (2016-2018) which is focused on developing a novel framework for optimised ensemble classifiers and ARC Linkage Project (2014-2017) which is focused on developing novel tools for roadside fire risk assessment using computational intelligence and pattern recognition techniques.
His teaching interests include programming (Java, C++), data structures and algorithms, software development, operating systems, computer architecture, emerging technologies, pattern recognition, digital image processing, neural networks and neural evolutionary computing. He is also involved in supervising research students. Currently he is supervising 4 PhD students. Overall, 34 research students have completed a research degree under his supervision.
If you are looking for a PhD/Masters research topic, please send your brief CV to Prof. Verma by e-mail.
Top 15 Publications (Ranking is taken from ERA 2008/2010)
1. Chowdhury, S., Verma, B. and Stockwell, D. (2015). A Novel Texture Feature based Multiple Classifier Technique for Roadside Vegetation Classification, Expert Systems with Applications, vol. 42, no. 12, pp. 5047-5055, Elsevier. Rank: Tier A Journal, Impact Factor: 2.24, 5-Year Impact Factor: 2.57.
2. Zhang, L., Verma, B. and Stockwell, D. (2015). Class-Semantic Color-Texture Textons for Vegetation Classification, 22nd International Conference on Neural Information Processing, 2015, Springer (Accepted on 06 August 2015). Rank: Tier A Conference, Flagship Conference.
3. Al-Sahaf, H., Zhang, M., Johnston, M. and Verma, B. (2015). Image Descriptor: A Genetic Programming Approach to Multiclass Texture Classification, IEEE Congress on Evolutionary Computation, pp. 2468-2475, IEEE Press. Rank: Tier A Conference, Flagship Conference of IEEE CI Society, Best Overall Paper Award.
4. McLeod, P., Verma, B. and Zhang, M. (2014). Optimizing Configuration of Neural Ensemble Network for Breast Cancer Diagnosis, IEEE International Joint Conference on Neural Networks, IEEE IJCNN, pp. 1087-1092. Rank: Tier A Conference, Flagship Conference of IEEE CI Society.
5. Rahman, A. and Verma, B. (2013). Effect of Ensemble Classifier Composition on Offline Cursive Character Recognition, Information Processing & Management, vol. 49, no. 4, pp. 852-864, Elsevier Science. Rank: Tier A Journal, Impact Factor: 1.265, 5-Year Impact Factor: 1.469.
6. Verma, B. and Rahman, A. (2012). Cluster Oriented Ensemble Classifier: Impact of Multi-cluster Characterisation on Ensemble Classifier Learning, IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 3, pp. 605-618, IEEE. Rank: Tier A, Impact Factor: 1.815, 5-Year Impact Factor: 2.573.
7. Lee, H. and Verma, B. (2012). Binary Segmentation Algorithm for English Cursive Handwriting Recognition, Pattern Recognition, vol. 45, no. 4, pp. 1306-1317, Elsevier. Rank: Tier A*, Impact Factor: 3.096, 5-Year Impact Factor: 3.613.
8. Rahman, A. and Verma, B. (2011). A Novel Layered Clustering based Approach for Generating Ensemble of Classifiers, IEEE Transactions on Neural Networks and Learning Systems, vol. 22, no. 5, pp. 781-792, IEEE. Rank: Tier A*, Impact Factor: 4.370, 5-Year Impact Factor: 4.308.
9. Verma, B., McLeod, P. and Klevansky, A. (2010). Classification of Benign and Malignant Patterns in Digital Mammograms for the Diagnosis of Breast Cancer, Expert Systems with Applications, vol. 37, no. 4, pp. 3344-3351, Elsevier. Rank: Tier A Journal, Impact Factor: 2.24, 5-Year Impact Factor: 2.57.
10. Verma, B., McLeod, P. and Klevansky, A. (2009). A Novel Soft Cluster Neural Network for the Classification of Suspicious Areas in Digital Mammograms, Pattern Recognition, vol. 42, no. 9, pp. 1845-1852, Elsevier. Rank: Tier A*, Impact Factor: 3.096, 5-Year Impact Factor: 3.613.
11. Verma, B. (2008). Novel Network Architecture and Learning Algorithm for the Classification of Mass Abnormalities in Digitized Mammograms, Artificial Intelligence in Medicine, vol. 42, no. 1, pp. 67-79, Elsevier. Rank: Tier A, Impact Factor: 1.357, 5-Year Impact Factor: 1.632.
12. Blumenstein, M., Liu, X. and Verma, B. (2007). An Investigation of the Modified Direction Feature for Cursive Character Recognition, Pattern Recognition, vol. 40, no. 2, pp. 376-388, Elsevier. Rank: Tier A*, Impact Factor: 3.096, 5-Year Impact Factor: 3.613.
13. Verma, B. and Kulkarni, S. (2004). Fuzzy Logic Based Interpretation and Fusion of Colour Queries, Fuzzy Sets and Systems, vol. 147, no. 1, pp. 99-118, Elsevier. Rank: Tier A, Impact Factor: 1.880, 5-Year Impact Factor: 2.263.
14. Verma, B. and Zakos, J. (2001). A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques, IEEE Transactions on Information Technology in Biomedicine, vol. 5, no. 1, pp. 5-14, IEEE. Rank: Tier A*, Impact Factor: 2.072, 5-Year Impact Factor: 2.584.
15. Verma, B. (1997). Fast Training of Multilayer Perceptrons (MLPs), IEEE Transactions on Neural Networks, vol. 8, no. 6, pp. 1314-1321, IEEE. Rank: Tier A*, Impact Factor: 4.370, 5-Year Impact Factor: 4.308.