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Brijesh Verma

Professor
School of Engineering and Technology
CQ University
Australia

Biography

"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 is the President of INNS (International Neural Network Society) Australia Chapter. 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 books: Roadside Video Data Analysis: Deep Learning, 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 (2749 Citations in Google Scholar, i10-index: 70, h-index: 28). 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."

Research Interest

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

Publications

  • Verma, B., & Kulkarni, S. (2004). A fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems. Applied soft computing., 5(1), 119-130.

  • Ghosh, M., Ghosh, R., & Verma, B. (2004). A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier. International journal of pattern recognition and artificial intelligence., 18(7), 1267-1284.

  • Ghosh, R., & Verma, B. (2003). A hierarchical method for finding optimal architecture and weights using evolutionary least square based learning. International journal of neural systems., 13(1), 13-24.

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