Professor Brijesh Verma Information and Communication Technology / School of Engineering and Technology

Professor and Director

PhD, SMIEEE, MINNS

Contact Details

Email: b.verma@cqu.edu.au

Phone: (07) 3295 1156 - Ext: 51156

Office Location

School of Engineering & Technology

CQUni, Level 21, Room 21.21, 160 Ann Street

Brisbane, QLD 4000, Australia

About Me

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.

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 5 research higher degree students. Overall, 35 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.

Refereed Articles

Top 10 Publications

  • Zhang, L., Verma, B. and Stockwell, D. (2016). Spatial Contextual Superpixel Model for Natural Roadside Vegetation Classification, Pattern Recognition, vol. 60, pp. 444–457, Elsevier. Rank: Tier A*, Impact Factor: 3.399, 5-Year Impact Factor: 3.707.
  • 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, 2015, Elsevier, Rank: Tier A, Impact Factor: 2.981, 5-Year Impact Factor: 2.879.
  • 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: 3.399, 5-Year Impact Factor: 3.707.
  • 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: 2.476, 5-Year Impact Factor: 3.018.
  • 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: 4.854, 5-Year Impact Factor: 5.167.
  • 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: 3.399, 5-Year Impact Factor: 3.707.
  • 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: 2.142, 5-Year Impact Factor: 2.136.
  • 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: 3.399, 5-Year Impact Factor: 3.707.
  • 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: 2.493, 5-Year Impact Factor: 2.87.
  • Verma, B. (1997). Fast Training of Multilayer Perceptrons (MLPs), IEEE Transactions on Neural Networks and Learning Systems, vol. 8, no. 6, pp. 1314-1321, IEEE. Rank: Tier A*, Impact Factor: 4.854, 5-Year Impact Factor: 5.167.

INFORMATION AND COMPUTING SCIENCES
Artificial Intelligence and Image Processing - Neural, Evolutionary and Fuzzy Computation
Artificial Intelligence and Image Processing - Pattern Recognition and Data Mining
Artificial Intelligence and Image Processing - Computer Vision
Conference paper

Shaheen, F., Verma, B., & Asafuddoula, M. (2016). Impact of automatic feature extraction in deep learning architecture. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016 (pp. 638-645). Piscataway, NJ.: IEEE. doi:10.1109/DICTA.2016.7797053

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Zhang, L., Verma, B., Stockwell, D., & Chowdhury, S. (2016). Aggregating pixel-level prediction and cluster-level texton occurrence within superpixel voting for roadside vegetation classification. In Proceedings of the International Joint Conference on Neural Networks Vol. 2016-October (pp. 3249-3255). Piscataway, NJ.: IEEE. doi:10.1109/IJCNN.2016.7727614

Zhang, L., Verma, B., Stockwell, D., & Chowdhury, S. (2016). Spatially constrained location prior for scene parsing. In Proceedings of the International Joint Conference on Neural Networks Vol. 2016-October (pp. 1480-1486). Piscataway, NJ.: IEEE. doi:10.1109/IJCNN.2016.7727373

Haidar, A., & Verma, B. (2016). A genetic algorithm based feature selection approach for rainfall forecasting in sugarcane areas. In Proceedings IEEE Symposium on Computational Intelligence, 2016 (SSCI) (pp. 1-8). Piscataway, NJ.: IEEE. Retrieved from http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7840087

Shaheen, F., & Verma, B. (2016). An ensemble of deep learning architectures for automatic feature extraction. In IEEE Symposium Series on Computational Intelligence 2016 (SSCI) (pp. 1-5). Piscataway, NJ.: IEEE.

Wang, Z., Verma, B., Walsh, K. B., Subedi, P., & Koirala, A. (2016). Automated mango flowering assessment via refinement segmentation. In D. Bailey, G. S. Gupta, & S. Marsland (Eds.), Proceedings of the 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 66-71). Piscataway, NJ: IEEE. doi:10.1109/IVCNZ.2016.7804426

Chowdhury, S., Verma, B., Roberts, J., Corbet, N., & Swain, D. (2016). Deep learning based computer vision technique for automatic heat detection in cows. In A. Wee-Chung Liew, B. Lovell, C. Fookes, J. Zhou, Y. Gao, M. Blumenstein, & Z. Wang (Eds.), International Conference on Digital Image Computing, 2016: Techniques and applications: DICTA 2016: (pp. 698-703). Piscataway, NJ.: IEEE. Retrieved from http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7794373

Chowdhury, S., Verma, B., & Zhang, L. (2016). Position gradient and plane consistency based feature extraction. In H. Akira, O. Seiichi, K. Doya, I. Kazushi, L. Minho, & L. Derong (Eds.), Neural Information Processing: Lecture Notes in Computer Science, volume 9950 Vol. 9948 LNCS (pp. 673-681). Heidelberg, Germany: Springer. doi:10.1007/978-3-319-46672-9_75

Zhang, L., & Verma, B. (2016). Rule-based grass biomass classification for roadside fire risk assessment. In H. Akira, O. Seiichi, K. Doya, I. Kazushi, L. Minho, & Derong (Eds.), Neural Information Processing: Lecture Notes in Computer Science, volume 9950 Vol. 9950 LNCS (pp. 636-644). Heidelberg, Germany: Springer. doi:10.1007/978-3-319-46681-1_75

Moore, P., Verma, B., & Li, M. (2015). A neural network based method to determine initial object positions for segmentation. In Proceedings of the 2015 11th International Conference on Natural Computation (ICNC 2015), 15-17 August 2015, Zhangjiajie, China (pp. 622-628). Piscataway, NJ.: IEEE. doi:10.1109/ICNC.2015.7378061

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Zhang, L., Verma, B., & Stockwell, D. (2015). Class-semantic color-texture textons for vegetation classification. In Neural information processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9–12, 2015 : proceedings, Part I (pp. 354-362). Cham: Springer. doi:10.1007/978-3-319-26532-2_39

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Zhang, L., & Verma, B. (2015). Class-semantic textons with superpixel neighborhoods for natural roadside vegetation classification. In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) : Adelaide, Australia, 23-25 November 2015 (pp. 244-251). Piscataway, NJ.: IEEE. doi:10.1109/DICTA.2015.7371246

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Lensen, A., Al-Sahaf, H., Zhang, M., & Verma, B. (2015). Genetic programming for algae detection in river images. In 2015 IEEE Congress on Evolutionary Computation (CEC) : Proceedings, 25-28 May 2015, Sendai, Japan. (pp. 2468-2475). Piscataway, NJ.: IEEE. doi:10.1109/CEC.2015.7257191

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Al-Sahaf, H., Zhang, M., Johnston, M., & Verma, B. (2015). Image descriptor : a genetic programming approach to multiclass texture classification. In 2015 IEEE Congress on Evolutionary Computation (CEC) : Proceedings, 25-28 May 2015, Sendai, Japan. (pp. 2460-2467). Piscataway, NJ.: IEEE. doi:10.1109/CEC.2015.7257190

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Chowdhury, S., Verma, B., Tom, M., & Zhang, M. (2015). Pixel characteristics based feature extraction approach for roadside object detection. In 2015 International Joint Conference on Neural Networks (IJCNN) : 12-17 July 2015, Killarney, Ireland. (pp. 1-8). USA: IEEE. doi:10.1109/IJCNN.2015.7280599

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McLeod, P., & Verma, B. (2015). Polynomial prediction of neurons in neural network classifier for breast cancer diagnosis. In Proceedings of the 2015 11th International Conference on Natural Computation (ICNC 2015), 15-17 August 2015, Zhangjiajie, China (pp. 777-782). Piscataway, NJ.: IEEE. doi:10.1109/ICNC.2015.7378089

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Zhang, L., Verma, B., & Stockwell, D. (2015). Roadside vegetation classification using color intensity and moments. In Proceedings of the 2015 11th International Conference on Natural Computation (ICNC 2015), 15-17 August 2015, Zhangjiajie, China (pp. 1250-1255). Piscataway, N.J.: IEEE. doi:10.1109/ICNC.2015.7378170

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Kinattukara Jobachan, T., & Verma, B. (2015). Wavelet based fuzzy clustering technique for the extraction of road objects. In Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), Istanbul, Turkey, 2-5 August 2015.. (pp. 1-7). Piscataway, NJ.: IEEE. doi:10.1109/FUZZ-IEEE.2015.7337887

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Kinattukara Jobachan, T., & Verma, B. (2014). A neural ensemble approach for segmentation and classification of road images. In Neural information processing : 21st international conference, (ICONIP 2014), Kuching, Malaysia, November 3-6, 2014, proceedings, Part III. (pp. 183-193). Switzerland: Springer. doi:10.1007/978-3-319-12643-2

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Chowdhury, S., & Verma, B. (2014). A novel feature extraction technique to retrieve vegetation class for fire risk assessment. In 2014 8th international conference on signal processing and communication systems, (ICSPCS), Gold Coast, Australia,15-17 December, 2014, proceedings. (pp. 1-6). Piscataway, NJ: IEEE. doi:10.1109/ICSPCS.2014.7021066

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Chowdhury, S., Verma, B., & Stockwell, D. (2014). Analysis of hybrid classification approach to differentiate dense and non-dense grass regions. In Simulated evolution and learning : 10th international conference, SEAL 2014, Dunedin, New Zealand, December 15-18, 2014, proceedings (pp. 835-846). Switzerland: Springer. doi:10.1007/978-3-319-13563-2

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Chiu, C. Y., & Verma, B. (2014). Multi-objective evolutionary algorithm based optimization of neural network ensemble classifier. In 2014 8th international conference on signal processing and communication systems, (ICSPCS), Gold Coast, Australia,15-17 December, 2014, proceedings. (pp. 1-5). Piscataway, NJ: IEEE. doi:10.1109/ICSPCS.2014.7021091

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McLeod, P., Verma, B., & Zhang, M. (2014). Optimizing configuration of neural ensemble network for breast cancer diagnosis. In 2014 international joint conference on neural networks (IJCNN), July 6-11, 2014, Beijing, China. (pp. 1087-1092). Piscataway, NJ: IEEE. doi:10.1109/IJCNN.2014.6889707

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Rahman, A., & Verma, B. (2013). Cluster oriented ensemble classifiers using multi-objective evolutionary algorithm. In International Joint Conference on Neural Networks (IJCNN 2013), Dallas, Texas, 4-9 August 2013. (pp. 1-6). Piscataway, NJ: IEEE. doi:10.1109/IJCNN.2013.6706822

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Kinattukara Jobachan, T., & Verma, B. (2013). Clustering based neural network approach for classification of road images. In International Conference o Soft Computing and Pattern Recognition (SoCPaR 2013), Hanoi, Vietnam, 15-18 December 2013. (pp. 178-183). Piscataway, NJ: IEEE.

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Chiu, C. Y., & Verma, B. (2013). Effect of varying hidden neurons and data size on clusters, layers, diversity and accuracy in neural ensemble classifier. In 2013 IEEE 16th International Conference on Computational Science and Engineering (CSE 2013), Sydney, Australia, 3-5 December 2013. (pp. 455-459). Piscataway, NJ: IEEE Computer Society. doi:10.1109/CSE.2013.212

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McLeod, P., & Verma, B. (2013). Effects of large constituent size in variable neural ensemble classifier for breast mass classification. In Neural Information Processing : 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings, Part 2. (pp. 525-532). Heidelberg, Germany: Springer. doi:10.1007/978-3-642-42051-1_65

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Kinattukara Jobachan, T., & Verma, B. (2013). Hierarchical segment learning method for road objects extraction and classification. In 2013 IEEE 16th International Conference on Computational Science and Engineering (CSE 2013), Sydney, Australia, 3-5 December 2013. (pp. 432-438). Piscataway, NJ: IEEE Computer Society. doi:10.1109/CSE.2013.72

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Chiu, C. Y., Verma, B., & Li, M. (2013). Impact of variability in data on accuracy and diversity of neural network based ensemble classifiers. In International Joint Conference on Neural Networks (IJCNN 2013), Dallas, Texas, 4-9 August 2013. (pp. 1999-2003). Piscataway, NJ: IEEE. doi:10.1109/IJCNN.2013.6706986

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McLeod, P., & Verma, B. (2012). A multilayered ensemble architecture for the classification of masses in digital mammograms. In AI 2012: Advances in Artificial Intelligence. (pp. 85-94). Germany: Springer. Retrieved from http://www.dx.doi.org/10.1007/978-3-642-35101-3

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Li, M., Guo, W., Verma, B., & Lee, H. (2012). A neural networks-based fitting to high energy stopping power data for heavy ions in solid matter. In 2012 International Joint Conference on Neural Networks. (pp. 1-6). New York: IEEE Computational Intelligence Society. Retrieved from http://dx.doi.org/10.1109/ijcnn.2012.6252478

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Rahman, A., Verma, B., & Stockwell, D. (2012). An hierarchical approach towards road image segmentation. In 2012 International Joint Conference on Neural Networks. (pp. 295-302). USA: IEEE. Retrieved from http://www.dx.doi.org/10.1109/ijcnn.2012.6252403

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McLeod, P., & Verma, B. (2012). Clustered ensemble neural network for breast mass classification in digital mammography. In 2012 International Joint Conference on Neural Networks. (pp. 1266-1271). USA: IEEE. Retrieved from http://www.dx.doi.org/10.1109/ijcnn.2012.6252539

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Ghassem Pour, S., McLeod, P., Verma, B., & Maeder, A. (2012). Comparing data mining with ensemble classification of breast cancer masses in digital mammograms. In Proceedings of the Second Australian Workshop on Artificial Intelligence in Health (AIH 2012). (pp. 55-64). Aachen, Germany: CEUR-WS, Sun SITE Central Europe operated under the umbrella of RWTH Aachen University.

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Rahman, A., & Verma, B. (2012). Influence of unstable patterns in layered cluster oriented ensemble classifier. In 2012 International Joint Conference on Neural Networks. (pp. 421-428). USA: IEEE.

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Verma, B., & Stockwell, D. (2011). An automated system for the analysis of the status of road safety using neural networks. In Neural information processing : 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, proceedings. Part 3. (pp. 530-537). Heidelberg: Springer. doi:10.1007/978-3-642-24965-5_60

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Chiu, C. Y., & Verma, B. (2011). An evolutionary algorithm based optimization of neural ensemble classifiers. In Neural information processing : 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, proceedings. Part 3. (pp. 292-298). Heidelberg: Springer. doi:10.1007/978-3-642-24965-5_32

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Rahman, A., & Verma, B. (2011). Ensemble classifier composition : impact on feature based offline cursive character recognition. In Proceedings of International Joint Conference on Neural Networks (IJCNN), San Jose, CA, 31 July-5 August 2011. (pp. 801-807). USA: IEEE. doi:10.1109/IJCNN.2011.6033303

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McLeod, P., & Verma, B. (2010). A classifier with clustered sub classes for the classification of suspicious areas in digital mammograms. In 2010 IEEE World Congress on Computational Intelligence (IEEE WCCI 2010). International Joint Conference on Neural Networks (IJCNN), 18-23 July 2010, Barcelona, Spain. (pp. 31-38). USA: IEEE. doi:10.1109/IJCNN.2010.5596832

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Rahman, A., & Verma, B. (2010). A novel ensemble classifier approach using weak classifier learning on overlapping clusters. In 2010 IEEE World Congress on Computational Intelligence (IEEE WCCI 2010). International Joint Conference on Neural Networks (IJCNN), 18-23 July 2010, Barcelona, Spain. (pp. 328-334). USA: IEEE. doi:10.1109/IJCNN.2010.5596332

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Zajaczkowski, J., & Verma, B. (2010). An evolutionary algorithm based approach for selection of topologies in hierarchical fuzzy systems. In 2010 IEEE World Congress on Computational Intelligence (IEEE WCCI 2010) : IEEE Congress on Evolutionary Computation (IEEE CEC), 18-23 July 2010, Barcelona, Spain. (pp. 976-983). USA: IEEE.

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Zajaczkowski, J., & Verma, B. (2010). MOEA based hierarchical fuzzy control over the set of user-defined initial conditions. In 2010 IEEE World Congress on Computational Intelligence (IEEE WCCI 2010) : IEEE Congress on Fuzzy Systems (Fuzz-IEEE 2010), 18-23 July 2010, Barcelona, Spain. (pp. 2326-2333). USA: IEEE.

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Rahman, A., Yao, X., & Verma, B. (2010). Non–uniform layered clustering for ensemble classifier generation and optimality. In ICONIP 2010 : Neural information processing : theory and algorithms, 17th international conference, proceedings, part II, 20-25 November 2010, Sydney, Australia (pp. 551-558). Heidelberg, Germany: Springer. doi:10.1007/978-3-642-17537-4_67

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Lee, H., & Verma, B. (2010). Over-segmentation and neural binary validation for cursive handwriting recognition. In 2010 IEEE World Congress on Computational Intelligence (IEEE WCCI 2010). International Joint Conference on Neural Networks (IJCNN), 18-23 July 2010, Barcelona, Spain. (pp. 3233-3237). USA: IEEE. doi:10.1109/IJCNN.2010.5596579

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Verma, B., Lee, H., & Zakos, J. (2009). An automatic intelligent language classifier. In Advances in neuro-information processing : 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 2008, revised selected papers, Part II (pp. 639-646). Berlin Heidelberg: Springer-Verlag. Retrieved from http://dx.doi.org/10.1007/978-3-642-03040-6_78

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Lee, H., & Verma, B. (2009). Binary segmentation with neural validation for cursive handwriting recognition. In Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 14-19, 2009. (pp. 1730-1735). NJ, USA: IEEE. Retrieved from http://dx.doi.org/10.1109/ijcnn.2009.5178955

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Verma, B. (2009). Impact of multiple clusters on neural classification of ROIs in digital mammograms. In Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 14-19, 2009. (pp. 3220-3230). NJ, USA: IEEE. Retrieved from http://dx.doi.org/10.1109/ijcnn.2009.5178942

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McLeod, P., Verma, B., & Park, M. (2009). Soft clustering and support vector machine based technique for the classification of abnormalities in digital mammograms. In Proceedings of the 2009 Fifth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2009), Melbourne, Victoria, 7-10 December 2009 (pp. 185-189). NJ, USA: IEEE. Retrieved from http://dx.doi.org/10.1109/ISSNIP.2009.5416794R

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Lee, H., & Verma, B. (2008). A novel multiple experts and fusion based segmentation algorithm for cursive handwriting recognition. In Proceedings : International Joint Conference on Neural Networks (IJCNN 2008) : (part of the 2008 IEEE World Congress on Computational Intelligence (WCCI 2008)), June 1-8, 2008. (pp. 2994-2999). USA: IEEE. doi:10.1109/IJCNN.2008.4634219

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Zajaczkowski, J., & Verma, B. (2008). Hierarchical fuzzy control for the inverted pendulum. In Proceedings : 7th international conference, Simulated Evolution and Learning (SEAL 2008), Melbourne, Australia, December 7-10, 2008. (pp. 534-543). Berlin: Springer-Verlag. doi:10.1007/978-3-540-89694-4_54

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McLeod, P., & Verma, B. (2008). Impact of soft clustering on classification of suspicious areas in digital mammograms. In Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2008), 15-18 December 2008, Sydney, Australia. (pp. 109-114). USA: IEEE. Retrieved from http://dx.doi.org/10.1109/issnip.2008.4761971

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Lee, H., & Verma, B. (2008). Over-segmentation and validation strategy for off-line cursive handwriting recognition. In Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2008), 15-18 December 2008, Sydney, Australia. (pp. 91-96). USA: IEEE. Retrieved from http://dx.doi.org/10.1109/issnip.2008.4761968

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Hassan, S., & Verma, B. (2008). Parallel neural-based hybrid data mining ensemble. In Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2008), 15-18 December 2008, Sydney, Australia. (pp. 115). USA: IEEE.

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Fan, X., & Verma, B. (2005). A comparative experimental analysis of separate and combined facial features for GA-ANN based technique. In 6th International Conference on Computational Intelligence and Multimedia Applications. (pp. 279-284). New Jersey: IEEE.

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Verma, B. (2003). A contour code feature based segmentation for handwriting recognition. In Proceedings of Seventh International Conference on Document analysis and recognition (ICDAR'03), United Kingdom, 3-6 August, 2003. (pp. 1203-1207). United States: IEEE Computer Society.

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Verma, B., Lu, J., Ghosh, M., & Ghosh, R. (2004). A feature extraction technique for online handwriting recognition. In 2004 Joint International Neural Networks Conference (IJCNN), and of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). (pp. 1337-1341). Piscataway , NJ, USA: Institute of Electrical and Electronics Engineers Inc..

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Panchal, R., & Verma, B. (2004). A fusion of neural network based auto-associator and classifier for the classification of microcalcification patterns. In Proceedings of the 11th International Conference on Neural Information Processing. (pp. 794-799). Berlin, Germany: Springer.

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Zhang, P., Kumar, K., & Verma, B. (2005). A hybrid classifier for mass classification with different kinds of features in mammography. In Fuzzy systems and knowledge discovery : second international conference, FSKD 2005, Changsha, China, August 27-29, 2005 : proceedings. (pp. 316-319). Germany: Springer-Verlag.

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Hassan, S., & Verma, B. (2007). A hybrid data mining approach for knowledge extraction and classification in medical databases. In Proceedings of Seventh International Conference on Intelligent Systems Design and Applications 22-24 October 2007. (pp. 503-508). USA: IEEE. Retrieved from http://dx.doi.org/10.1109/isda.2007.48

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Blumenstein, M., Liu, X., & Verma, B. (2004). A modified direction feature for cursive character recognition. In 2004 Joint International Neural Networks Conference (IJCNN), and of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). (pp. 2983-2988). Piscataway, NJ, USA: Institute of Electrical and Electronics Engineers, Inc..

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Verma, B. (2006). A Neural learning algorithm for the diagnosis of breast cancer. In IEEE World Congress on Computational Intelligence.. Piscataway, USA: IEEE.

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Goyal, A., & Verma, B. (2007). A neural network based approach for the vehicle classification. In Proceedings of the first IEEE Symposium on Computational Intelligence in Image and Signal Processing 2007 (CIISP 2007), Honolulu, HI, USA, April 2007. (pp. 226-231). USA: IEEE. Retrieved from http://dx.doi.org/10.1109/CIISP.2007.369173

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Bray, J., Li, X., He, W., & Verma, B. (2006). A Neural network based technique for automatic classification of road cracks. In Proceedings : International Joint Conference on Neural Networks (IJCNN 2006) (part of the 2006 IEEE World Congress on Computational Intelligence (WCCI 2006)), Vancouver, Canada, 16th - 21st July, 2006IEEE World Congress on Computational Intelligence. (pp. 1886-1891). Piscatway, NJ: IEEE.

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Verma, B., & Ghosh, M. (2003). A neural-evolutionary approach for feature and architecture selection in online handwriting recognition. In Proceedings of Seventh International Conference on Document Analysis and Recognition (ICDAR'03), United Kingdom, 3-6 August, 2003. (pp. 1203-1207). United States: IEEE Computer Society.

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Zhang, P., Verma, B., & Kumar, K. (2004). A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography. In 2004 Joint International Neural Networks Conference (IJCNN), and of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). (pp. 2303-2308). Piscataway, NJ, USA: Institute of Electrical and Electronics Engineers, Inc..

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Zakos, J., & Verma, B. (2005). A novel context matching based technique for web document retrieval. In Eighth International Conference on Document Analysis and Recognition, 2005. Proceedings. (pp. 909-913). New Jersey: IEEE.

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Verma, B., Panchal, R., & Kumar, K. (2003). A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications. In Proceedings of the International Joint Conference on Neural Networks 2003, Portland, Oregon, July 20-24 2003. (pp. 2033-2038). United States: The Institute of Electrical and Electronics Engineers. doi:10.1109/IJCNN.2003.1223720

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Verma, B., & Lee, H. (2007). A segmentation based adaptive approach for cursive handwritten text recognition. In Proceedings of International Joint Conference on Neural Networks, Orlando, USA, 12-17 August 2007. (pp. 2212-2216). Orlando, USA: IEEE. Retrieved from http://dx.doi.org/10.1109/ijcnn.2007.4371301

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Tao, Y., Muthukkumarasamy, V., Blumenstein, M., & Verma, B. (2003). A texture feature extraction technique using 2D-DFT and hamming distance. In Proceedings of Fifth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2003), Xia"an, China, 27-30 September 2003. (pp. 120-125). United States: IEEE Computer Society.

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Kumar, K., Zhang, P., & Verma, B. (2006). Application of decision trees for mass classification in mammography. In Advances in natural computation and Data Mining : Second International Conference on Natural Computation, (ICNC 2006), Xi'an, China, September 24-28, 2006 : Proceedings. Germany: Springer.

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Li, M., Fan, X., Verma, B., Balsys, R., & O'Connor, D. (2005). Artificial neural network techniques for analysis of ion backscattering spectra. In Proceedings of the International Conference on Artificial Intelligence, IC-AI '05, June 27-30, 2005, Las Vegas, Nevada, U.S.A. (pp. 1-7). Las Vegas: CSREA Press.

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Panchal, R., & Verma, B. (2006). Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network. In Proceedings of the 13th International Conference on Neural Information Processing (ICONIP2006). (pp. 127-136). Berlin, Germany: Springer-Verlag.

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Panchal, R., & Verma, B. (2005). Classification of breast abnormalities in digital mammograms using image and BI-RADS features in conjunction with neural network. In Proceedings of the IEEE International Joint Conference on Neural Networks. (pp. 2487-2492). New Jersey: IEEE. Retrieved from http://dx.doi.org/10.1109/ijcnn.2005.1556293

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McLeod, P., & Verma, B. (2007). Clustering and least square based neural technique for learning and identification of suspicious areas within digital mammograms. In Proceedings of International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), Sivakasi, Tamil Nadu, India, 13-15 December 2007. (pp. 190-194). U.S.A.: IEEE Computer Society. Retrieved from http://dx.doi.org/10.1109/iccima.2007.327

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McLeod, P., Verma, B., & Panchal, R. (2007). Combining SOM based clustering and MGS for classification of suspicious areas within digital mammograms. In Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia, 3-6 December 2007. (pp. 413-418). USA: IEEE. Retrieved from http://dx.doi.org/10.1109/issnip.2007.4496879

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Hassan, S., & Verma, B. (2007). Decisions fusion strategy : towards hybrid cluster ensemble. In Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia, 3-6 December 2007. (pp. 377-382). USA: IEEE.

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Fan, X., & Verma, B. (2004). Face recognition : a new feature selection and classification technique. In Complex 2004 : Proceedings of the 7th Asia-Pacific Complex Systems Conference, Cairns Convention Centre, Cairns, Australia, 6-10 December 2004. (pp. 713-721). Rockhampton, Qld.: Central Queensland University.

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Kulkarni, S., & Verma, B. (2003). Fuzzy logic based texture queries for CBIR. In Proceedings of Fifth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2003), Xi'an, China, 27-30 September 2003. (pp. 223-238). United States: IEEE Computer Society.

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Fan, X., & Verma, B. (2005). GA-ANN based technique for face recognition : PCA features vs average grey level value features. In Workshop on Learning Algorithms for Pattern Recognition : in conjunction with the 18th Australian Joint Conference on Artificial Intelligence (pp. 8-14). Sydney: UTS.

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Zakos, J., Verma, B., Li, X., & Kulkarni, S. (2003). Intelligent encoding of concepts in web document retrieval. In Proceedings of Fifth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2003), Xi'an, China, 27-30 September 2003. (pp. 72-77). United States: IEEE Computer Society.

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Zhang, P., Verma, B., & Kumar, K. (2003). Neural vs. statistical classifier in conjunction with genetic algorithm feature selection in digital mammography. In IEEE Congress on Evolutionary Computation (CEC '03), Canberra, Australia, 8-12 December 2003. (pp. 1206-1213). Piscataway, NJ: IEEE - Press. doi:10.1109/CEC.2003.1299806

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Zakos, J., Zhang, P., & Verma, B. (2005). Optimization of parameters for effective web information retrieval using an evolutionary algorithm. In Proceedings of the International Joint Conference on Neural Networks, Montreal, Canada, 2005. (pp. 582-587). New Jersey, USA.: IEEE.

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Li, X., He, W., Dong, Z., Verma, B., Yu, K., Koh, T., . . . Ling, S. (2003). Real time web vehicle classifier. In 7th International Symposium on Digital Processing and Communication Systems, PSPC'03 : and the 2nd Workshop on the Internet, Telecommunications and Signal Processing WITSP'03 (pp. 428-434). Wollongong, N.S.W.: The University of Wollongong.

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Panchal, R., & Verma, B. (2004). Significance of neural association of microcalcification patterns for their classification in digital mammography. In Complex 2004 : Proceedings of the 7th Asia-Pacific Complex Systems Conference, Cairns Convention Centre, Cairns, Australia, 6-10 December 2004. (pp. 729-737). Rockhampton, Qld.: Central Queensland University.

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Verma, B., Muthukkumarasamy, V., & He, C. (2003). Unsupervised clustering of texture features using SOM and Fourier Transform. In Proceedings of the International Joint Conference on Neural Networks 2003, Portland, Oregon, July 20-24 2003. (pp. 1237-1242). USA: IEEE. doi:10.1109/IJCNN.2003.1223870

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Journal article

Zhang, L., Verma, B., & Stockwell, D. (2016). Spatial contextual superpixel model for natural roadside vegetation classification. Pattern Recognition, 60, 444-457. doi:10.1016/j.patcog.2016.05.013

Li, M., & Verma, B. (2016). Nonlinear curve fitting to stopping power data using RBF neural networks. Expert systems with applications., 45, 161-171. doi:10.1016/j.eswa.2015.09.033

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Chowdhury, S., Verma, B., & Stockwell, D. (2015). A novel texture feature based multiple classifier technique for roadside vegetation classification. Expert systems with applications., 42(12), 5047-5055. doi:10.1016/j.eswa.2015.02.047

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Stockwell, D., Zhang, L., & Verma, B. (2015). Segmentation of geophysical data: a big data friendly approach. Procedia computer science., 53, 39-47. doi:10.1016/j.procs.2015.07.277

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Chowdhury, S., Verma, B., & Stockwell, D. (2014). A novel hybrid learning technique for roadside vegetation classification. Australian journal of intelligent information processing systems., 14(1), 1-6.

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Rahman, A., & Verma, B. (2013). Cluster based ensemble classifier generation by joint optimization of accuracy and diversity. International journal of computational intelligence and applications., 12(4), 1-13. doi:10.1142/S1469026813400038

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Rahman, A., & Verma, B. (2013). Cluster-based ensemble of classifiers. Expert systems : the journal of knowledge engineering., 30(3), 270-282. doi:10.1111/j.1468-0394.2012.00637.x

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Rahman, A., & Verma, B. (2013). Effect of ensemble classifier composition on offline cursive character recognition. Information processing and management., 49(4), 852-864. doi:10.1016/j.ipm.2012.12.010

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Rahman, A., & Verma, B. (2013). Ensemble classifier generation using non-uniform layered clustering and genetic algorithm. Knowledge-based systems., 43, 30-42. doi:10.1016/j.knosys.2013.01.002

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Chiu, C. Y., & Verma, B. (2013). Relationship between data size, accuracy, diversity and clusters in neural network ensembles. International journal of computational intelligence and applications., 12(4), 1-11. doi:10.1142/S1469026813400051

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McLeod, P., & Verma, B. (2013). Variable hidden neuron ensemble for mass classification in digital mammograms. IEEE computational intelligence magazine., 8(1), 68-76. doi:10.1109/MCI.2012.2228598

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Lee, H., & Verma, B. (2012). Binary segmentation algorithm for English cursive handwriting recognition. Pattern recognition : the journal of the Pattern Recognition Society., 45(4), 1306-1317. Retrieved from http://dx.doi.org/10.1016/j.patcog.2011.09.015

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Zajaczkowski, J., & Verma, B. (2012). Selection and impact of different topologies in multi-layered hierarchical fuzzy systems. Applied intelligence., 36(3), 564-584. doi:10.1007/s10489-011-0277-0

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Lee, H., & Verma, B. (2011). Binary validation as segmentation for cursive handwriting recognition. Asian journal of information technology., 10(5), 180-191. Retrieved from http://dx.doi.org/10.3923/ajit.2011.180.191

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Verma, B., & Rahman, A. (2011). Cluster-oriented ensemble classifier : impact of multi-cluster characterisation on ensemble classifier learning. IEEE transactions on knowledge and data engineering., 24(4), 605-618. doi:10.1109/TKDE.2011.28

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McLeod, P., & Verma, B. (2011). Multi-cluster support vector machine classifier for the classification of suspicious areas in digital mammograms. International journal of computational intelligence and applications., 10(4), 481-494. Retrieved from http://dx.doi.org/10.1142/s1469026811003203

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Rahman, A., & Verma, B. (2011). Novel layered clustering-based approach for generating ensemble of classifiers. IEEE transactions on neural networks., 22(5), 781-792. doi:10.1109/TNN.2011.2118765

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Verma, B., & Lee, H. (2011). Segment confidence-based binary segmentation (SCBS) for cursive handwritten words. Expert systems with applications., 38(9), 11167-11175. Retrieved from http://dx.doi.org/10.1016/j.eswa.2011.02.162

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Lee, H., Verma, B., & Park, M. (2010). A novel approach based on fusion of three neural experts for handwriting recognition. Australian journal of intelligent information processing systems., 12(3), 25-30.

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Verma, B., McLeod, P., & Klevansky, A. (2010). Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer. Expert systems with applications., 37(4), 3344-3351. doi:10.1016/j.eswa.2009.10.016

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Zajaczkowski, J., & Verma, B. (2009). A compositional method using an evolutionary alborithm for finding fuzzy rules in 3-layered hierarchical fuzzy structure. International journal of computational intelligence and applications., 8(4), 467-485.

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Verma, B., McLeod, P., & Klevansky, A. (2009). A novel soft cluster neural network for the classification of suspicious areas in digital mammograms. Pattern recognition : the journal of the Pattern Recognition Society., 42(9), 1845-1852.

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Verma, B., & Hassan, S. (2009). Hybrid ensemble approach for classification. Applied intelligence., 34(2), 258-278. doi:10.1007/s10489-009-0194-7

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Li, M., Guo, W., Verma, B., Tickle, K., & O'Connor, J. (2009). Intelligent methods for solving inverse problems of backscattering spectra with noise : a comparison between neural networks and simulated annealing. Neural computing & applications., 18(5), 423-430. Retrieved from http://dx.doi.org/10.1007/s00521-008-0219-x

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Fan, X., & Verma, B. (2009). Selection and fusion of facial features for face recognition. Expert systems with applications., 36(3P2), 7157-7169.

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Verma, B. (2008). Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms. Artificial intelligence in medicine., 42(1).

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Li, M., Verma, B., Fan, X., & Tickle, K. (2008). RBF neural networks for solving the inverse problem of backscattering spectra. Neural computing & applications., 17(4), 391-397. Retrieved from http://dx.doi.org/10.1007/s00521-007-0138-2

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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.

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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.

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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. Retrieved from http://search.ebscohost.com/

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Verma, B., Blumenstein, M., & Ghosh, M. (2004). A novel approach for structural feature extraction : contour vs. direction. Pattern recognition letters., 25(9), 975-988.

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Zakos, J., & Verma, B. (2006). A Novel context-based technique for web information retrieval. World Wide Web., 9(4), 485-503. doi:10.1007/s11280-006-0223-y

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Verma, B., & Zhang, P. (2007). A Novel neural-genetic algorithm to find the most significant combination of features in digital mammograms. Applied soft computing., 7(2), 612-625. doi:10.1016/j.asoc.2005.02.008

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Blumenstein, M., Liu, X., & Verma, B. (2007). An Investigation of the modified direction feature for cursive character recognition. Pattern recognition : the journal of the Pattern Recognition Society., 40(2), 376-388. Retrieved from http://ezproxy.cqu.edu.au/

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Zakos, J., & Verma, B. (2006). Concept-based term weighting for web information retrieval. International journal of computational intelligence and applications., 6(2), 193-207. doi:10.1142/S1469026806001915

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Verma, B., & Kulkarni, S. (2004). Fuzzy logic based interpretation and fusion of color queries. Fuzzy sets and systems., 147(1), 99-118. doi:10.1016/S0165-0114(03)00223-9

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Panchal, R., & Verma, B. (2006). Neural classification of mass abnormalities with different types of features in digital mammography. International journal of computational intelligence and applications., 6(1), 61-75.

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Zhang, P., Verma, B., & Kumar, K. (2005). Neural vs statistical classifier in conjunction with genetic algorithm based feature selection. Pattern recognition letters., 26(7), 909-919. doi:10.1016/j.patrec.2004.09.053

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Panchal, R., & Verma, B. (2006). Neural-association of microcalcification patterns for their reliable classification in digital mammography. International journal of pattern recognition and artificial intelligence., 20(7), 971-983. doi:10.1142/S0218001406005125

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Book chapter

Lee, H., Verma, B., Li, M., & Rahman, A. (2012). Machine learning techniques in handwriting recognition : problems and solutions. In Unknown (Ed.), Machine learning algorithms for problem solving in computational applications : intelligent techniques (pp. 12-29). USA: IGI Global. doi:10.4018/978-1-4666-1833-6

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Verma, B. (2012). Neural network based classifier ensembles : a comparative analysis. In Unknown (Ed.), Machine learning algorithms for problem solving in computational applications : intelligent techniques (pp. 229-239). USA: IGI Global. doi:10.4018/978-1-4666-1833-6

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Hassan, S., & Verma, B. (2009). Hybrid data mining for medical applications. In M. R. Syed, & S. N. Syed (Eds.), Handbook of research on modern systems analysis and design technologies and applications (pp. 523-543). Hershey, PA: Information Science Reference.

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Verma, B. (2009). Multicluster class-based classification for the diagnosis of suspicious areas in digital mammograms. In T. Pham (Ed.), Computational biology : issues and applications in oncology (pp. 113-124). New York, USA: Springer.

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Verma, B., & Blumenstein, M. (2008). Fusion of segmentation strategies for off-line cursive handwriting recognition. In B. Verma, & M. Blumenstein (Eds.), Pattern recognition technologies and applications : recent advances (pp. 1-16). USA: IGI Global.

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Verma, B., Zhang, P., & Kumar, K. (2006). A hybrid approach based on genetic algorithms in conjunction with statistical methods for the diagnosis of breast cancer. In T. D. Pham, H. Yan, & D. I. Crane (Eds.), Advanced computational methods for biocomputing and bioimaging (pp. 167-188). Commack, N.Y.: Nova.

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Verma, B., & Ghosh, R. (2004). Combination strategies for finding optimal neural network architecture and weights. In J. C. Rajapakse, & L. Wang (Eds.), Neural information processing : research and development (pp. 294-319). Germany: Springer.

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Verma, B., & Kulkarni, S. (2006). Neural networks for content based image retrieval. In Y. Zhang (Ed.), Semantic-based visual information retrieval (pp. p.). Hershey, USA: IRM.

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Verma, B., & Panchal, R. (2006). Neural networks for the classification of benign and malignant patterns in digital mammograms. In J. Fulcher (Ed.), Advances in applied artificial intelligence (pp. 251-272). USA: IGI. Retrieved from http://www.igi-pub.com/books/details.asp?id=5582

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Book

Jiao, L. C., Selvaraj, H., Verma, B., & Yao, X. (2003). Fifth International Conference on Computational Intelligence and Multimedia Applications : ICCIMA 2003, 27-30 September, Xi'an, China : proceedings. Los Alamitos, Calif.: IEEE Computer Society. doi:10.1109/ICCIMA.2003.1238080

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Selvaraj, H., Verma, B., & Carvalho, A. (2005). Sixth International Conference on Computational Intelligence and Multimedia Applications [electronic resource] : ICCIMA 2005 : proceedings : 16-18 August 2005, Las Vegas, Nevada. Los Alamitos, Calif.: IEEE Computer Society. doi:10.1109/ICCIMA.2005.51

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HE Term 2 - 2017
COIT20256 - Data Structures and Algorithms
Lecturer - COIT20256 - HE Term 2 - 2017
COIT20245 - Introduction to Programming
Lecturer - COIT20245 - HE Term 2 - 2017
HE Term 2 - 2016
COIT20256 - Data Structures and Algorithms
Lecturer - COIT20256 - HE Term 2 - 2016
COIT20245 - Introduction to Programming
Lecturer - COIT20245 - HE Term 2 - 2016
HE Term 2 - 2015
COIT29222 - Programming Principles
Lecturer - COIT29222 - HE Term 2 - 2015
COIT20245 - Introduction to Programming
Lecturer - COIT20245 - HE Term 2 - 2015
HE Term 2 - 2014
COIT29222 - Programming Principles
Lead Lecturer - COIT29222 - HE Term 2 - 2014
COIT23001 - Object Oriented Development
Lead Lecturer - COIT23001 - HE Term 2 - 2014
HE Term 1 - 2014
COIT29222 - Programming Principles
Lead Lecturer - COIT29222 - HE Term 1 - 2014
COIT23001 - Object Oriented Development
Lead Lecturer - COIT23001 - HE Term 1 - 2014
HE Term 2 - 2013
COIT29222 - Programming Principles
Lead Lecturer - COIT29222 - HE Term 2 - 2013
COIT23001 - Object Oriented Development
Lead Lecturer - COIT23001 - HE Term 2 - 2013
HE Term 1 - 2013
COIT29222 - Programming Principles
Lecturer - COIT29222 - HE Term 1 - 2013
COIT23001 - Object Oriented Development
Lecturer - COIT23001 - HE Term 1 - 2013
HE Term 2 - 2012
COIT29222 - Programming Principles
Unit Coordinator
COIT11222 - Programming Fundamentals
Unit Coordinator
HE Term 1 - 2012
COIT29222 - Programming Principles
Unit Coordinator
COIT11222 - Programming Fundamentals
Unit Coordinator
HE Term 2 - 2011
COIT29222 - Structured Programming
Unit Coordinator
HE Term 1 - 2011
COIT29222 - Structured Programming
Unit Coordinator