1. Presentations:

(1) “Automatic Target Recognition using multiple-aspect sonar images”, IEEE Congress on Evolutionary Computation 2014, Beijing, China. (International)

(2) “Vessel track correlation and association using fuzzy logic and Echo State Networks”, IEEE Congress on Evolutionary Computation 2014, Beijing, China. (International)

(3) “Explicit feature mapping via multi-layer perceptron and its application to Mine-Like Objects detection”, IJCNN 2014, Beijing, China. (International)

(4) “Cost-Sensitive Boosting Algorithms for Imbalanced Multi-instance Datasets”, 2013 Canadian Conference on Artificial Intelligence, Regina, Saskatchewan, Canada. (International)

(5) “Ensemble of Multiple Kernel SVM Classifiers”, Canadian Conference on AI 2014, Montreal, Quebec, Canada. (International)

(6) “Resampling and Cost-Sensitive Methods for Imbalanced Multi-instance Learning”, 2013 IEEE International Conference on Data Mining (ICDM), Dallas, Texas, USA. (International)

(7) “Machine learning algorithm for Autonomous Underwater Vehicles operations”, 2012 University of Ottawa Research Day, Ottawa, Ontario, Canada (Poster, Presentation, Institutional)

(8) “Boosting Support Vector Machines for Imbalanced Data Sets”, 2008 Tamale seminar in University of Ottawa, Ottawa, Ontario, Canada (Presentation, Institutional)

(9) “Resampling and Cost-Sensitive Methods for Imbalanced Multi-instance Learning”, 2013 Tamale seminar in University of Ottawa, Ottawa, Ontario, Canada (Presentation, Institutional)

(10) “Cost-Sensitive Boosting Algorithms for Imbalanced Multi-instance Datasets. In Advances in Artificial Intelligence”, 2013 Tamale seminar in University of Ottawa, Ottawa, Ontario, Canada (Presentation, Institutional)

2. Paper Reviewed:

(1) “Cost-free Learning for SVM Classifiers in the Class Imbalanced Problems”, (Blind review) ICML 2014, Beijing, China.
(2) Li, Q., Mao, Y., “Boosting Methods for Imbalanced Data Classification: An in-depth Study”, Pattern Recognition, November 17, 2012.