Complete List of Publications by Projects

Current Research Interests: Big Data analytics in public security (Attack and Disaster Preparation/Recovery/Response; Cyber Security; Internal Security; and Predictive Policing [Channel NewsAsia][ConvergenceAsia]) and fraud (Government, Banking, and Insurance)

Phua, C. (2015). Analytics for Urban Solutions: From Data Fusion to Actionable Awareness. Proc. of NCS TechConnect 2015. [pdf][prezi]

Gokarn, I. and Phua, C. (2015). Understanding Characteristics of Insider Threats by Using Feature Extraction. Proc. of SAS Global Forum 2015. [pdf]

Phua, C., Feng, Y., Ji, J. and Soh, T. (2014). Visual and Predictive Analytics on Singapore News: Experiments on GDELT, Wikipedia, and ^STI. Computing Research Repository, arXiv:1404.1996. [paper][prezi]

Phua, C., Ji, J., Chng, K. and Lee, Y. (2013). Four Steps to Visual Analytics for Cyber Security. Proc. of VAST13. [paper][prezi][video]

Past Research Interests: Web data gathering (scraping and crawling of deep Web, designing and consuming of high-speed APIs and feeds), security data mining (data breach detection, phishing detection, and plagiarism detection), data mining-based crime detection, data mining-based fraud detection [presentation], and my previous scope did expand a little into mobile computing, computational engineering, computational finance, and other applications

Conort, X., Chua, H., Cao, H., Phua, C. and Yap, G. (2014). Big Data Analytics for Flight Arrival Time Prediction. Proc. of 2014 INFORMS Conference on Business Analytics and Operations Research. [prezi]

Oentaryo, R., Lim, E., Zhu, F., Lo, D., Finegold, M., Phua, C., Cheu, E., Yap, G., Sim, K., Nguyen, M., Perera, K., Neupane, B., Faisal, M., Aung, Z., Woon, W., Chen, W., Patel, D. and Berrar, D. (2014). Detecting Fraudulent Patterns in Online Advertising: A Data Mining Approach. Journal of Machine Learning Research, 15(Jan): pp. 99-140. [pdf][bib]

Gomes, B., Phua, C. and Krishnaswamy, S. (2013). Where Will You Go? Mobile Data Mining for Next Place Prediction. Proc. of DaWaK13, pp. 146-158. [pdf]

Chia, M. and Phua, C. (2013). Longitudinal Development of Physical Fitness Among Singaporean Youths - Insights and Lessons from Data Mining. Adaptation Biology and Medicine, 7: Chapter 25. [pdf]

Phua, C., Cheu, E., Yap, G., Sim, K. and Nguyen, M. (2012). Feature Engineering for Click Fraud Detection. Proc. of ACML12 Workshop on Fraud Detection in Mobile Advertising. [paper][poster][prezi][feature list][click fraud data]

Phua, C., Cao, H., Gomes, B. and Nguyen, M. (2012). Predicting Near-Future Churners and Win-Backs in the Telecommunications Industry. Computing Research Repository, abs/1210.6891. [pdf]

Feng, M., Loy, L., Sim, K., Phua, C., Zhang, F. and Guan, C. (2012). Artifact Correction with Robust Statistics for Non-Stationary Intracranial Pressure Signal Monitoring. Proc. of ICPR12, pp. 557-560. [pdf]

Sim, K., Gopalkrishnan, V., Phua, C. and Cong, G. (2012). 3D Subspace Clustering for Value Investing. IEEE Intelligent Systems, 29(2): pp. 52-59. [pdf]

Phua, C. (2009). Protecting Organizations from Personal Data Breaches. Computer Fraud and Security, January 2009, pp. 13-18. [pdf]

Phua, C. and Ashrafi, M. (2008). Security Data Mining: A Survey Introducing Tamper-Resistance. Data Mining for Business Applications, Chapter 8, pp. 97-110. [pdf]

Wattalyzer Project [I²R] [SP] [PA] - An Integrated Solution for Smart Grid Condition Monitoring through Advanced Sensing and Real-Time Analytics

Activity Monitoring and UI Plasticity for supporting Ageing with Mild Dementia at Home Project [I²R] [IPAL] [NUS] (completed)

Biswas, J., Endelin, R., Phua, C., Aung, A., Tolstikov, A., Jiaqi, Z., Tiberghien, T., Aloulou, H., Yap, P. and Mokhtari, M. (2015). Activity Recognition in Assistive Living Facilities with Incremental, Approximate Ground Truth, Proc. of ICOST15. [pdf]

Aung, A., Lin, K., Lee, A., Phua, C., Tiberghien, T., Aloulou, H., Liu, Y., Zhang, X., Biswas, J. and Yap, P. (2013). Challenges, Experiences and Lessons Learned from Deploying Patient Monitoring and Assistance System at Dementia Care Hostel, Proc. of ICOST13, pp. 292-297. [pdf]

Aloulou, H., Mokhtari, M., Tiberghien, T., Biswas, J., Phua, C., Lin, K. and Yap, P. (2013). Deployment of Assistive Living Technology in a Nursing Home Environment: Methods and Lessons Learned. BMC Medical Informatics and Decision Making, 13: 42. [pdf]

Cao, H., Nguyen, M., Phua, C., Krishnaswamy, S. and Li, X. (2012). An Integrated Framework for Human Activity Classification. Proc. of UbiComp12, pp. 331-340. [pdf]

Lee, A., Liu, Y., Zhang, X., Phua, C., Sim, K., Zhu, J., Biswas, J., Dong, J. and Mokhtari, M. (2012). ACARP: Auto Correct Activity Recognition Rules using Process Analysis Toolkit (PAT). Proc. of ICOST12, pp. 182-189. [pdf]

Biswas, J., Aung, A., Tolstikov, A., Lin, K., Maniyeri, J., Foo, V., Lee, A., Phua, C., Zhu, J., Huynh, H., Tiberghien, T., Aloulou, H. and Mokhtari M. (2011). From Context to Micro-context - Issues and Challenges in Sensorizing Smart Spaces for Assistive Living. Proc. of ANT11, pp. 288-295. [pdf]

Sim, K., Phua, C., Yap, G., Biswas, J. and Mokhtari, M. (2011). Activity Recognition using Correlated Pattern Mining for People with Dementia. Proc. of EMBC11, pp. 7593-7597. [pdf]

Tolstikov, A., Phua, C., Biswas, J. and Huang, W. (2011). Multiple People Activity Recognition using MHT over DBN. Proc. of ICOST11, pp. 313-318. [pdf]

Phua, C., Sim, K. and Biswas, J. (2011). Multiple People Activity Recognition Using Simple Sensors. Proc. of PECCS11, pp. 224-231. [pdf]

Ambient Intelligence for Home-based Elderly Care Project (completed)

We worked on erroneous-plan recognition for people with dementia. Specifically, in eating habits for home-alone person [bibsonomy].

Phua, C., Roy, P. C., Aloulou, H., Biswas, J., Tolstikov, A., Foo, V., Aung, A., Huang, W., Feki, M., Maniyeri, J., Chu, A. and Xu, D. (2011). State-of-the-Art Assistive Technology for People with Dementia. Handbook of Research on Ambient Intelligence and Smart Environments: Trends and Perspectives, pp. 300-319. [pdf][presentation]

Roy, P. C., Giroux, S., Bouchard, B., Bouzouane, A., Phua, C., Tolstikov, A. and Biswas, J. (2011). A Possibilistic Approach for Activity Recognition in Smart Homes for Cognitive Assistance to Alzheimer's Patients. Activity Recognition in Pervasive Intelligent Environments, pp. 33-58. [pdf]

Roy, P. C., Giroux, S., Bouchard, B., Bouzouane, A., Phua, C., Tolstikov, A. and Biswas, J. (2010). Possibilistic Behavior Recognition in Smart Homes for Cognitive Assistance. Proc. of AAAI10 Workshop on Plan, Activity, and Intent Recognition, pp. 53-60. [pdf]

Sim, K., Yap, G., Phua, C., Biswas, J., Aung, A., Tolstikov, A., Huang, W. and Yap, P. (2010). Improving the Accuracy of Erroneous-Plan Recognition System for Activities of Daily Living. Proc. of HealthCom10, pp. 28-35. [pdf]

Biswas, J., Tolstikov, A., Maniyeri, J., Foo, V., Aung, A., Phua, C., Huang, W., Shue, L., Gopalakrishnan, K., Lee, J. and Yap, P. (2010). Health and Wellness Monitoring through Wearable and Ambient Sensors: Exemplars from Home-based Care of Elderly with Mild Dementia. Annals of Telecommunications, 65(9-10): pp. 505-521. [pdf]

Phua, C., Foo, V., Biswas, J., Tolstikov, A., Aung, A., Maniyeri, J., Huang, W., That, M., Xu, D. and Chu, A. (2009). 2-Layer Erroneous-Plan Recognition for Dementia Patients in Smart Homes. Proc. of HealthCom09, pp. 21-28. [pdf][presentation]

Phua, C., Biswas, J., Tolstikov, A., Foo, V., Huang, W., Maniyeri, J., Aung, A., Roy, P. C., Aloulou, H., Feki, M., Giroux, S., Bouzouane, A. and Bouchard, B. (2009). Plan Recognition based on Sensor Produced Micro-Context for Eldercare. Proc. of RO-MAN09 Workshop on Context-Awareness in Smart Environments: Background, Achievements, and Challenges. [pdf]

Aloulou, H., Feki, M., Phua, C. and Biswas, J. (2009). Efficient Incremental Plan Recognition Method for Cognitive Assistance. Proc. of ICOST09, pp. 225-228. [pdf]

Identity Crime and Data Mining Project [Veda] (completed)

We worked on a fraud detection system for credit card applications. It is based on the idea that any successful fraudster, within certain time frames, will exhibit consistent, communal, temporal, spatial, and persistent characteristics which are distinguishable from the normal credit applications. It goes beyond the conventional industry technique of ID number, address, and phone number verification.

During the course of this project, a number of public presentations were made.

For reference, in reverse order of my PhD dissertation [pdf] and honours dissertation [pdf][presentation], the papers related to these presentations are listed and attached.

Phua, C., Smith-Miles, K., Lee, V. and Gayler, R. (2012). Resilient Identity Crime Detection. IEEE Transactions on Knowledge and Data Engineering, 24(3): pp. 533-546. [pdf][bib][Papers which have plagiarized Resilient Identity Crime Detection]

Phua, C., Lee, V., and Smith-Miles, K. (2008). Utility of Real-time Decision-making in Commercial Data Stream Mining Domains. Proc. of ICSSSM08, pp. 46-51. [pdf][presentation]

Phua, C., Smith-Miles, K., Lee, V. and Gayler, R. (2007). Adaptive Spike Detection for Resilient Data Stream Mining. Proc. of AusDM07, pp. 181-188. [pdf][presentation]

Phua, C., Lee, V., Gayler, R., and Smith-Miles, K. (2006). Temporal Representation in Spike Detection of Sparse Personal Identity Streams. Proc. of PAKDD06 Workshop on Intelligence and Security Informatics, pp. 115-126. [pdf]

Phua, C., Lee, V., Smith-Miles, K. and Gayler, R. (2007). Adaptive Communal Detection in Search of Adversarial Identity Crime. Proc. of SIGKDD07 Workshop on Domain-Driven Data Mining, pp. 1-10. [pdf][presentation]

Phua, C., Gayler, R., Smith-Miles, K. and Lee, V. (2006). Communal Detection of Implicit Personal Identity Streams. Proc. of ICDM06 Workshop on Mining Evolving and Streaming Data, pp. 620-625. [pdf]

Phua, C., Gayler, R., Lee, V. and Smith-Miles, K. (2009). On the Communal Analysis Suspicion Scoring for Identity Crime in Streaming Credit Applications. European Journal of Operational Research, 195: pp. 595-612. [pdf]

Phua, C., Gayler, R., Lee, V. and Smith-Miles, K. (2005). On the Approximate Communal Fraud Scoring of Credit Applications. Proc. of CSCC05. [pdf][presentation][synthetic credit card application fraud data]

Phua, C., Lee, V. and Smith-Miles, K. (2009). The Personal Name Problem and a Recommended Data Mining Solution. Encyclopedia of Data Warehousing and Mining (2^nd Edition), pp. 1524-1531. [pdf][data]

Phua, C., Lee, V., Smith-Miles, K. and Gayler, R. (2010). A Comprehensive Survey of Data Mining-based Fraud Detection Research. Computing Research Repository, abs/1009.6119. [pdf][bib of all references]

Phua, C., Alahakoon, D. and Lee, V. (2004). Minority Report in Fraud Detection: Classification of Skewed Data. SIGKDD Explorations, 6(1): pp. 50-59. [pdf][data][WEKA ARFF car insurance fraud data]