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Research Portfolio

Tony specialises in artificial intelligence and machine learning research, stemming from his pioneering work in probabilistic neural networks (PNN) and extending to federated machine learning, enabling distributed AI solutions in energy-depleted and resource-constrained edge environments.

His work focuses on advancing federated machine learning, exemplified by innovative applications in physics-informed AI, smart environments, IIoT security, and ambient intelligence modelling.

His research has been supported by ARC grants and industry awards, and has led to the supervision of more than 12 PhD graduates, many of whom have progressed to prominent roles in academia and industry. Tony is a frequent keynote speaker at leading machine learning conferences and a recipient of multiple internal and external research awards.

Research Summary

Refereed Journals and Conference Articles

  1. Adel, A., Das, B. and Jan, T. 2026, ‘Federated predictive maintenance with NASA BP930 satellite battery in Industry 5.0’, Journal of Intelligent Manufacturing, [Q1, Impact Factor: 7.4]
  2. Jones, N., Whaiduzzaman, M. and Jan, T. 2026, ‘Provenance-Based Cybersecurity for LLMs Using Lossless Graph Compression for Computational Efficiency’, IEEE Transactions on Artificial Intelligence, vol. 1, no. 1, pp. 1–10. [Q1, Impact Factor: 4.8].
  3. Whaiduzzaman, M., Monalisa, N.T., Himi, S.T., Sultana, S., Jan, T. and Barros, A., 2026. Enhancing IIoT Security Using Digital Twins in Industry 5.0: A Systematic Literature Review. Information, 17(2) [Q2, Impact Factor: 2.9].
  4. Tiwana, M.S., Braytee, A., Karimi, F., Bandara, M., Jan, T. and Prasad, M., 2026. DisasterSense: Event Classification in Social Media Using RAKE-Based Pattern Mining and Dictionary-Driven Named Entity Recognition. Machine Learning with Applications, p.100882 [Q1, Impact Factor: 4.9].
  5. Nguyen, T.D., Alazab, A., Khraisat, A. and Jan, T., 2026. Feature reduction in federated learning for intrusion detection in IoT networks. Cybersecurity, 9(1), p.102 [Q1, Impact Factor: 3.7].
  6. Kumar, C., Samal, U., Jan, T., Shaukat, K. and Prasad, M., 2026. Causal modeling in software defect prediction: bridging expert knowledge and novice insight. Automated Software Engineering, 33(3), p.80 [Q2, Impact Factor: 3.7].
  7. Kammari, K.S., Khanna, M., Annambhotla, Y.L., Anuradha, K.D., Namrata, P.N.S., Nagaraja, K.V., Smitha, T.V., Jan, T. and Naik, G.R., 2025. A Comprehensive Review of Deepfake Detection Techniques Utilizing Remote Photoplethysmography. IEEE Access [Q1, Impact Factor: 3.4].
  8. Adel, A., Alani, N.H., Jan, T. and Prasad, M., 2025. A review of major ICT failures and recovery strategies: Strengthening digital resilience. Computers & Security, p.104678 [Q1, Impact Factor: 5.4].
  9. Tom, A.K., Khraisat, A., Jan, T., Whaiduzzaman, M., Nguyen, T.D. and Alazab, A., 2025. Survey of federated learning for cyber threat intelligence in industrial IoT: techniques, applications and deployment models. Future Internet, 17(9), p.409 [Q2, Impact Factor: 3.6].
  10. Khraisat, A., Alazab, A., Alazab, M., Obeidat, A., Singh, S. and Jan, T., 2025. Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy. Discover Internet of Things, 5(1), p.72 [Q1, Impact Factor: 4.7].
  11. Skipworth, S., Chatteur, F., Maher, M.L. and Jan, T., 2025. The acceptance of artificial intelligence (AI) by an Australian interior design and design education community. International Journal of Design Creativity and Innovation, 13(2), pp.93-113 [Q1, Impact Factor: 2.5].
  12. Oliullah, K., Whaiduzzaman, M., Mahi, M.J.N., Jan, T. and Barros, A., 2025. A machine learning based authentication and intrusion detection scheme for IoT users anonymity preservation in fog environment. PLOS ONE, 20(6), p.e0323954. [Q1, Impact Factor: 2.6].
  13. Ki, C., Sivakumar, R., Mulerikkal, J., Gupta, M. and Jan, T., 2025. A comprehensive survey of machine learning and deep learning approaches for anomaly detection in high-performance computing systems. The Journal of Supercomputing, 81(8), pp.1-45 [Q2, Impact Factor: 2.7].
  14. Das, B., Adel, A., Jan, T. and Wahiduzzaman, M.D., 2025. Water quality management using federated deep learning in developing Southeastern Asian Country. Water Resources Management, 39(4), pp.1893-1909 [Q1, Impact Factor: 3.9].
  15. Ankalaki, S., Rajesh, A.A., Pallavi, M., Hukkeri, G.S., Jan, T. and Naik, G.R., 2025. Cyber Attack Prediction: From Traditional Machine Learning to Generative Artificial Intelligence. IEEE Access [Q1, Impact Factor: 3.4].
  16. Jones, N., Whaiduzzaman, M., Jan, T., Adel, A., Alazab, A. and Alkreisat, A., 2025. A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models. Future Internet, 17(3), p.113 [Q2, Impact Factor: 2.8].
  17. Khraisat, A., Alazab, A., Alazab, M., Jan, T., Singh, S. and Uddin, M.A., 2025. Securing federated learning: a defense strategy against targeted data poisoning attack. Discover Internet of Things, 5(1), p.16, [Q1, Impact Factor: 4.7].
  18. Shrestha, S., Huang, X., Saleem, K., Bevinakoppa, S. and Jan, T., 2024. Quantification of Interference Constraint for Small Cells in Low SINR Regime with Steepest Ascent Method. IEEE Access [Q1, Impact Factor: 3.4].
  19. Khraisat, A., Alazab, A., Singh, S., Jan, T. and Jr. Gopez, A., 2024. Survey on Federated Learning for Intrusion Detection System: Concept, Architectures, Aggregation Strategies, Challenges, and Future Directions. ACM Computing Surveys [Q1, Impact Factor: 23.8].
  20. Sulimani, H., Sulimani, R., Ramezani, F., Naderpour, M., Huo, H., Jan, T. and Prasad, M., 2024. HybOff: a Hybrid Offloading approach to improve load balancing in fog environments. Journal of Cloud Computing, 13(1), p.113 [Q1, Impact Factor: 3.7].
  21. Pradyumna, G.R., Hegde, R.B., Bommegowda, K.B., Jan, T. and Naik, G.R., 2024. Empowering healthcare with IoMT: Evolution, machine learning integration, security, and interoperability challenges. IEEE Access [Q1, Impact Factor: 3.4].
  22. Adel, A., Alani, N.H., Whiteside, S.T. and Jan, T., 2024. Who is Watching Whom? Military and Civilian Drone: Vision Intelligence Investigation and Recommendations. IEEE Access [Q1, Impact Factor: 3.4].
  23. Butt, S., Shaukat, K., Alam, T.M. and Jan, T., 2024. Patents and Public Health: State Responsibility to Opt for a Balanced Approach. Societies, 14(8), p.152 [Q2, Impact Factor: 1.7].
  24. Das, B., Adel, A., Jan, T. and Wahiduzzaman, M.D., 2024. Water Quality Management using Federated Deep Learning in Developing Southeastern Asian Country. Water Resources Management, pp.1-17 [Q1, Impact Factor: 3.9].
  25. Marshall, J., Thompson-Whiteside, S. and Jan, T., 2024. Barriers to the adoption of industry 4.0 technologies within the Australian fashion industry. International Journal of Fashion Design, Technology and Education, pp.1-11 [Q1, Impact Factor: 3.1].
  26. Rukmini, P.G., Hegde, R.B., Basavarajappa, B.K., Bhat, A.K., Pujari, A.N., Gargiulo, G.D., Gunawardana, U., Jan, T. and Naik, G.R., 2024. Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare—A Survey. Sensors, 24(13), p.4301 [Q1, Impact Factor: 3.4].
  27. Adel, A. and Jan, T., 2024. Watch the Skies: A Study on Drone Attack Vectors, Forensic Approaches, and Persisting Security Challenges. Future Internet, 16(7) [Q2, Impact Factor: 2.8].
  28. Siddique, A., Shaukat, K. and Jan, T., 2024. An Intelligent Mechanism to Detect Multi-Factor Skin Cancer. Diagnostics, 14(13), p.1359 [Q2, Impact Factor: 3.0].
  29. Nobel, S.N., Sultana, S., Singha, S.P., Chaki, S., Mahi, M.J.N., Jan, T., Barros, A. and Whaiduzzaman, M., 2024. Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data. Information, 15(6), p.298 [Q2, Impact Factor: 3.1].
  30. Adel, A., Alani, N.H. and Jan, T., 2024. Factories of the future in industry 5.0—Softwarization, Servitization, and Industrialization. Internet of Things, 28, p.101431 [Q1, Impact Factor: 6.0].
  31. Rana, S., Hosen, M.J., Tonni, T.J., Rony, M.A.H., Fatema, K., Hasan, M.Z., Rahman, M.T., Khan, R.T., Jan, T. and Whaiduzzaman, M., 2024. DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features. Sensors, 24(9), p.2830 [Q1, Impact Factor: 3.4].
  32. Begum, M., Shorif, S.B., Uddin, M.S., Ferdush, J., Jan, T., Barros, A. and Whaiduzzaman, M., 2024. Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition for Enhanced Imperceptibility and Robustness. Algorithms, 17(1), p.32 [Q2, Impact Factor: 2.3].
  33. Azizi, N., Jan, T., Miao, Y., Haass, O. and Sidnal, N., 2024. Industry 4.0 and Beyond: Fortifying Industrial Cybersecurity for Sustainable Growth. In Strengthening Industrial Cybersecurity to Protect Business Intelligence (pp. 229-243). IGI Global.
  34. Gupta, G., Raja, K., Gupta, M., Jan, T., Whiteside, S.T. and Prasad, M., 2023. A Comprehensive Review of DeepFake Detection Using Advanced Machine Learning and Fusion Methods. Electronics, 13(1), p.95 [Q2, Impact Factor: 2.6].
  35. Hossain, M.E., Faruqui, N., Mahmud, I., Jan, T., Whaiduzzaman, M. and Barros, A., 2023. DPMS: Data-driven promotional management system of universities using deep learning on social media. Applied Sciences, 13(22), p.12300 [Q2, Impact Factor: 2.5].
  36. Sulimani, H., Sajjad, A.M., Alghamdi, W.Y., Kaiwartya, O., Jan, T., Simoff, S. and Prasad, M., 2023. Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment. Transactions on Emerging Telecommunications Technologies, 34(11), p.e4650 [Q1, Impact Factor: 3.7].
  37. Whaiduzzaman, M., Sakib, A., Khan, N.J., Chaki, S., Shahrier, L., Ghosh, S., Rahman, M.S., Mahi, M.J.N., Barros, A., Fidge, C. and Thompson-Whiteside, S. Jan, T., 2023 Concept to Reality: An Integrated Approach to Testing Software User Interfaces, Applied Sciences, 13(21), 11997 [Q2, Impact Factor: 2.7].
  38. Tan J, Goyal S B, Singh Rajawat A, Jan T, Azizi N and Prasad M., 2023 Anti-Counterfeiting and Traceability Consensus Algorithm Based on Weightage to Contributors in a Food Supply Chain of Industry 4.0 Sustainability 15 7855 Online: http://dx.doi.org/10.3390/su15107855 [Q1, Impact Factor: 3.889].
  39. Thakur, G. S., Sahu, S. K., Swamy, N. K., Gupta, M., Jan, T., & Prasad, M., 2023, Review of Soft Computing Techniques in Monitoring Cardiovascular Disease in the Context of South Asian Countries. Applied Sciences (Switzerland), 13(9555) [Q2, Impact Factor: 2.7].
  40. Singh Rajawat, A., Goyal, S. B., Bedi, P., Jan, T., Whaiduzzaman, W., & Prasad, M. (2023). Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT). Future Internet, 15(8). [Q2, Impact Factor: 3.4]
  41. Grover, P., Chaturvedi, K., Zi, X., Saxena, A., Prakash, S., Jan, T., & Prasad, M., 2023, Ensemble Transfer Learning for Distinguishing Cognitively Normal and Mild Cognitive Impairment Patients Using MRI. Algorithms, 16(8), 377. https://doi.org/10.3390/a16080377 [Q2, Impact Factor: 2.3].
  42. Alazab A, Khraisat A, Singh S, Jan T., 2023, Enhancing Privacy-Preserving Intrusion Detection through Federated Learning. Electronics.; 12(16):3382. https://doi.org/10.3390/electronics12163382 [Q2, Impact Factor: 2.9]
  43. Zhou, Z., Kanwal, A., Chaturvedi, K., Raza, R., Prakash, S., Jan, T. and Prasad, M., 2023, July. Deep learning-based classification of neurodegenerative diseases using gait dataset: A comparative study. In Proceedings of the 2023 International Conference on Robotics, Control and Vision Engineering (pp. 59-64).
  44. Malik, A., Jan, T. and Prasad, M., 2023. Landslide Susceptibility Prediction based on Decision Tree and Feature Selection Methods. Journal of the Indian Society of Remote Sensing, pp.1-16 [Q2, Impact Factor: 1.89].
  45. Rajawat, AS, Goyal, SB, Chauhan, C, Bedi, P, Prasad, M & Jan, T 2023, ‘Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm’, Electronics, 12(1), p. 217 [Q2, Impact Factor: 2.6].
  46. Nirbhav; Malik A.; Maheshwar; Jan T.; Prasad M., 2023 Landslide Susceptibility Prediction based on Decision Tree and Feature Selection Methods, Journal of the Indian Society of Remote Sensing, pp.1-16 [Q2, Impact Factor: 1.89].
  47. Tran, T.P., Jan, T. and Kew, S.N., 2022, September. Learning analytics for improved course delivery: applications and techniques. In Proceedings of the 6th International Conference on Digital Technology in Education (pp. 100-106).
  48. Haass, O., Akhavan, P., Miao, Y., Soltani, M., Jan, T. and Azizi, N., 2023. Organizational citizenship behaviour on organizational performance: A knowledge-based organization. Knowledge Management & E-Learning, 15(1), p.85 [Q2, Impact Factor: 2.33].
  49. Akhavan, P., Azizi, N., Akhtari, S., Haass, O., Jan, T. and Sajeev, S., 2023. Understanding critical success factors for implementing medical tourism in a multi-case analysis. Knowledge Management & E-Learning, 15(1), p.43 [Q2, Impact Factor: 2.33].
  50. Nii, Y, Raj, C, Tiwana, MS, Samarawickrama, M, Simoff, S, Jan, T & Prasad, M 2023, Understanding Social Media Engagement in Response to Disaster Fundraising Attempts During Australian Bushfires. in Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 17): International Conference on Intelligent Vision and Computing. Springer Nature, pp. 277-289. https://doi.org/10.1007/978-3-031-31164-2_23 [ERA listed]
  51. Tich, PT, Jan, T & Kew, SN 2023, Learning Analytics for Improved Course Delivery: Applications and Techniques. in ICDTE ’22: Proceedings of the 6th International Conference on Digital Technology in Education. ACM New York, NY, USA, pp. 100-106. https://doi.org/10.1145/3568739.3568758
  52. Rajawat, A. S., Goyal, S. B., Simoff, S., Jan, T., & Prasad, M., 2022. Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing. Applied Sciences (Switzerland), 12(21) [Q2, Impact Factor: 2.8].
  53. Sulimani, H., Sajjad, A.M., Alghamdi, W.Y., Kaiwartya, O., Jan, T., Simoff, S. and Prasad, M., 2022. Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment. Transactions on Emerging Telecommunications Technologies, p.e4650 [ERA-listed Q2, Impact Factor: 3.41].
  54. Junejo, A.K., Jokhio, I.A. and Jan, T., 2022. A Multi-Dimensional and Multi-Factor Trust Computation Framework for Cloud Services. Electronics, 11(13), p.1932 [Q2, Impact Factor: 2.6].
  55. Davison, C., Akhavan, P., Jan, T., Azizi, N., Fathollahi, S., Taheri, N., Haass, O. and Prasad, M., 2022. Evaluation of Sustainable Digital Currency Exchange Platforms Using Analytic Models. Sustainability, 14(10), p.5822 [Q2, Impact Factor: 3.251].
  56. Pare, S., Mittal, H., Sajid, M., Bansal, J.C., Saxena, A., Jan, T., Pedrycz, W., Prasad, M., 2021. Remote Sensing Imagery Segmentation: A Hybrid Approach. Remote Sensing 13, 4604 [Q1, Impact Factor: 4.509].
  57. Memon, T.D., Jurin, M., Kwan, P., Jan, T., Sidnal, N. and Nafi, N., 2021. Studying Learner’s Perception of Attaining Graduate Attributes in Capstone Project Units Using Online Flipped Classroom. Education Sciences, 11(11), p.698 [Q2, Impact Factor: 2.15].
  58. Baloch, A., Memon, T.D., Memon, F., Lal, B., Viyas, V. and Jan, T., 2021. Hardware Synthesize and Performance Analysis of Intelligent Transportation Using Canny Edge Detection Algorithm. Int. J. Eng. Manuf.(IJEM), 11, pp.22-32.
  59. Sulimani, H., Alghamdi, W.Y., Jan, T., Bharathy, G. and Prasad, M., 2021. Sustainability of Load Balancing Techniques in Fog Computing Environment. Procedia Computer Science, 191, pp.93-101 [Conference Article Impact Score: 2.0].
  60. Agarwal, A., Chivukula, A.S., Bhuyan, M.H., Jan, T., Narayan, B. and Prasad, M., 2020, November. Identification and Classification of Cyberbullying Posts: A Recurrent Neural Network Approach Using Under-Sampling and Class Weighting. In International Conference on Neural Information Processing (pp. 113-120). Springer, Cham [Core Rank: A]
  61. Go, J.H., Jan, T., Mohanty, M., Patel, O.P., Puthal, D. and Prasad, M., 2020, July. Visualization Approach for Malware Classification with ResNeXt. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-7). IEEE [Core Rank: B]
  62. Jan, T., Azami, P., Iranmanesh, S., Ameri Sianaki, O., Hajiebrahimi, S., 2020, Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City. Sensors, 20, 2276. [Q1, Impact factor: 3.03].
  63. Prabhu, C.S.R., Jan, T., Prasad, M. and Varadarajan, V., 2020. FOG ANALYTICS-A SURVEY. Malaysian Journal of Computer Science, pp.140-151. [Impact factor: 0.723].
  64. Iranmanesh, S., Raad, R., Raheel, M.S., Tubbal, F. and Jan, T., 2019. Novel DTN mobility-driven routing in autonomous drone Logistics networks. IEEE Access, 8, pp.13661-13673 [Q1, Impact Factor: 4.089].
  65. Jan, T., Iranmanesh, S. and Sajeev, A.S.M., 2019, December. Ensemble of Semi-Parametric Models for IoT Fog Modeling. In 2019 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 2995-2998). IEEE.
  66. Yousefi, A., Sianaki, O.A. and Jan, T., 2019, March. Big Data Analytics for Electricity Price Forecast. In Workshops of the International Conference on Advanced Information Networking and Applications (pp. 915-922). Springer, Cham.
  67. Jan, T. and Sajeev, A.S.M., 2018. Ensemble of Probabilistic Learning Networks for IoT Edge Intrusion Detection. International Journal of Computer Networks & Communications (IJCNC), Vol, 10. Pp 135-147 [ERA ranked].
  68. Jan, T., 2018, August. Ensemble of Semi-Supervised Models for IoT Resource Scheduling and Sharing. In 2018 IEEE 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (pp. 376-378). IEEE.
  69. Jan, T. and Sajeev, A.S.M., 2018, August. Boosted Probabilistic Neural Network for IoT Data Classification. In 2018 IEEE 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (pp. 408-411). IEEE.
  70. Jan, T., 2018, July. Ada-Boosted Locally Enhanced Probabilistic Neural Network for IoT Intrusion Detection. In Conference on Complex, Intelligent, and Software Intensive Systems (pp. 583-589). Springer, Cham.
  71. Jan, T., Bevinakoppa, S., 2018, Distributed Data Analytic Models for IoT Edge Computing Network. International Journal of Internet of Things and Web Services, 3, 67-71.
  72. Bevinakoppa, S., Alazab, A., Jan, T., 2018, Design of Computer Networking Courses with Major in Cyber Security. International Journal of Education and Learning Systems, 3, 111-116.
  73. Tran, T.P., Tsai, P., Jan, T. and He, X., 2012. Machine Learning Techniques for Network Intrusion Detection. In Machine Learning: Concepts, Methodologies, Tools and Applications (pp. 498-521).
  74. Yu, T., Simoff, S. and Jan, T., 2010. VQSVM: A case study for incorporating prior domain knowledge into inductive machine learning. Neurocomputing, 73(13-15), pp.2614-2623 [Q1, Impact Factor: 5.719].
  75. Tran, T.P., Tsai, P., Jan, T. and Kong, X., 2010. Network Intrusion Detection using Machine Learning and Voting techniques, Machine Learning, pp. 267-289.
  76. Tsai, P., Cao, L., Hintz, T. and Jan, T., 2009. A bi-modal face recognition framework integrating facial expression with facial appearance. Pattern Recognition Letters, 30(12), pp.1096-1109. [Q1, Impact Factor: 2.81].
  77. Tran, T.P., Tsai, P. and Jan, T., 2008, December. A Multi-expert Classification Framework with Transferable Voting for Intrusion Detection. In 2008 Seventh International Conference on Machine Learning and Applications (pp. 877-882). IEEE.
  78. Tran, T.P., Tsai, P. and Jan, T., 2008, December. An adjustable combination of linear regression and modified probabilistic neural network for anti-spam filtering. In Pattern Recognition, 2008. ICPR 2008. 19th International Conference on (pp. 1-4). IEEE.
  79. Tsai, P., Tran, T.P., Hintz, T. and Jan, T., 2008, December. An evaluation of bi-modal facial appearance+ facial expression face biometrics. In Pattern Recognition, 2008. ICPR 2008. 19th International Conference on (pp. 1-5). IEEE.
  80. Tsai, P., Tran, T.P., Hintz, T. and Jan, T., 2008. Discriminant Subspace Analysis for Uncertain Situation in Facial Recognition. In Recent Advances in Face Recognition. Pp.161-182.
  81. Tsai, P., Tran, T.P., Hintz, T. and Jan, T., 2008, October. Adaptive multiple experts system for personal identification using facial behaviour biometrics. In Multimedia Signal Processing, 2008 IEEE 10th Workshop on (pp. 660-665). IEEE
  82. Tsai, P., Hintz, T. and Jan, T., 2007, October. Facial behavior as behavior biometric? an empirical study. In Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on (pp. 3917-3922). IEEE.
  83. Yu, T., Jan, T., Simoff, S. and Debenham, J., 2007, August. A hierarchical VQSVM for imbalanced data sets. In Neural Networks, 2007. IJCNN 2007. International Joint Conference on (pp. 518-523). IEEE.
  84. Tsai, P., Jan, T. and Hintz, T., 2007, August. Kernel-based subspace analysis for face recognition. In Neural Networks, 2007. IJCNN 2007. International Joint Conference on (pp. 1127-1132). IEEE.
  85. Jan, T. and Debenham, J., 2007. Incorporating prior domain knowledge into inductive machine learning. J. Mach. Learn, pp.1-42.
  86. Yu, T., Debenham, J., Jan, T. and Simoff, S., 2006, August. Combine vector quantization and support vector machine for imbalanced datasets. In IFIP International Conference on Artificial Intelligence in Theory and Practice (pp. 81-88). Springer, Boston, MA.
  87. Yu, T., Jan, T., Debenham, J. and Simoff, S., 2006, July. Classify unexpected news impacts to stock price by incorporating time series analysis into support vector machine. In Neural Networks, 2006. IJCNN’06. International Joint Conference on (pp. 2993-2998). IEEE.
  88. Tran, T.P. and Jan, T., 2006, July. Boosted modified probabilistic neural network (BMPNN) for network intrusion detection. In Neural Networks, 2006. IJCNN’06. International Joint Conference on (pp. 2354-2361). IEEE.
  89. Tran, T.P., Jan, T. and Simmonds, A.J., 2006. A Multi-Expert Classification Framework for Network Misuse Detection. From Proceeding (544) Artificial Intelligence and Soft Computing.
  90. Yoo, P.D., Kim, M.H. and Jan, T., 2005, December. Financial forecasting: advanced machine learning techniques in stock market analysis. In 2005 IAE Section Multitopic Conference (pp. 1-7). IEEE.
  91. Yu, T., Jan, T., Debenham, J. and Simoff, S., 2005, December. Incorporate domain knowledge into support vector machine to classify price impacts of unexpected news. In AusDM 2005 Proc.-4th Australasian Data Mining Conf.-Collocated with the 18th Australian Joint Conf. on Artificial Intelligence, AI 2005 and the 2nd Australian Conf. on Artificial Life, ACAL 2005.
  92. Jan, T. and Kim, M., 2005, July. Vector quantized radial basis function neural network with embedded multiple local linear models for financial prediction. In Neural Networks, 2005. IJCNN’05. Proceedings. 2005 IEEE International Joint Conference on (Vol. 4, pp. 2538-2543). IEEE.
  93. Yoo, P.D., Kim, M.H. and Jan, T., 2005, November. Machine learning techniques and use of event information for stock market prediction: A survey and evaluation. In Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on (Vol. 2, pp. 835-841). IEEE.
  94. Tsai, P.H. and Jan, T., 2005, October. Expression-invariant face recognition system using subspace model analysis. In Systems, Man and Cybernetics, 2005 IEEE International Conference on (Vol. 2, pp. 1712-1717). IEEE
  95. Cheng, E.D., Piccardi, M. and Jan, T., 2005, September. Boat-generated acoustic target signal detection by use of an Adaptive Median CFAR and multi-frame integration algorithm. In Signal Processing Conference, 2005 13th European (pp. 1-4). IEEE.
  96. Jan, S.T., 2005, July. Efficient video object classifier using locality-enhanced support vector machines. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. (Vol. 3, pp. 1936-1940). IEEE.
  97. Jan, T. and Kim, M., 2005, July. Vector quantized radial basis function neural network with embedded multiple local linear models for financial prediction. In Neural Networks, 2005. IJCNN’05. Proceedings. 2005 IEEE International Joint Conference on (Vol. 4, pp. 2538-2543). IEEE.
  98. Tsai, P.H., Jan, T. and Hintz, T., 2005, July. Expression-invariant face recognition for small class problem. In Computational Intelligence for Measurement Systems and Applications, 2005. CIMSA. 2005 IEEE International Conference on (pp. 193-197). IEEE.
  99. Cheng, E.D., Piccardi, M. and Jan, T., 2004, December. Stochastic boats generated acoustic target signal detection in time-frequency domain. In Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, 2004. (pp. 429-432). IEEE.
  100. Piccardi, M. and Jan, T., 2004, October. Mean-shift background image modelling. In Image Processing, 2004. ICIP’04. 2004 International Conference on (Vol. 5, pp. 3399-3402). IEEE.
  101. Gunes, H., Piccardi, M. and Jan, T., 2004, October. Comparative beauty classification for pre-surgery planning. In Systems, Man and Cybernetics, 2004 IEEE International Conference on (Vol. 3, pp. 2168-2174). IEEE.
  102. Jan, T., Tsai, P.H., Piccardi, M. and Hintz, T., 2004, October. Efficient video object classifier using locality-enhanced support vector machines. In Systems, Man and Cybernetics, 2004 IEEE International Conference on (Vol. 7, pp. 6373-6377). IEEE.
  103. Jan, T., 2004, July. Neural network-based threat assessment for automated visual surveillance. In 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541) (Vol. 2, pp. 1309-1312). IEEE.
  104. Jan, T., Yu, T., Debenham, J. and Simoff, S., 2004, July. Financial prediction using modified probabilistic learning network with embedded local linear models. In 2004 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2004. CIMSA. (pp. 81-84). IEEE.
  105. Gunes, H., Piccardi, M. and Jan, T., 2004, June. Face and body gesture analysis for multimodal HCI. In Asia-Pacific Conference on Computer Human Interaction (pp. 583-588). Springer, Berlin, Heidelberg.
  106. Gunes, H., Piccardi, M. and Jan, T., 2004, June. Face and body gesture recognition for a vision-based multimodal analyser. In Proceedings of the Pan-Sydney area workshop on Visual information processing (pp. 19-28). Australian Computer Society.
  107. Gunes, H., Piccardi, M. and Jan, T., 2004, January. Automated classification of female facial beauty by image analysis and supervised learning. In Visual Communications and Image Processing 2004 (Vol. 5308, pp. 968-979). International Society for Optics and Photonics.
  108. Tsai, P.H., Jan, S. and Gunes, H., 2004. Video object encoder using selective local-space support vector machines. In Multimedia Signal Processing, 2004 IEEE 6th Workshop on (pp. 427-429). IEEE.
  109. Yu, T., Jan, T., Debenham, J. and Simoff, S., 2004. Incorporating Prior Domain Knowledge in Machine Learning: A Review. In AISTA 2004: International Conference on Advances in Intelligence Systems-Theory and Applications in cooperation with IEEE Computer Society.
  110. Gunes, H., Piccardi, M. and Jan, T., 2004. Bimodal Modelling of Facial and Upper-Body Gesture for Affective HCI. In Australian Computer Human Interaction (pp. 2-10), Ergonomics Society of Australia
  111. Jan, T., 2003, December. Combining analytic models with neural networks. In Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (IEEE Cat. No. 03EX795) (pp. 605-608). IEEE.
  112. Jan, T., 2003, December. Video object encoder using region-of-interest based neural network classifiers. In Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (IEEE Cat. No. 03EX795) (pp. 263-266). IEEE.
  113. Jan, T., Piccardi, M. and Hintz, T., 2003, September. Neural network classifiers for automated video surveillance. In 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No. 03TH8718) (pp. 729-738). IEEE.
  114. Jan, T., 2003, July. Robust short-term prediction using combination of linear regression and modified probabilistic neural network model. In Neural Networks, 2003. Proceedings of the International Joint Conference on (Vol. 4, pp. 2478-2481). IEEE.
  115. Piccardi, M. and Jan, T., 2003. Recent advances in computer vision. Industrial Physicist, 9(1), pp.18-21.
  116. Jan, T., 2003, March. Combination of linear and general regression neural network for robust short-term financial prediction. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 228-235). Springer, Berlin, Heidelberg.
  117. Jan, T., Piccardi, M. and Hintz, T., 2003, September. Neural network classifiers for automated video surveillance. In Neural Networks for Signal Processing, 2003. NNSP’03. 2003 IEEE 13th Workshop on (pp. 729-738). IEEE.
  118. Jan, T., Zaknich, A. and Attikiouzel, Y., 2000. Separation of signals with overlapping spectra using signal characterisation and hyperspace filtering. In Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000 (pp. 327-332). IEEE.
  119. Jan, T. and Zaknich, A., 1999, July. An adjustable model for linear to nonlinear regression. In Neural Networks, 1999. IJCNN’99. International Joint Conference on (Vol. 2, pp. 846-850). IEEE.