List of Publications
2023:
- Kandath, H., Ferdaus, M. M., Wei, N. Z., Bangjian, Z., Sundaram, S., Li, X., & Jayavelu, S. (2023). PASE: An autonomous sequential framework for the state estimation of dynamical systems. Expert Systems with Applications (Impact Factor: 8.665), 119414.
- Dam, T., Pratama, M., Ferdaus, M. M., Anavatti, S., & Abbas, H. (2023, March). Scalable Adversarial Online Continual Learning. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part III (pp. 373-389). Cham: Springer Nature Switzerland.
- Lee, G. Y., Dam, T., Ferdaus, M. M., Poenar, D. P., & Duong, V. N. (2023). WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images. IEEE Geoscience and Remote Sensing Letters (Impact Factor: 5.343).
2022:
- Solomon, I., Jayavelu, S., Ferdaus, M. M., & Kumar, U. (2022, October). Data Oversampling with Structure Preserving Variational Learning. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM-2022) (pp. 4490-4494).
- Boonlia, H., Dam, T., Ferdaus, M. M., Anavatti, S. G., & Mullick, A. (2022, October). Improving Self-Supervised Learning for Out-Of-Distribution Task via Auxiliary Classifier. In 2022 IEEE International Conference on Image Processing (IEEE–ICIP 2022) (pp. 3036-3040).
- Dam, T., Ferdaus, M. M., Pratama, M., Anavatti, S. G., Jayavelu, S., & Abbass, H. (2022, October). Latent Preserving Generative Adversarial Network for Imbalance classification. In 2022 IEEE International Conference on Image Processing (IEEE-ICIP 2022) (pp. 3712-3716).
- Ferdaus, M. M., Zhou, B., Yoon, J. W., Low, K. L., Pan, J., Ghosh, J., … & Senthilnath, J. (2022). Significance of activation functions in developing an online classifier for semiconductor defect detection. Knowledge-Based Systems (Impact Factor: 8.139), 248, 108818.
- Woo, C. J., Goh, S. K., Alam, S., Ferdaus, M. M., & Ellejmi, M. (2022). A runway exit prediction model with visually explainable machine decisions, ICRAT 2022 (Best paper award).
Woo, C. J., Goh, S. K., Alam, S., Ferdaus, M. M., & Ellejmi, M. (2022). A runway exit prediction model with visually explainable machine decisions.df∗, Pratama, M.∗, Ferdaus, M. M.∗, Anavatti, S. G., Abbass, H. AScalable Adversarial Online Continual Learning, ECML-PKDD 202
2021:
- Ferdaus, M. M., Zaman, F., & Chakrabortty, R. K. (2021). Performance improvement of a parsimonious learning machine using metaheuristic approaches. IEEE Transactions on Cybernetics, 52(8), 7277-7290 (Impact Factor: 19.118).
- Ferdaus, M. M., Chakrabortty, R. K., & Ryan, M. J. (2021). Multiobjective automated type-2 parsimonious learning machine to forecast time-varying stock indices online. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(5), 2874-2887 (Impact Factor: 11.471).
- Dam, T., Ferdaus, M. M., Anavatti, S. G., Jayavelu, S., & Abbass, H. A. (2021, October). Does Adversarial Oversampling Help us?. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM-2021) (pp. 2970-2973).
- Aggarwal, D., Senthilnath, J., Kumar, U., Yadav, V., Kulkarni, S., Ferdaus, M. M., & Xiaoli, L. (2021, December). SGDOL: Self-evolving Generative and Discriminative Online Learning for Data Stream Classification. In 2021 International Conference on Data Mining Workshops (IEEE-ICDMW 2021) (pp. 322-330).
- Pan, J., Low, K. L., Ghosh, J., Jayavelu, S., Ferdaus, M. M., Lim, S. Y., … & Thean, A. V. Y. (2021). Transfer learning-based artificial intelligence-integrated physical modeling to enable failure analysis for 3 nanometer and smaller silicon-based CMOS transistors. ACS Applied Nano Materials, 4(7), 6903-6915 (Impact Factor: 6.140).
2020:
- Ferdaus, M. M., Pratama, M., Anavatti, S. G., Garratt, M. A., & Lughofer, E. (2020). PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles. Information sciences, 512, 481-505 (Impact Factor: 8.233).
- Ferdaus, M. M., Anavatti, S. G., Pratama, M., & Garratt, M. A. (2020). Towards the use of fuzzy logic systems in rotary wing unmanned aerial vehicle: a review. Artificial Intelligence Review, 53(1), 257-290 (Impact Factor: 9.588).