| Mixture of spectral generative adversarial networks for imbalanced hyperspectral image classification T Dam, SG Anavatti, HA Abbass IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2020 | 15 | 2020 |
| Improving ClusterGAN Using Self-Augmented Information Maximization of Disentangling Latent Spaces T Dam, SG Anavatti, HA Abbass https://arxiv.org/abs/2107.12706, 0 | 13* | |
| A clustering algorithm based TS fuzzy model for tracking dynamical system data T Dam, AK Deb Journal of the Franklin Institute 354 (13), 5617-5645, 2017 | 12 | 2017 |
| Block sparse representations in modified fuzzy c-regression model clustering algorithm for ts fuzzy model identification T Dam, A Deb 2015 IEEE Symposium Series on Computational Intelligence, 1687-1694, 2015 | 11 | 2015 |
| Interval type-2 modified fuzzy c-regression model clustering algorithm in ts fuzzy model identification T Dam, AK Deb 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1671-1676, 2016 | 8 | 2016 |
| Watt-effnet: A lightweight and accurate model for classifying aerial disaster images GY Lee, T Dam, MM Ferdaus, DP Poenar, VN Duong IEEE Geoscience and Remote Sensing Letters 20, 1-5, 2023 | 7 | 2023 |
| Latent preserving generative adversarial network for imbalance classification T Dam, MM Ferdaus, M Pratama, SG Anavatti, S Jayavelu, H Abbass 2022 IEEE International Conference on Image Processing (ICIP), 3712-3716, 2022 | 7 | 2022 |
| Does adversarial oversampling help us? T Dam, MM Ferdaus, SG Anavatti, S Jayavelu, HA Abbass Proceedings of the 30th ACM International Conference on Information …, 2021 | 7 | 2021 |
| GATE: A guided approach for time series ensemble forecasting MR Sarkar, SG Anavatti, T Dam, MM Ferdaus, M Tahtali, S Ramasamy, ... Expert Systems with Applications 235, 121177, 2024 | 6 | 2024 |
| D-fj: Deep neural network based factuality judgment A Mullick, S Pal, P Chanda, A Panigrahy, A Bharadwaj, S Singh, T Dam Technology 50, 173, 2019 | 6 | 2019 |
| TS fuzzy model identification by a novel objective function based fuzzy clustering algorithm T Dam, AK Deb 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL …, 2014 | 6 | 2014 |
| Scalable adversarial online continual learning T Dam, M Pratama, MDM Ferdaus, S Anavatti, H Abbas Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 5 | 2022 |
| Rainfall-runoff prediction using a Gustafson-Kessel clustering based Takagi-Sugeno Fuzzy model S Dey, T Dam 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2021 | 4 | 2021 |
| Interval type-2 recursive fuzzy C-means clustering algorithm in the TS fuzzy model identification T Dam, AK Deb 2015 IEEE Symposium Series on Computational Intelligence, 22-29, 2015 | 4 | 2015 |
| Enhancing wind power forecast precision via multi-head attention transformer: An investigation on single-step and multi-step forecasting MR Sarkar, SG Anavatti, T Dam, M Pratama, B Al Kindhi 2023 International Joint Conference on Neural Networks (IJCNN), 1-8, 2023 | 3 | 2023 |
| Developing generative adversarial networks for classification and clustering: Overcoming class imbalance and catastrophic forgetting T Dam UNSW Sydney, 2022 | 3 | 2022 |
| A Web based Analog Signals, Network and Measurement Laboratory TD A. K. Deb Int. Conf. on Soft Computing, Artificial Intelligence, Pattern Recognition …, 2013 | 3 | 2013 |
| Improving self-supervised learning for out-of-distribution task via auxiliary classifier H Boonlia, T Dam, MM Ferdaus, SG Anavatti, A Mullick 2022 IEEE International Conference on Image Processing (ICIP), 3036-3040, 2022 | 2 | 2022 |
| Unlocking the capabilities of explainable few-shot learning in remote sensing GY Lee, T Dam, MM Ferdaus, DP Poenar, VN Duong Artificial Intelligence Review 57 (7), 169, 2024 | 1 | 2024 |
| X-Fuzz: An Evolving and Interpretable Neurofuzzy Learner for Data Streams MM Ferdaus, T Dam, S Alam, DT Pham IEEE Transactions on Artificial Intelligence, 2024 | 1 | 2024 |
Sreenatha Anavattiuniversity of New South Wales在 adfa.edu.au 的电子邮件经过验证
