Dhika Pratama

Research Interests

Computer vision and multimedia computation Data mining and knowledge discovery Deep learning Fuzzy computation Machine learning Neural, Evolutionary and Fuzzy Computation

APrf Dhika Pratama

Enterprise Fellow

School of Computer Science and Information Technology

College of Engineering and Information Technology

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

Available For Media Comment.

Assoc. Prof. Mahardhika Pratama is currently an associate professor-level enterprise fellow in AI at the academic unit of STEM, University of South Australia, Adelaide, Australia. For further details, please visit www.mpratama.com. 

Date Position Institution name
2025 - ongoing Associate Professor-Level Enterprise Fellow Adelaide University
2022 - 2025 Associate Professor-Level Enterprise Fellow University of South Australia
2017 - 2022 Assistant Professor Nanyang Technological University
2015 - 2017 Lecturer La Trobe University
2014 - 2015 Research Fellow University of Technology Sydney

Date Type Title Institution Name Country Amount
2024 Award Outstanding Associate Editor Award of IEEE Trans on Neural Networks and Learning Systems IEEE Computational Intelligence Society United States -
2019 Award Amity Researcher Award in Data Streams Amity University India -
2019 Award IEEE Transactions on Fuzzy Systems Publication Award IEEE Computational Intelligence Society United States -
2014 Award UNSW Publication Award UNSW Sydney Australia -
2013 Award UNSW Publication Award UNSW Sydney Australia -
2011 Award Prestigious Engineering Achievement Award institute of engineer singapore Singapore -

Date Institution name Country Title
2012 - 2014 UNSW Sydney Australia Doctor of Philosophy
2010 - 2011 Nanyang Technological University Singapore Master of Science
2006 - 2010 Sepuluh Nopember Institute of Technology Indonesia Bachelor of Engineering

Year Citation
2025 Andonovski, G., Leite, D., Precup, R. E., Gomide, F., Pratama, D., & Škrjanc, I. (2025). Advancements in data-driven evolving fuzzy and neuro-fuzzy control: a comprehensive survey. Applied Soft Computing, online(114058), 1-41.
DOI Scopus1
2025 Stržinar, Ž., Škrjanc, I., Pratama, M., & Pregelj, B. (2025). Evolving clustering of time series for unsupervised analysis of industrial data streams. Computers & Industrial Engineering, 209(111508), 1-16.
DOI Scopus1
2025 Chen, Y., Yu, F., Zhang, Q., & Pratama, M. (2025). Energy-efficient adaptive perception for autonomous driving via lightweight policy learning and simulation-based optimization. Knowledge-Based Systems, online, 26 pages.
DOI Scopus1
2025 Ma'sum, M. A., Pratama, M., Lughofer, E., Liu, L., Habibullah., & Kowalczyk, R. (2025). Few-shot continual learning via flat-to-wide approaches. IEEE Transactions on Neural Networks and Learning Systems, 36(5), 8966-8978.
DOI Scopus1 WoS3 Europe PMC1
2025 A Hady, M., Hu, S., Pratama, M., Cao, Z., & Kowalczyk, R. (2025). Multi-agent reinforcement learning for resources allocation optimization: a survey. Artificial Intelligence Review: an international science and engineering journal, 58(354), 1-49.
DOI Scopus2 WoS1
2025 Furqon, M. T., Pratama, M., Shiddiqi, A., Liu, L., Habibullah, H., & Dogancay, K. (2025). Time and frequency synergy for source-free time-series domain adaptations. Information Sciences, 695(121734), 20 pages.
DOI Scopus3 WoS1
2024 Fristiana, A. H., Alfarozi, S. A. I., Permanasari, A. E., Pratama, M., & Wibirama, S. (2024). A survey on Hyperparameters optimization of deep learning for time series classification. IEEE Access, 12, 191162-191198.
DOI Scopus7 WoS6
2024 Paeedeh, N., Pratama, M., Masum, M. A., Mayer, W., Cao, Z., & Kowalczyk, R. (2024). Cross-domain few-shot learning via adaptive transformer networks. Knowledge-based Systems, 288(111458), 1-9.
DOI Scopus17 WoS16
2024 Furqon, M., Pratama, M., Liu, L., Habibullah, H., & Dogancay, K. (2024). Mixup domain adaptations for dynamic remaining useful life predictions. Knowledge-based Systems, 295(111783), 1-13.
DOI Scopus14 WoS14
2024 Paeedeh, N., Pratama, M., Wibirama, S., Mayer, W., Cao, Z., & Kowalczyk, R. (2024). Few-shot class incremental learning via robust transformer approach. Information Sciences, 675(120751), 1-21.
DOI Scopus2
2024 Weng, W., Pratama, M., Zhang, J., Chen, C., Yapp Kien Yie, E., & Ramasamy, S. (2024). Cross-Domain Continual Learning via CLAMP. Information Sciences, 676(120813), 1-23.
DOI Scopus3 WoS2
2024 Ma'sum, M. A., Sarkar, M. D. R., Pratama, M., Ramasamy, S., Anavatti, S., Liu, L., . . . Kowalczyk, R. (2024). Dynamic long-term time-series forecasting via meta transformer networks. IEEE Transactions on Artificial Intelligence, 5(8), 4258-4581.
DOI Scopus1
2024 Masum, M. A., Pratama, M., Ramasamy, S., Liu, L., & Habibullah, H. (2024). Unsupervised few-shot continual learning for remote sensing image scene classification. IEEE Transactions on Geoscience and Remote Sensing, 62(4707214), 1-14.
DOI Scopus3 WoS2
2023 Ding, W., Abdel Basset, M., Hawash, H., Pratama, M., & Pedrycz, W. (2023). Generalizable segmentation of COVID-19 infection from multi-site tomography scans: a federated learning framework. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(1), 126-139.
DOI Scopus13 WoS10
2023 Tan, C. S., Gupta, A., Yew Soon, O., Pratama, M., Tan, P. S., & Siew Kei, L. (2023). Pareto optimization with small data by learning across common objective spaces. Scientific Reports, 13(7842), 1-15.
DOI Scopus12 WoS8 Europe PMC4
2023 Ma'sum, M. A., Pratama, M., Lughofer, E., Ding, W., & Jatmiko, W. (2023). Assessor-guided learning for continual environments. Information Sciences, 640(119088), 1-17.
DOI Scopus14 WoS12
2023 Sarkar, M. R., Anavatti, S., Dam, T., Ferdaus, M. M., Tahtali, M., Ramasamy, S., & Pratama, M. (2023). GATE: A guided approach for time series ensemble forecasting. Expert Systems with Applications, in press, 1-44.
DOI Scopus9 WoS9
2023 Weiwei, W., Pratama, M., Za'in, C., De Carvalho, M., Appan, R., Ashfahani, A., & Yapp Kien Yee, E. (2023). Autonomous cross domain adaptation under extreme label scarcity. IEEE Transactions on Neural Networks and Learning Systems, 34(10), 6839-6850.
DOI Scopus7 WoS7
2023 Lughofer, E., & Pratama, M. (2023). Evolving multi-user fuzzy classifier system with advanced explainability and interpretability aspects. Information Fusion, 91, 458-476.
DOI Scopus18 WoS17
2023 Ashfahani, A., & Pratama, M. (2023). Unsupervised continual learning in streaming environments. IEEE Transactions on Neural Networks and Learning Systems, 34(12, article no. 3163362), 9992-10003.
DOI Scopus22 WoS24 Europe PMC4
2022 Aradya, A. M. S., Ashfahani, A., Angelina, F., Pratama, M., de Mello, R. F., & Sundaram, S. (2022). Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination. Information Sciences, 607, 638-653.
DOI Scopus13 WoS7
2022 Lughofer, E., & Pratama, M. (2022). Online sequential ensembling of predictive fuzzy systems. Evolving Systems, 13(2), 361-386.
DOI Scopus7 WoS7
2022 De Carvalho, M., Pratama, M., Zhang, J., & Yapp Kien Yee, E. (2022). ACDC: online unsupervised cross-domain adaptation. Knowledge-Based Systems, 253(253, article no.109486), 1-15.
DOI Scopus9 WoS8
2022 Renchunzi, X., & Pratama, M. (2022). Automatic online multi-source domain adaptation. Information Sciences, 582, 480-494.
DOI Scopus15 WoS15
2022 Pratama, M., Lughofer, E., & Angelov, P. (2022). Editorial: Special issue on recent progress in autonomous machine learning. Information Sciences, 595, 195-196.
DOI
2021 Weiwei, W., Pratama, M., Ashfahani, A., & Yapp Kien Yee, E. (2021). Online semisupervised learning approach for quality monitoring of complex manufacturing process. Complexity, 2021(3005276), 1-16.
DOI Scopus3
2021 Al Mahasneh, A. J., Anavatti, S. G., Garratt, M. A., & Pratama, M. (2021). Stable adaptive controller based on generalized regression neural networks and sliding mode control for a class of nonlinear time-varying systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4, article no. 8721510), 2525-2535.
DOI Scopus24 WoS20
2021 AL Sharuee, M. T., Liu, F., & Pratama, M. (2021). Sentiment analysis: dynamic and temporal clustering of product reviews. Applied Intelligence, 51(1), 51-70.
DOI Scopus30 WoS23
2021 Pratama, M., Za'in, C., Lughofer, E., Pardede, E., & Rahayu, D. A. P. (2021). Scalable teacher forcing network for semi-supervised large scale data streams. Information Sciences, 576, 407-431.
DOI Scopus18 WoS16
2021 Cai, Q., Alam, S., Pratama, M., & Liu, J. (2021). Robustness evaluation of multipartite complex networks based on percolation theory. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(10), 6244-6257.
DOI Scopus30 WoS28
2021 Fubing, M., Weiwei, W., Pratama, M., & Yee, E. Y. K. (2021). Continual learning via inter-task synaptic mapping. Knowledge-Based Systems, 222, 1-11.
DOI Scopus22 WoS20
2021 Samanta, S., Pratama, M., & Sundaram, S. (2021). Bayesian neuro-fuzzy inference system for temporal dependence estimation. IEEE Transactions on Fuzzy Systems, 29(9, article no. 9115257), 2479-2490.
DOI Scopus9 WoS4
2021 Lughofer, E., Pratama, M., & Skrjanc, I. (2021). Online bagging of evolving fuzzy systems. Information Sciences, 570, 16-33.
DOI Scopus29 WoS24
2020 Pratama, M., Dimla, E., Tjahjowidodo, T., Pedrycz, W., & Lughofer, E. (2020). Online tool condition monitoring based on parsimonious ensemble+. IEEE Transactions on Cybernetics, 50(2), 664-677.
DOI Scopus50 WoS37 Europe PMC5
2020 Samanta, S., Pratama, M., Sundaram, S., & Srikanth, N. (2020). Learning elastic memory online for fast time series forecasting. Neurocomputing, 390, 315-326.
DOI Scopus10 WoS10
2020 Cai, Q., Pratama, M., Alam, S., Ma, C., & Liu, J. (2020). Breakup of directed multipartite networks. IEEE Transactions on Network Science and Engineering, 7(3, article no. 8620361), 947-960.
DOI Scopus8 WoS8
2020 Kurnianingsih., Nugroho, L. E., Widyawan., Lazuardi, L., Prabuwono, A. S., & Pratama, M. (2020). Anomaly detection for elderly home care. International Journal of Business Intelligence and Data Mining, 16(4), 418-444.
DOI Scopus1
2020 Pratama, M., Pedrycz, W., & Webb, G. I. (2020). An incremental construction of deep neuro fuzzy system for continual learning of nonstationary data streams. IEEE Transactions on Fuzzy Systems, 28(7, article no. 8826232), 1315-1328.
DOI Scopus52 WoS42
2020 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.
DOI Scopus38 WoS31
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.
DOI Scopus45 WoS40
2020 Das, M., Pratama, M., & Ghosh, S. K. (2020). SARDINE: a self-adaptive recurrent deep incremental network model for spatio-temporal prediction of remote sensing data. ACM Transactions on Spatial Algorithms and Systems, 6(3, article no. 16), 1-26.
DOI Scopus11 WoS10
2020 Cai, Q., Alam, S., Pratama, M., & Wang, Z. (2020). Percolation theories for multipartite networked systems under random failures. Complexity, 2020(article no. 3974503), 1-12.
DOI Scopus1 WoS1
2020 Lughofer, E., Zavoianu, A. C., Pollak, R., Pratama, M., Meyer Heye, P., Zörrer, H., . . . Radauer, T. (2020). On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks. Information Sciences, 537, 425-451.
DOI Scopus27 WoS22
2020 Yang, D., Luan, W., Qiao, L., & Pratama, M. (2020). Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery. Applied Energy, 268(114696), 1-14.
DOI Scopus109 WoS103
2020 Ferdaus, M. M., Pratama, M., Anavatti, S. G., Garratt, M. A., & Pan, Y. (2020). Generic evolving self-organizing neuro-fuzzy control of bio-inspired unmanned aerial vehicles. IEEE Transactions on Fuzzy Systems, 28(8, article no. 8718263), 1542-1556.
DOI Scopus39 WoS39
2020 Ashfahani, A., Pratama, M., Lughofer, E., & Ong, Y. S. (2020). DEVDAN: deep evolving denoising autoencoder. Neurocomputing, 390, 297-314.
DOI Scopus102 WoS87
2019 Pan, Y., Yang, C., Pratama, M., & Yu, H. (2019). Composite learning adaptive backstepping control using neural networks with compact supports. International Journal of Adaptive Control and Signal Processing, 33(12), 1726-1738.
DOI Scopus15 WoS12
2019 Za'in, C., Pratama, M., & Pardede, E. (2019). Evolving large-scale data stream analytics based on scalable PANFIS. Knowledge-Based Systems, 166, 186-197.
DOI Scopus8 WoS6
2019 Ferdaus, M. M., Pratama, M., Anavatti, S. G., & Garratt, M. A. (2019). PALM: an incremental construction of hyperplanes for data stream regression. IEEE Transactions on Fuzzy Systems, 27(11, article no. 8613834), 2115-2129.
DOI Scopus53 WoS44
2019 Cai, Q., Pratama, M., & Alam, S. (2019). Interdependency and vulnerability of multipartite networks under target node attacks. Complexity, 2019(2680972), 1-16.
DOI Scopus17 WoS11
2019 Kocer, B. B., Tjahjowidodo, T., Pratama, M., & Seet, G. G. L. (2019). Inspection-while-flying: an autonomous contact-based nondestructive test using UAV-tools. Automation in Construction, 106(102895), 1-15.
DOI Scopus50 WoS45
2019 Ferdaus, M. M., Anavatti, S. G., Garratt, M. A., & Pratama, M. (2019). Development of C-means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle. Journal of Artificial Intelligence and Soft Computing Research, 9(2), 99-109.
DOI Scopus18 WoS13
2019 Ferdaus, M. M., Pratama, M., Anavatti, S. G., & Garratt, M. A. (2019). Online identification of a rotary wing unmanned aerial vehicle from data streams. Applied Soft Computing Journal, 76, 313-325.
DOI Scopus13 WoS12
2019 Pratama, M., Dimla, E., Lai, C. Y., & Lughofer, E. (2019). Metacognitive learning approach for online tool condition monitoring. Journal of Intelligent Manufacturing, 30(4), 1717-1737.
DOI Scopus27 WoS21
2019 Pratama, M., & Wang, D. (2019). Deep stacked stochastic configuration networks for lifelong learning of non-stationary data streams. Information Sciences, 495, 150-174.
DOI Scopus72 WoS64
2019 Gupta, D., Pratama, M., Ma, Z., Li, J., & Prasad, M. (2019). Financial time series forecasting using twin support vector regression. PLoS ONE, 14(3, article no. e0211402), 1-27.
DOI Scopus45 WoS33 Europe PMC4
2019 Anh, N., Suresh, S., Pratama, M., & Srikanth, N. (2019). Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system. Knowledge-Based Systems, 169, 28-38.
DOI Scopus26 WoS20
2019 Lughofer, E., Zavoianu, A. C., Pollak, R., Pratama, M., Meyer Heye, P., Zörrer, H., . . . Radauer, T. (2019). Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models. Journal of Process Control, 76, 27-45.
DOI Scopus49 WoS41
2019 Samanta, S., Pratama, M., & Sundaram, S. (2019). A novel Spatio-Temporal Fuzzy Inference System (SPATFIS) and its stability analysis. Information Sciences, 505, 84-99.
DOI Scopus22 WoS19
2019 Ding, W., Yen, G. G., Beliakov, G., Triguero, I., Pratama, M., Zhang, X., & Li, H. (2019). IEEE Access Special Section Editorial: Data Mining and Granular Computing in Big Data and Knowledge Processing. IEEE Access, 7, 47682-47686.
DOI Scopus5 WoS3
2018 Pratama, M., Lughofer, E., Sundaram, S., Mouchaweh, M. S., Skrjanc, I., & Mostafa, F. (2018). Editorial: Special issue on Advanced Soft Computing for Prognostic Health Management. Applied Soft Computing Journal, 72, 552-554.
DOI
2018 Er, M. J., Wang, N., Pratama, M., Sharma, S., & Lian, Z. (2018). Preface. Intelligent Marine and Aerial Vehicles Theory and Applications, vii-viii.
2018 Lughofer, E., Pratama, M., & Skrjanc, I. (2018). Incremental rule splitting in generalized evolving fuzzy systems for autonomous drift compensation. IEEE Transactions on Fuzzy Systems, 26(4, article no. 8039219), 1854-1865.
DOI Scopus78 WoS72
2018 Pratama, M., Pedrycz, W., & Lughofer, E. (2018). Evolving ensemble fuzzy classifier. IEEE Transactions on Fuzzy Systems, 26(5, article no. 8265083), 2552-2567.
DOI Scopus113 WoS94
2018 AL Sharuee, M. T., Liu, F., & Pratama, M. (2018). Sentiment analysis: an automatic contextual analysis and ensemble clustering approach and comparison. Data and Knowledge Engineering, 115, 194-213.
DOI Scopus50 WoS37
2018 Lughofer, E., Pollak, R., Zavoianu, A. C., Pratama, M., Meyer Heye, P., Zörrer, H., . . . Radauer, T. (2018). Self-adaptive evolving forecast models with incremental PLS space updating for on-line prediction of micro-fluidic chip quality. Engineering Applications of Artificial Intelligence, 68, 131-151.
DOI Scopus38 WoS30
2018 Pratama, M., Angelov, P. P., Lughofer, E., & Joo Er, M. (2018). Parsimonious random vector functional link network for data streams. Information Sciences, 430-431, 519-537.
DOI Scopus57 WoS43
2018 Caesarendra, W., Pratama, M., Kosasih, B., Tjahjowidodo, T., & Glowacz, A. (2018). Parsimonious network based on a fuzzy inference system (PANFIS) for time series feature prediction of low speed slew bearing prognosis. Applied Sciences (Switzerland), 8(12, article no. 2656), 1-21.
DOI Scopus55 WoS49
2018 Lughofer, E., & Pratama, M. (2018). Online active learning in data stream regression using uncertainty sampling based on evolving generalized fuzzy models. IEEE Transactions on Fuzzy Systems, 26(1, article no. 7820039), 292-309.
DOI Scopus98 WoS81
2017 Pratama, M., Lughofer, E., Lim, C. P., Rahayu, W., Dillon, T., & Budiyono, A. (2017). pClass+: a novel evolving semi-supervised classifier. International Journal of Fuzzy Systems, 19(3), 863-880.
DOI Scopus32 WoS11
2017 Za'in, C., Pratama, M., Lughofer, E., & Anavatti, S. G. (2017). Evolving type-2 web news mining. Applied Soft Computing Journal, 54, 200-220.
DOI Scopus50 WoS30
2017 Pratama, M., Lughofer, E., Er, M. J., Anavatti, S., & Lim, C. P. (2017). Data driven modelling based on recurrent interval-valued metacognitive scaffolding fuzzy neural network. Neurocomputing, 262, 4-27.
DOI Scopus35 WoS29
2017 Pratama, M., Zhang, G., Er, M. J., & Anavatti, S. (2017). An incremental type-2 meta-cognitive extreme learning machine. IEEE Transactions on Cybernetics, 47(2, article no. 7390064), 339-353.
DOI Scopus70 WoS55 Europe PMC3
2017 Pratama, M., Lu, J., Lughofer, E., Zhang, G., & Er, M. J. (2017). An incremental learning of concept drifts using evolving type-2 recurrent fuzzy neural networks. IEEE Transactions on Fuzzy Systems, 25(5, article no. 7542529), 1175-1192.
DOI Scopus144 WoS133
2017 Lughofer, E., Kindermann, S., Pratama, M., & Rubio, J. D. J. (2017). Top-down sparse fuzzy regression modeling from data with improved coverage. International Journal of Fuzzy Systems, 19(5), 1645-1658.
DOI Scopus7 WoS5
2017 Zhang, Y., Er, M. J., Zhao, R., & Pratama, M. (2017). Multiview convolutional neural networks for multidocument extractive summarization. IEEE Transactions on Cybernetics, 47(10, article no. 7756666), 3230-3242.
DOI Scopus74 WoS46 Europe PMC12
2017 Venkatesan, R., Er, M. J., Dave, M., Pratama, M., & Wu, S. (2017). A novel online multi-label classifier for high-speed streaming data applications. Evolving Systems, 8(4), 303-315.
DOI Scopus39 WoS27
2017 Pratama, M., Lughofer, E., & Wang, D. (2017). Online real-time learning strategies for data streams for Neurocomputing. Neurocomputing, 262, 1-3.
DOI Scopus2 WoS2
2016 Er, M. J., Zhang, Y., Wang, N., & Pratama, M. (2016). Attention pooling-based convolutional neural network for sentence modelling. Information Sciences, 373, 388-403.
DOI Scopus149 WoS115
2016 Pratama, M., Lu, J., Anavatti, S., Lughofer, E., & Lim, C. P. (2016). An incremental meta-cognitive-based scaffolding fuzzy neural network. Neurocomputing, 171, 89-105.
DOI Scopus98 WoS67
2016 Pratama, M., Lu, J., & Zhang, G. (2016). Evolving type-2 fuzzy classifier. IEEE Transactions on Fuzzy Systems, 24(3, article no. 7175005), 574-589.
DOI Scopus134 WoS94
2016 Pratama, M., Lu, J., Lughofer, E., Zhang, G., & Anavatti, S. (2016). Scaffolding type-2 classifier for incremental learning under concept drifts. Neurocomputing, 191, 304-329.
DOI Scopus87 WoS49
2015 Pratama, M., Anavatti, S. G., & Lu, J. (2015). Recurrent classifier based on an incremental metacognitive-based scaffolding algorithm. IEEE Transactions on Fuzzy Systems, 23(6, article no. 7039239), 2048-2066.
DOI Scopus92 WoS58
2015 Lughofer, E., Cernuda, C., Kindermann, S., & Pratama, M. (2015). Generalized smart evolving fuzzy systems. Evolving Systems, 6(4), 269-292.
DOI Scopus162 WoS137
2015 Pratama, M., Anavatti, S. G., Er, M. J., & Lughofer, E. D. (2015). pClass: an effective classifier for streaming examples. IEEE Transactions on Fuzzy Systems, 23(2, article no. 6776566), 369-386.
DOI Scopus121 WoS85
2014 Pratama, M., Anavatti, S. G., & Lughofer, E. (2014). Genefis: Toward an effective localist network. IEEE Transactions on Fuzzy Systems, 22(3), 547-562.
DOI Scopus165 WoS134
2014 Pratama, M., Anavatti, S. G., Angelov, P. P., & Lughofer, E. (2014). PANFIS: A novel incremental learning machine. IEEE Transactions on Neural Networks and Learning Systems, 25(1), 55-68.
DOI Scopus252 WoS222 Europe PMC8
2013 Pratama, M., Er, M. J., Li, X., Oentaryo, R. J., Lughofer, E., & Arifin, I. (2013). Data driven modeling based on dynamic parsimonious fuzzy neural network. Neurocomputing, 110, 18-28.
DOI Scopus72 WoS55

Year Citation
2022 Pratama, M., Ashfahani, A., & Lughofer, E. (2022). Unsupervised continual learning via self-adaptive deep clustering approach. In F. Cuzzolin, K. Cannons, & V. Lomonaco (Eds.), Event/exhibition information: First International Workshop, CSSL 2021, Virtual, online, 19/08/2021-20/08/2021
Source details - Title: Continual Semi-Supervised Learning (Vol. 13418 LNAI, pp. 48-61). Switzerland: Springer.
DOI Scopus3 WoS1
2019 Lughofer, E., Zavoianu, A. C., Pratama, M., & Radauer, T. (2019). Automated process optimization in manufacturing systems based on static and dynamic prediction models. In E. Lughofer, & M. Sayed-Mouchaweh (Eds.), Source details - Title: Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications (pp. 485-531). Switzerland: Springer Nature Switzerland AG.
DOI Scopus2
2019 Ashfahani, A., Pratama, M., Lughofer, E., Cai, Q., & Sheng, H. (2019). An online rfid localization in the manufacturing shopfloor. In E. Lughofer, & M. Sayed-Mouchaweh (Eds.), Source details - Title: Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications (pp. 287-309). Switzerland: Springer Nature Switzerland AG.
DOI Scopus23
2018 Za'in, C., Pratama, M., Prasad, M., Puthal, D., Lim, C. P., & Seera, M. (2018). Motor fault detection and diagnosis based on a meta-cognitive random vector functional link network. In M. Sayed-Mouchaweh (Ed.), Source details - Title: Fault Diagnosis of Hybrid Dynamic and Complex Systems (pp. 15-44). Switzerland: Springer International Publishing AG.
DOI Scopus5
2018 Hassanein, O., Anavatti, S. G., Pratama, M., & Ray, T. (2018). Autonomous underwater vehicles. In M. J. Er, N. Wing, M. Pratama, S. Sharma, & Z. Lian (Eds.), Source details - Title: Intelligent Marine and Aerial Vehicles: Theory and Applications (pp. 77-125). US: Nova Science Publishers, Inc..
Scopus2

Year Citation
2025 Masum, M. A., Pratama, D., Ramasamy, S., Liu, L., Habibullah, H., & Kowalczyk, R. (2025). PROL: Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning. In IEEE/CVF International Conference on Computer Visions (ICCV 2025) (pp. 1-11). US: IEEE/CVF.
2025 Ma'sum, M. A., Pratama, M., Liu, L., Habibullah, H., & Kowalczyk, R. (2025). Federated few-shot class-incremental learning. In The Thirteenth International Conference on Learning Representations (ICLR 2025) (pp. 1-30). US: ICLR.
2025 Ma'sum, M. A., Pratama, M., Ramasamy, S., Liu, L., Habibullah, H., & Kowalczyk, R. (2025). Vision and language synergy for rehearsal free continual learning. In The Thirteenth International Conference on Learning Representations (ICLR 2025) (pp. 1-31). US: ICLR.
2025 Ma'sum, M. A., Pratama, M., Liu, L., Habibullah, H., & Kowalczyk, R. (2025). FEDERATED FEW-SHOT CLASS-INCREMENTAL LEARNING. In 13th International Conference on Learning Representations Iclr 2025 (pp. 101524-101553).
Scopus1
2025 Ma'Sum, M. A., Pratama, M., Ramasamy, S., Liu, L., Habibullah, H., & Kowalczyk, R. (2025). VISION AND LANGUAGE SYNERGY FOR REHEARSAL FREE CONTINUAL LEARNING. In 13th International Conference on Learning Representations Iclr 2025 (pp. 15997-16027).
2024 Rakaraddi, A., Siew Kei, L., Pratama, M., & de Carvalho, M. (2024). Graph mining under data scarcity. In IJCNN 2024 Conference Proceedings (pp. 7 pages). US: IEEE.
DOI
2024 Tan, C. S., Gupta, A., Ong, Y. S., Lam, S. K., Pratama, M., & Tan, P. S. (2024). Pareto set representation learning with application to multi-criteria order optimization. In 2024 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 947-951). US: IEEE.
DOI
2024 Ma'sum, M. A., Pratama, M., Ramasamy, S., Liu, L., Habibullah, H., & Kowalczyk, R. (2024). PIP: prototypes-injected prompt for Federated Class Incremental Learning. In CIKM ’24 Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 1670-1679). US: Association for Computing Machinery.
DOI Scopus2
2024 de Carvalho, M., Pratama, M., Zhang, J., Haoyan, C., & Yapp, E. (2024). Towards cross-domain continual learning. In Proceedings: 2024 IEEE 40th International Conference on Data Engineering (ICDE) (pp. 1131-1142). US: IEEE.
DOI Scopus1
2023 Sarkar, M. D. R., Anavatti, S., Dam, T., Pratama, M., & Kindhi, B. A. (2023). Enhancing wind power forecast precision via multi-head attention transformer: an investigation on single-step and multi-step forecasting. In Proceedings of the International Joint Conference on Neural Networks (IJCNN) Vol. 2023-June (pp. 1-8). US: IEEE.
DOI Scopus12 WoS7
2023 De Carvalho, M., Pratama, M., Zhang, J., & Sun, Y. (2023). Class-incremental learning via knowledge amalgamatio. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Vol. 13715 (pp. 36-50). Switzerland: Springer.
DOI Scopus7 WoS4
2023 Dam, T., Pratama, M., Ferdaus, M. D. M., Anavatti, S., & Abbas, H. (2023). Scalable adversarial online continual learning. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Vol. 13715 (pp. 373-389). Switzerland: Springer.
DOI
2022 Aradya, A. M. S., Sharma, G., Pratama, M., & Sundaram, S. (2022). Adaptive Latent Transformation (ALT) for the classification of resting state - fMRI. In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 206-210). US: IEEE.
DOI
2022 Dam, T., Ferdaus, M. D. M., Pratama, M., Anavatti, S., Jayavelu, S., & Abbas, H. (2022). Latent preserving generative adversarial network for imbalance classification. In International Conference on Image Processing (pp. 3712-3716). US: IEEE.
DOI Scopus8 WoS6
2022 Rakaraddi, A., Lam, S. K., Pratama, M., & De Carvalho, M. (2022). Reinforced continual learning for graphs. In CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 1666-1674). US: Association for Computing Machinery.
DOI Scopus24 WoS15
2022 Sakti, W. W., Anam, K., Pratama, M., Bukhori, S., Hanggara, F. S., & Liswanto, B. (2022). Brain-Computer Interface based on Neural Network with Dynamically Evolved for Hand Movement Classification. In 2022 Fortei International Conference on Electrical Engineering Fortei Icee 2022 Proceeding (pp. 72-75). IEEE.
DOI Scopus1
2022 Hirzi, N. M., Ma'sum, M. A., Pratama, M., & Jatmiko, W. (2022). Large-scale 3D point cloud semantic segmentation with 3D U-Net ASPP sparse CNN. In IWBIS 2022 - 7th International Workshop on Big Data and Information Security, Proceedings (pp. 59-64). IEEE: IEEE.
DOI Scopus1
2021 Kocer, B. B., Hady, M. A., Kandath, H., Pratama, M., & Kovac, M. (2021). Deep neuromorphic controller with dynamic topology for aerial robots. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2021-May (pp. 110-116). US: I E E E Computer Society.
DOI Scopus11 WoS8
2021 Abka, A. F., Pratama, M., & Jatmiko, W. (2021). Cross-lingual summarization: English - Bahasa Indonesia. In Proceedings - IWBIS 2021: 6th International Workshop on Big Data and Information Security (pp. 53-58). US: Institute of Electrical and Electronics Engineers.
DOI Scopus1
2021 Gates, W., Jati, G., Intan P, R. D., Pratama, M., & Jatmiko, W. (2021). A modest system of feature-based stereo visual odometry. In Proceedings - IWBIS 2021: 6th International Workshop on Big Data and Information Security (pp. 47-52). US: IEEE.
DOI
2021 Ashfahani, A., Pratama, M., Lughofer, E., & Yee, E. Y. K. (2021). Autonomous deep quality monitoring in streaming environments. In 2021 International Joint Conference on Neural Networks(IJCNN) Proceedings Vol. 2021-July (pp. 1-8). US: Institute of Electrical and Electronics Engineers.
DOI Scopus3 WoS2
2021 Rakaraddi, A., & Pratama, M. (2021). Unsupervised learning for identifying high eigenvector centrality nodes: a graph neural network approach. In IEEE BigData 2021 Program Schedule (pp. 4945-4954). US: IEEE.
DOI Scopus10 WoS8
2020 Aradhya, A. M. S., Sundaram, S., & Pratama, M. (2020). Metaheuristic Spatial Transformation (MST) for accurate detection of Attention Deficit Hyperactivity Disorder (ADHD) using rs-fMRI. In R. Barbieri (Ed.), Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Vol. 2020-July (pp. 2829-2832). US: Institute of Electrical and Electronics Engineers.
DOI Scopus19 WoS12
2020 Anam, K., Bukhori, S., Hanggara, F. S., & Pratama, M. (2020). Subject-independent classification on brain-computer interface using autonomous deep learning for finger movement recognition. In R. Barbieri (Ed.), Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Vol. 2020-July (pp. 447-450). US: Institute of Electrical and Electronics Engineers.
DOI Scopus9 WoS9
2020 Za'in, C., Ashfahani, A., Pratama, M., Lughofer, E., & Pardede, E. (2020). Scalable teacher-forcing networks under spark environments for large-scale streaming problems. In G. Castellano, C. Castiello, & C. Mencar (Eds.), IEEE Conference on Evolving and Adaptive Intelligent Systems Vol. 2020-May (pp. 1-8). US: Institute of Electrical and Electronics Engineers.
DOI Scopus3 WoS3
2020 Wei, S., Shen, H., Li, Q., Pratama, M., Shunmei, M., Long, H., & Xia, Y. (2020). PSVM: quantitative analysis method of intelligent system risk in independent host environment. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST Vol. 322 LNICST (pp. 544-552). Germany: Institute for Computer Sciences Social Informatics and Telecommunications Engineering.
DOI Scopus1
2020 Lughofer, E., & Pratama, M. (2020). Online sequential ensembling of fuzzy systems. In G. Castellano, C. Castiello, & C. Mencar (Eds.), IEEE Conference on Evolving and Adaptive Intelligent Systems Vol. 2020-May (pp. 1-10). US: Institute of Electrical and Electronics Engineers.
DOI Scopus2
2020 Das, M., Pratama, M., & Tjahjowidodo, T. (2020). A self-evolving mutually-operative recurrent network-based model for online tool condition monitoring in delay scenario. In R. Gupta, Y. Liu, J. Tang, & B. Prakash (Eds.), Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 2775-2783). US: Association for Computing Machinery.
DOI Scopus9 WoS8
2020 Das, M., Pratama, M., Zhang, J., & Ong, Y. S. (2020). A skip-connected evolving recurrent neural network for data stream classification under label latency scenario. In AAAI 2020 - 34th AAAI Conference on Artificial Intelligence Vol. 34 (pp. 3717-3724). US: AAAI Press.
DOI Scopus15 WoS12
2020 Samanta, S., Pratama, M., Sundaram, S., & Srikanth, N. (2020). A dual network solution (DNS) for lag-free time series forecasting. In Proceedings of the International Joint Conference on Neural Networks (pp. 1-8). US: Institute of Electrical and Electronics Engineers.
DOI Scopus8 WoS5
2019 Pratama, M., Za'in, C., Ashfahani, A., Ong, Y. S., & Ding, W. (2019). Automatic construction of multi-layer perceptron network from streaming examples. In International Conference on Information and Knowledge Management Proceedings (pp. 1171-1180). PEOPLES R CHINA, Beijing: ASSOC COMPUTING MACHINERY.
DOI Scopus52 WoS44
2019 Das, M., Pratama, M., Ashfahani, A., & Samanta, S. (2019). FERNN: a fast and evolving recurrent neural network model for streaming data classification. In D. Wang, & K. Doya (Eds.), Proceedings of the International Joint Conference on Neural Networks Vol. 2019-July (pp. 2-8). US: Institute of Electrical and Electronics Engineers.
DOI Scopus11 WoS8
2019 Kocer, B. B., Tiryaki, M. E., Pratama, M., Tjahjowidodo, T., & Seet, G. G. L. (2019). Aerial robot control in close proximity to ceiling: a force estimation-based nonlinear MPC. In IEEE International Conference on Intelligent Robots and Systems (pp. 2813-2819). US: Institute of Electrical and Electronics Engineers.
DOI Scopus21 WoS17
2019 Pratama, M., De Carvalho, M., Xie, R., Lughofer, E., & Lu, J. (2019). ATL: autonomous knowledge transfer from many streaming processes. In CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 269-278). US: Association for Computing Machinery.
DOI Scopus17 WoS23
2019 Za'in, C., Pratama, M., Ashfahani, A., Pardede, E., & Sheng, H. (2019). Big data analytic based on scalable PANFIS for RFID localization. In L. O'Conner (Ed.), Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 1687-1692). US: Institute of Electrical and Electronics Engineers.
DOI Scopus2 WoS1
2019 Ashfahani, A., & Pratama, M. (2019). Autonomous deep learning: continual learning approach for dynamic environments. In 19th SIAM International Conference on Data Mining, SDM 2019 (pp. 666-674). US: Society for Industrial and Applied Mathematics.
DOI Scopus84 WoS59
2019 Pratama, M., Za'in, C., Ashfahani, A., Ong, Y. S., & Ding, W. (2019). Automatic construction of multi-layer perceptron network from streaming examples. In CIKM ’19 Proceedings of the 28th ACM International Conference on Information & Knowledge Management (pp. 1171-1180). US: Association for Computing Machinery.
DOI
2019 Das, M., Pratama, M., Savitri, S., & Zhang, J. (2019). MUSE-RNN: a multilayer self-evolving recurrent neural network for data stream classification. In J. Wang, K. Shim, & X. Wu (Eds.), Proceedings - IEEE International Conference on Data Mining, ICDM Vol. 2019-November (pp. 110-119). US: IEEE.
DOI Scopus28 WoS22
2019 Carvalho, M., & Pratama, M. (2019). Improving shallow neural network by compressing deep neural network. In S. S (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 1382-1387). US: Institute of Electrical and Electronics Engineers.
DOI Scopus1
2019 Al Mahasneh, A. J., Anavatti, S. G., Garratt, M. A., & Pratama, M. (2019). Evolving general regression neural networks using limited incremental evolution for data-driven modeling of non-linear dynamic systems. In S. Sundaram (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 335-341). US: Institute of Electrical and Electronics Engineers.
DOI Scopus8 WoS7
2019 Lughofer, E., Pratama, M., Eitzinger, C., & Radauer, T. (2019). Dynamic anomaly detection based on recursive independent component analysis of multi-variate residual signals. In P. Fuster-Parra, & O. V. Sierra (Eds.), 33rd Annual European Simulation and Modelling Conference 2019, ESM 2019 (pp. 105-113). Belgium: EUROSIS.
2019 Ferdaus, M. M., Anavatti, S. G., Al Mahasneh, A. J., Pratama, M., & Garratt, M. A. (2019). Development of hyperplane-based adaptive T-S fuzzy controller for micro aerial robots. In Proceedings - 2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2019 (pp. 50-56). US: Institute of Electrical and Electronics Engineers.
DOI Scopus2 WoS2
2019 Huang, S., Ashfahani, A., & Pratama, M. (2019). Wireless indoor positioning using online machine learning. In M. Wani (Ed.), Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 (pp. 1885-1890). US: Institute of Electrical and Electronics Engineers.
DOI Scopus1
2019 Pratama, M., Ashfahani, A., & Hady, A. (2019). Weakly supervised deep learning approach in streaming environments. In C. Baru (Ed.), Proceedings 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 1195-1202). US: Institute of Electrical and Electronics Engineers.
DOI Scopus20 WoS18
2019 Kocer, B. B., Kumtepeli, V., Tjahjowidodo, T., Pratama, M., Tripathi, A., Seet Gim Lee, G., & Wang, Y. (2019). UAV control in close proximities - ceiling effect on battery lifetime. In R. Bilof (Ed.), Proceedings - 2019 2nd International Conference on Intelligent Autonomous Systems, ICoIAS 2019 (pp. 193-197). US: Institute of Electrical and Electronics Engineers.
DOI Scopus13 WoS12
2019 Kandath, H., Hady, M. A., Pratama, M., & Feng, N. B. (2019). Robust evolving neuro-fuzzy control of a novel tilt-rotor vertical takeoff and landing aircraft. In IEEE International Conference on Fuzzy Systems Vol. 2019-June (pp. 1-6). US: Institute of Electrical and Electronics Engineers.
DOI Scopus4 WoS2
2019 Ferdaus, M. M., Hady, M. A., Pratama, M., Kandath, H., & Anavatti, S. G. (2019). RedPAC: a simple evolving neuro-fuzzy-based intelligent control framework for quadcopter. In IEEE International Conference on Fuzzy Systems Vol. 2019-June (pp. 1-7). US: Institute of Electrical and Electronics Engineers.
DOI Scopus4 WoS2
2019 Ferdaus, M. M., Anavatti, S. G., Garratt, M. A., & Pratama, M. (2019). Red-FLC: an adaptive fuzzy logic controller with reduced learning parameters. In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (pp. 513-518). US: Institute of Electrical and Electronics Engineers.
DOI
2019 Samanta, S., Hartanto, A., Pratama, M., Sundaram, S., & Srikanth, N. (2019). RIT2FIS: a recurrent interval type 2 fuzzy inference system and its rule base estimation. In D. Wang, & K. Doya (Eds.), Proceedings of the International Joint Conference on Neural Networks Vol. 2019-July (pp. 1-8). US: Institute of Electrical and Electronics Engineers.
DOI Scopus3 WoS1
2019 Ferdaus, M. M., Anavatti, S. G., Pratama, M., & Garratt, M. A. (2019). A novel self-organizing neuro-fuzzy based intelligent control system for a AR.Drone quadcopter. In S. Sundaram (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 2026-2032). US: Institute of Electrical and Electronics Engineers.
DOI Scopus5 WoS4
2019 Ferdaus, M. M., Pratama, M., Anavatti, S. G., & Garratt, M. A. (2019). A generic self-evolving neuro-fuzzy controller based high-performance hexacopter altitude control system. In L. O'Conner (Ed.), Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 2784-2791). US: Institute of Electrical and Electronics Engineers.
DOI Scopus12 WoS13
2019 Aradhya, A. M. S., Joglekar, A., Suresh, S., & Pratama, M. (2019). Deep transformation method for discriminant analysis of multi-channel resting state fMRI. In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 Vol. 33 (pp. 2556-2563). US: IEEE Computer Society.
DOI Scopus14 WoS12
2018 Ferdaus, M. M., Anavatti, S. G., Garratt, M. A., & Pratama, M. (2018). Fuzzy clustering based modelling and adaptive controlling of a flapping wing micro air vehicle. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings Vol. 2018-January (pp. 1-6). US: Institute of Electrical and Electronics Engineers.
DOI
2018 Prasad, M., Chang, L. C., Gupta, D., Pratama, M., Sundaram, S., & Lin, C. T. (2018). Online video streaming for human tracking based on weighted resampling particle filter. In S. Ozawa, M. Pratama, A. Roy, A. W. Tan, & P. P. Angelov (Eds.), Procedia Computer Science Vol. 144 (pp. 2-12). Netherlands: Elsevier BV.
DOI Scopus2 WoS1
2018 Singh, J., Prasad, M., Daraghmi, Y. A., Tiwari, P., Yadav, P., Bharill, N., . . . Saxena, A. (2018). Fuzzy logic hybrid model with semantic filtering approach for pseudo relevance feedback-based query expansion. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings Vol. 2018-January (pp. 1-7). US: Institute of Electrical and Electronics Engineers.
DOI Scopus36
2018 Lughofer, E., Pollak, R., Zavoianu, A. C., Meyer Heye, P., Zorrer, H., Eitzinger, C., . . . Pratama, M. (2018). Evolving time-series based prediction models for quality criteria in a multi-stage production process. In Y. Manolopoulos, L. Iliadis, P. Angelov, & E. Lughofer (Eds.), 2018 IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2018 (pp. 1-10). US: Institute of Electrical and Electronics Engineers.
DOI Scopus5
2018 Ferdaus, M. M., Pratama, M., Anavatti, S. G., & Garratt, M. A. (2018). Evolving neuro-fuzzy system based online identification of a bio-inspired flapping wing micro aerial vehicle. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings Vol. 2018-January (pp. 1-8). US: Institute of Electrical and Electronics Engineers.
DOI
2018 Ferdaus, M. M., Anavatti, S. G., Garratt, M. A., & Pratama, M. (2018). Evolving fuzzy inference system based online identification and control of a quadcopter unmanned aerial vehicle. In Proceeding - ICAMIMIA 2017: International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (pp. 223-228). US: Institute of Electrical and Electronics Engineers.
DOI Scopus24 WoS4
2018 Huang, S., Mohanty, S., Ashfahani, A., & Pratama, M. (2018). The study on indoor localization for manufacturing execution system. In 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018 (pp. 1863-1867). US: Institute of Electrical and Electronics Engineers.
DOI Scopus4 WoS3
2018 Huang, S., Shoaib, S., Ashfahani, A., & Pratama, M. (2018). Similarity measures in development of an indoor localization system. In 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018 (pp. 1420-1425). US: Institute of Electrical and Electronics Engineers.
DOI Scopus3
2018 Za'in, C., Pratama, M., Lughofer, E., Ferdaus, M., Cai, Q., & Prasad, M. (2018). Big data analytics based on PANFIS MapReduce. In S. Ozawa, M. Pratama, A. Roy, A. W. Tan, & P. P. Angelov (Eds.), Procedia Computer Science Vol. 144 (pp. 140-152). Netherlands: Elsevier BV.
DOI Scopus3 WoS3
2017 Lughofer, E., Pratama, M., & Skrjanc, I. (2017). Incremental rule splitting in generalized evolving fuzzy regression models. In I. Skrjanc, & S. Blazic (Eds.), IEEE Conference on Evolving and Adaptive Intelligent Systems Vol. 2017-May (pp. 1-8). US: Institute of Electrical and Electronics Engineers.
DOI Scopus3 WoS2
2017 Ferdaus, M. M., Anavatti, S. G., Garratt, M. A., & Pratama, M. (2017). Fuzzy clustering based nonlinear system identification and controller development of Pixhawk based quadcopter. In 9th International Conference on Advanced Computational Intelligence, ICACI 2017 (pp. 223-230). US: Institute of Electrical and Electronics Engineers.
DOI Scopus10 WoS25
2017 Biswas, S., Anavatti, S. G., Garratt, M. A., & Pratama, M. (2017). Simultaneous replanning with vectorized particle swarm optimization algorithm. In 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016 (pp. 1-6). US: Institute of Electrical and Electronics Engineers.
DOI Scopus8
2017 Pratama, M., Angelov, P. P., Lu, J., Lughofer, E., Seera, M., & Lim, C. P. (2017). A randomized neural network for data streams. In Proceedings of the International Joint Conference on Neural Networks Vol. 2017-May (pp. 3423-3430). US: Institute of Electrical and Electronics Engineers.
DOI Scopus9 WoS6
2017 Chou, K. P., Li, D. L., Prasad, M., Pratama, M., Su, S. Y., Lu, H., . . . Lin, W. C. (2017). Robust facial alignment for face recognition. In J. E. Moody, S. J. Hanson, & R. P. Lippmann (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10636 LNCS (pp. 497-504). Germany: Springer.
DOI Scopus5 WoS4
2017 Ferdaus, M. M., Anavatti, S. G., Garratt, M. A., & Pratama, M. (2017). Fuzzy Clustering based Modelling and Adaptive Controlling of a Flapping Wing Micro Air Vehicle. In 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) (pp. 1914-1919). HI, Honolulu: IEEE.
WoS5
2017 Singh, J., Prasad, M., Daraghmi, Y. A., Tiwari, P., Yadav, P., Bharill, N., . . . Saxena, A. (2017). Fuzzy LogicHybrid Model with Semantic Filtering Approach for Pseudo Relevance Feedback-based Query Expansion. In 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) (pp. 1907-1913). HI, Honolulu: IEEE.
WoS3
2016 Er, M. J., Liu, F., Wang, N., Zhang, Y., & Pratama, M. (2016). User-level twitter sentiment analysis with a hybrid approach. In L. Cheng, Q. Liu, & A. Ronzhin (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9719 (pp. 426-433). Germany: Springer.
DOI Scopus12 WoS6
2016 Pratama, M., Lughofer, E., Er, M. J., Rahayu, W., & Dillon, T. (2016). Evolving type-2 recurrent fuzzy neural network. In Proceedings of the International Joint Conference on Neural Networks Vol. 2016-October (pp. 1841-1848). US: Institute of Electrical and Electronics Engineers.
DOI Scopus11 WoS9
2016 Venkatesan, R., Er, M. J., Wu, S., & Pratama, M. (2016). A novel online real-time classifier for multi-label data streams. In Proceedings of the International Joint Conference on Neural Networks Vol. 2016-October (pp. 1833-1840). US: Institute of Electrical and Electronics Engineers.
DOI Scopus15 WoS11
2016 Zhang, Y., Er, M. J., Venkatesan, R., Wang, N., & Pratama, M. (2016). Sentiment classification using comprehensive attention recurrent models. In Proceedings of the International Joint Conference on Neural Networks Vol. 2016-October (pp. 1562-1569). US: Institute of Electrical and Electronics Engineers.
DOI Scopus49 WoS22
2016 Zhang, Y., Er, M. J., & Pratama, M. (2016). Extractive document summarization based on convolutional neural networks. In IECON Proceedings (Industrial Electronics Conference) (pp. 918-922). US: Institute of Electrical and Electronics Engineers.
DOI Scopus33 WoS19
2016 Anavatti, S. G., Biswas, S., Colvin, J. T., & Pratama, M. (2016). A hybrid algorithm for efficient path planning of autonomous ground vehicles. In 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016 (pp. 1-6). US: Institute of Electrical and Electronics Engineers.
DOI Scopus5
2016 Pratama, M., Lu, J., & Zhang, G. (2016). A novel meta-cognitive extreme learning machine to learning from data streams. In L. O'Conner (Ed.), Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 (pp. 2792-2797). US: Institute of Electrical and Electronics Engineers.
DOI Scopus5 WoS3
2015 Pratama, M., Lu, J., & Zhang, G. (2015). An incremental interval type-2 neural fuzzy classifier. In A. Yazici (Ed.), IEEE International Conference on Fuzzy Systems Vol. 2015-November (pp. 1-8). US: Institute of Electrical and Electronics Engineers.
DOI Scopus5 WoS10
2014 Pratama, M., Anavatti, S. G., & Lughofer, E. (2014). An incremental classifier from data streams. In A. Likas, K. Blekas, & D. Kalles (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 8445 LNCS (pp. 15-28). GREECE, Ioannina: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus6 WoS5
2014 Pratama, M., Er, M. J., Anavatti, S. G., Lughofer, E., Wang, N., & Arifin, I. (2014). A novel meta-cognitive-based scaffolding classifier to sequential non-stationary classification problems. In IEEE International Conference on Fuzzy Systems (pp. 369-376). PEOPLES R CHINA, Beijing: IEEE.
DOI Scopus30 WoS10
2014 Pratama, M., Lu, J., Anavatti, S. G., & Iglesias, J. A. (2014). A recurrent meta-cognitive-based Scaffolding classifier from data streams. In IEEE Ssci 2014 2014 IEEE Symposium Series on Computational Intelligence Eals 2014 2014 IEEE Symposium on Evolving and Autonomous Learning Systems Proceedings (pp. 132-139). FL, Orlando: IEEE.
DOI Scopus1
2013 Pratama, M., Anavatti, S. G., Garratt, M., & Lughofer, E. (2013). Online identification of complex multi-input-multi-output system based on generic evolving neuro-fuzzy inference system. In Proceedings of the 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems Eais 2013 2013 IEEE Symposium Series on Computational Intelligence Ssci 2013 (pp. 106-113). SINGAPORE, Singapore: IEEE.
DOI Scopus7 WoS4
2013 Pratama, M., Anavatti, S. G., & Lughofer, E. (2013). Evolving fuzzy rule-based classifier based on GENEFIS. In IEEE International Conference on Fuzzy Systems (pp. 8 pages). INDIA, Hyderabad: IEEE.
DOI Scopus23 WoS8
2013 Lughofer, E., Cernuda, C., & Pratama, M. (2013). Generalized flexible fuzzy inference systems. In M. A. Wani, G. Tecuci, M. Boicu, M. Kubat, T. M. Khoshgoftaar, & N. Seliya (Eds.), Proceedings 2013 12th International Conference on Machine Learning and Applications Icmla 2013 Vol. 2 (pp. 1-7). FL, Miami: IEEE.
DOI Scopus5 WoS3
2011 Pratama, M., Er, M. J., Li, X., San, L., Richard, J. O., Zhai, L. Y., . . . Arifin, I. (2011). Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification. In D. Liu, H. Zhang, M. Polycarpou, C. Alippi, & H. He (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6676 LNCS (pp. 525-534). PEOPLES R CHINA, Guilin: SPRINGER-VERLAG BERLIN.
DOI Scopus3 WoS4
2011 Pratama, M., Er, M. J., Li, X., Gan, O. P., Oentaryo, R. J., Linn, S., . . . Arifin, I. (2011). Tool wear prediction using evolutionary Dynamic Fuzzy Neural (EDFNN) Network. In IECON Proceedings Industrial Electronics Conference (pp. 4739-4744). AUSTRALIA, Melbourne: IEEE.
DOI Scopus6 WoS3

Courses I teach

  • COMP 2019 AI and Machine Learning (2025)
  • COMP 2019 AI and Machine Learning (2024)

Date Role Research Topic Program Degree Type Student Load Student Name
2024 Co-Supervisor An autonomous real-time satellite tasking and dynamic resource allocation based on distributed, multi-agent deep reinforcement learning Doctor of Philosophy Doctorate Full Time Mr Mingjun Fan
2024 Co-Supervisor Dynamic optimization of heterogeneous constellation resources Doctor of Philosophy Doctorate Full Time Mr Mohamad Abdul Hady
2024 Co-Supervisor A hybrid intrinsic knowledge representation and reasoning in deep reinforcement learning Doctor of Philosophy Doctorate Full Time Yugu Li
2023 Principal Supervisor Handling Dynamic Environments with Extremely Few Examples Doctor of Philosophy Doctorate Full Time Mr Naeem Paeedeh
2023 Co-Supervisor A framework for natural language communication with uninhabited aerial vehicles in air traffic management Doctor of Philosophy Doctorate Full Time Miss Aradhika Guha
2023 Co-Supervisor 109376-Multi-Modal Information Fusion Doctor of Philosophy Doctorate Full Time Ifrah Siddiqui
2023 Co-Supervisor Natural Language Summarisation of Causal Events Doctor of Philosophy Doctorate Full Time Mr Cong-Linh Le
2023 Principal Supervisor Onboard Dynamic Optimization of Heterogeneous Satellite Constellation Resources Doctor of Philosophy Doctorate Full Time Mr Monirul Islam Pavel
2022 Principal Supervisor span class="cf0">Advanced time series domain adaptation Doctor of Philosophy Doctorate Full Time Muhammad Tanzil Furqon

Date Role Board name Institution name Country
2022 - ongoing Board Member Industrial AI research center University of South Australia Australia

Date Role Committee Institution Country
2025 - ongoing Member Program Committee ICCV 2025 United States
2025 - ongoing Member Senior Program Committee IJCAI 2025 Canada
2025 - ongoing Member Workshop Chair IEEE PHMAP 2025 Singapore
2025 - ongoing Member Tutorial and Keynote Chair IEEE SSCI 2022 Singapore
2025 - ongoing Member Publication Chair IEEE SSCI 2018 India
2025 - ongoing Member Local Chair IEEE ICBK 2018 Singapore
2025 - ongoing Member Program Committee ICLR Singapore

Date Role Editorial Board Name Institution Country
2025 - ongoing Associate Editor Neural Networks INNS United States
2025 - ongoing Associate Editor Knowledge-based Systems Elsevier Netherlands
2025 - ongoing Associate Editor Information Sciences elsevier Netherlands
2025 - ongoing Associate Editor Complex and Intelligent Systems Springer Nature Germany
2022 - ongoing Associate Editor IEEE Transactions on Neural Networks and Learning Systems IEEE Computational Intelligence Society United States
2022 - ongoing Associate Editor IEEE Transactions on Fuzzy Systems IEEE Computational Intelligence Society United States

Date Engagement Type Partner Name
2024 - ongoing Collaboration BAE Systems

Date Title Type Institution Country
2022 - ongoing Detailed Assessor Grant Assessment Australian Research Council -

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