The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Mahardhika Pratama sits on this spectrum.
This scientist: 223 publications — 54th percentile
54% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Mahardhika Pratama sits on this spectrum.
This scientist: 34 D-Index — 16th percentile
16% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Mahardhika Pratama is affiliated with the University of South Australia in Australia and has made significant contributions to the field of computer science, with a particular focus on artificial intelligence, computer vision, signal processing, information systems, and control and systems engineering. The research output predominantly concentrates on areas such as domain adaptation and few-shot learning, data stream mining techniques, machine learning including extreme learning machines (ELM), multimodal machine learning applications, anomaly detection techniques, neural networks, and time series analysis and forecasting.
The scientist's recent publications include studies that address both theoretical and applied aspects in these specialized areas. Selected works include:
Frequent co-authors in Mahardhika Pratama's research include Edwin Lughofer, Ryszard Kowalczyk, M. Anwar Ma'sum, Lin Liu, and Sreenatha G. Anavatti.
Publications are often disseminated through venues such as arXiv (Cornell University), Information Sciences, Knowledge-Based Systems, UniSA Research Outputs Repository (University of South Australia), and IEEE Transactions on Neural Networks and Learning Systems.
The body of work primarily centers on computer science with strong roots in artificial intelligence. The convergence of machine learning techniques with practical applications such as continual learning from nonstationary data streams, and self-adaptive control systems for micro aerial vehicles, reflects a multidisciplinary approach intersecting signal processing and control engineering.
Mahardhika Pratama;Sreenatha G. Anavatti;Plamen P. Angelov;Edwin Lughofer
Mahardhika Pratama;Sreenatha G. Anavatti;Edwin Lughofer
Edwin Lughofer;Carlos Cernuda;Stefan Kindermann;Mahardhika Pratama
Mahardhika Pratama;Jie Lu;Edwin Lughofer;Guangquan Zhang
Meng Joo Er;Yong Zhang;Ning Wang;Mahardhika Pratama
Mahardhika Pratama;Jie Lu;Guangquan Zhang
Mahardhika Pratama;Sreenatha G. Anavatti;Meng Joo;Edwin David Lughofer
Di Yang;Weixin Luan;Lu Qiao;Mahardhika Pratama
Mahardhika Pratama;Witold Pedrycz;Edwin Lughofer
Mahardhika Pratama;Jie Lu;Sreenatha Anavatti;Edwin Lughofer
Edwin Lughofer;Mahardhika Pratama
Mahardhika Pratama;Sreenatha G. Anavatti;Jie Lu
Yong Zhang;Meng Joo Er;Rui Zhao;Mahardhika Pratama
Mahardhika Pratama;Jie Lu;Edwin Lughofer;Guangquan Zhang
Andri Ashfahani;Mahardhika Pratama;Edwin Lughofer;Yew-Soon Ong
Edwin Lughofer;Mahardhika Pratama;Igor Skrjanc
Andri Ashfahani;Mahardhika Pratama
Mahardhika Pratama;Guangquan Zhang;Meng Joo Er;Sreenatha Anavatti
Mahardhika Pratama;Meng Joo Er;Xiang Li;Richard J. Oentaryo
Mahardhika Pratama;Dianhui Wang;Dianhui Wang
Wahyu Caesarendra;Mahardhika Pratama;Buyung Kosasih;Tegoeh Tjahjowidodo
Andri Ashfahani;Mahardhika Pratama
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