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Computer Science

D-Index
34
Citations
4501
World Ranking
12239
National Ranking
373

Overview

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:

  • "Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery," 2020, Applied Energy
  • "DEVDAN: deep evolving denoising autoencoder," 2020, UniSA Research Outputs Repository (University of South Australia)
  • "An incremental construction of deep neuro fuzzy system for continual learning of nonstationary data streams," 2020, UniSA Research Outputs Repository (University of South Australia)
  • "PAC: a novel self-adaptive neuro-fuzzy controller for micro aerial vehicles," 2020, UniSA Research Outputs Repository (University of South Australia)
  • "Robustness Evaluation of Multipartite Complex Networks Based on Percolation Theory," 2020, IEEE Transactions on Systems Man and Cybernetics Systems

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.

Best Publications

  • PANFIS: A Novel Incremental Learning Machine

    Mahardhika Pratama;Sreenatha G. Anavatti;Plamen P. Angelov;Edwin Lughofer

  • GENEFIS: Toward an Effective Localist Network

    Mahardhika Pratama;Sreenatha G. Anavatti;Edwin Lughofer

  • Generalized smart evolving fuzzy systems

    Edwin Lughofer;Carlos Cernuda;Stefan Kindermann;Mahardhika Pratama

  • An Incremental Learning of Concept Drifts Using Evolving Type-2 Recurrent Fuzzy Neural Networks

    Mahardhika Pratama;Jie Lu;Edwin Lughofer;Guangquan Zhang

  • Attention pooling-based convolutional neural network for sentence modelling

    Meng Joo Er;Yong Zhang;Ning Wang;Mahardhika Pratama

  • Evolving Type-2 Fuzzy Classifier

    Mahardhika Pratama;Jie Lu;Guangquan Zhang

  • pClass: An Effective Classifier for Streaming Examples

    Mahardhika Pratama;Sreenatha G. Anavatti;Meng Joo;Edwin David Lughofer

  • Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery

    Di Yang;Weixin Luan;Lu Qiao;Mahardhika Pratama

  • Evolving Ensemble Fuzzy Classifier

    Mahardhika Pratama;Witold Pedrycz;Edwin Lughofer

  • An incremental meta-cognitive-based scaffolding fuzzy neural network

    Mahardhika Pratama;Jie Lu;Sreenatha Anavatti;Edwin Lughofer

  • Online Active Learning in Data Stream Regression Using Uncertainty Sampling Based on Evolving Generalized Fuzzy Models

    Edwin Lughofer;Mahardhika Pratama

  • Recurrent Classifier Based on an Incremental Metacognitive-Based Scaffolding Algorithm

    Mahardhika Pratama;Sreenatha G. Anavatti;Jie Lu

  • Multiview Convolutional Neural Networks for Multidocument Extractive Summarization

    Yong Zhang;Meng Joo Er;Rui Zhao;Mahardhika Pratama

  • Scaffolding type-2 classifier for incremental learning under concept drifts

    Mahardhika Pratama;Jie Lu;Edwin Lughofer;Guangquan Zhang

  • DEVDAN: Deep evolving denoising autoencoder

    Andri Ashfahani;Mahardhika Pratama;Edwin Lughofer;Yew-Soon Ong

  • Incremental Rule Splitting in Generalized Evolving Fuzzy Systems for Autonomous Drift Compensation

    Edwin Lughofer;Mahardhika Pratama;Igor Skrjanc

  • Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments.

    Andri Ashfahani;Mahardhika Pratama

  • An Incremental Type-2 Meta-Cognitive Extreme Learning Machine

    Mahardhika Pratama;Guangquan Zhang;Meng Joo Er;Sreenatha Anavatti

  • Data driven modeling based on dynamic parsimonious fuzzy neural network

    Mahardhika Pratama;Meng Joo Er;Xiang Li;Richard J. Oentaryo

  • Deep stacked stochastic configuration networks for lifelong learning of non-stationary data streams

    Mahardhika Pratama;Dianhui Wang;Dianhui Wang

  • Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis

    Wahyu Caesarendra;Mahardhika Pratama;Buyung Kosasih;Tegoeh Tjahjowidodo

  • Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments

    Andri Ashfahani;Mahardhika Pratama

Frequent Co-Authors

Edwin Lughofer
Edwin Lughofer Johannes Kepler University of Linz
Meng Joo Er
Meng Joo Er Dalian Maritime University
Jie Lu
Jie Lu University of Technology Sydney
Suresh Sundaram
Suresh Sundaram Indian Institute of Science
Guangquan Zhang
Guangquan Zhang University of Technology Sydney
Chee Peng Lim
Chee Peng Lim Swinburne University of Technology
Dianhui Wang
Dianhui Wang La Trobe University
Igor Škrjanc
Igor Škrjanc University of Ljubljana
Ning Wang
Ning Wang Dalian Maritime University

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