World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
12821
World Ranking
3448
National Ranking
98

Research.com Recognitions

  • 2020 - IEEE Fellow For contributions to evolutionary learning and optimization

Overview

Hussein A. Abbass is affiliated with the University of New South Wales in Australia. Their research spans primarily the fields of Computer Science and Engineering, with a focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Aerospace Engineering, and Media Technology.

Their work covers several key topics including Robotic Path Planning Algorithms, Distributed Control Multi-Agent Systems, Modular Robots and Swarm Intelligence, Human-Automation Interaction and Safety, Advanced Vision and Imaging, Image Processing Techniques and Applications, and Reinforcement Learning in Robotics.

Abbass has contributed to numerous publications, frequently appearing in venues such as arXiv (Cornell University), IEEE Computational Intelligence Magazine, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Transactions on Neural Networks and Learning Systems, and IEEE Transactions on Artificial Intelligence.

Recent notable papers include:

  • Towards Real-Time Monocular Depth Estimation for Robotics: A Survey, 2022, IEEE Transactions on Intelligent Transportation Systems
  • BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs, 2020, Pattern Recognition
  • IEEE Transactions on Emerging Topics in Computational Intelligence, 2021, IEEE Transactions on Emerging Topics in Computational Intelligence
  • Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments, 2020, Frontiers in Neuroscience
  • Modified continuous Ant Colony Optimisation for multiple Unmanned Ground Vehicle path planning, 2022, Expert Systems with Applications

Their frequent co-authors include Sreenatha G. Anavatti, Matthew Garratt, Carlos A. Coello Coello, Kathryn Kasmarik, and Aya Hussein.

In addition to journal articles, Hussein A. Abbass has authored books published by Springer International Publishing, such as "Shepherding UxVs for Human-Swarm Teaming" (2021).

The researcher has been recognized as an IEEE Fellow in 2020 for contributions to evolutionary learning and optimization.

Best Publications

  • PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems

    H.A. Abbass;R. Sarker;C. Newton

  • The self-adaptive Pareto differential evolution algorithm

    H.A. Abbass

  • MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach

    H.A. Abbass

  • An evolutionary artificial neural networks approach for breast cancer diagnosis

    Hussein A. Abbass

  • Advances in Computational Intelligence

    Jing Liu;Cesare Alippi;Bernadette Bouchon-Meunier;Garrison W. Greenwood

  • THE PARETO DIFFERENTIAL EVOLUTION ALGORITHM

    Hussein A. Abbass;Ruhul A. Sarker

  • Data Mining: A Heuristic Approach

    Hussein Abbass;Charles Newton;Ruhul Sarker

  • Speeding up backpropagation using multiobjective evolutionary algorithms

    Hussein A. Abbass

  • Hierarchical Deep Reinforcement Learning for Continuous Action Control

    Zhaoyang Yang;Kathryn Merrick;Lianwen Jin;Hussein A. Abbass

  • Multiobjective optimization for dynamic environments

    L.T. Bui;H.A. Abbass;J. Branke

  • A Memetic Pareto Evolutionary Approach to Artificial Neural Networks

    Hussein A. Abbass

  • Towards Real-Time Monocular Depth Estimation for Robotics: A Survey

    Unknown

  • Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust

    Hussein A. Abbass

  • 2012 IEEE Congress on Evolutionary Computation

    Hussein Abbass;Daryl Essam;Ruhul Sarker

  • Pareto neuro-evolution: constructing ensemble of neural networks using multi-objective optimization

    H.A. Abbass

  • Multimodal Fusion for Objective Assessment of Cognitive Workload: A Review

    Essam Debie;Raul Fernandez Rojas;Justin Fidock;Michael Barlow

  • Convolutional Neural Networks Using Dynamic Functional Connectivity for EEG-Based Person Identification in Diverse Human States

    Min Wang;Heba El-Fiqi;Jiankun Hu;Hussein A. Abbass

  • Neural-Based Learning Classifier Systems

    H.H. Dam;H.A. Abbass;C. Lokan;Xin Yao

  • A Monogenous MBO Approach to Satisfiability

    Hussein A. Abbass

  • Adaptive Cross-Generation Differential Evolution Operators for Multiobjective Optimization

    Xin Qiu;Jian-Xin Xu;Kay Chen Tan;Hussein A. Abbass

  • Grammar model-based program evolution

    Y. Shan;R.I. McKay;R. Baxter;H. Abbass

  • Fitness inheritance for noisy evolutionary multi-objective optimization

    Lam T. Bui;Hussein A. Abbass;Daryl Essam

  • Classification Rule Discovery with Ant Colony Optimization.

    Bo Liu;Hussein A. Abbass;Robert I. McKay

Frequent Co-Authors

Ruhul A. Sarker
Ruhul A. Sarker University of New South Wales
Kay Chen Tan
Kay Chen Tan Hong Kong Polytechnic University
Jing Liu
Jing Liu Xidian University
Daryl Essam
Daryl Essam University of New South Wales
Saber M. Elsayed
Saber M. Elsayed University of New South Wales
Jiankun Hu
Jiankun Hu University of New South Wales
Kalyanmoy Deb
Kalyanmoy Deb Michigan State University
David E. Goldberg
David E. Goldberg University of Illinois at Urbana-Champaign
Jürgen Branke
Jürgen Branke University of Warwick
Xiaodong Li
Xiaodong Li University of Virginia

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