What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary areas of investigation include Artificial intelligence, Mathematical optimization, Machine learning, Multi-objective optimization and Evolutionary computation.
Hussein A. Abbass interconnects Genetic algorithm and Process in the investigation of issues within Artificial intelligence.
His work carried out in the field of Machine learning brings together such families of science as Classifier, Data mining and Adaptive control.
The Multi-objective optimization study combines topics in areas such as Evolutionary programming, Fitness approximation, Stochastic optimization, Meta-optimization and Robustness.
The various areas that Hussein A. Abbass examines in his Evolutionary computation study include Management science, Crossover and Risk management.
The study incorporates disciplines such as Configuration space, Differential evolution, Mutation and Conflicting objectives in addition to Pareto principle.
His most cited work include:
- PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems (433 citations)
- The self-adaptive Pareto differential evolution algorithm (364 citations)
- An evolutionary artificial neural networks approach for breast cancer diagnosis (318 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Mathematical optimization, Evolutionary computation and Evolutionary algorithm.
His work deals with themes such as Genetic algorithm and Pattern recognition, which intersect with Artificial intelligence.
His work in the fields of Machine learning, such as Learning classifier system and Supervised learning, overlaps with other areas such as Generalization.
Multi-objective optimization, Optimization problem, Pareto principle, Differential evolution and Metaheuristic are the primary areas of interest in his Mathematical optimization study.
He frequently studies issues relating to Management science and Evolutionary computation.
Evolutionary algorithm is closely attributed to Fitness landscape in his study.
He most often published in these fields:
- Artificial intelligence (46.35%)
- Machine learning (19.29%)
- Mathematical optimization (17.41%)
What were the highlights of his more recent work (between 2017-2022)?
- Artificial intelligence (46.35%)
- Swarm behaviour (10.59%)
- Machine learning (19.29%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Swarm behaviour, Machine learning, Pattern recognition and Artificial neural network.
His work carried out in the field of Artificial intelligence brings together such families of science as Task analysis and Function.
His studies deal with areas such as Robot, Task, Boids and Situation awareness as well as Swarm behaviour.
In general Machine learning study, his work on Evolutionary algorithm and Interpretability often relates to the realm of Swarming, thereby connecting several areas of interest.
His Pattern recognition research integrates issues from Generative grammar, Missing data and Robustness.
His Artificial neural network study incorporates themes from Decision tree, Interpretation and Tree.
Between 2017 and 2022, his most popular works were:
- Hierarchical Deep Reinforcement Learning for Continuous Action Control (49 citations)
- Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust (22 citations)
- Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Hussein A. Abbass focuses on Artificial intelligence, Swarm behaviour, Artificial neural network, Task analysis and Task.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Function and Pattern recognition.
His biological study spans a wide range of topics, including Tree and Interpretation.
His Swarm behaviour research incorporates elements of Boids, Workload, Robot, Teleoperation and Situation awareness.
His Artificial neural network research incorporates themes from State space and Decision rule.
The study incorporates disciplines such as Mathematical optimization and Process in addition to Task analysis.
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