What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His scientific interests lie mostly in Mathematical optimization, Algorithm, Artificial intelligence, Swarm intelligence and Particle swarm optimization.
His research related to Metaheuristic, Genetic algorithm and Swarm behaviour might be considered part of Mathematical optimization.
His Artificial intelligence research incorporates elements of Machine learning and Pattern recognition.
His Binary search algorithm research is multidisciplinary, relying on both Best-first search, Min-conflicts algorithm, Beam search and A* search algorithm.
His Best-first search study combines topics from a wide range of disciplines, such as Consistent heuristic and Null-move heuristic.
Hossein Nezamabadi-pour combines subjects such as Pattern search, Fringe search, Killer heuristic, Incremental heuristic search and Heuristic with his study of A* search algorithm.
His most cited work include:
- GSA: A Gravitational Search Algorithm (3486 citations)
- BGSA: binary gravitational search algorithm (467 citations)
- An Improved Multi-Objective Harmony Search for Optimal Placement of DGs in Distribution Systems (184 citations)
What are the main themes of his work throughout his whole career to date?
Hossein Nezamabadi-pour mostly deals with Artificial intelligence, Pattern recognition, Algorithm, Mathematical optimization and Data mining.
Hossein Nezamabadi-pour has researched Artificial intelligence in several fields, including Machine learning and Computer vision.
The study incorporates disciplines such as Filter, Selection and Image retrieval in addition to Pattern recognition.
His study in the field of Optimization problem is also linked to topics like Electric power system.
The study incorporates disciplines such as Convergence and Benchmark in addition to Mathematical optimization.
In his research on the topic of Metaheuristic, Heuristic is strongly related with Search algorithm.
He most often published in these fields:
- Artificial intelligence (39.25%)
- Pattern recognition (25.70%)
- Algorithm (23.36%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (39.25%)
- Feature selection (17.29%)
- Pattern recognition (25.70%)
In recent papers he was focusing on the following fields of study:
Hossein Nezamabadi-pour mainly focuses on Artificial intelligence, Feature selection, Pattern recognition, Algorithm and Classifier.
His research in Artificial intelligence intersects with topics in Machine learning and Computer vision.
His Feature selection study incorporates themes from Feature extraction, Data mining and Curse of dimensionality.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Entropy and Cluster analysis.
His studies in Classifier integrate themes in fields like Gravitational search algorithm, Training set, Metaheuristic, Swarm behaviour and Data set.
The Training set study combines topics in areas such as Imbalanced data and Search algorithm.
Between 2017 and 2021, his most popular works were:
- A comprehensive survey on gravitational search algorithm (67 citations)
- Multilabel feature selection: A comprehensive review and guiding experiments (27 citations)
- A label-specific multi-label feature selection algorithm based on the Pareto dominance concept (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His main research concerns Artificial intelligence, Feature selection, Pattern recognition, Gravitational search algorithm and Data mining.
His Artificial intelligence research incorporates elements of Machine learning and Identification.
His studies deal with areas such as Algorithm, Image retrieval, Filter and Curse of dimensionality as well as Feature selection.
His Pattern recognition research includes themes of Entropy, Swarm behaviour and Construction method.
His work deals with themes such as Tree structure and Genetic algorithm, which intersect with Data mining.
His Optimization problem study results in a more complete grasp of Mathematical optimization.
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