What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Cluster analysis, Data mining, Artificial intelligence, Krill herd algorithm and Optimization problem.
Laith Mohammad Abualigah specializes in Cluster analysis, namely Document clustering.
His research is interdisciplinary, bridging the disciplines of Metaheuristic and Data mining.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
The Krill herd algorithm study combines topics in areas such as Swarm behaviour, Computational intelligence and Text document.
His Optimization problem study combines topics from a wide range of disciplines, such as Variety, Chaotic, Harmony search and Benchmark.
His most cited work include:
- A comprehensive review (354 citations)
- Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering (269 citations)
- A new feature selection method to improve the document clustering using particle swarm optimization algorithm (241 citations)
What are the main themes of his work throughout his whole career to date?
Laith Mohammad Abualigah spends much of his time researching Artificial intelligence, Optimization problem, Cluster analysis, Data mining and Benchmark.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
Laith Mohammad Abualigah interconnects Optimization algorithm and Metaheuristic in the investigation of issues within Optimization problem.
The Cluster analysis study combines topics in areas such as Harmony search, Text document and Krill herd algorithm.
His studies in Data mining integrate themes in fields like Wireless sensor network, Computational intelligence and Document clustering.
His studies deal with areas such as CURE data clustering algorithm and Clustering high-dimensional data as well as Canopy clustering algorithm.
He most often published in these fields:
- Artificial intelligence (35.90%)
- Optimization problem (33.33%)
- Cluster analysis (29.49%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (35.90%)
- Optimization problem (33.33%)
- Benchmark (23.08%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Optimization problem, Benchmark, Feature selection and Machine learning.
Laith Mohammad Abualigah combines topics linked to Natural language processing with his work on Artificial intelligence.
His Optimization problem study is concerned with the field of Algorithm as a whole.
His Benchmark research includes elements of Domain, Theoretical computer science, Metaheuristic, Genetic algorithm and Swarm behaviour.
In his study, Particle swarm optimization is strongly linked to Data mining, which falls under the umbrella field of Feature selection.
His Cluster analysis research is multidisciplinary, incorporating elements of Range and Dragonfly algorithm.
Between 2020 and 2021, his most popular works were:
- A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments (63 citations)
- Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications (47 citations)
- The Arithmetic Optimization Algorithm (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Laith Mohammad Abualigah spends much of his time researching Optimization problem, Benchmark, Metaheuristic, Data mining and Optimization algorithm.
He has included themes like Local optimum, Sine cosine algorithm, Local search, Differential evolution and CloudSim in his Optimization problem study.
The various areas that he examines in his Benchmark study include Swarm behaviour, Theoretical computer science, Nature inspired and Meta heuristic.
His Swarm behaviour research incorporates themes from Mutation, Text mining, Feature selection, Document clustering and Pattern recognition.
His study in Metaheuristic is interdisciplinary in nature, drawing from both Computational complexity theory, Genetic algorithm, Multiplication and Particle swarm optimization.
Data mining is closely attributed to Computational intelligence in his study.
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