What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Mathematical optimization
His primary areas of investigation include Artificial intelligence, Mathematical optimization, Metaheuristic, Harmony search and Optimization problem.
His work focuses on many connections between Artificial intelligence and other disciplines, such as Genetic algorithm, that overlap with his field of interest in Pattern recognition.
His Mathematical optimization research integrates issues from Algorithm and Natural selection, Selection.
His Metaheuristic research includes elements of Variety, Hill climbing, Local search and Cluster analysis.
Mohammed Azmi Al-Betar focuses mostly in the field of Harmony search, narrowing it down to matters related to Particle swarm optimization and, in some cases, Memetic algorithm and Search algorithm.
His research investigates the connection between Optimization problem and topics such as Swarm intelligence that intersect with issues in Combinatorial optimization.
His most cited work include:
- A comprehensive review (354 citations)
- Grey wolf optimizer: a review of recent variants and applications (187 citations)
- A harmony search algorithm for university course timetabling (154 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Mathematical optimization, Harmony search, Metaheuristic and Algorithm.
Mohammed Azmi Al-Betar combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence.
The study incorporates disciplines such as Selection and Benchmark in addition to Mathematical optimization.
Mohammed Azmi Al-Betar has researched Harmony search in several fields, including Particle swarm optimization and Heuristics.
His Metaheuristic research is multidisciplinary, incorporating perspectives in Variety and Continuous optimization.
His work deals with themes such as Swarm behaviour, Feature selection and Support vector machine, which intersect with Algorithm.
He most often published in these fields:
- Artificial intelligence (49.71%)
- Mathematical optimization (48.55%)
- Harmony search (36.99%)
What were the highlights of his more recent work (between 2019-2021)?
- Algorithm (28.90%)
- Metaheuristic (34.10%)
- Optimization problem (20.23%)
In recent papers he was focusing on the following fields of study:
Algorithm, Metaheuristic, Optimization problem, Data mining and Artificial intelligence are his primary areas of study.
Mohammed Azmi Al-Betar works mostly in the field of Algorithm, limiting it down to concerns involving Feature selection and, occasionally, Benchmark, Classifier and Optimization algorithm.
His Optimization problem research focuses on Local search and how it relates to Computational intelligence.
His study on Artificial intelligence also encompasses disciplines like
- Pattern recognition which is related to area like Noise reduction,
- Machine learning, which have a strong connection to Herd immunity.
His study looks at the intersection of Particle swarm optimization and topics like Harmony search with Heuristics.
His research integrates issues of Natural selection and Selection in his study of Mathematical optimization.
Between 2019 and 2021, his most popular works were:
- An improved Dragonfly Algorithm for feature selection (23 citations)
- EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods (20 citations)
- Link-based multi-verse optimizer for text documents clustering (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His main research concerns Algorithm, Feature selection, Metaheuristic, Artificial intelligence and Benchmark.
The Algorithm study combines topics in areas such as Time domain, Optimization algorithm, Classifier and Biometrics.
His Feature selection study integrates concerns from other disciplines, such as Swarm behaviour, Dragonfly algorithm and Fitness function.
Mohammed Azmi Al-Betar interconnects Machine learning and Pattern recognition in the investigation of issues within Artificial intelligence.
His biological study deals with issues like Noise reduction, which deal with fields such as Harmony search, Genetic algorithm and Particle swarm optimization.
His Hill climbing research is within the category of Mathematical optimization.
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