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 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.
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
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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A comprehensive review
Asaju Laaro Bolaji;Mohammed Azmi Al-Betar;Mohammed A. Awadallah;Ahamad Tajudin Khader.
soft computing (2016)
Grey wolf optimizer: a review of recent variants and applications
Hossam Faris;Ibrahim Aljarah;Mohammed Azmi Al-Betar;Seyedali Mirjalili.
Neural Computing and Applications (2018)
A harmony search algorithm for university course timetabling
Mohammed Azmi Al-Betar;Ahamad Tajudin Khader.
Annals of Operations Research (2012)
A survey on applications and variants of the cuckoo search algorithm
Mohammad Shehab;Ahamad Tajudin Khader;Mohammed Azmi Al-Betar.
Applied Soft Computing (2017)
Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering
Laith Mohammad Abualigah;Ahamad Tajudin Khader;Mohammed Azmi Al-Betar;Osama Ahmad Alomari.
Expert Systems With Applications (2017)
Artificial bee colony algorithm, its variants and applications: A survey.
Asaju La'aro Bolaji;Ahamad Tajudin Khader;Mohammed Azmi Al-Betar;Mohammed A. Awadallah.
University Course Timetabling Using a Hybrid Harmony Search Metaheuristic Algorithm
M. A. Al-Betar;A. T. Khader;M. Zaman.
systems man and cybernetics (2012)
$$eta$$ β -Hill climbing: an exploratory local search
Mohammed Azmi Al-Betar.
Neural Computing and Applications (2017)
Novel selection schemes for harmony search
Mohammed Azmi Al-Betar;Mohammed Azmi Al-Betar;Iyad Abu Doush;Ahamad Tajudin Khader;Mohammed A. Awadallah.
Applied Mathematics and Computation (2012)
Variants of the Flower Pollination Algorithm: A Review
Zaid Abdi Alkareem Alyasseri;Zaid Abdi Alkareem Alyasseri;Ahamad Tajudin Khader;Mohammed Azmi Al-Betar;Mohammed A. Awadallah.
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