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 study are Artificial intelligence, Feature selection, Metaheuristic, Machine learning and Swarm intelligence.
In his research, Benchmark is intimately related to Optimization problem, which falls under the overarching field of Artificial intelligence.
His studies deal with areas such as Algorithm, Feature, Data mining and Genetic algorithm as well as Feature selection.
His Metaheuristic study which covers Mutation operator that intersects with Feature, Task and Sigmoid function.
His work on Fitness proportionate selection and Tournament selection as part of general Machine learning research is frequently linked to Swarming and Data flow diagram, bridging the gap between disciplines.
His Swarm intelligence research incorporates elements of Context, Global optimization, Variety and Salp swarm algorithm, Optimization algorithm.
His most cited work include:
- Harris hawks optimization: Algorithm and applications (565 citations)
- Optimizing connection weights in neural networks using the whale optimization algorithm (278 citations)
- An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (240 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Machine learning, Feature selection, Data mining and Cluster analysis.
His Artificial intelligence research focuses on Particle swarm optimization and how it connects with Genetic algorithm and Intrusion detection system.
His work on Evolutionary algorithm and Naive Bayes classifier as part of general Machine learning study is frequently linked to Set, bridging the gap between disciplines.
His research in Feature selection tackles topics such as Crossover which are related to areas like Supervised learning.
His study on Decision tree is often connected to Process as part of broader study in Data mining.
Ibrahim Aljarah interconnects Swarm intelligence, Optimization problem and Global optimization in the investigation of issues within Metaheuristic.
He most often published in these fields:
- Artificial intelligence (49.15%)
- Machine learning (34.75%)
- Feature selection (28.81%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (49.15%)
- Feature selection (28.81%)
- Machine learning (34.75%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Feature selection, Machine learning, Cluster analysis and Metaheuristic.
His Artificial intelligence research integrates issues from Particle swarm optimization and Pattern recognition.
His work deals with themes such as Evolutionary algorithm, Data mining and Moth flame optimization, which intersect with Feature selection.
The Machine learning study combines topics in areas such as Local optimum and Feature extraction.
His studies in Metaheuristic integrate themes in fields like Optimization problem, Locality, Dragonfly algorithm and Nature inspired.
His work in Optimization problem tackles topics such as Benchmark which are related to areas like Optimal design.
Between 2019 and 2021, his most popular works were:
- An enhanced associative learning-based exploratory whale optimizer for global optimization (42 citations)
- Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection (40 citations)
- Salp swarm algorithm: Theory, literature review, and application in extreme learning machines (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Ibrahim Aljarah spends much of his time researching Artificial intelligence, Feature selection, Metaheuristic, Machine learning and Optimization problem.
His Feature selection research is multidisciplinary, incorporating perspectives in Evolutionary algorithm, Data mining, Salp swarm algorithm and Dimensionality reduction.
His Evolutionary algorithm study incorporates themes from Expert system, Aggregate and Benchmark.
The various areas that Ibrahim Aljarah examines in his Metaheuristic study include Local optimum, Locality, Dragonfly algorithm and Cluster analysis.
His work on Swarm intelligence as part of his general Machine learning study is frequently connected to Open source, thereby bridging the divide between different branches of science.
His Optimization problem research includes themes of Local search, Hill climbing and Global optimization.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
