What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Hossam Faris mainly focuses on Artificial intelligence, Machine learning, Feature selection, Metaheuristic and Artificial neural network.
Within one scientific family, he focuses on topics pertaining to Optimization problem under Artificial intelligence, and may sometimes address concerns connected to Benchmark.
In the subject of general Machine learning, his work in Tournament selection is often linked to Swarming, thereby combining diverse domains of study.
His Feature selection study incorporates themes from Salp swarm algorithm, Algorithm, Data mining and Genetic algorithm.
The concepts of his Metaheuristic study are interwoven with issues in Mutation operator, Swarm intelligence, Fitness proportionate selection, Task and Optimization algorithm.
As part of one scientific family, he deals mainly with the area of Artificial neural network, narrowing it down to issues related to the Local optimum, and often Evolutionary algorithm and Multilayer perceptron.
His most cited work include:
- Salp Swarm Algorithm (1035 citations)
- Harris hawks optimization: Algorithm and applications (565 citations)
- Optimizing connection weights in neural networks using the whale optimization algorithm (278 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, Artificial neural network, Metaheuristic and Feature selection.
His Artificial intelligence study combines topics in areas such as Particle swarm optimization and Pattern recognition.
His study in the fields of Backpropagation and Random forest under the domain of Machine learning overlaps with other disciplines such as Android and Medical diagnosis.
He interconnects Evolutionary algorithm and Data mining in the investigation of issues within Artificial neural network.
His study in Metaheuristic is interdisciplinary in nature, drawing from both Extreme learning machine, Swarm intelligence and Optimization problem.
His Feature selection research incorporates elements of Salp swarm algorithm, Context and The Internet.
He most often published in these fields:
- Artificial intelligence (44.44%)
- Machine learning (32.16%)
- Artificial neural network (23.39%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (44.44%)
- Machine learning (32.16%)
- Feature selection (18.71%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Machine learning, Feature selection, Metaheuristic and Cluster analysis.
His Artificial intelligence study integrates concerns from other disciplines, such as Particle swarm optimization and Pattern recognition.
His Machine learning research integrates issues from Local optimum and Metaheuristic algorithms.
His Feature selection study combines topics from a wide range of disciplines, such as Evolutionary algorithm, Context and The Internet.
The concepts of his Metaheuristic study are interwoven with issues in Extreme learning machine, Locality, Dragonfly algorithm, Swarm intelligence and Optimization problem.
His Optimization problem study is concerned with the field of Mathematical optimization as a whole.
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
- The Internet
Hossam Faris spends much of his time researching Artificial intelligence, Metaheuristic, Machine learning, Feature selection and Data mining.
His Artificial intelligence study combines topics in areas such as Genetic algorithm and Particle swarm optimization.
Hossam Faris interconnects Cluster analysis, Extreme learning machine, Locality, Dragonfly algorithm and Optimization problem in the investigation of issues within Metaheuristic.
His Machine learning research includes themes of Local optimum and Metaheuristic algorithms.
His Feature selection research is multidisciplinary, incorporating elements of Salp swarm algorithm, Evolutionary algorithm and Dimensionality reduction.
When carried out as part of a general Data mining research project, his work on Intrusion detection system is frequently linked to work in Current, therefore connecting diverse disciplines of study.
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