What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Operating system
Artificial intelligence, Mathematical optimization, Machine learning, Particle swarm optimization and Intrusion detection system are his primary areas of study.
The various areas that Ajith Abraham examines in his Artificial intelligence study include Tree and Data mining.
His study in Machine learning is interdisciplinary in nature, drawing from both Fuzzy set, Web mining, Process and Personalization.
His work carried out in the field of Particle swarm optimization brings together such families of science as Fuzzy logic, Genetic algorithm and Control theory.
His Intrusion detection system study combines topics in areas such as Decision tree, Soft computing, Network security and Support vector machine.
His Evolutionary algorithm research is multidisciplinary, incorporating perspectives in Evolutionary computation and Benchmark.
His most cited work include:
- Differential Evolution Using a Neighborhood-Based Mutation Operator (885 citations)
- Automatic Clustering Using an Improved Differential Evolution Algorithm (550 citations)
- Feature deduction and ensemble design of intrusion detection systems (514 citations)
What are the main themes of his work throughout his whole career to date?
Ajith Abraham mainly focuses on Artificial intelligence, Mathematical optimization, Machine learning, Artificial neural network and Particle swarm optimization.
Ajith Abraham interconnects Data mining and Pattern recognition in the investigation of issues within Artificial intelligence.
His biological study spans a wide range of topics, including Support vector machine, Feature selection and Cluster analysis.
His Mathematical optimization study frequently links to related topics such as Algorithm.
His Machine learning research includes elements of Neuro-fuzzy and Fuzzy logic.
His studies in Particle swarm optimization integrate themes in fields like Genetic algorithm and Swarm behaviour.
He most often published in these fields:
- Artificial intelligence (34.83%)
- Mathematical optimization (18.33%)
- Machine learning (16.25%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (34.83%)
- Algorithm (11.17%)
- Pattern recognition (7.00%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Algorithm, Pattern recognition, Artificial neural network and Cluster analysis.
Artificial intelligence and Machine learning are frequently intertwined in his study.
Ajith Abraham does research in Machine learning, focusing on Evolutionary algorithm specifically.
His Algorithm research includes themes of Histogram, Swarm behaviour and Benchmark.
His Pattern recognition research incorporates elements of Image quality, Image and Thresholding.
Optimization problem is a subfield of Mathematical optimization that he studies.
Between 2016 and 2021, his most popular works were:
- Metaheuristic design of feedforward neural networks (182 citations)
- Industry 4.0: A bibliometric analysis and detailed overview (124 citations)
- A survey on region based image fusion methods (52 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Operating system
His primary scientific interests are in Artificial intelligence, Algorithm, Pattern recognition, Artificial neural network and Benchmark.
His research in Artificial intelligence intersects with topics in Machine learning and Interval.
Ajith Abraham interconnects Classifier, Pareto principle and Data mining in the investigation of issues within Machine learning.
He has researched Algorithm in several fields, including Histogram, Swarm behaviour and Thresholding.
His Artificial neural network study combines topics in areas such as Deep learning, CUDA and k-nearest neighbors algorithm.
He combines subjects such as Value, Mathematical optimization, Search algorithm and Finite impulse response with his study of Benchmark.
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