What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Operating system
His scientific interests lie mostly in Learning automata, Algorithm, Mathematical optimization, Particle swarm optimization and Artificial intelligence.
His biological study spans a wide range of topics, including Wireless sensor network, Distributed computing and Cellular automaton.
His study in Algorithm is interdisciplinary in nature, drawing from both Evolutionary algorithm, GrowCut algorithm, Weighted Majority Algorithm and Cluster analysis.
The Mathematical optimization study combines topics in areas such as Convergence, Random graph, Task and Benchmark.
His Particle swarm optimization research is multidisciplinary, relying on both Value, Enhanced Data Rates for GSM Evolution, Bayesian network and Markov chain.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Data mining and Population-based incremental learning.
His most cited work include:
- A Gaussian Firefly Algorithm (118 citations)
- A MATHEMATICAL FRAMEWORK FOR CELLULAR LEARNING AUTOMATA (114 citations)
- UTILIZING DISTRIBUTED LEARNING AUTOMATA TO SOLVE STOCHASTIC SHORTEST PATH PROBLEMS (92 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Learning automata, Artificial intelligence, Algorithm, Theoretical computer science and Mathematical optimization.
The Learning automata study combines topics in areas such as Wireless sensor network, Computer network, Distributed computing and Cellular automaton.
His Distributed computing study combines topics in areas such as Grid computing and Destination-Sequenced Distance Vector routing.
Mohammad Reza Meybodi combines subjects such as Machine learning and Set with his study of Artificial intelligence.
His Algorithm study combines topics from a wide range of disciplines, such as Cluster analysis and Graph.
As a part of the same scientific family, Mohammad Reza Meybodi mostly works in the field of Theoretical computer science, focusing on Evolutionary algorithm and, on occasion, Evolutionary computation.
He most often published in these fields:
- Learning automata (51.61%)
- Artificial intelligence (25.50%)
- Algorithm (23.69%)
What were the highlights of his more recent work (between 2017-2021)?
- Learning automata (51.61%)
- Theoretical computer science (18.07%)
- Automaton (14.66%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Learning automata, Theoretical computer science, Automaton, Artificial intelligence and Social network.
His research in Learning automata intersects with topics in Graph, Wireless sensor network, Set, Node and Algorithm.
Within one scientific family, Mohammad Reza Meybodi focuses on topics pertaining to Graph under Algorithm, and may sometimes address concerns connected to Shortest path problem.
His study in Theoretical computer science is interdisciplinary in nature, drawing from both Structure, Modularity, Convergence and Cellular automaton.
His study focuses on the intersection of Automaton and fields such as Reinforcement learning with connections in the field of Evolutionary algorithm.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Peer-to-peer.
Between 2017 and 2021, his most popular works were:
- Minimum positive influence dominating set and its application in influence maximization: a learning automata approach (34 citations)
- Link prediction in weighted social networks using learning automata (25 citations)
- An adaptive bi-flight cuckoo search with variable nests for continuous dynamic optimization problems (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Operating system
Mohammad Reza Meybodi spends much of his time researching Learning automata, Theoretical computer science, Automaton, Artificial intelligence and Optimization problem.
His studies in Learning automata integrate themes in fields like Set, Local search, Mathematical optimization and Social network.
His biological study spans a wide range of topics, including Structure, Graph and Cellular automaton.
In his study, Range is inextricably linked to Machine learning, which falls within the broad field of Artificial intelligence.
Optimization problem is a subfield of Algorithm that Mohammad Reza Meybodi explores.
His work deals with themes such as Scheduling and Swarm behaviour, which intersect with Algorithm.
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