What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Chunyan Miao mostly deals with Artificial intelligence, Machine learning, Fuzzy cognitive map, Artificial neural network and Computer security.
Chunyan Miao is interested in Fuzzy logic, which is a field of Artificial intelligence.
The Machine learning study combines topics in areas such as Categorization, Metric, Data mining and Image retrieval.
His studies deal with areas such as Set, Cognitive map and Inference as well as Fuzzy cognitive map.
His studies in Artificial neural network integrate themes in fields like Credit crunch, Range, Credit rating and Scale.
The Computer security study combines topics in areas such as Entropy, Entropy, Entropy and Risk analysis.
His most cited work include:
- A Survey of Trust and Reputation Management Systems in Wireless Communications (229 citations)
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey (190 citations)
- Exploiting Geographical Neighborhood Characteristics for Location Recommendation (185 citations)
What are the main themes of his work throughout his whole career to date?
Chunyan Miao mainly investigates Artificial intelligence, Machine learning, Knowledge management, Human–computer interaction and Multi-agent system.
His Artificial intelligence research is multidisciplinary, relying on both Natural language processing and Pattern recognition.
His Machine learning study typically links adjacent topics like Data mining.
Chunyan Miao has researched Knowledge management in several fields, including Intelligent agent and Crowdsourcing.
His Crowdsourcing research incorporates elements of Workload and Task.
His study looks at the intersection of Human–computer interaction and topics like Multimedia with Educational technology and Instructional simulation.
He most often published in these fields:
- Artificial intelligence (32.18%)
- Machine learning (13.24%)
- Knowledge management (9.16%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (32.18%)
- Machine learning (13.24%)
- Distributed computing (5.91%)
In recent papers he was focusing on the following fields of study:
Chunyan Miao focuses on Artificial intelligence, Machine learning, Distributed computing, Graph and Task.
His biological study spans a wide range of topics, including Natural language processing, Forgetting and Pattern recognition.
His work deals with themes such as Focus and Metric, which intersect with Machine learning.
His research in Distributed computing intersects with topics in Scalability, Deep learning, The Internet, Scheme and Information privacy.
His Graph research incorporates themes from Recurrent neural network, Structure, Information retrieval, Graph and Natural language.
Chunyan Miao combines subjects such as Persona, Empathy, Social psychology, Personality and Empirical research with his study of Task.
Between 2019 and 2021, his most popular works were:
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey (190 citations)
- EEG-Based Emotion Recognition Using Regularized Graph Neural Networks (18 citations)
- Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
His main research concerns Artificial intelligence, Pattern recognition, Distributed computing, Task and Machine learning.
Chunyan Miao studies Artificial intelligence, namely Feature extraction.
His Pattern recognition study combines topics in areas such as Relationship extraction, Facial recognition system, Point and Cluster analysis.
Chunyan Miao has included themes like Scalability, Deep learning, The Internet and Server in his Distributed computing study.
Chunyan Miao interconnects Empathy, Social psychology, Personality and Empirical research in the investigation of issues within Task.
His work on Feature selection, Recommender system and Regret as part of general Machine learning research is frequently linked to Diversity, bridging the gap between disciplines.
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