What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Quantum mechanics
Data mining, Artificial intelligence, Dempster–Shafer theory, Mathematical optimization and Fuzzy logic are his primary areas of study.
His studies deal with areas such as Measure, Structure, Node, Analytic hierarchy process and Complex network as well as Data mining.
His Node course of study focuses on Centrality and Weighted network and Degree.
His work deals with themes such as Machine learning and Pattern recognition, which intersect with Artificial intelligence.
His Dempster–Shafer theory research incorporates elements of Uncertainty analysis and Operations research.
In his work, Entropy is strongly intertwined with Entropy, which is a subfield of Mathematical optimization.
His most cited work include:
- Supplier selection using AHP methodology extended by D numbers (305 citations)
- Supplier selection using AHP methodology extended by D numbers (305 citations)
- A new fuzzy dempster MCDM method and its application in supplier selection (194 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Data mining, Artificial intelligence, Mathematical optimization, Complex network and Algorithm.
He usually deals with Data mining and limits it to topics linked to Measure and Function.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition.
Mathematical optimization is often connected to Selection in his work.
His studies examine the connections between Complex network and genetics, as well as such issues in Node, with regards to Degree.
His Algorithm study combines topics in areas such as Path and Shortest path problem.
He most often published in these fields:
- Data mining (40.43%)
- Artificial intelligence (33.08%)
- Mathematical optimization (28.18%)
What were the highlights of his more recent work (between 2018-2021)?
- Measure (15.77%)
- Entropy (9.49%)
- Complex network (21.75%)
In recent papers he was focusing on the following fields of study:
Yong Deng focuses on Measure, Entropy, Complex network, Data mining and Function.
His studies in Measure integrate themes in fields like Fuzzy set, Theoretical computer science, Mathematical optimization and Degree.
His research on Entropy also deals with topics like
- Probability distribution that intertwine with fields like Negation and Algorithm,
- Statistical physics which is related to area like Tsallis entropy and Degenerate energy levels.
His work carried out in the field of Complex network brings together such families of science as Node, Centrality, Dimension and Identification.
Many of his studies on Data mining apply to Information quality as well.
The various areas that he examines in his Function study include Frame, Choquet integral, Fuzzy logic, Sensor fusion and Flexibility.
Between 2018 and 2021, his most popular works were:
- D-AHP method with different credibility of information (71 citations)
- D-AHP method with different credibility of information (71 citations)
- Combining conflicting evidence using the DEMATEL method (70 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Quantum mechanics
His primary scientific interests are in Artificial intelligence, Entropy, Basic probability, Probability distribution and Computational intelligence.
His Artificial intelligence research focuses on Fuzzy logic in particular.
His Entropy research also works with subjects such as
- Fuzzy decision, Mathematical optimization and Fuzzy set most often made with reference to Measurement uncertainty,
- Iris flower data set, Correlation coefficient and Decision tree most often made with reference to Basic belief.
Yong Deng combines subjects such as Data mining, Base, Uncertainty modeling, Belief distribution and Operations research with his study of Computational intelligence.
He combines Data mining and Field in his studies.
His research integrates issues of Dempster–Shafer theory, Analytic hierarchy process and Human error probability, Human reliability in his study of Machine learning.
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