H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 73 Citations 15,358 358 World Ranking 648 National Ranking 52

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Supplier selection using AHP methodology extended by D numbers

Xinyang Deng;Yong Hu;Yong Deng;Yong Deng;Sankaran Mahadevan.
Expert Systems With Applications (2014)

490 Citations

Short communication: Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment

Yong Deng;Yuxin Chen;Yajuan Zhang;Sankaran Mahadevan.
soft computing (2012)

283 Citations

A new fuzzy dempster MCDM method and its application in supplier selection

Yong Deng;Felix T. S. Chan.
Expert Systems With Applications (2011)

275 Citations

Thermal conductivity enhancement of polyethylene glycol/expanded vermiculite shape-stabilized composite phase change materials with silver nanowire for thermal energy storage

Yong Deng;Jinhong Li;Tingting Qian;Weimin Guan.
Chemical Engineering Journal (2016)

243 Citations

A Method of Converting Z-number to Classical Fuzzy Number

Bingyi Kang;Daijun Wei;Ya Li;Yong Deng.
(2012)

232 Citations

Generalized evidence theory

Yong Deng.
Applied Intelligence (2015)

228 Citations

Enhanced thermal conductivity of PEG/diatomite shape-stabilized phase change materials with Ag nanoparticles for thermal energy storage

Tingting Qian;Jinhong Li;Xin Min;Weimin Guan.
Journal of Materials Chemistry (2015)

221 Citations

Identifying influential nodes in weighted networks based on evidence theory

Daijun Wei;Daijun Wei;Xinyang Deng;Xiaoge Zhang;Yong Deng;Yong Deng.
Physica A-statistical Mechanics and Its Applications (2013)

220 Citations

An improved method to construct basic probability assignment based on the confusion matrix for classification problem

Xinyang Deng;Qi Liu;Yong Deng;Sankaran Mahadevan.
Information Sciences (2016)

209 Citations

Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory

Huawei Guo;Wenkang Shi;Yong Deng.
systems man and cybernetics (2006)

175 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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