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 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.
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
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.
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
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.
Supplier selection using AHP methodology extended by D numbers
Xinyang Deng;Yong Hu;Yong Deng;Yong Deng;Sankaran Mahadevan.
Expert Systems With Applications (2014)
Short communication: Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment
Yong Deng;Yuxin Chen;Yajuan Zhang;Sankaran Mahadevan.
soft computing (2012)
A new fuzzy dempster MCDM method and its application in supplier selection
Yong Deng;Felix T. S. Chan.
Expert Systems With Applications (2011)
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)
A Method of Converting Z-number to Classical Fuzzy Number
Bingyi Kang;Daijun Wei;Ya Li;Yong Deng.
(2012)
Generalized evidence theory
Yong Deng.
Applied Intelligence (2015)
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)
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)
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)
Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory
Huawei Guo;Wenkang Shi;Yong Deng.
systems man and cybernetics (2006)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
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