What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Quantum mechanics
Jianqiang Wang spends much of his time researching Fuzzy set, Data mining, Artificial intelligence, Multiple-criteria decision analysis and Fuzzy logic.
He mostly deals with Intuitionistic fuzzy in his studies of Fuzzy set.
He studied Data mining and Rule-based machine translation that intersect with Operations research.
His Artificial intelligence study combines topics in areas such as Machine learning and Extension.
His work deals with themes such as Ranking and Term, which intersect with Multiple-criteria decision analysis.
The Fuzzy number research Jianqiang Wang does as part of his general Fuzzy logic study is frequently linked to other disciplines of science, such as Extant taxon, Rank and Evidential reasoning approach, therefore creating a link between diverse domains of science.
His most cited work include:
- Understanding the High Activity of Fe-N-C Electrocatalysts in Oxygen Reduction: Fe/Fe3C Nanoparticles Boost the Activity of Fe-N(x). (860 citations)
- Ordered Mesoporous Black TiO2 as Highly Efficient Hydrogen Evolution Photocatalyst (593 citations)
- Model Predictive Multi-Objective Vehicular Adaptive Cruise Control (271 citations)
What are the main themes of his work throughout his whole career to date?
Jianqiang Wang mostly deals with Chemical engineering, Artificial intelligence, Fuzzy logic, Mathematical optimization and Multiple-criteria decision analysis.
His research investigates the link between Chemical engineering and topics such as Adsorption that cross with problems in Polymer chemistry.
The concepts of his Artificial intelligence study are interwoven with issues in Term, Machine learning and Computer vision.
He does research in Fuzzy logic, focusing on Fuzzy set specifically.
Jianqiang Wang is interested in Multi criteria decision, which is a field of Mathematical optimization.
His Multiple-criteria decision analysis research incorporates elements of Ranking and Data mining.
He most often published in these fields:
- Chemical engineering (17.67%)
- Artificial intelligence (15.88%)
- Fuzzy logic (11.35%)
What were the highlights of his more recent work (between 2019-2021)?
- Chemical engineering (17.67%)
- Fuzzy logic (11.35%)
- Artificial intelligence (15.88%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Chemical engineering, Fuzzy logic, Artificial intelligence, Probabilistic logic and Operations research.
Within one scientific family, Jianqiang Wang focuses on topics pertaining to Electrochemistry under Chemical engineering, and may sometimes address concerns connected to Scanning electron microscope and Catalysis.
The various areas that Jianqiang Wang examines in his Fuzzy logic study include Computational intelligence, Selection, Decision support system, Mathematical optimization and Ranking.
The study of Ranking is intertwined with the study of Multiple-criteria decision analysis in a number of ways.
His Artificial intelligence research incorporates themes from Machine learning and Pattern recognition.
His Probabilistic logic study combines topics from a wide range of disciplines, such as Probability distribution, Normalization, ELECTRE, Failure mode and effects analysis and Term.
Between 2019 and 2021, his most popular works were:
- The differences in hotel selection among various types of travellers: A comparative analysis with a useful bounded rationality behavioural decision support model (52 citations)
- A Promoted Charge Separation/Transfer System from Cu Single Atoms and C 3 N 4 Layers for Efficient Photocatalysis (40 citations)
- Extended Z-MABAC Method Based on Regret Theory and Directed Distance for Regional Circular Economy Development Program Selection With Z-Information (34 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Quantum mechanics
His main research concerns Chemical engineering, Probabilistic logic, Artificial intelligence, Fuzzy logic and ELECTRE.
His Chemical engineering research includes elements of Electrochemistry, Adsorption and Scanning electron microscope.
His research integrates issues of Structure, Multiple-criteria decision analysis, Rule-based machine translation and Binary relation in his study of Probabilistic logic.
The Multiple-criteria decision analysis study combines topics in areas such as Probability distribution, Ranking and Data mining.
His studies in Artificial intelligence integrate themes in fields like Field and Machine learning.
The study incorporates disciplines such as Ranking, Sensitivity, Computational intelligence and Operations research in addition to Fuzzy logic.
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