2023 - Research.com Computer Science in United Kingdom Leader Award
The scientist’s investigation covers issues in Evidential reasoning approach, Artificial intelligence, Data mining, Machine learning and Fuzzy logic. His Evidential reasoning approach research is multidisciplinary, incorporating perspectives in Fuzzy set, Process and Dempster–Shafer theory. His Artificial intelligence research includes themes of Multiple-criteria decision analysis and Decision matrix.
His work investigates the relationship between Data mining and topics such as Legal expert system that intersect with problems in Pipeline and Test data. His research integrates issues of Decision theory and Rule-based system in his study of Machine learning. His studies deal with areas such as Risk management and Operations research as well as Decision analysis.
His main research concerns Evidential reasoning approach, Artificial intelligence, Machine learning, Process and Mathematical optimization. His work deals with themes such as Multiple-criteria decision analysis and Inference, which intersect with Evidential reasoning approach. As part of his studies on Artificial intelligence, he often connects relevant subjects like Nonlinear programming.
He interconnects Reliability, Decision theory and Expert system in the investigation of issues within Machine learning. His Process research includes elements of New product development and Operations research. His study looks at the intersection of Decision support system and topics like Risk analysis with Risk assessment.
Jian-Bo Yang mainly investigates Evidential reasoning approach, Artificial intelligence, Machine learning, Process and Operations research. His research in Evidential reasoning approach intersects with topics in Multiple-criteria decision analysis, Belief distribution and Rule-based system. He combines subjects such as Risk analysis and Decision analysis with his study of Multiple-criteria decision analysis.
His Artificial intelligence research incorporates elements of Triage and Identification. His work in Machine learning addresses subjects such as Reliability, which are connected to disciplines such as Group decision-making. In his study, which falls under the umbrella issue of Process, Quality is strongly linked to Data-driven.
Evidential reasoning approach, Artificial intelligence, Machine learning, Data mining and Belief distribution are his primary areas of study. His Evidential reasoning approach study necessitates a more in-depth grasp of Statistics. He has included themes like Analytic hierarchy process, Survey data collection, Aggregate and Patient experience in his Artificial intelligence study.
His Machine learning study incorporates themes from Business case, Reliability, Decision support system and Patient satisfaction. His Data mining research is multidisciplinary, incorporating elements of Classifier and Statistic. Jian-Bo Yang studied Process and Group decision-making that intersect with Decision matrix and Ranking.
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On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
Jian-Bo Yang;Dong-Ling Xu.
An evidential reasoning approach for multiple-attribute decision making with uncertainty
Jian-Bo Yang;M.G. Singh.
Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties
Jian Bo Yang.
Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean
Ying-Ming Wang;Kwai-Sang Chin;Gary Ka Kwai Poon;Jian-Bo Yang.
Belief rule-base inference methodology using the evidential reasoning Approach-RIMER
Jian-Bo Yang;Jun Liu;Jin Wang;How-Sing Sii.
Environmental impact assessment using the evidential reasoning approach
Ying Ming Wang;Ying Ming Wang;Jian Bo Yang;Dong Ling Xu.
Multiple Criteria Decision Support in Engineering Design
Pratyush Sen;Jian-Bo Yang.
Evidential reasoning rule for evidence combination
Jian-Bo Yang;Dong-Ling Xu.
On the centroids of fuzzy numbers
Ying-Ming Wang;Jian-Bo Yang;Dong-Ling Xu;Kwai-Sang Chin.
The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties
Jian-Bo Yang;Jian-Bo Yang;Y. M. Wang;Y. M. Wang;Dong-Ling Xu;Kwai-Sang Chin.
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