scholarly journals Group Decision Making Using Bayesian Network Inference with Qualitative Expert Knowledge

10.5772/10076 ◽  
2010 ◽  
Author(s):  
Wichian ◽  
Nipat Jongsawat
Keyword(s):  
Decision Making ◽  
Bayesian Network ◽  
Group Decision Making ◽  
Network Inference ◽  
Group Decision ◽  
Expert Knowledge ◽  
Bayesian Network Inference

scholarly journals Group decision-making with Fermatean fuzzy soft expert knowledge

2022 ◽  
Author(s):  
Muhammad Akram ◽  
Ghous Ali ◽  
José Carlos R. Alcantud ◽  
Aneesa Riaz
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Expert Knowledge ◽  
Solar Panel ◽  
Practical Application ◽  
Essential Properties ◽  
Production Technologies ◽  
Theoretical Standpoint ◽  
Selection Of

AbstractWith the rapid growth of population, the global impact of solar technology is increasing by the day due to its advantages over other power production technologies. Demand for solar panel systems is soaring, thus provoking the arrival of many new manufacturers. Sale dealers or suppliers face an uncertain problem to choose the most adequate technological solution. To effectively address such kind of issues, in this paper we propose the Fermatean fuzzy soft expert set model by combining Fermatean fuzzy sets and soft expert sets. We describe this hybrid model with numerical examples. From a theoretical standpoint, we demonstrate some essential properties and define operations for this setting. They comprise the definitions of complement, union and intersection, the OR operation and the AND operation. Concerning practice in this new environment, we provide an algorithm for multi-criteria group decision making whose productiveness and authenticity is dutifully tested. We explore a practical application of this approach (that is, the selection of a suitable brand of solar panel system). Lastly, we give a comparison of our model with certain related mathematical tools, including fuzzy and intuitionistic fuzzy soft expert set models.

scholarly journals Extraction of Belief Knowledge from a Relational Database for Quantitative Bayesian Network Inference

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
LiMin Wang
Keyword(s):  
Bayesian Network ◽  
Relational Database ◽  
Network Inference ◽  
Probabilistic Reasoning ◽  
Expert Knowledge ◽  
Learning Framework ◽  
Three Phase ◽  
Rule Analysis ◽  
Effectiveness And Efficiency ◽  
Bayesian Network Inference

The problem of extracting knowledge from a relational database for probabilistic reasoning is still unsolved. On the basis of a three-phase learning framework, we propose the integration of a Bayesian network (BN) with the functional dependency (FD) discovery technique. Association rule analysis is employed to discover FDs and expert knowledge encoded within a BN; that is, key relationships between attributes are emphasized. Moreover, the BN can be updated by using an expert-driven annotation process wherein redundant nodes and edges are removed. Experimental results show the effectiveness and efficiency of the proposed approach.

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