A Fuzzy Rule-Based Expert System for Evaluating Intellectual Capital

A fuzzy rule-based expert system is developed for evaluating intellectual capital. A fuzzy linguistic approach assists managers to understand and evaluate the level of each intellectual capital item. The proposed fuzzy rule-based expert system applies fuzzy linguistic variables to express the level of qualitative evaluation and criteria of experts. Feasibility of the proposed model is demonstrated by the result of intellectual capital performance evaluation for a sample company.

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X-Ortho: Fuzzy Rule Based Expert System for Diagnosing Infective Diseases of Hinge Joint Knee

2019 International Conference on Automation, Computational and Technology Management (ICACTM) ◽

10.1109/icactm.2019.8776831 ◽

2019 ◽

Author(s):

Gagandeep Kaur ◽

Anshu Vashisth ◽

Ranbir Singh Batth

Keyword(s):

Expert System ◽

Fuzzy Rule ◽

Rule Based ◽

Hinge Joint

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Embedding evolutionary multiobjective optimization into fuzzy linguistic combination method for fuzzy rule-based classifier ensembles

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) ◽

10.1109/fuzz-ieee.2014.6891842 ◽

2014 ◽

Cited By ~ 1

Author(s):

Krzysztof Trawinski ◽

Oscar Cordon ◽

Arnaud Quirin

Keyword(s):

Multiobjective Optimization ◽

Fuzzy Rule ◽

Combination Method ◽

Classifier Ensembles ◽

Rule Based ◽

Evolutionary Multiobjective Optimization ◽

Rule Based Classifier ◽

Fuzzy Linguistic

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Integration of fuzzy logic in the SEIRD compartmental model for the analysis of intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics

International Journal of Modern Physics C ◽

10.1142/s0129183122500917 ◽

2022 ◽

Author(s):

Zakaria Shams Siam ◽

Rubyat Tasnuva Hasan ◽

Hossain Ahamed ◽

Samiya Kabir Youme ◽

Soumik Sarker Anik ◽

...

Keyword(s):

Fuzzy Logic ◽

Compartmental Model ◽

Fuzzy Rule ◽

Transmission Dynamics ◽

Intervention Measures ◽

Proposed Model ◽

Total Death ◽

Linguistic Intervention ◽

Fuzzy Linguistic ◽

Death Cases

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we have proposed a fuzzy rule-based Susceptible-Exposed-Infectious-Recovered-Death ([Formula: see text]) compartmental model considering a new dynamic transmission possibility variable as a function of time and three different fuzzy linguistic intervention variables to delineate the intervention and transmission heterogeneity on SARS-CoV-2 viral infection. We have analyzed the datasets of active cases and total death cases of China and Bangladesh. Using our model, we have predicted active cases and total death cases for China and Bangladesh. We further presented the correspondence of different intervention measures in relaxing the transmission possibility. The proposed model delineates the correspondence between the intervention measures as fuzzy subsets and the predicted active cases and total death cases. The prediction made by our system fitted the collected dataset very well while considering different fuzzy intervention measures. The integration of fuzzy logic in the classical compartmental model also produces more realistic results as it generates a dynamic transmission possibility variable. The proposed model could be used to control the transmission of SARS-CoV-2 as it deals with the intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics.

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