FUZZY LOGIC MODELS FOR IMPROVING ACCURACY AND EFFICIENCY IN KNOWLEDGE MANAGEMENT SYSTEMS

Swapnil Porwal; Imad Ansari

doi:10.29121/shodhkosh.v5.i2.2024.6204

Authors

Swapnil Porwal Research Scholar, Glocal University, Saharanpur.
Dr. Imad Ansari Research Supervisor, Glocal University, Saharanpur.

DOI:

https://doi.org/10.29121/shodhkosh.v5.i2.2024.6204

Keywords:

Fuzzy Logic, Knowledge Management Systems, Decision-Making, Accuracy, Efficiency, Artificial Intelligence

Abstract [English]

The Research Project examines the enhancement of accuracy and efficiency of Knowledge Management Systems with fuzzy logic models. Traditional KMS and fuzzy logic-based KMS were comparatively analyzed; 50 organizations were used as the basis of data collection. Means of accuracy and efficiency were measured via descriptive statistics and the hypothesis was tested to ensure the results were in significance. Results indicate that fuzzy logic implement KMS had a better level of accuracy (88%) than traditional (72%) and saved time used in decision-making (average of 45 minutes) to 28 minutes. These improvements were also proven to be significant according to the statistical results.
On the whole, the research finds out that fuzzy logic contributes to the improvement of reliability and efficiency of knowledge management. The study also indicates the potential of fuzziness logic integration with machine learning and artificial intelligence to create superior KMS models which have the potential to cope with real time dynamics of a dynamic organizational setting.

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