A Distributed Fuzzy Expert System on Edge-Cloud Architecture for Technology Commercialization Success Prediction

Document Type : Original Article

Authors

1 Corresponding author, Assistant professor, Department of Industrial Engineering, Faculty of Modern Technologies, Hakim Sabzevari University, Sabzevar, Iran. Email: j.yazdi@hsu.ac.ir

2 Assistant professor, Department of Economics, Faculty of Letters and Humanities, Hakim Sabzevari University, Sabzevar, Iran. Email: drmohammadi1358@gmail.com

10.22091/jdaid.2026.16404.1077

Abstract

Accurately predicting the commercialization potential of emerging technologies is essential for guiding investment decisions and shaping innovation policies. However, most existing fuzzy expert systems are based on centralized architectures, which limit their scalability when applied to large technology portfolios in open innovation environments. This study introduces a distributed fuzzy inference system (D-FIS) implemented within an edge–cloud computing framework to overcome these limitations. The proposed model evaluates technology commercialization using 32 indicators covering technological, financial, market, and regulatory dimensions. These indicators were identified through the fuzzy Delphi method, while decision-making is performed using Mamdani fuzzy inference and centroid defuzzification. To improve computational efficiency, rule evaluation is distributed across edge nodes, whereas the cloud layer is responsible for aggregating inference results and maintaining synchronization of membership functions. The framework was validated using a real-world dataset comprising technology commercialization cases. Experimental results showed an overall prediction accuracy of 72.73%, with a specificity of 94.44% and a sensitivity of 75.00%. Compared with a conventional centralized implementation, the proposed approach reduced inference latency by 64% while demonstrating near-linear scalability across up to 32 computing nodes. These findings indicate that the proposed D-FIS offers an interpretable and scalable decision-support framework for assessing commercialization risks under uncertainty and can facilitate collaborative decision-making in open innovation ecosystems.

Keywords

Main Subjects

References

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Journal of Data Analytics and Intelligent Decision-making
Volume 2, Issue 3 - Serial Number 6
September 2026
Pages 62-81

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