Apriori-Enhanced Transformer with Federated Graph Neural Networks for Real-Time Dairy Process Optimization and Predictive Maintenance

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : V. Manochitra, A. Shaik Abdul Khadir

10.14445/23488549/IJECE-V12I7P133

How to Cite?

V. Manochitra, A. Shaik Abdul Khadir, "Apriori-Enhanced Transformer with Federated Graph Neural Networks for Real-Time Dairy Process Optimization and Predictive Maintenance," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 7, pp. 415-427, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I7P133

Abstract:

The modernization of dairy production demands intelligent, adaptive systems capable of optimizing workflows, predicting equipment failures, and minimizing human intervention. This paper proposes a Hybrid Deep Rule-Based Learning Framework that integrates Apriori algorithm-enhanced Transformer architectures for dynamic rule extraction and real-time process optimization. The system adaptively learns operational rules from heterogeneous dairy workflows while leveraging Federated Graph Neural Networks (GNNs) to analyze machine interdependencies across distributed production units. This decentralized approach ensures data privacy while enabling predictive maintenance, fault localization, and efficient resource allocation. Experimental results across multiple dairy plant simulations demonstrate a workflow optimization accuracy of 98.1%, significantly reducing downtime and enhancing overall yield. The proposed framework represents a scalable, intelligent automation solution for smart dairy manufacturing environments, ensuring real-time adaptability, enhanced decision-making, and reduced reliance on manual oversight.

Keywords:

Apriori algorithm, Transformer, Federated learning, Graph neural networks, Predictive maintenance, Dairy process optimization, Real-time automation, Workflow mining, Intelligent manufacturing.

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