Transfer Learning Approaches for Cross-Domain Industrial Process Optimization

  • Authors

    • Dr. Mohammed Asif Khan Professor Jamia Millia Islamia, India Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2022PI5H1V

    Published 03-04-2022

  • Transfer Learning, Cross-Domain Learning, Industrial Process Optimization, Machine Learning, Domain Adaptation, Feature Representation, Parameter Transfer, Industrial AI

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    M. Asif Khan, “Transfer Learning Approaches for Cross-Domain Industrial Process Optimization”, IJMLPA, vol. 5, no. 1, pp. 01–12, Mar. 2022, doi: 10.67228/3142788X/IJMLPA-2022PI5H1V.

  • Abstract

    Industrial process optimization plays a crucial role in improving operational efficiency, reducing costs, and enhancing product quality across diverse manufacturing and production domains. However, developing robust machine learning models for optimization often requires large amounts of labeled data, which may be scarce or unavailable in many industrial settings. Transfer learning offers a promising solution by leveraging knowledge from related source domains to improve learning performance in target domains with limited data. This paper presents a comprehensive review and analysis of transfer learning approaches tailored for cross-domain industrial process optimization. We categorize and discuss various methodologies including instance-based, feature-based, parameter-based, and relational transfer learning techniques, highlighting their applicability and challenges in industrial environments. Experimental case studies across different industrial domains demonstrate the effectiveness of transfer learning in bridging domain gaps and enhancing optimization outcomes. Finally, we outline future research directions to advance transfer learning frameworks for scalable, adaptive, and real-time industrial process optimization.

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