Machine Learning Enhanced Framework for Big Data Modeling with Application in Industry 4.0

In the dynamic milieu of Industry 4.0, characterized by the deluge of big data, this research promulgates a groundbreaking framework that harnesses machine learning (ML) to optimize big data modeling processes, addressing the intricate requirements and challenges of contemporary industrial domains. Traditional data processing mechanisms falter in the face of the sheer volume, velocity, and variety of big data, necessitating more robust, intelligent solutions. This paper delineates the development and application of an innovative ML-augmented framework, engineered to interpret and model complex, multifaceted data structures more efficiently and accurately than has been feasible with conventional methodologies. Central to our approach is the integration of advanced ML strategies—including but not limited to deep learning and neural networks—with sophisticated analytics tools, collectively capable of automated decision-making, predictive analysis, and trend identification in real-time scenarios. Beyond theoretical formulation, our research rigorously evaluates the framework through empirical analysis and industrial case studies, demonstrating tangible enhancements in data utility, predictive accuracy, operational efficiency, and scalability within various Industry 4.0 contexts. The results signify a marked improvement over existing models, particularly in handling high-dimensional data and facilitating actionable insights, thereby empowering industrial entities to navigate the complexities of digital transformation. This exploration underscores the potential of machine learning as a pivotal ally in evolving data strategies, setting a new precedent for data-driven decision-making paradigms in the era of Industry 4.0.

@article{Kazbekova2024,
  title     = {Machine Learning Enhanced Framework for Big Data Modeling with Application in Industry 4.0},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {3},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Gulnur Kazbekova and Zhuldyz Ismagulova and Botagoz Zhussipbek and Yntymak Abdrazakh and Gulzipa Iskendirova and Nurgul Toilybayeva},
  doi       = {10.14569/IJACSA.2024.0150332},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150332}
}