Decision Making in Industry 4.0 Scenarios Supported by Imbalanced Data Classification

Para, Jesús; Ser, Javier Del; Aguirre, Aitor; Nebro, Antonio J.

doi:10.1007/978-3-319-99626-4_11

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Para et al [94] conducted a study on decision making in Industry 4.0 scenarios, focusing on imbalanced data classification. They proposed a practical methodology for data analysis in industrial environments, emphasizing the importance of hypothesis generation dynamics among multidisciplinary experts prior to data capture.…”

Section: Focus Of Research Impact On Industrial Applicationsmentioning

confidence: 99%

Time Series Prediction in Industry 4.0: A Comprehensive Review and Prospects for Future Advancements

Kashpruk,

Piskor-Ignatowicz,

Baranowski

2023

Applied Sciences

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Time series prediction stands at the forefront of the fourth industrial revolution (Industry 4.0), offering a crucial analytical tool for the vast data streams generated by modern industrial processes. This literature review systematically consolidates existing research on the predictive analysis of time series within the framework of Industry 4.0, illustrating its critical role in enhancing operational foresight and strategic planning. Tracing the evolution from the first to the fourth industrial revolution, the paper delineates how each phase has incrementally set the stage for today’s data-centric manufacturing paradigms. It critically examines how emergent technologies such as the Internet of things (IoT), artificial intelligence (AI), cloud computing, and big data analytics converge in the context of Industry 4.0 to transform time series data into actionable insights. Specifically, the review explores applications in predictive maintenance, production optimization, sales forecasting, and anomaly detection, underscoring the transformative impact of accurate time series forecasting on industrial operations. The paper culminates in a call to action for the strategic dissemination and management of these technologies, proposing a pathway for leveraging time series prediction to drive societal and economic advancement. Serving as a foundational compendium, this article aims to inform and guide ongoing research and practice at the intersection of time series prediction and Industry 4.0.

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Section: Focus Of Research Impact On Industrial Applicationsmentioning

confidence: 99%