Work

Enhancing predictive accuracy in manufacturing through AI/ML-driven data labeling

Year: 2025

Type: article

Abstract: This research focuses on data-labeling techniques using AI and ML that are overcoming challenges in manufacturing. Data labeling that supports supervised learning plays a key role in allowing machines to spot patterns and get their predictions correct. The study themes give importance to gathering data in real time during manufacturing, developing advanced ways to improve labels are added and upgraded, and achieving great results, such as making decisions more efficiently, having instant insights into operations, and building systems that can learn independently. The automatic data labeling process is important for reaching intelligent, predictive, and autonomous manufacturing.

Source: International Journal of Science and Research Archive

DOI: 10.5281/zenodo.17309431

Author: Vikram, Srinivas

Language: English

Topic: Impact of AI and Big Data on Business and Society

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W7087619640

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