Modernizing quality management with formal languages and neural networks
Abstract
This paper explores the integration of formal languages and neural networks into quality management systems to enhance efficiency and sustainability. Formal languages standardize regulatory documents, reducing misinterpretation and simplifying modification, contributing to innovative infrastructure (SDG 9). Recurrent neural networks (RNNs) automate document analysis, non-conformance detection, and decision-making, improving production efficiency and promoting responsible consumption (SDG 12). Automation in quality management reduces costs, enhances competitiveness, and aligns with decent work and economic growth (SDG 8). Standardizing documentation and automating quality control enhance workforce competencies and support quality education (SDG 4). These technologies strengthen regulatory transparency, reduce legal risks, and improve governance, supporting strong institutions (SDG 16). The proposed approach fosters sustainable development through digitalization and automation, ensuring efficiency, innovation, and compliance with environmental and social standards.
Keywords
Automated control system; Document structure; Formal language; Intelligent system; Quality management
Full Text:
PDFDOI: http://doi.org/10.11591/ijece.v15i4.pp4031-4042
Copyright (c) 2025 Irbulat Utepbergenov, Shara Toibayeva
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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578
This journal is published by theĀ Institute of Advanced Engineering and Science (IAES).