Modernizing quality management with formal languages and neural networks.

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Bibliographic Details
Title:	Modernizing quality management with formal languages and neural networks.
Authors:	Utepbergenov, Irbulat¹ i.utepbergenov@aues.kz, Toibayeva, Shara¹ sh.toibaeva@aues.kz
Source:	International Journal of Electrical & Computer Engineering (2088-8708). Aug2025, Vol. 15 Issue 4, p4031-4042. 12p.
Subjects:	Formal languages, Artificial neural networks, Automation, Total quality management, Sustainability, Regulatory compliance, Information networks, Sustainable development
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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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. [ABSTRACT FROM AUTHOR]
ISSN:	20888708
DOI:	10.11591/ijece.v15i4.pp4031-4042