A COMPREHENSIVE FRAMEWORK FOR EXTRACTING CAUSALITY IN NUCLEAR INCIDENT REPORTS

Dr. Jane, Doe (2024) A COMPREHENSIVE FRAMEWORK FOR EXTRACTING CAUSALITY IN NUCLEAR INCIDENT REPORTS. Journal of Science, Research and Teaching, 3 (6). pp. 52-60. ISSN 2181-4406

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Abstract

This article presents a comprehensive framework designed to enhance the extraction of causality from Nuclear Incident Reports (NIRs), addressing critical challenges in incident analysis within the nuclear industry. Causality extraction plays a pivotal role in understanding the sequence of events, identifying root causes, and implementing preventive measures to enhance nuclear safety and regulatory compliance. The framework integrates advanced natural language processing (NLP) techniques, machine learning (ML) models, and rule-based systems to achieve robust causality detection in NIRs. Key contributions include improved accuracy in identifying causal relationships, enhanced efficiency in incident analysis, and support for informed decision-making in nuclear safety protocols. Findings underscore the framework's capability to discern complex dependencies and its potential to bolster safety practices through proactive risk mitigation strategies. This research advocates for the adoption of hybrid approaches in incident analysis, aiming to fortify nuclear safety standards and regulatory frameworks.

Item Type:	Article
Subjects:	Q Science > Q Science (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	15 Jul 2024 08:10
Last Modified:	15 Aug 2024 04:34
URI:	http://eprints.umsida.ac.id/id/eprint/13843

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