Implementation of Artificial Neural Network Using Scaled Conjugate Gradient in ISO 9001:2015 Audit Findings Classification
Ralph Sherwin A. Corpuz

Dr. Ralph Sherwin A. Corpuz, Director of Quality Assurance and Assistant Professor, Electronics Engineering Department, Technological University of the Philippines, Manila, Philippines.
Manuscript received on 12 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 420-425 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1014078219/19©BEIESP | DOI: 10.35940/ijrte.B1014.078219
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Abstract: Auditors of Quality Management System (QMS) face challenges in generating accurate audit reports due to some factors that can be attributed to technical competence, experience, time, auditee reaction, and other factors. Incorrect clauses cited in audit reports may result to loss of integrity of the auditor and the auditing procedure itself, hence, it is important that auditors should be careful in citing clauses of the standard to avoid chaos and complaints from auditees. To resolve this issue, this paper presents the implementation of Artificial Neural Network (ANN) using Scaled Conjugate Gradient (SCG) algorithm to classify audit findings based on the clauses of the ISO 9001:2015 QMS Requirements international standard. In this paper, the author explored how the neural network can predict the correct clause of the standard according to text patterns of audit findings. Based on modelling results, the neural network has generated a Cross Entropy (CE) values of 6.39, 18.09, 18.09 and Percentage Error (PE) values of 21.83, 21.58, and 22.39 in training, testing, and validation environments, respectively. Moreover, the model has achieved a combined Classification Accuracy (CA) of 96%, as for which, based on the actual implementation, the model has accurately predicted 95% of the audit findings analyzed.
Index Terms: Artificial Neural Network, Scaled Conjugate Gradient, Text Classification, ISO 9001 Audit Findings

Scope of the Article: Classification