Open AccessPrediction of ISO 9001:2015 Audit Reports According to its Major Clauses using Recurrent Neural Networks
Author(s) -
Ken Jon M. Tarnate,
Madhavi Devaraj
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1018.078219
Subject(s) - computer science , audit , recurrent neural network , deep learning , word embedding , artificial intelligence , artificial neural network , quality assurance , process (computing) , machine learning , quality management , dimension (graph theory) , data mining , embedding , management system , accounting , operations management , engineering , external quality assessment , business , operating system , mathematics , pure mathematics
The Quality Assurance Department of the educational sectors is rapidly generating digital documents. The continuous increase of digital documents may become a risk and challenge in the future. Interpreting and analyzing those digital data in a short period of time is very critical and crucial for the top management to support their decisions. By this purpose, this paper explored the possibility of machine learning and data mining process to improve the Quality Assurance Management System process, specifically in the Quality Audit procedures and generation of management reports. The researchers developed a machine learning model that predicts an audit report according to the major clauses of the ISO 9001:2015 Quality Management System (QMS) Requirements. The proposed data mining process helps the top management to identify which principles of the ISO 9001:2015 QMS Requirements they are lacking. The authors used four different Recurrent Neural Networks (RNNs) as a classifier; (1) Long Short Term-Memory (LSTM), (2) Bidirectional-LSTM, (3) Deep-LSTM and a (4) Deep-Bidirectional-LSTM Recurrent Neural Networks with a combine word representation models (word encoding plus an embedding dimension layer). The Deep-Bidirectional-LSTM outperformed the other three RNN models. Where it achieved an average classification accuracy of 91.10%