Prediction of ISO 9001:2015 Audit Reports According to its Major Clauses using Recurrent Neural Networks

Tarnate, Ken Jon M.; Devaraj, Madhavi

doi:10.35940/ijrte.b1018.078219

Cited by 4 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To effectively harness the potential of data mining in university settings, top managers and strategic leaders must address certain challenges and develop specific skills. These challenges include data privacy and security concerns, ensuring data accuracy and reliability, integrating different data sources from various departments, implementing appropriate data mining algorithms, and interpreting and effectively communicating the findings derived from data mining analysis (Tarnate & Devaraj, 2019). In order to tackle these challenges, top managers and strategic leaders in universities must develop skills in data management, data analysis, and data visualization.…”

Section: University Data Strategymentioning

confidence: 99%

The Role of the Data Management Centre for Data Sustainability at Academic University for: Literature Review

Karno,

Salleh,

Ismail

et al. 2023

IJARBSS

View full text Add to dashboard Cite

Informed decision-making is increasingly recognised as a standard for policymaking in many fields, including academia and universities. However, many challenges exist in identifying the appropriate evidence, disseminating it to different stakeholders, and implementing it in various settings. Academic universities brought together international experts and stakeholders to increase knowledge in the field of evidence-informed policy, develop a research agenda, strengthen international collaborations, and create a community for sharing experience, research, and best practices. Consequently, the effects of all this new smart dashboard insight are not fully realized. It is imperative to develop a system that can collect, integrate, manage, and analyse on-and off-farm data in real time for practical and relevant actions. Purpose: The University Data Management Centre (UDMC) has been called a "groundbreaking" area for university strategy, and it is among the top future trends for academic universities. Hence, this study aims to systematically review UDMC practices and services, primarily focusing on the challenges, services, and skills along with motivational factors associated with them. Design/methodology/approach -A systematic literature review method was used, focusing on literature produced between 2015 and 2022 to understand the latest trends. Findings: The role of the University Data Management Centre is gradually gaining importance among staff and academic universities; however, it is still poorly practised. Albeit, it is better observed in developed countries than developing countries; however, there are lots of challenges associated with UDMC practices by top managers and strategic universities. These challenges demand certain sets of skills be developed for better practices and services. Active

show abstract

Section: University Data Strategymentioning

confidence: 99%

The Role of the Data Management Centre for Data Sustainability at Academic University for: Literature Review

Karno,

Salleh,

Ismail

et al. 2023

IJARBSS

View full text Add to dashboard Cite

show abstract

“…As it is shown in (Figure 6) every cost model context in scientific papers explained in a native language. Our solution is based on bag‐of‐words (BOW) model 22 and variability feature model (FM) 23 that defines the vocabulary of known words (context of cost models). To create BOW we follow these steps.…”

Section: Our Frameworkmentioning

confidence: 99%

Capitalizing the database cost models process through a service‐based pipeline

Ouared

Ouhammou

2021

Concurrency and Computation

View full text Add to dashboard Cite

Designing a database cost model is one of the main research topics related to the physical design phase. It follows the evolution of database technology in order to evaluate and quantify the performance metrics (e.g., response time, energy consumption, etc.). Therefore, it makes the community researchers sensitive to the generated results.However, reusing and comparing database cost models require extracting related information manually from the research publications. This process is error-prone and time-consuming. Unfortunately, many researchers claim the difficulty of surveying and reproducing cost models already published in several/journal articles and/or reports. This difficulty is due to the absence of a process describing the cost model itself formally as well as the context of its utilization. This article presents an approach enabling the extraction of cost models information (context, parameters, features, etc.) as a set of orchestrated services. These services are implemented using natural language processing and machine-learning techniques via a work-flow pipeline inspired by DevOps practices. We illustrate our approach on a case study to stress the feasibility and benefits of our proposal by emphasizing the reproduction and automatization facilities.

show abstract

“…In a more closely related study, Tarnate et al (2020) evaluated different types of recurrent neural networks (RNN) in classifying text-based ISO 9001 audit reports based on major clauses. They compared the effects of word encoding, word embedding and word encoding-embedding methods to optimize the learning performance of the classification model.…”

Section: Introductionmentioning

confidence: 99%

ISO 9001:2015 Quality Management System Requirements and Audit Findings Classification Using Support Vector Machine and Long Short-Term Memory Neural Network: An Optimization Method

Corpuz

2021

MJST

View full text Add to dashboard Cite

Generating accurate and timely internal and external audit reports may seem difficult for some auditors due to limited time or expertise in matching the correct clauses of the standard with the textual statement of findings. To overcome this gap, this paper presents the design of text classification models using support vector machine (SVM) and long short-term memory (LSTM) neural network in order to automatically classify audit findings and standard requirements according to text patterns. Specifically, the study explored the optimization of datasets, holdout percentage and vocabulary of learned words called NumWords, then analyzed their capability to predict training accuracy and timeliness performance of the proposed text classification models. The study found that SVM (96.74%) and LSTM (97.54%) were at par with each other in terms of the best training accuracy, although SVM (67.96±17.93 seconds [s]) was found to be significantly faster than LSTM (136.67±96.42 s) in any dataset size. The study proposed optimization formulas that highlight dataset and holdout as predictors of accuracy, while dataset and NumWords as predictors of timeliness. In terms of actual implementation, both classification models were able to accurately classify 20 out of 20 sample audit findings at 1 and 3 s, respectively. Hence, the extent of choosing between the two algorithms depend on the datasets size, learned words, holdout percentage, and workstation speed. This paper is part of a series, which explores the use of Artificial Intelligence (AI) techniques in optimizing the performance of QMS in the context of a state university.

show abstract

Prediction of ISO 9001:2015 Audit Reports According to its Major Clauses using Recurrent Neural Networks

Cited by 4 publications

References 10 publications

The Role of the Data Management Centre for Data Sustainability at Academic University for: Literature Review

The Role of the Data Management Centre for Data Sustainability at Academic University for: Literature Review

Capitalizing the database cost models process through a service‐based pipeline

ISO 9001:2015 Quality Management System Requirements and Audit Findings Classification Using Support Vector Machine and Long Short-Term Memory Neural Network: An Optimization Method

Contact Info

Product

Resources

About