MARC details
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| 003 - CONTROL NUMBER IDENTIFIER |
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MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251125110754.0 |
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| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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ta |
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230720t20232023my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0009488 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
KK .H36 2023 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Wang Hao, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Artificial intelligence-based customer requirement classification in quality management system using natural language processing for autonomous vehicles / |
| Statement of responsibility, etc. |
Wang Hao |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Date of production, publication, distribution, manufacture, or copyright notice |
© 2023 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xvi, 128 pages : |
| Other physical details |
Illustration ; |
| Dimensions |
30 cm.+ |
| Accompanying material |
1 CD ROM |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
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text |
| 337 ## - MEDIA TYPE |
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rdamedia |
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unmediated |
| 337 ## - MEDIA TYPE |
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rdamedia |
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computer |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
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volume |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
computer disc |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
College of Engineering |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical reference |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Major vehicle companies have created visible and significant outcomes and progress both in automotive and traffic systems. A quality management system could help vehicle companies identify customer requirements to fulfil them. Consumer demands are given in the format of large amount text data, classifying it becomes time, labour, and cost consuming. In addition, artificial intelligence (AI) techniques have many advantages, with the potential to replace humans. However, the popularity of autonomous vehicles faces not only technological difficulties, but also social barriers. Autonomous vehicle development needs a more efficient working process based on quality management systems. Most previous research on customer demands, requirements, and opinions are not comprehensive enough to provide deeper and more applicable data for car makers to develop and improve their products. Furthermore, there is no comprehensive corpus that is derived from a comprehensive customer requirements matrix. The conventional way to transfer customer requirements into usable text data is with low efficiency. Even though some Natural Language Processing (NLP) methods have been introduced into customer requirements classification, the performance of these methods have yet to be proven for a customised corpus in autonomous vehicle’s customer requirements classification. This study aims to build an efficient and simplified product development process based on quality management systems and embedded AI technology to create more comprehensive customer requirements that can be collected as input for autonomous vehicle developers. This will create a specific customer requirement corpus to compare the different conventional NLP algorithms based on a customised corpus and to compare the NaiveBayes, Maxent, and SVM Algorithms as well as improve the accuracy of classification by using a Convolution Neural Network (CNN). A three convolution layers with max pooling CNN is used in this study. Different quality management systems are compared, and a novel autonomous vehicle development process is created in this study. Then, Comprehensive customer requirements are collected by gathering former research outcomes based on different aspects. After that, A specific corpus based on both comprehensive customer requirements and NLP method is created. Then this corpus is expressed in the format of labelled data matrix and fed into different NLP algorithms to improve the efficiency of classification of customer requirements pertaining to autonomous vehicles. The training:testing dataset ratio set in 75:25, the datasets extended from 144 to 276 sentences, and the hyperparameters are optimised for CNN to get the best results. Meanwhile, for conventional classifiers use the common parameters. Average time is used for expressing the efficiency of the classification. Confusion matrix is used to evaluate the classification results for the conventional algorithms. Accuracy results is used for comparison. Results showed that the NLP method is applicable for autonomous vehicle development, and showed high efficiency in customer requirements’ classification, which could help reduce costs for car manufacturers. The classification accuracy using conventional classifiers reached lower than 50% for both training set ratio. The classification accuracy reached 90.48% under a 25% testing ratio and 100% under a 75% training ratio by using the CNN. In conclusion, even though this research is based on a customised corpus that cannot possibly compare with other research, the Convolution Neural Network showed its advantage in improving the accuracy of the classification when dealing with the specified corpus. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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College of Engineering |
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Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Thesis |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |