MARC details
| 000 -LEADER |
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| fixed length control field |
03916ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20260525141640.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
260525t20262026my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010526 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FTKEE .R34 2025 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Rafiuddin Bin Abdubrani, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Driver fatigue classification based on electroencephalogram signal / |
| Statement of responsibility, etc. |
Rafiuddin Bin Abdubrani |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2026 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2026 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiv, 164 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Electrical & Electronics Engineering Technology |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2026 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Include bibliographical reference |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Driver fatigue is a critical risk factor for road accidents, underscoring the need for accurate and reliable detection systems. This research proposes an advanced EEG-based driver fatigue classification framework that integrates enhanced signal pre-processing with a hybrid deep learning architecture. The novelty of this research lies in the integration of an ICA–CWT pre-processing pipeline with enhanced modified z-score normalisation to produce high-fidelity EEG features, combined with a hybrid DBN–DQN architecture that significantly improves fatigue detection performance under crosssubject variability. The methodology employs independent component analysis combined with continuous wavelet transform (ICA-CWT) using the Morlet mother wavelet to improve artefact removal and time-frequency resolution. An enhanced modified z-score (EMZS) normalisation technique is introduced to stabilise feature distributions by reducing the influence of outliers. Extracted features from delta, theta, and alpha sub-bands are processed by a hybrid model combining a deep belief network (DBN) and a deep Q-network (DQN). The DBN performs hierarchical feature learning, while the DQN enhances decision-making through reinforcement learning principles, enabling improved classification accuracy. Experimental evaluation was conducted using stratified 5-fold cross-validation on a publicly available EEG dataset, with performance compared against traditional deep learning baselines (CNN and RNN) under identical conditions. The proposed method addresses key limitations of CNN/RNN models, such as poor frequency resolution and high sensitivity to noise, resulting in lower robustness in EEG-based fatigue studies. The proposed DBN-DQN framework achieved an average accuracy of 98.21%, precision of 98.15%, sensitivity (recall) of 98.24%, specificity of 98.18%, and an F1-score of 98.19%. These results significantly outperform CNN and RNN baselines, demonstrating the superiority of the proposed hybrid approach in EEGbased fatigue detection. The findings validate that integrating ICA-CWT, EMZS normalisation, and a hybrid DBN-DQN architecture substantially enhances fatigue classification performance. Future research should focus on real-time implementation, multimodal integration with additional physiological signals, and deployment of lightweight models for embedded systems. Overall, the study strengthens the link between methodology, results, and application by showing that ICA–CWT improves feature clarity, EMZS enhances data robustness, and the hybrid DBN–DQN architecture delivers highly reliable classification suitable for practical deployment in future intelligent transportation systems. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Electrical & Electronics Engineering Technology |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |