MARC details
| 000 -LEADER |
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| fixed length control field |
03486ntm 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 |
20251230175348.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
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 |
251230t20252025my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010194 (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 .M34 2025 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Md Mahmudul Hasan, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Hybrid bigru-bilstm with multi-head attention for eeg-based microsleep detection / |
| Statement of responsibility, etc. |
Md Mahmudul Hasan |
| 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 |
2025 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2025 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 133 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 (Master of Science) -- Universiti Malaysia Pahang – 2025 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Include bibliographical reference |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Microsleep poses a serious risk to high-risk occupations like driving, flying, and industrial work. Microsleep can result in serious accidents and fatalities and are frequently brought on by exhaustion, irregular circadian rhythms, or tedious work. This emphasizes the critical need for precise and timely detection systems. The accuracy, versatility, and practicality of current detection techniques are still constrained, despite developments in related domains. By putting forth an intelligent EEG-based system for early microsleep identification and alarm production, this study overcomes these constraints. This study presents a thorough detection framework that uses the sensitivity of electroencephalogram (EEG) signals to identify unique cortical brain activity patterns. To guarantee robustness, the process combines both controlled experimental data and publicly available EEG datasets, starting with data gathering utilizing an eight-channel Unicorn Black EEG headset. Utilizing sophisticated feature extraction methods, significant insights are extracted from unprocessed EEG signals. To improve detection accuracy, both conventional machine learning models including Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) and deep learning models including Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and a new Hybrid BiGRU-BiLSTM with Multi-Head Attention are used. Comparative evaluations demonstrate the higher performance of the suggested hybrid design, which attains 98.89% classification accuracy, 99.16% specificity, 97.77% MCC, 99.05% precision, 98.58% recall, and 98.81% F1-score. Further evidence of its generalizability comes from validation on a publicly available dataset. Additionally, the study offers a hardware feedback system prototype and an intuitive graphical user interface (GUI) for real-time driver alertness monitoring and accident prevention. In summary, this study fills important gaps in existing microsleep detection techniques and offers a scalable solution that can be implemented in safety-critical areas. Future approaches include investigating cross-domain applications in industrial safety systems, transportation, and aviation, as well as diversifying datasets and improving hardware integration. |
| 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 |
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| 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 |