MARC details
| 000 -LEADER |
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| fixed length control field |
03128ntm 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 |
20261007111741.0 |
| 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 |
261005t20262026my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010806 (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) |
FK .A36 2026 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Muhammad Afnan Hafidz Mohd Taslim, |
| Relator term |
author. |
| 245 12 - TITLE STATEMENT |
|---|
| Title |
A set of diagonal embedding algorithm for improving robustness with tamper detection in image watermarking / |
| Statement of responsibility, etc. |
Muhammad Afnan Hafidz Mohd Taslim |
| 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, 149 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 |
|---|
| 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 Computing |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang - 2026 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Detecting suicidal intention from social media text is a challenging task because such expressions are often subtle, context-dependent, and linguistically diverse. Although Convolutional Neural Network (CNN) models have shown promise in text classification, individual CNN architectures remain limited in two important ways: they capture only specific types of spatial features according to their own architectural design, and they often show weaker generalizability across varied social media expressions. These limitations reduce their ability to represent the full range of linguistic patterns associated with suicidal intent. To address this problem, this thesis proposes a heterogeneous ensemble of CNN models for suicidal intention detection in social media text. The approach combines three distinct CNN architectures with different pooling strategies, activation functions, and attention-related mechanisms so that complementary textual features can be captured more effectively than with a single model. The study used a large, labeled Reddit dataset for model development and training, followed by evaluation against individual CNNs and other deep learning baselines. The proposed ensemble CNN achieved an accuracy of 97.56%, outperforming the individual CNN models and other compared deep learning methods. Its generalization capability was further examined using two unforeseen datasets from Reddit and Twitter, where the model maintained strong performance despite differences in platform - specific language patterns and expression styles. These findings indicate that combining heterogeneous CNN architectures can improve spatial feature extraction and strengthen classification performance for suicidal intention detection. Overall, this study contributes a CNN-based ensemble framework that provides a promising approach for identifying suicidal intention in social media text within the evaluated experimental setting. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| 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 |