000			03128ntm a2200337 i 4500
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005			20261007111741.0
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008			261005t20262026my a|||fr|||| 000 0 eng d
020			_aTHE0010806 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFK .A36 2026 r Thesis
100	1		_aMuhammad Afnan Hafidz Mohd Taslim, _eauthor.
245	1	2	_aA set of diagonal embedding algorithm for improving robustness with tamper detection in image watermarking / _cMuhammad Afnan Hafidz Mohd Taslim
264		1	_aKuantan, Pahang : _bUMPSA, _c2026
264		4	_c© 2026
300			_axiv, 149 pages : _billustrations ; _c30 cm. + _e1 CD-ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Computing
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang - 2026
504			_aIncludes bibliographical references
520	3		_aDetecting 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	2	0	_aFaculty of Computing _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c105661 _d105667