MARC details
| 000 -LEADER |
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| fixed length control field |
03983ntm 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 |
20260805094658.0 |
| 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 |
260805t20262026my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010798 (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 .B46 2026 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Bhowmik Abhijit, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
EDUAE-LSTM : |
| Statement of responsibility, etc. |
Bhowmik Abhijit |
| Remainder of title |
a model for detecting fake reviews in the education using an educational attention-enhanced lstm and random forest technique / |
| 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 |
xiii, 129 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM. |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2026 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
The increasing reliance on online student review systems in higher education has intensified the need to ensure the authenticity and reliability of such feedback. Educational reviews differ significantly from product and service reviews due to multi-aspect sentiments, domain-specific terminology, limited metadata availability, and strict privacy regulations. These unique characteristics make existing fake review detection methods from other domains unsuitable for academic settings, where rating–comment inconsistencies, subjective bias, and strategically manipulated evaluations pose challenges to fair and data-driven teacher assessment. This research aims to address these challenges by developing a hybrid analytical framework specifically designed for the educational domain by fulfilling three objectives namely, i) Feature identification, ii) Model development and iii) Evaluation of the developed model. The proposed approach integrates an Education Attention-Enhanced Long Short-Term Memory model (EduAE-LSTM) for aspect-based sentiment extraction with a Random Forest classifier for fake review detection. A large-scale teacher evaluation dataset from the American International University-Bangladesh (AIUB), containing over 2.2 million entries, was utilized alongside benchmark datasets such as SemEval-2014 Task 4, MOOC Reviews, and Coursera Course Reviews. Linguistic, contextual, and behavioral features, including polarity, subjectivity, rating–comment consistency, and review length were operationalized to enable accurate and interpretable classification. Experimental results demonstrate that the EduAE-LSTM surpasses standard CNN, GRU, and LSTM baselines, achieving an F1-score of 0.86 and ROC-AUC of 0.95. When integrated with the Random Forest classifier, the hybrid model achieved 96.9% accuracy, 0.9999 precision, 0.9620 recall, and a 0.9806 F1-score, outperforming all comparative machine learning models. Domain validation using expert judgments yielded a 96% agreement rate and no significant difference in predictions (p > 0.05). Cross-domain evaluations further confirmed the robustness of the framework, with F1-scores ranging from 0.94 to 0.99 across benchmark datasets. This research contributes both theoretically and practically by advancing NLP methodologies for education-specific text mining, proposing a validated hybrid deep+ensemble architecture for fake review detection, and offering an interpretable, data-driven tool to strengthen transparency and trust in academic evaluation systems. The societal impact aligns with SDG 4 (Quality Education) by enhancing the credibility of educational assessments, and SDG 16 (Peace, Justice, and Strong Institutions) through promoting ethical, trustworthy, and data-driven institutional decision-making. |
| 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 |