MARC details
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03224ntm a2200313 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251216111123.0 |
| 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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ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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251216t20252025my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010157 (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) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKOM .H39 2025 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Siti Hawa Binti Apandi, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Web page classification based on recursive Convolutional Neural Network (CNN) and word cloud image |
| Statement of responsibility, etc. |
Siti Hawa Binti Apandi |
| 264 ## - 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 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xi, 131 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
30 cm. + |
| Accompanying material |
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 |
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| 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 – 2025 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
The increasing quantity and diversity of web content pose tremendous challenges to web page categorization, particularly in handling unstructured data and adapting to the dynamic nature of web content. Traditional classification methods relying on Convolutional Neural Networks (CNN) are confronted with hierarchical feature learning and long-range dependencies in text data. To overcome these constraints, this research proposes a novel web page classification model by integrating Recursive CNN with word cloud images as an alternative feature representation approach. The study aims to improve the accuracy of classification, interpretability, and computational expense by applying CNN's image processing capability to text data. The research process entails three phases: (1) Identification of effective text representation methods, (2) Developing a CNN-based classification model, and (3) Performance evaluation of the model against existing classification techniques. Web page text is extracted from HTML source codes, vectorized using the bag-of-words method, and depicted as word cloud images with high-frequency words presented in bold. The Recursive CNN model is trained using a training dataset of 391-word cloud images (274 Gaming and 117 Online Video Streaming) and tuned through grid search hyperparameter tuning. The maximum validation accuracy of the model is 89.50% for Adam optimizer, learning rate of 0.001, batch size 32, and 20 epochs, with a training time of 1 minute and 34 seconds. The overall accuracy is 89.50%, confirming the effectiveness of Recursive CNN in detecting deeper word relationships and reducing misclassification errors. Comparative analysis with existing CNN-based models demonstrates the improved adaptability and efficiency of the proposed method, confirming that word cloud images can be a good input representation for deep learning-based classification tasks. The findings provide a new benchmark for classifying web pages, reaffirming the feasibility of recursive feature extraction approaches as an instrument for future document classification, sentiment, and automated content filtering applications. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| 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 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |