000			03224ntm a2200313 i 4500
003			MY-KuUP
005			20251216111123.0
006			t||||fr|||| 000 0
007			ta
008			251216t20252025my a|||fr|||| 000 0 eng d
020			_aTHE0010157 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFKOM .H39 2025 r Thesis
100	1		_aSiti Hawa Binti Apandi, _eauthor.
245	1	0	_aWeb page classification based on recursive Convolutional Neural Network (CNN) and word cloud image _cSiti Hawa Binti Apandi
264			_aKuantan, Pahang : _bUMPSA, _c2025
264			_c© 2025
300			_axi, 131 pages : _billustrations ; _c30 cm. + _eCD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Computing
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2025
504			_aThe 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	2	0	_aFaculty of Computing _xDissertations
650		0	_aUniversities and colleges _xDissertations
942			_2lcc _cTHESIS
999			_c103739 _d103745