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Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

Mesfer Al Duhayyim1,*, Badriyya B. Al-onazi2, Mohamed K. Nour3, Ayman Yafoz4, Amal S. Mehanna5, Ishfaq Yaseen6, Amgad Atta Abdelmageed6, Gouse Pasha Mohammed6

1 Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia
2 Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Makkah 24211, Saudi Arabia
4 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
5 Department of Digital Media, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11845, Egypt
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Mesfer Al Duhayyim. Email: email

Computer Systems Science and Engineering 2023, 46(3), 2755-2772. https://doi.org/10.32604/csse.2023.034609

Abstract

Natural Language Processing (NLP) for the Arabic language has gained much significance in recent years. The most commonly-utilized NLP task is the ‘Text Classification’ process. Its main intention is to apply the Machine Learning (ML) approaches for automatically classifying the textual files into one or more pre-defined categories. In ML approaches, the first and foremost crucial step is identifying an appropriate large dataset to test and train the method. One of the trending ML techniques, i.e., Deep Learning (DL) technique needs huge volumes of different types of datasets for training to yield the best outcomes. The current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machine-based Arabic Corpus Classification (DODHBM-ACC) model in this background. The presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding process. For Arabic text classification, the DHBM technique is utilized. This technique is a hybrid version of the Deep Boltzmann Machine (DBM) and Deep Belief Network (DBN). It has the advantage of learning the decisive intention of the classification process. To adjust the hyperparameters of the DHBM technique, the Dice Optimization Algorithm (DOA) is exploited in this study. The experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC model. The outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

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APA Style
Duhayyim, M.A., Al-onazi, B.B., Nour, M.K., Yafoz, A., Mehanna, A.S. et al. (2023). Optimal deep hybrid boltzmann machine based arabic corpus classification model. Computer Systems Science and Engineering, 46(3), 2755-2772. https://doi.org/10.32604/csse.2023.034609
Vancouver Style
Duhayyim MA, Al-onazi BB, Nour MK, Yafoz A, Mehanna AS, Yaseen I, et al. Optimal deep hybrid boltzmann machine based arabic corpus classification model. Comput Syst Sci Eng. 2023;46(3):2755-2772 https://doi.org/10.32604/csse.2023.034609
IEEE Style
M.A. Duhayyim et al., "Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model," Comput. Syst. Sci. Eng., vol. 46, no. 3, pp. 2755-2772. 2023. https://doi.org/10.32604/csse.2023.034609



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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