Ibrahim M. Alwayle¹, Hala J. Alshahrani², Saud S. Alotaibi³, Khaled M. Alalayah¹, Amira Sayed A. Aziz⁴, Khadija M. Alaidarous¹, Ibrahim Abdulrab Ahmed⁵, Manar Ahmed Hamza^6,*

Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 153-168, 2023, DOI:10.32604/iasc.2023.034718

Abstract Abstractive text summarization is crucial to produce summaries of natural language with basic concepts from large text documents. Despite the achievement of English language-related abstractive text summarization models, the models that support Arabic language text summarization are fewer in number. Recent abstractive Arabic summarization models encounter different issues that need to be resolved. Syntax inconsistency is a crucial issue resulting in the low-accuracy summary. A new technique has achieved remarkable outcomes by adding topic awareness in the text summarization process that guides the module by imitating human awareness. The current research article presents Abstractive Arabic Text Summarization using Hyperparameter Tuned… More >

Badriyya B. Al-onazi¹, Najm Alotaibi², Jaber S. Alzahrani³, Hussain Alshahrani⁴, Mohamed Ahmed Elfaki⁴, Radwa Marzouk⁵, Mahmoud Othman⁶, Abdelwahed Motwakel^7,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1537-1554, 2023, DOI:10.32604/cmc.2023.034196

Abstract Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or classes. When the number of labels is limited to one, the task becomes single-label text categorization. The Arabic texts include unstructured information also like English texts, and that is understandable for machine learning (ML) techniques, the text is changed and demonstrated by numerical value. In recent times, the dominant method for natural language processing (NLP) tasks is recurrent neural network (RNN), in general, long short term memory (LSTM) and convolutional neural network (CNN). Deep learning (DL) models are currently presented for deriving… More >

Fethi Fkih^1,2,*, Mohammed Alsuhaibani¹, Delel Rhouma^1,2, Ali Mustafa Qamar¹

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5871-5886, 2023, DOI:10.32604/cmc.2023.035910

Abstract Text classification is an essential task for many applications related to the Natural Language Processing domain. It can be applied in many fields, such as Information Retrieval, Knowledge Extraction, and Knowledge modeling. Even though the importance of this task, Arabic Text Classification tools still suffer from many problems and remain incapable of responding to the increasing volume of Arabic content that circulates on the web or resides in large databases. This paper introduces a novel machine learning-based approach that exclusively uses hybrid (stylistic and semantic) features. First, we clean the Arabic documents and translate them to English using translation tools.… More >

Ibrahim M. Alwayle¹, Badriyya B. Al-onazi², Mohamed K. Nour³, Khaled M. Alalayah¹, Khadija M. Alaidarous¹, Ibrahim Abdulrab Ahmed⁴, Amal S. Mehanna⁵, Abdelwahed Motwakel^6,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2947-2961, 2023, DOI:10.32604/csse.2023.034456

Abstract Online reviews regarding purchasing services or products offered are the main source of users’ opinions. To gain fame or profit, generally, spam reviews are written to demote or promote certain targeted products or services. This practice is called review spamming. During the last few years, various techniques have been recommended to solve the problem of spam reviews. Previous spam detection study focuses on English reviews, with a lesser interest in other languages. Spam review detection in Arabic online sources is an innovative topic despite the vast amount of data produced. Thus, this study develops an Automated Spam Review Detection using… More >

Mrim M. Alnfiai^1,2, Nabil Almalki^1,3, Fahd N. Al-Wesabi^4,*, Mesfer Alduhayyem⁵, Anwer Mustafa Hilal⁶, Manar Ahmed Hamza⁶

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2639-2652, 2023, DOI:10.32604/iasc.2023.034069

Abstract Text-To-Speech (TTS) is a speech processing tool that is highly helpful for visually-challenged people. The TTS tool is applied to transform the texts into human-like sounds. However, it is highly challenging to accomplish the TTS outcomes for the non-diacritized text of the Arabic language since it has multiple unique features and rules. Some special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic language. But, such signs are not frequently used in the texts written in the Arabic language since its speakers and readers can guess them… More >

Najm Alotaibi¹, Badriyya B. Al-onazi², Mohamed K. Nour³, Abdullah Mohamed⁴, Abdelwahed Motwakel^5,*, Gouse Pasha Mohammed⁵, Ishfaq Yaseen⁵, Mohammed Rizwanullah⁵

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3121-3137, 2023, DOI:10.32604/iasc.2023.033915

Abstract Opinion Mining (OM) studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide. Though the interest in OM studies in the Arabic language is growing among researchers, it needs a vast number of investigations due to the unique morphological principles of the language. Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features. The comparative OM studies in the English language are wide and novel. But, comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage. The unique… More >

Moeen Tayyab¹, Ayyaz Hussain^2,*, Usama Mir³, M. Aqeel Iqbal⁴, Muhammad Haneef⁵

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6177-6193, 2023, DOI:10.32604/cmc.2023.034669

Abstract The news ticker is a common feature of many different news networks that display headlines and other information. News ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory authorities. In this paper, we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news channel. The primary emphasis of this research is on ticker recognition methods and storage schemes. To that end, the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification method. The proposed learning architecture considers the grouping of homogeneous-shaped classes. This incorporates linguistic taxonomy in… More >

Badriyya B. Al-onazi¹, Saud S. Alotaib², Saeed Masoud Alshahrani^3,*, Najm Alotaibi⁴, Mrim M. Alnfiai⁵, Ahmed S. Salama⁶, Manar Ahmed Hamza⁷

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5447-5465, 2023, DOI:10.32604/cmc.2023.033564

Abstract The text classification process has been extensively investigated in various languages, especially English. Text classification models are vital in several Natural Language Processing (NLP) applications. The Arabic language has a lot of significance. For instance, it is the fourth mostly-used language on the internet and the sixth official language of the United Nations. However, there are few studies on the text classification process in Arabic. A few text classification studies have been published earlier in the Arabic language. In general, researchers face two challenges in the Arabic text classification process: low accuracy and high dimensionality of the features. In this… More >

Abdelwahed Motwakel^1,*, Badriyya B. Al-onazi², Jaber S. Alzahrani³, Radwa Marzouk⁴, Amira Sayed A. Aziz⁵, Abu Sarwar Zamani¹, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3097-3113, 2023, DOI:10.32604/csse.2023.033945

Abstract With a population of 440 million, Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users. 11 million monthly Twitter users were active and posted nearly 27.4 million tweets every day. In order to develop a classification system for the Arabic language there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification effective. In this view, this article introduces a Dolphin Swarm Optimization with Convolutional Deep Belief Network for Short Text Classification (DSOCDBN-STC) model on Arabic Corpus. The… More >

Mohammed Hadwan^1,2,*, Hamzah A. Alsayadi^3,4, Salah AL-Hagree⁵

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3471-3487, 2023, DOI:10.32604/cmc.2023.033457

Abstract The attention-based encoder-decoder technique, known as the trans-former, is used to enhance the performance of end-to-end automatic speech recognition (ASR). This research focuses on applying ASR end-to-end transformer-based models for the Arabic language, as the researchers’ community pays little attention to it. The Muslims Holy Qur’an book is written using Arabic diacritized text. In this paper, an end-to-end transformer model to building a robust Qur’an vs. recognition is proposed. The acoustic model was built using the transformer-based model as deep learning by the PyTorch framework. A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic… More >