Abdelwahed Motwakel^1,*, Hala J. Alshahrani², Jaber S. Alzahrani³, Ayman Yafoz⁴, Heba Mohsen⁵, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹, Mohamed I. Eldesouki⁶

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2741-2757, 2023, DOI:10.32604/csse.2023.034721

Abstract Currently, individuals use online social media, namely Facebook or Twitter, for sharing their thoughts and emotions. Detection of emotions on social networking sites’ finds useful in several applications in social welfare, commerce, public health, and so on. Emotion is expressed in several means, like facial and speech expressions, gestures, and written text. Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning (DL) and natural language processing (NLP) domains. This article proposes a Deer Hunting Optimization with Deep Belief Network Enabled Emotion Classification (DHODBN-EC) on English Twitter Data in this study. The presented… More >

Mesfer Al Duhayyim^1,*, Badriyya B. Al-onazi², Jaber S. Alzahrani³, Hussain Alshahrani⁴, Mohamed Ahmed Elfaki⁴, Abdullah Mohamed⁵, Ishfaq Yaseen⁶, Gouse Pasha Mohammed⁶, Mohammed Rizwanullah⁶, Abu Sarwar Zamani⁶

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3049-3065, 2023, DOI:10.32604/csse.2023.033836

Abstract Sentiment analysis (SA) of the Arabic language becomes important despite scarce annotated corpora and confined sources. Arabic affect Analysis has become an active research zone nowadays. But still, the Arabic language lags behind adequate language sources for enabling the SA tasks. Thus, Arabic still faces challenges in natural language processing (NLP) tasks because of its structure complexities, history, and distinct cultures. It has gained lesser effort than the other languages. This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis (MVODRL-AA) on Arabic Corpus. The presented MVODRL-AA model majorly concentrates on identifying and classifying effects or emotions… More >

Nourmeen Lotfy¹, Abdulaziz Shehab^1,2,*, Mohammed Elhoseny^1,3, Ahmed Abu-Elfetouh¹

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1227-1249, 2023, DOI:10.32604/cmc.2023.039185

Abstract Despite the extensive effort to improve intelligent educational tools for smart learning environments, automatic Arabic essay scoring remains a big research challenge. The nature of the writing style of the Arabic language makes the problem even more complicated. This study designs, implements, and evaluates an automatic Arabic essay scoring system. The proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing tasks. Then, it comprises two main components: the grading engine and the adaptive fusion engine. The grading engine employs string-based and corpus-based similarity algorithms separately. After that, the adaptive fusion… 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 >

Abdelwahed Motwakel^1,*, Najm Alotaibi², Eatedal Alabdulkreem³, Hussain Alshahrani⁴, Mohamed Ahmed Elfaki⁴, Mohamed K Nour⁵, Radwa Marzouk⁶, Mahmoud Othman⁷

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1249-1266, 2023, DOI:10.32604/csse.2023.034227

Abstract Sentiment Analysis (SA) of natural language text is not only a challenging process but also gains significance in various Natural Language Processing (NLP) applications. The SA is utilized in various applications, namely, education, to improve the learning and teaching processes, marketing strategies, customer trend predictions, and the stock market. Various researchers have applied lexicon-related approaches, Machine Learning (ML) techniques and so on to conduct the SA for multiple languages, for instance, English and Chinese. Due to the increased popularity of the Deep Learning models, the current study used diverse configuration settings of the Convolution Neural Network (CNN) model and conducted… More >

YANYA CHEN^1,#, HONGYUAN WU^2,#, RUISI ZHOU^5,#, HELING DONG⁴, XUEFANG ZHANG², XUEWEI WU¹, WENSHAN CHEN¹, YANTING YOU^5,*, YIFEN WU^3,*

BIOCELL, Vol.47, No.6, pp. 1353-1364, 2023, DOI:10.32604/biocell.2023.027346

Abstract Background: The relationship between microRNA (miRNA) expression patterns and tumor mutation burden (TMB) in uterine corpus endometrial carcinoma (UCEC) was investigated in this study. Methods: The UCEC dataset from The Cancer Genome Atlas (TCGA) database was used to identify the miRNAs that differ in expression between high TMB and low TMB sample sets. The total sample sets were divided into a training set and a test set. TMB levels were predicted using miRNA-based signature classifiers developed by Lasso Cox regression. Test sets were used to validate the classifier. This study investigated the relationship between a miRNA-based signature classifier and three… More >

Ou Ye^1,2, Mimi Wang¹, Zhenhua Yu^1,*, Yan Fu¹, Shun Yi¹, Jun Deng²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5675-5696, 2023, DOI:10.32604/cmc.2023.037503

Abstract Currently, the video captioning models based on an encoder-decoder mainly rely on a single video input source. The contents of video captioning are limited since few studies employed external corpus information to guide the generation of video captioning, which is not conducive to the accurate description and understanding of video content. To address this issue, a novel video captioning method guided by a sentence retrieval generation network (ED-SRG) is proposed in this paper. First, a ResNeXt network model, an efficient convolutional network for online video understanding (ECO) model, and a long short-term memory (LSTM) network model are integrated to construct… More >

Manar Ahmed Hamza^1,*, Hala J. Alshahrani², Khaled Tarmissi³, Ayman Yafoz⁴, Amal S. Mehanna⁵, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹, Mohamed I. Eldesouki⁶

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3303-3319, 2023, DOI:10.32604/csse.2023.034823

Abstract The term ‘corpus’ refers to a huge volume of structured datasets containing machine-readable texts. Such texts are generated in a natural communicative setting. The explosion of social media permitted individuals to spread data with minimal examination and filters freely. Due to this, the old problem of fake news has resurfaced. It has become an important concern due to its negative impact on the community. To manage the spread of fake news, automatic recognition approaches have been investigated earlier using Artificial Intelligence (AI) and Machine Learning (ML) techniques. To perform the medicinal text classification tasks, the ML approaches were applied, and… More >

Mesfer Al Duhayyim^1,*, Badriyya B. Al-onazi², Mohamed K. Nour³, Ayman Yafoz⁴, Amal S. Mehanna⁵, Ishfaq Yaseen⁶, Amgad Atta Abdelmageed⁶, Gouse Pasha Mohammed⁶

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2755-2772, 2023, DOI: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… More >

Hala J. Alshahrani¹, Abdulkhaleq Q. A. Hassan², Khaled Tarmissi³, Amal S. Mehanna⁴, Abdelwahed Motwakel^5,*, Ishfaq Yaseen⁵, Amgad Atta Abdelmageed⁵, Mohamed I. Eldesouki⁶

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4255-4272, 2023, DOI:10.32604/cmc.2023.034821

Abstract Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunter-prey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform… More >