Arwa Saif Fadel^1,2,*, Osama Ahmed Abulnaja¹, Mostafa Elsayed Saleh¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4419-4444, 2023, DOI:10.32604/cmc.2023.037112

Abstract Aspect-based sentiment analysis (ABSA) is a fine-grained process. Its fundamental subtasks are aspect term extraction (ATE) and aspect polarity classification (APC), and these subtasks are dependent and closely related. However, most existing works on Arabic ABSA content separately address them, assume that aspect terms are preidentified, or use a pipeline model. Pipeline solutions design different models for each task, and the output from the ATE model is used as the input to the APC model, which may result in error propagation among different steps because APC is affected by ATE error. These methods are impractical for real-world scenarios where the… More >

Arfat Ahmad Khan¹, Rashid Jahangir^2,*, Roobaea Alroobaea³, Saleh Yahya Alyahyan⁴, Ahmed H. Almulhi³, Majed Alsafyani³, Chitapong Wechtaisong⁵

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4085-4100, 2023, DOI:10.32604/cmc.2023.036797

Abstract Automatic Speaker Identification (ASI) involves the process of distinguishing an audio stream associated with numerous speakers’ utterances. Some common aspects, such as the framework difference, overlapping of different sound events, and the presence of various sound sources during recording, make the ASI task much more complicated and complex. This research proposes a deep learning model to improve the accuracy of the ASI system and reduce the model training time under limited computation resources. In this research, the performance of the transformer model is investigated. Seven audio features, chromagram, Mel-spectrogram, tonnetz, Mel-Frequency Cepstral Coefficients (MFCCs), delta MFCCs, delta-delta MFCCs and spectral… More >

Huma Israr^1,*, Safdar Abbas Khan¹, Muhammad Ali Tahir¹, Muhammad Khuram Shahzad¹, Muneer Ahmad¹, Jasni Mohamad Zain^2,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2981-3009, 2023, DOI:10.32604/cmc.2023.035814

Abstract In bilingual translation, attention-based Neural Machine Translation (NMT) models are used to achieve synchrony between input and output sequences and the notion of alignment. NMT model has obtained state-of-the-art performance for several language pairs. However, there has been little work exploring useful architectures for Urdu-to-English machine translation. We conducted extensive Urdu-to-English translation experiments using Long short-term memory (LSTM)/Bidirectional recurrent neural networks (Bi-RNN)/Statistical recurrent unit (SRU)/Gated recurrent unit (GRU)/Convolutional neural network (CNN) and Transformer. Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively, with a scalable data set, make precise predictions on unseen data. The trained models yielded… More >

Badriyya B. Al-onazi¹, Mohamed K. Nour², Hussain Alshahran³, Mohamed Ahmed Elfaki³, Mrim M. Alnfiai⁴, Radwa Marzouk⁵, Mahmoud Othman⁶, Mahir M. Sharif⁷, Abdelwahed Motwakel^8,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3413-3429, 2023, DOI:10.32604/cmc.2023.035303

Abstract Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities. Several models have been available in the literature for sign language detection and classification for enhanced outcomes. But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks. This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning (ASLGC-DHOML) model. The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures. The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected… 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 >

Badriyya B. Al-onazi¹, Abdulkhaleq Q. A. Hassan², Mohamed K. Nour³, Mesfer Al Duhayyim^4,*, Abdullah Mohamed⁵, Amgad Atta Abdelmageed⁶, Ishfaq Yaseen⁶, Gouse Pasha Mohammed⁶

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2575-2591, 2023, DOI:10.32604/cmc.2023.033531

Abstract Sentiment Analysis (SA), a Machine Learning (ML) technique, is often applied in the literature. The SA technique is specifically applied to the data collected from social media sites. The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process. In this background, the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets (QPSODL-SAAT). The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic. Initially, the data pre-processing is performed to convert the raw tweets into a… More >

Badriyya B. Al-onazi¹, Mohamed K. Nour², Hassan Alshamrani³, Mesfer Al Duhayyim^4,*, Heba Mohsen⁵, Amgad Atta Abdelmageed⁶, Gouse Pasha Mohammed⁶, Abu Sarwar Zamani⁶

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2529-2544, 2023, DOI:10.32604/iasc.2023.034623

Abstract Gender analysis of Twitter could reveal significant socio-cultural differences between female and male users. Efforts had been made to analyze and automatically infer gender formerly for more commonly spoken languages’ content, but, as we now know that limited work is being undertaken for Arabic. Most of the research works are done mainly for English and least amount of effort for non-English language. The study for Arabic demographic inference like gender is relatively uncommon for social networking users, especially for Twitter. Therefore, this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter (OMSDAE-GIAT) model.… 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 >

Bimal Kumar Ghimire¹, Chang Yeon Yu², Seung Hyun Kim¹, Ill Min Chung^1,*

Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1439-1467, 2023, DOI:10.32604/phyton.2023.023076

Abstract Miscanthus, is a promising bioenergy crop, considered superior to other bioenergy crops because of its higher water and nutrient use efficiency, cold tolerance, and higher production of biomass. Broadleaf weeds and grass weeds, cause major problems in the Miscanthus field. A field experiment was conducted in 2018 and 2019, to assess the effects of pre-emergence (alachlor and napropamide) and post-emergence herbicides (nicosulfuron, dicamba, bentazon, and glufosinate ammonium) on broadleaf and grass weeds in M. sinensis and M. sacchariflorus fields. The weed control efficiency and phytotoxicity of pre- and post-emergence herbicides were evaluated at 30 days after treatment (DAT) and compared… More >