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Chaotic Elephant Herd Optimization with Machine Learning for arabic Hate Speech Detection

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Intelligent Automation & Soft Computing 2024年第3期39卷 567-583页

作者： Badriyya B.Al-onazi Jaber S.Alzahrani Najm Alotaibi Hussain Alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Heba Mohsen Abdelwahed Motwakel Department of language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies Riyadh12735Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqra11911Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New CairoNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipAl-Kharj16278Saudi Arabia

In recent years,the usage of social networking sites has considerably increased in the Arab *** has empowered individuals to express their opinions,especially in ***,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different *** is attributed to business owners’understanding of social media’s importance for business ***,the arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and *** speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil *** this background,the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection(CEHOML-HSD)model in the context of the arabic *** presented CEHOML-HSD model majorly concentrates on identifying and categorising the arabic text into hate speech and *** attain this,the CEHOML-HSD model follows different sub-processes as discussed *** the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF ***,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the arabic ***,the CEHO approach is employed for fine-tuning the parameters involved in *** CEHO approach is developed by combining the chaotic functions with the classical EHO *** design of the CEHO algorithm for parameter tuning shows the novelty of the work.A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD *** comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.

关键词： arabic language machine learning elephant herd optimization TF-IDF vectorizer hate speech detection

An Assessment of English Instruction for Elementary-Level Students in Jeddah Governorate, as Perceived by English Supervisors and Educators

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Sino-US English Teaching 2024年第2期21卷 55-72页

作者： Issa AlQurashi Taif University TaifSaudi Arabia

This study investigated the perceptions of English educators and supervisors in Jeddah Governorate regarding the process of teaching English to elementary students.A survey was conducted using a sample size of 94 educators and 10 *** data indicate that respondents considered English instruction at the elementary level essential for expanding kids’perspectives,improving academic performance,and promoting international *** main advantages cited are the development of English language skills and the promotion of early *** not as easily noticeable,the disadvantages include potential negative impacts on an individual’s proficiency in arabic and their sense of national *** highlighted challenges encompass insufficient teacher training,student reluctance towards English,limited resources,and school *** proposed techniques focused on prioritizing English instructors’training,ensuring the use of appropriate content,utilizing technology,and promoting awareness of students and *** current research found different obstacles in teaching English at elementary *** overcome these obstacles,it will be essential to enhance teacher competencies,develop efficient teaching methods,get the backing of stakeholders,assign adequate resources,and carry out continuous *** research can also contribute to a better understanding of how early English learning impacts on arabic identity and proficiency.

关键词： English language instruction elementary education teacher perceptions Saudi Arabia difficulties advantages arabic language national identity

Embedding Extraction for arabic Text Using the AraBERT Model

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Computers, Materials & Continua 2022年第7期72卷 1967-1994页

作者： Amira Hamed Abo-Elghit Taher Hamza Aya Al-Zoghby Faculty of Computers and Information Department of Computer SciencesMansoura UniversityMansoura35516Egypt Faculty of Computers and Artificial Intelligence Department of Computer SciencesDamietta UniversityDamietta34517Egypt

Nowadays,we can use the multi-task learning approach to train a machine-learning algorithm to learn multiple related tasks instead of training it to solve a single *** this work,we propose an algorithm for estimating textual similarity scores and then use these scores in multiple tasks such as text ranking,essay grading,and question answering *** used several vectorization schemes to represent the arabic texts in the SemEval2017-task3-subtask-D *** used schemes include lexical-based similarity features,frequency-based features,and pre-trained model-based ***,we used contextual-based embedding models such as arabic Bidirectional Encoder Representations from Transformers(AraBERT).We used the AraBERT model in two different ***,as a feature extractor in addition to the text vectorization schemes’*** fed those features to various regression models to make a prediction value that represents the relevancy score between arabic text ***,AraBERT is adopted as a pre-trained model,and its parameters are fine-tuned to estimate the relevancy scores between arabic textual *** evaluate the research results,we conducted several experiments to compare the use of the AraBERT model in its two *** terms of Mean Absolute Percentage Error(MAPE),the results showminor variance between AraBERT v0.2 as a feature extractor(21.7723)and the fine-tuned AraBERT v2(21.8211).On the other hand,AraBERT v0.2-Large as a feature extractor outperforms the finetuned AraBERT v2 model on the used data set in terms of the coefficient of determination(R2)values(0.014050,−0.032861),respectively.

关键词： Semantic textual similarity arabic language embeddings AraBERT pre-trained models regression contextual-based models concurrency concept

Automated arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

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Computers, Materials & Continua 2023年第3期74卷 5447-5465页

作者： Badriyya B.Al-onazi Saud S.Alotaib Saeed Masoud Alshahrani Najm Alotaibi Mrim M.Alnfiai Ahmed S.Salama Manar Ahmed Hamza Department of language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The text classification process has been extensively investigated in various languages,especially *** classification models are vital in several Natural language Processing(NLP)*** arabic language has a lot of *** instance,it is the fourth mostly-used language on the internet and the sixth official language of ***,there are few studies on the text classification process in arabic.A few text classification studies have been published earlier in the arabic *** general,researchers face two challenges in the arabic text classification process:low accuracy and high dimensionality of the *** this study,an Automated arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning(AATC-HTHDL)model is *** major goal of the proposed AATC-HTHDL method is to identify different class labels for the arabic *** first step in the proposed model is to pre-process the input data to transform it into a useful *** Term Frequency-Inverse Document Frequency(TF-IDF)model is applied to extract the feature ***,the Convolutional Neural Network with Recurrent Neural Network(CRNN)model is utilized to classify the arabic *** the final stage,the Crow Search Algorithm(CSA)is applied to fine-tune the CRNN model’s hyperparameters,showing the work’s *** proposed AATCHTHDL model was experimentally validated under different parameters and the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.

关键词： Hybrid deep learning natural language processing arabic language text classification parameter tuning

Consensus-Based Ensemble Model for arabic Cyberbullying Detection

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Computer Systems Science & Engineering 2022年第4期41卷 241-254页

作者： Asma A.Alhashmi Abdulbasit A.Darem Northern Border University Arar9280Saudi Arabia

Due to the proliferation of internet-enabled smartphones,many people,particularly young people in arabic society,have widely adopted social media platforms as a primary means of communication,interaction and friendship *** technological advances in smartphones and communication have enabled young people to keep in touch and form huge social networks from all over the ***,such networks expose young people to cyberbullying and offen-sive content that puts their safety and emotional well-being at serious ***,many solutions have been proposed to automatically detect cyberbully-ing,most of the existing solutions have been designed for English speaking *** morphologically rich languages-such as the arabic language-lead to data sparsity ***,render solutions developed for another language are ineffective once applied to the arabic language *** this end,this study focuses on improving the efﬁcacy of the existing cyberbullying detection models for arabic content by designing and developing a Consensus-based Ensemble Cyberbullying Detection Model.A diverse set of heterogeneous classiﬁers from the traditional machine and deep learning technique have been trained using arabic cyberbullying labeled dataset collected fromﬁve different *** outputs of the selected classiﬁers are combined using consensus-based decision-making in which the F1-Score of each classiﬁer was used to rank the classiﬁ***,the Sigmoid function,which can reproduce human-like decision making,is used to infer theﬁnal *** outcomes show the efﬁcacy of the proposed model comparing to the other studied classiﬁ*** overall improvement gained by the proposed model reaches 1.3%comparing with the best trained classiﬁ*** its effectiveness for arabic language content,the proposed model can be generalized to improve cyberbullying detection in other languages.

关键词： Consensus cyberbullying detection arabic language offensive contents ensemble learning deep learning

Sailfish Optimizer with Deep Transfer Learning-Enabled arabic Handwriting Character Recognition

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Computers, Materials & Continua 2023年第3期74卷 5467-5482页

作者： Mohammed Maray Badriyya B.Al-onazi Jaber S.Alzahrani Saeed Masoud Alshahrani Najm Alotaibi Sana Alazwari Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recognition of the arabic characters is a crucial task incomputer vision and Natural language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character ***, many features usually create difficulties due to high dimensionality *** this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this *** Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches.

关键词： arabic language handwritten character recognition deep learning feature extraction hyperparameter tuning

Battle Royale Optimization with Fuzzy Deep Learning for arabic Sentiment Classification

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Computer Systems Science & Engineering 2023年第8期46卷 2619-2635页

作者： Manar Ahmed Hamza Hala J.Alshahrani Jaber S.Alzahrani Heba Mohsen Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Aspect-Based Sentiment Analysis(ABSA)on arabic corpus has become an active research topic in recent *** refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided *** of the prevailing arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as *** literature,concerning the arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and *** sources were used for processing and analyzing the arabic language content like ***,an important challenge in this domain is the unavailability of sufficient and reliable *** this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for arabic Aspect Based Sentiment Classification(BROFDL-AASC)*** aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the arabic *** the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful ***,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of ***,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM *** scenario establishes the novelty of current *** performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

关键词： arabic corpus aspect based sentiment analysis arabic language deep learning battle royale optimization natural language processing

Parameter Tuned Machine Learning Based Emotion Recognition on arabic Twitter Data

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Computer Systems Science & Engineering 2023年第9期46卷 3423-3438页

作者： Ibrahim M.Alwayle Badriyya B.Al-onazi Jaber S.Alzahrani Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Mahmoud Othman Abdelwahed Motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran55461Saudi Arabia Department of language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityNajran24211Saudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

arabic is one of the most spoken languages across the ***,there are fewer studies concerning Sentiment Analysis(SA)in *** recent years,the detected sentiments and emotions expressed in tweets have received significant *** substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the arabic *** common models are available:Machine Learning and lexicon-based approaches to address emotion classification *** this motivation,the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification(TLBOML-ERC)model for Sentiment Analysis on tweets made in the arabic *** presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in arabic *** attain this,the proposed TLBOMLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding *** addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in arabic *** improve the efficacy of the DAE model,the Teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the *** proposed TLBOML-ERC method was experimentally validated with the help of an arabic tweets *** obtained results show the promising performance of the proposed TLBOML-ERC model on arabic emotion classification.

关键词： arabic language Twitter data machine learning teaching and learning-based optimization sentiment analysis emotion classification

Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on arabic Corpus

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Computer Systems Science & Engineering 2023年第12期47卷 3049-3065页

作者： Mesfer Al Duhayyim Badriyya B.Al-onazi Jaber S.Alzahrani Hussain Alshahrani Mohamed Ahmed Elfaki Abdullah Mohamed Ishfaq Yaseen Gouse Pasha Mohammed Mohammed Rizwanullah Abu Sarwar Zamani Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAflaj16273Saudi Arabia Department of language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sentiment analysis(SA)of the arabic language becomes important despite scarce annotated corpora and confined *** affect Analysis has become an active research zone *** still,the arabic language lags behind adequate language sources for enabling the SA ***,arabic still faces challenges in natural language processing(NLP)tasks because of its structure complexities,history,and distinct *** has gained lesser effort than the other *** paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis(MVODRL-AA)on arabic *** presented MVODRL-AAmodelmajorly concentrates on identifying and classifying effects or emotions that occurred in the arabic ***,the MVODRL-AA model follows data pre-processing and word ***,an n-gram model is utilized to generate word embeddings.A deep Q-learning network(DQLN)model is then exploited to identify and classify the effect on the arabic *** last,the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model,showing the novelty of the work.A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA *** simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.

关键词： arabic language arabic corpus natural language processing affect analysis deep learning