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

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

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Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

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Computers, Materials & Continua 2023年第8期76卷 1537-1554页

作者： Badriyya BAl-onazi Najm Alotaibi Jaber SAlzahrani hussain alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Mahmoud Othman Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi 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 EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or *** the number of labels is limited to one,the task becomes single-label text *** 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 *** recent times,the dominant method for natural language processing(NLP)tasks is recurrent neural network(RNN),in general,long short termmemory(LSTM)and convolutional neural network(CNN).Deep learning(DL)models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection,medical image analysis,and so *** paper introduces aModified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition(MDFO-EMTRR)model *** presentedMDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic *** achieve this,theMDFO-EMTRRtechnique encompasses data pre-processing to transform the input data into compatible ***,the ELM model is utilized for the representation and recognition of the Arabic *** last,the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier *** experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.

关键词： Arabic corpus dragonfly algorithm machine learning text mining extreme learning machine

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Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment Analysis

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Computer Systems Science & Engineering 2023年第10期47卷 1249-1266页

作者： Abdelwahed Motwakel Najm Alotaibi Eatedal Alabdulkreem hussain alshahrani MohamedAhmed Elfaki Mohamed K Nour Radwa Marzouk Mahmoud Othman Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi 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 EgyptNew Cairo11835Egypt

Sentiment Analysis(SA)of natural language text is not only a challenging process but also gains significance in various Natural Language Processing(NLP)*** SA is utilized in various applications,namely,education,to improve the learning and teaching processes,marketing strategies,customer trend predictions,and the stock *** researchers have applied lexicon-related approaches,Machine Learning(ML)techniques and so on to conduct the SA for multiple languages,for instance,English and *** 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 SA for Hindi movie *** current study introduces an Effective Improved Metaheuristics with Deep Learning(DL)-Enabled Sentiment Analysis for Movie Reviews(IMDLSA-MR)*** presented IMDLSA-MR technique initially applies different levels of pre-processing to convert the input data into a compatible ***,the Term Frequency-Inverse Document Frequency(TF-IDF)model is exploited to generate the word vectors from the pre-processed *** Deep Belief Network(DBN)model is utilized to analyse and classify the ***,the improved Jellyfish Search Optimization(IJSO)algorithm is utilized for optimal fine-tuning of the hyperparameters related to the DBN model,which shows the novelty of the *** experimental analyses were conducted to validate the better performance of the proposed IMDLSA-MR *** comparative study outcomes highlighted the enhanced performance of the proposed IMDLSA-MR model over recent DL models with a maximum accuracy of 98.92%.

关键词： Corpus linguistics sentiment analysis natural language processing deep learning word embedding

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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

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Awareness and Behaviour Related to Orthodontic Treatment among School Children in Aseer Region, Kingdom of Saudi Arabia

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Open Journal of Stomatology 2019年第4期9卷 87-94页

作者： M. Zakirulla hussain Almubarak Sahar N. Fageeh Abeer A. Alghothimi Saleh Khalid Alqahtani Faris Mohammed Alqahtani Fahad Thamer alshahrani Department of Pediatric Dentistry and Orthodontic Sciences College of Dentistry King Khalid University Abha KSA Department of the Diagnostic Science and Oral Biology (DDS) College of Dentistry King Khalid University Abha KSA

Background: Awareness of malocclusion and the need to make corrections have increasingly become prevalent among the young population. In the period of adolescence, physical appearance is crucial in the construction of personal identity, including one’s relationship with one’s own body. Orthodontic treatment includes improved oral health and enhanced psychological wellbeing. Early orthodontic treatment for children will be crucial to improving oral health. The present study aims to assess the awareness towards orthodontic treatment among school children’s of Aseer region, Saudi Arabia. Materials and Methods: A total of 835 (450 boys and 385 girls) school children’s, of age group between 10 - 15 years were included in the study. A total of 16 schools in the all Aseer region were surveyed during January to May 2018. A pre-structured self-administered questionnaire consisting of 12 questions with multiple answers were given to the children after the clinical examination to assess their knowledge and attitude towards Orthodontic treatment. Statistical Analysis: The survey data was collected and organized into Microsoft Excel spreadsheets (Microsoft Inc., USA), and was statistically analyzed utilizing the Statistical Package for the Social Sciences version 20.0 software (IBM Inc., USA). The statistical test used here was the chi-square test and P values less than 0.05 were considered to be statistically significant (P Result: A total of 292 (65%) of males and 285 (74%) of females have heard of an orthodontist. 234 (52%) of males said that orthodontist would align their teeth. 203 (45%) and 250 (65%) of males and females respectively are aware that few teeth needed to be removed for aligning irregular teeth. Majority of the children are aware of taking braces treatment at an earlier age would improve facial appearance. 320 (83%) and 227 (59%) of female children aware of that irregular teeth can affect chewing ability and speech respectively. 261 (58%) of males knew that or

关键词： Orthodontic Treatment Malocclusion Children Oral Health Behaviour

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