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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.als.ahrani 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

Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

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Intelligent Automation & Soft Computing 2023年第6期36卷 3139-3155页

作者： hala j.als.ahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah22230Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based *** stay in the leading positions for business as well as personal *** popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email *** filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the *** prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully ***,this study provides a solution related to email message body text automatic classification into phishing and email *** paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on Applied Linguistics for Email *** presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam *** the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further ***,the SRRNN method can be useful in recognizing and classifying spam *** hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter *** investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

关键词： Email classification applied linguistics improved fruitfly optimization deep learning recurrent neural network

Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

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Intelligent Automation & Soft Computing 2023年第6期36卷 3203-3219页

作者： hala j.als.ahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related *** exponential generation of text data on the Internet must be leveraged to gain knowledgeable *** extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and *** Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the *** current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)*** presented ALTS-MLODL technique aims to summarize the text documents in the English *** accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of ***,the MLO algorithm is used for the effectual selection of the extracted *** the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter *** exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization *** validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were *** experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.

关键词： Text summarization deep learning hyperparameter tuning applied linguistics multi-leader optimizer

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

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Computers, Materials & Continua 2023年第6期75卷 4767-4783页

作者： Abdelwahed Motwakel hala j.als.ahrani Abdulkhaleq Q.A.Hassan Khaled Tarmissi Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari 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 English College of Science and Arts at MahayilKing Khalid UniversityMuhayil63763Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life *** new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the ***,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable ***,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus *** has become one of the popular social media platforms and allows anyone to post *** study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 *** SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 *** SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish ***,the BGRU model is utilized to recognise and classify sen-timents present in the ***,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification *** experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.

关键词： Applied linguistics deep learning sentiment analysis COVID-19 pandemic sine cosine optimization twitter

Study of Utilization of Embedded Metal Nanoparticles in Dielectric Thin Film for Humidity Sensing

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Photonic Sensors 2020年第2期10卷 155-161页

作者： hala j.EL-KHOZONDAR Waleed S.MOHAMMED Islamic University of Gaza Electrical Engineering DepartmentGaza-108Palestine Bangkok University BU-CROCCSSchool Of EngineeringPathumthani 12120Thailand

This paper presents a theoretical study of the utilization of the shift in the reflection peak of the thin dielectric film with embedded metal nanoparticles(NPs)towards humidity and vapor *** presence of the NPs in the film results in a complex effective ***,the reflected light at the superstrate-film interface causes a phase shift when the index of the surrounding is *** alters the reflected spectrum of the formed Fabry-Perot,for both the reflection peak wavelength and ***,the dynamic range of the proposed sensor is optimized through the variation of the film thickness and nanoparticle metal type,as well as the volume fraction.

关键词： Nanoparticles polymer film humidity sensor

Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

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Computers, Materials & Continua 2023年第5期75卷 4137-4154页

作者： Manar Ahmed Hamza hala j.als.ahrani Abdulkhaleq Q.A.Hassan Abdulbaset Gaddah Nasser Allheeib Suleiman Ali alsaif Badriyya B.Al-onazi Heba Mohsen 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 English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Computer Deanship of Preparatory Year and Supporting StudiesImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic *** number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a *** and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion *** the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web ***,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social *** presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social *** attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature *** irony detection and classification,the DBN model is exploited in this *** last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)*** experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark *** simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.

关键词： Computational linguistics natural language processing deep learning irony detection social media

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

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Computers, Materials & Continua 2023年第5期75卷 4255-4272页

作者： hala j.als.ahrani Abdulkhaleq Q.A.Hassan Khaled Tarmissi Amal S.Mehanna Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityMuhayil63763Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab *** false information is harmful to society and *** and detecting the spread of fake news in Arabic becomes *** artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake ***,fake news in Arabic is identified by utilizing AI *** article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic *** HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful ***,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and ***,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN *** performance validation of the HPOHDL-FND technique is tested using two Arabic *** outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

关键词： Arabic corpus fake news detection deep learning hunter prey optimizer classification model

Modified Sine Cosine Optimization with Adaptive Deep Belief Network for Movie Review Classification

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Intelligent Automation & Soft Computing 2023年第7期37卷 283-300页

作者： hala j.als.ahrani Abdulbaset Gaddah Ehab S.Alnuzaili Mesfer Al Duhayyim Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of English College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer63311Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi 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

Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in *** linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural *** is classified as a negative or positive assessment articulated through *** can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been *** learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise *** this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)*** major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review ***,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding *** the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this *** last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different *** comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.

关键词： Computational linguistics movie review analysis sentiment analysis sentiment classification deep learning

Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

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Computer Systems Science & Engineering 2023年第9期46卷 3303-3319页

作者： Manar Ahmed Hamza hala j.als.ahrani Khaled Tarmissi Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki 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 Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘corpus’refers to a huge volume of structured datasets containing machine-readable *** texts are generated in a natural communicative *** explosion of social media permitted individuals to spread data with minimal examination and filters *** to this,the old problem of fake news has *** has become an important concern due to its negative impact on the *** manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)*** perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite ***,a huge effort is required from the human side to generate the labelled training *** recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news *** unlock social media data,an automatic text classifier is highly helpful in the domain of *** current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)*** presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual *** attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding ***,the QCLSTM model is utilized for *** boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the *** proposed QCLSTM-FNC approach was experimentally validated against a benchmark *** QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.

关键词： English corpus fake news detection social media natural language processing artificial intelligence deep learning