Reading Loss in Arabic Language During COVID-19 in the UAE and Proposed Solutions:The Perspectives of Primary-Grade Arabic Language Teachers

The COVID-19 pandemic caused significant disruptions in the field of education worldwide,including in the United Arab Emirates.Teachers and students had to adapt to remote learning and virtual classrooms,leading to various challenges in maintaining educational standards.The sudden transition to remote teaching could have a negative impact on students’reading abilities,especially in the Arabic language.To gain insight into the unique challenges encountered by Arabic language teachers in the UAE,a survey was conducted to explore their assessment of teaching quality,student-teacher interaction,and learning outcomes amidst the COVID-19 pandemic.The results of the survey revealed a significant decline of student reading abilities and identified several major issues in online Arabic language teaching.These issues included limited interaction between students and teachers,challenges in monitoring students’class participation and performance,and challenges in effectively assessing students’reading skills.The results also demonstrated some other challenges faced by Arabic language teachers,including a lack of preparedness,a lack of subscription to relevant platforms,and a lack of resources for online learning.Several solutions to these challenges are proposed,including reevaluating the balance between depth and breadth in the curriculum,integrating language skills into the curriculum more effectively,providing more comprehensive teacher professional development,implementing student grouping strategies,utilizing retired and expert teachers in specific content areas,allocating time for interventions,and improving support from both teachers and parents to ensure the quality of online learning.

Heritage of Arabic Language in South Caucasus： Arabic Inscriptions From Trialeti

In this article the author would like to discuss the usage of Arabic language in South Georgia as language of religious and ethnical minority in circumstances of Russian rule. The best examples of this are Arabic inscriptions on gravestones. In this article the author describes several inscriptions from Trialeri, South Georgia, which, because of unknown reasons, have never been studied by scientists before. Another important reason why the author has paid importance to those inscriptions is their recent condition and danger to lose them forever. The author has studied inscriptions from three main cemeteries： Beshtasheni, Tikma-Dash, and Minayasar. As a result of his research the author can say that Arabic language have been used by Muslims of Turk origins in Trialeti till Soviet time. They were using this language to preserve their identity, but it used to work only about a century. Soviet rule tried to destroy all identities and religions and especially after WWII they have reached more or less success. Today the Muslim population of Trialtei does not understand Arabic, they just know that their religion is Islam and in the graves with Arabic inscriptions lay their predecessors.

The Effect of Using L1 （Arabic Language） in the L2 （English Language） Classroom on the Achievement in General English of Foundation Year Students in King Abdulaziz University

This study aims at investigating the effect of using L 1 （Arabic Language） while teaching a target language （English Language） on the achievement in General English of foundation year students in King Abdulaziz University. To achieve the purpose of the study, the researcher used an experimental design： an experimental group and a control group. The independent variable is using L1 while teaching English in very limited and specified areas. The dependent variable is students＇ achievement in general English. The statistics used is the t-test. The population of the study was all students enrolled in the foundation year 1431/1432 in the ELI （English Language Institute） at King Abdulaziz University. The sample of the study consisted of 50 students taking North Star in the sections A and B as a university requirement in the foundation year 1431/1432 in King Abdulaziz University. The results of the study were in favor of banning Arabic in the English language classroom as shown in the mean scores of the control and experimental groups in the tables. It is recommended that teachers and instructors should be trained to use teaching strategies that help them use English only in the English language classroom.

Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters RecognitionModel

Handwritten character recognition becomes one of the challenging research matters.More studies were presented for recognizing letters of various languages.The availability of Arabic handwritten characters databases was confined.Almost a quarter of a billion people worldwide write and speak Arabic.More historical books and files indicate a vital data set for many Arab nationswritten in Arabic.Recently,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)technique.Themajor intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language.It initially pre-processes the handwritten images to improve their quality of them.Then,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors.Next,the deep echo state network(DESN)model is utilized to classify handwritten characters.Finally,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance.Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique.The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

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

In recent years,the usage of social networking sites has considerably increased in the Arab world.It has empowered individuals to express their opinions,especially in politics.Furthermore,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different scales.This is attributed to business owners’understanding of social media’s importance for business development.However,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 nouns.Hate speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil societies.In 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 language.The presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and normal.To attain this,the CEHOML-HSD model follows different sub-processes as discussed herewith.At the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF vectorizer.Secondly,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic language.Lastly,the CEHO approach is employed for fine-tuning the parameters involved in SVM.This CEHO approach is developed by combining the chaotic functions with the classical EHO algorithm.The 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 approach.The comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.

A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning

This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken Arabic.The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models.The image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning technique.The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of ATSL.It offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute community.This work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.

The Influence of the First Language （Arabic） on Learning English as a Second Language in Jordanian Schools, and Its Relation to Educational Policy： Structural Errors

This study was conducted to investigate the influence of the L 1 （first language） （Arabic） on learning ESL （English as a second language） in Jordanian schools, and its relation to education policy. The sample of the study consisted of 266 high school graduates Jordanian students in the academic year 2013-2014. A translation test consisted of 24 items and divided to eight areas was constructed to arrive at the objectives of the study. The study concluded that the percentage of total errors committed by the study sample in all areas exceeds percentage of correct answers （wrong answers is 52.48＇%, correct answers is 47.52%）, and the students committed more transfer errors in the types of verb to be, addition to, andpassive voice than other types of errors as a result of the effect of the L1.

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

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 classes.But, many features usually create difficulties due to high dimensionality issues.Against 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 objective.The 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.

Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

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

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Arabic is one of the most spoken languages across the globe.However,there are fewer studies concerning Sentiment Analysis(SA)in Arabic.In recent years,the detected sentiments and emotions expressed in tweets have received significant interest.The 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 language.Two common models are available:Machine Learning and lexicon-based approaches to address emotion classification problems.With 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 language.The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets.To attain this,the proposed TLBOMLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding process.In addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in Arabic tweets.To improve the efficacy of the DAE model,the Teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the parameters.The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset.The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.

Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent days.ABSA 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 text.Most of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as lexicons.In 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 tools.These sources were used for processing and analyzing the Arabic language content like lexicons.However,an important challenge in this domain is the unavailability of sufficient and reliable resources.In this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)technique.The aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic language.In the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful format.Besides,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of sentiments.Furthermore,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM model.This scenario establishes the novelty of current study.The performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

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

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-AAmodelmajorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic corpus.Firstly,the MVODRL-AA model follows data pre-processing and word embedding.Next,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 corpus.At 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 model.The simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.

Predicting Violence-Induced Stress in an Arabic Social Media Forum

Social Media such as Facebook plays a substantial role in virtual com-munities by sharing ideas and ideologies among different populations over time.Social interaction analysis aids in defining people’s emotions and aids in assessing public attitudes,towards different issues such as violence against women and chil-dren.In this paper,we proposed an Arabic language prediction model to identify the issue of Violence-Induced Stress in social media.We searched for Arabic posts of many countries through Facebook application programming interface(API).We discovered that the stress state of a battered woman is usually related to her friend’s stress states on Facebook.We applied a large real database from Facebook platforms to analytically investigate the correlation of violence-induced stress states and the victim interactions on social media.We extracted a set of tex-tual,spatial,and interaction attributes from various features.Therefore,we are proposing a hybrid model–an interaction graph model incorporated in a deep learning neural model to leverage post content and interaction data for vio-lence-induced stress detection.Experiments depict that our proposed hybrid mod-el can enhance the prediction performance by 10%in F1-measure.Also,considering the user interaction information can learn an interesting phenomenon,where,the sparse social interactions of violence-induced stress stressed victims is higher by around 15%percent non-battered users,signifying that the structure of the friends of such victims is less connected than non-stressed users.

Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.

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

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 supervisors.The data indicate that respondents considered English instruction at the elementary level essential for expanding kids’perspectives,improving academic performance,and promoting international involvement.The main advantages cited are the development of English language skills and the promotion of early education.Although not as easily noticeable,the disadvantages include potential negative impacts on an individual’s proficiency in Arabic and their sense of national identification.The highlighted challenges encompass insufficient teacher training,student reluctance towards English,limited resources,and school disparities.The proposed techniques focused on prioritizing English instructors’training,ensuring the use of appropriate content,utilizing technology,and promoting awareness of students and educators.The current research found different obstacles in teaching English at elementary stages.To 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 evaluations.Further research can also contribute to a better understanding of how early English learning impacts on Arabic identity and proficiency.

Embedding Extraction for Arabic Text Using the AraBERT Model

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 task.In 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 systems.We used several vectorization schemes to represent the Arabic texts in the SemEval2017-task3-subtask-D dataset.The used schemes include lexical-based similarity features,frequency-based features,and pre-trained model-based features.Also,we used contextual-based embedding models such as Arabic Bidirectional Encoder Representations from Transformers(AraBERT).We used the AraBERT model in two different variants.First,as a feature extractor in addition to the text vectorization schemes’features.We fed those features to various regression models to make a prediction value that represents the relevancy score between Arabic text units.Second,AraBERT is adopted as a pre-trained model,and its parameters are fine-tuned to estimate the relevancy scores between Arabic textual sentences.To evaluate the research results,we conducted several experiments to compare the use of the AraBERT model in its two variants.In 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.

Consensus-Based Ensemble Model for Arabic Cyberbullying Detection

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 mak-ing.The technological advances in smartphones and communication have enabled young people to keep in touch and form huge social networks from all over the world.However,such networks expose young people to cyberbullying and offen-sive content that puts their safety and emotional well-being at serious risk.Although,many solutions have been proposed to automatically detect cyberbully-ing,most of the existing solutions have been designed for English speaking con-sumers.The morphologically rich languages-such as the Arabic language-lead to data sparsity problems.Thus,render solutions developed for another language are ineffective once applied to the Arabic language content.To 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 platforms.The 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 classifiers.Then,the Sigmoid function,which can reproduce human-like decision making,is used to infer thefinal decision.The outcomes show the efficacy of the proposed model comparing to the other studied classifiers.The overall improvement gained by the proposed model reaches 1.3%comparing with the best trained classifier.Besides its effectiveness for Arabic language content,the proposed model can be generalized to improve cyberbullying detection in other languages.

The Application of Spanning Tree Algorithm in Arabic Language Rules for Contextual Analysis

In this paper we deal with the application of spanning tree algorithm in Arabic language rules for contextual analysis. We define a special multiplication of a matrix and a vector, and construct a mathematical model of spelling rules for the different types of characters,Inseparates,Separates and Diacritics. We design the output algorithm in contextual analysis by the spanning tree structure.