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.

Enhancement of the Antigenotoxic and Antioxidant Actions of Eugenol from Spice Clove and the Stabilizer Gum Arabic on Colorectal Carcinogenesis

Spices are defined as any aromatic condiment of plant origin used to alter the flavor and aroma of foods. Besides flavor and aroma, many spices have antioxidant activity, mainly related to the presence in cloves of phenolic compounds, such as flavonoids, terpenoids and eugenol. In turn, the most common uses of gum arabic are in the form of powder for addition to soft drink syrups, cuisine and baked goods, specifically to stabilize the texture of products, increase the viscosity of liquids and promote the leavening of baked products (e.g., cakes). Both eugenol, extracted from cloves, and gum arabic, extracted from the hardened sap of two species of the Acacia tree, are dietary constituents routinely consumed virtually throughout the world. Both of them are also widely used medicinally to inhibit oxidative stress and genotoxicity. The prevention arm of the study included groups: Ia, IIa, IIIa, Iva, V, VI, VII, VIII. Once a week for 20 weeks, the controls received saline s.c. while the experimental groups received DMH at 20 mg/kg s.c. During the same period and for an additional 9 weeks, the animals received either water, 10% GA, EUG, or 10% GA + EUG by gavage. The treatment arm of the study included groups Ib, IIb, IIIb e IVb, IX, X, XI, XII). Once a week for 20 weeks, the controls received saline s.c. while the experimental groups received DMH at 20 mg/kg s.c. During the subsequent 9 weeks, the animals received either water, 10% GA, EUG or 10% GA + EUG by gavage. The novelty of this study is the investigation of their use alone and together for the prevention and treatment of experimental colorectal carcinogenesis induced by dimethylhydrazine. Our results show that the combined use of 10% gum arabic and eugenol was effective, with antioxidant action in the colon, as well as reducing oxidative stress in all colon segments and preventing and treating genotoxicity in all colon segments. Furthermore, their joint administration reduced the number of aberrant crypts and the number of aberrant crypt foci (ACF) in the distal segment and entire colon, as well as the number of ACF with at least 5 crypts in the entire colon. Thus, our results also demonstrate the synergistic effects of 10% gum arabic together with eugenol (from cloves), with antioxidant, antigenotoxic and anticarcinogenic actions (prevention and treatment) at the doses and durations studied, in the colon of rats submitted to colorectal carcinogenesis induced by dimethylhydrazine.

Effect of Gum Arabic from Acacia senegal var. kerensis as an Improver on the Rheological Properties of Wheat Flour Dough

Dough improvers are substances with functional characteristics used in baking industry to enhance dough properties. Currently, the baking industry is faced with increasing demand for natural ingredients owing to increasing consumer awareness, thus contributing to the rising demand for natural hydrocolloids. Gum Arabic from Acacia senegal var. kerensis is a natural gum exhibiting excellent water binding and emulsification capacity. However, very little is reported on how it affects the rheological properties of wheat dough. The aim of this study was therefore, to determine the rheological properties of wheat dough with partial additions of gum Arabic as an improver. Six treatments were analyzed comprising of: flour-gum blends prepared by adding gum Arabic to wheat flour at different levels (1%, 2% and 3%), plain wheat flour (negative control), commercial bread flour and commercial chapati flour (positive controls). The rheological properties were determined using Brabender Farinograph, Brabender Extensograph and Brabender Viscograph. Results showed that addition of gum Arabic significantly (p chapati. These findings support the need to utilize gum Arabic from Acacia senegal var. kerensis as a dough improver.

The Enforcement of Occupational Safety and Health Requirements in Public and Private Sectors in the Emirate of Abu Dhabi, the United Arab Emirates

Research Problem: In Abu Dhabi, limited implementation of OSH Regulations contributes to the general unawareness among employees and workers about occupational hazards and safety measures, resulting in slow responsiveness toward enforcement measures and a lack of self-regulatory approaches within companies. Purpose: The purpose of this study is to examine the implementation methods practised in Abu Dhabi with those in developed countries with established OSH regulatory bodies. Methodology: Qualitative and quantitative research methods were employed to gather primary research data. Workers from various industries in Abu Dhabi were sampled on purpose and asked to respond to questionnaires and interviews on OSH protocol awareness and implementation, and circumstances of workplace incidence. Results: The findings of this study showed that the enforcement of OSH requirements in UAE positively correlated to a reduction in the rate of work-related injury and improved business performance. The quantitative research data showed that the energy sector had the highest score (15) while the tourism sector had the lowest score (5.3) in occupational health systems and improvements in business efficiency and productivity. Implications: The outcomes of this study shed light on the importance of implementing OSH Guidelines for companies to empower their safety managers to fully enforce OSH requirements in their organisations. In conclusion, effective OSH enforcement requires cooperation between general workers and OSH managers and facilitation from business owners.

Arabic Optical Character Recognition:A Review

This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingly.The study describes the characteristics of the Arabic language,different types of OCR systems,different stages of the Arabic OCR system,the researcher’s contributions in each step,and the evaluationmetrics for OCR.The study reviews the existing datasets for the Arabic OCR and their characteristics.Additionally,this study implemented some preprocessing and segmentation stages of Arabic OCR.The study compares the performance of the existing methods in terms of recognition accuracy.In addition to researchers’OCRmethods,commercial and open-source systems are used in the comparison.The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters.Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment,and few approaches were tested on pagelevel scripts.Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low,under 75%for printed text,and further improvement is needed.Moreover,most of the current approaches are offline OCR systems,and there is no remarkable contribution to online OCR systems.

Tigris, Euphrates, and Shatt Al-Arab River System: Historic and Modern Attempts to Manage and Restore Iraq’s Lifeline

In Iraq, the principal rivers are the Tigris, Shatt Al-Arab and Euphrates. From their headwater sources in the mountains of eastern Türkiye, these rivers descend through valleys and gorges and flow into the uplands of Syria and northern and central alluvial plain of Iraq. The Euphrates and Tigris Rivers confluence to form the Shatt Al-Arab river at Al-Qurnah which flows into the Persian Gulf. From sources in the Zagros Mountains other tributaries join the Tigris from the east. The Tigris and Euphrates rivers flow in a southeastern direction through the central plain and discharge into the Mesopotamian Marshes, which include permanent marshes, lakes, and riparian habitat. The rivers and their tributaries drain an area of 879,790 km2 which includes almost the entire area of Iraq as well as land in Syria, Türkiye, Kuwait and Iran. The region has historical importance as part of the Fertile Crescent region and where Mesopotamian civilization first emerged. The post war reconstruction efforts in the Yusifiyah township, an important food production region for Baghdad, illustrate the importance of these water resources. In addition, the advent of soil tunnels by Iraqi insurgents within the riverine corridors will make reconstruction of this resource more complex. The primary objectives of this study are to assess lessons learned, manage, and restore the Tigris, Euphrates, and Shatt Al-Arab river system lifeline in Iraq.

Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis(ABSA)is a fine-grained process.Its fundamental subtasks are aspect termextraction(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 ATE task is the base task for APC,and its result impacts the accuracy of APC.Thus,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a singlemodel.This paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text representation.The LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its context.Moreover,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training dataset.This paper examined the effect of data augmentation on the multi-task model for Arabic ABSA.Extensive experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC techniques.Superior results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation word embedding-based method(FastText)on the combined dataset.

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%.

血清HSP47、β1ARAb、sST2与老年原发性高血压伴心肌肥厚的相关性研究

目的 分析血清HSP47、β1ARAb、sST2与老年原发性高血压伴心肌肥厚的相关性。方法 选择该院心内科2020年1月至2022年12月收治的老年原发性高血压伴心肌肥厚患者50例为甲组,老年原发性高血压50例为乙组,并选择同一时期入院体检的健康人群50例为丙组,比较三组血清HSP47、β1ARAb、sST2与老年原发性高血压伴心肌肥厚的相关性。结果 甲组、乙组的尿素、LDL-C、收缩压、舒张压显著高于丙组,HDL-C低于丙组;乙组的尿素、空腹血糖显著高于甲组,HDL-C低于甲组(P<0.05);三组患者的肌酐、TC、TG比较差异不具有统计学意义;甲组、乙组除LVPWTD升高之外,LVEDD、IVSTD、LVEF、LAD均显著降低(P<0.05);三组患者的FS、LVESD比较差异不具有统计学意义(P>0.05);与丙组相比,甲组、乙组的血清HSP47、β1ARAb水平显著增高,s ST2水平显著降低(P<0.05);血清HSP47、β1ARAb、sST2水平与LVEDD、LVPWTD、IVSTD、LVEF之间相关性比较差异为负相关(P<0.01),与LAD呈正相关(P<0.01);血清HSP47、β1ARAb、sST2水平的曲线下面积分别为0.83 (95%CI:0.760-0.912)、0.892(95%CI:0.831-0.955)、0.735(95%CI:0.641-0.828),差异有统计学意(P<0.05)。结论 血清HSP47、β1ARAb、sST2在老年原发性高血压患者的心肌肥厚中具备一定的相关性,该项指标可作为老年原发性高血压患者诊断的相关标准,为患者的临床诊治提供一定的指导,值得推广应用。

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

Nowadays,the usage of socialmedia 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 hunterprey 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 the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

Despite the extensive effort to improve intelligent educational tools for smart learning environments,automatic Arabic essay scoring remains a big research challenge.The nature of the writing style of the Arabic language makes the problem even more complicated.This study designs,implements,and evaluates an automatic Arabic essay scoring system.The proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing tasks.Then,it comprises two main components:the grading engine and the adaptive fusion engine.The grading engine employs string-based and corpus-based similarity algorithms separately.After that,the adaptive fusion engine aims to prepare students’scores to be delivered to different feature selection algorithms,such as Recursive Feature Elimination and Boruta.Then,some machine learning algorithms such as Decision Tree,Random Forest,Adaboost,Lasso,Bagging,and K-Nearest Neighbor are employed to improve the suggested system’s efficiency.The experimental results in the grading engine showed that Extracting DIStributionally similar words using the CO-occurrences similarity measure achieved the best correlation values.Furthermore,in the adaptive fusion engine,the Random Forest algorithm outperforms all other machine learning algorithms using the(80%–20%)splitting method on the original dataset.It achieves 91.30%,94.20%,0.023,0.106,and 0.153 in terms of Pearson’s Correlation Coefficient,Willmot’s Index of Agreement,Mean Square Error,Mean Absolute Error,and Root Mean Square Error metrics,respectively.

Toward a sustainable growth path in Arab economies:an extension of classical growth model

Background/Objectives:Many economies are on the trajectory of alternative growth drivers other than conventional capital and labor.Access to credit facilities is a pertinent indicator of economic growth.In line with the United Nations Sustainable Development Goals(UNSDGs-8)agenda,the national goal for sustainable development for most economies and Arab economies is no exception.Therefore,the current study adopts a traditional growth model by exploring the relationship between gross domestic product(GDP)per capita,credit for private sectors,ratio of exports,real GDP,and per labor force participants for selected Arab economies annually from 2001 to 2020.Research design:This study leverages the Fourier Kwiatkowski–Phillips–Schmidt–Shin(KPSS)unit root test and second-generation panel econometrics as estimation techniques,such as Westerlund and Edgerton panel cointegration test,and the use of two estimators,namely the augmented mean group(AMG)and common correlated error mean group(CCEMG),to obtain robust results.Findings:Empirical findings from Westerlund and Edgerton panel cointegration tests validate the long-run equilibrium relationship among the outlined variables.Further empirical results indicate that the share of exports is negatively significant with economic growth in countries such as Kuwait,Lebanon,Tunisia,and Jordan.Additionally,savings and labor force participation have a positive relationship with economic growth in individual countries such as Algeria and Bahrain.As per the panel,there is no significant relationship between labor force participation and economic growth.This indicates that the skilled labor force enhanced economic growth.Conclusions:These findings come with inherent far-reaching policy suggestions for economies and panels.Further details on country-specific policy actions are presented in the concluding section.

Novel Machine Learning–Based Approach for Arabic Text Classification Using Stylistic and Semantic Features

Text classification is an essential task for many applications related to the Natural Language Processing domain.It can be applied in many fields,such as Information Retrieval,Knowledge Extraction,and Knowledge modeling.Even though the importance of this task,Arabic Text Classification tools still suffer from many problems and remain incapable of responding to the increasing volume of Arabic content that circulates on the web or resides in large databases.This paper introduces a novel machine learning-based approach that exclusively uses hybrid(stylistic and semantic)features.First,we clean the Arabic documents and translate them to English using translation tools.Consequently,the semantic features are automatically extracted from the translated documents using an existing database of English topics.Besides,the model automatically extracts from the textual content a set of stylistic features such as word and character frequencies and punctuation.Therefore,we obtain 3 types of features:semantic,stylistic and hybrid.Using each time,a different type of feature,we performed an in-depth comparison study of nine well-known Machine Learning models to evaluate our approach and used a standard Arabic corpus.The obtained results show that Neural Network outperforms other models and provides good performances using hybrid features(F1-score=0.88%).

Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

The news ticker is a common feature of many different news networks that display headlines and other information.News ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory authorities.In this paper,we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news channel.The primary emphasis of this research is on ticker recognition methods and storage schemes.To that end,the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification method.The proposed learning architecture considers the grouping of homogeneousshaped classes.This incorporates linguistic taxonomy in a unified manner to address the imbalance in data distribution which leads to individual biases.Furthermore,experiments with a novel ArabicNews Ticker(Al-ENT)dataset that provides accurate character-level and character components-level labeling to evaluate the effectiveness of the suggested approach.The proposed method attains 96.5%,outperforming the current state-of-the-art technique by 8.5%.The study reveals that our strategy improves the performance of lowrepresentation correlated character classes.

Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or classes.When the number of labels is limited to one,the task becomes single-label text categorization.The 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 value.In 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 on.This paper introduces aModified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition(MDFO-EMTRR)model onArabicCorpus.The presentedMDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic text.To achieve this,theMDFO-EMTRRtechnique encompasses data pre-processing to transform the input data into compatible format.Next,the ELM model is utilized for the representation and recognition of the Arabic text.At last,the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier results.The experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.

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.

Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

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 useful format.Then,the word2vec model is applied to generate the feature vectors.The Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the sentiments.Finally,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU model.The proposed QPSODL-SAAT model was experimentally validated using the standard datasets.An extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.

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.

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.