Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to signi...Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to significant challenges,one of the most pressing being cyberbullying.This issue has become a major concern in modern society,particularly due to its profound negative impacts on the mental health and well-being of its victims.In the Arab world,where social media usage is exceptionblly high,cyberbullying has become increasingly prevalent,necessitating urgent attention.Early detection of harmful online behavior is critical to fostering safer digital environments and mitigating the adverse efcts of cyberbullying.This underscores the importance of developing advanced tools and systems to identify and address such behavior efectively.This paper investigates the development of a robust cyberbullying detection and classifcation system tailored for Arabic comments on YouTube.The study explores the efectiveness of various deep learning models,including Bi-LSTM(Bidirectional Long Short Term Memory),LSTM(Long Short-Term Memory),CNN(Convolutional Neural Networks),and a hybrid CNN-LSTM,in classifying Arabic comments into binary classes(bullying or not)and multiclass categories.A comprehensive dataset of 20,000 Arabic YouTube comments was collected,preprocessed,and labeled to support these tasks.The results revealed that the CNN and hybrid CNN-LSTM models achieved the highest accuracy in binary classification,reaching an impressive 91.9%.For multiclass dlassification,the LSTM and Bi-LSTM models outperformed others,achieving an accuracy of 89.5%.These findings highlight the efctiveness of deep learning approaches in the mitigation of cyberbullying within Arabic online communities.展开更多
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 accordingl...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.展开更多
Protein-energy malnutrition (PEM) as a result of poor nutrition, especially for deprived resourced households, is a big health concern in the world. According to the World Health Organisation, PEM accounts for 49% of ...Protein-energy malnutrition (PEM) as a result of poor nutrition, especially for deprived resourced households, is a big health concern in the world. According to the World Health Organisation, PEM accounts for 49% of the 10.4 million deaths of children under five that take place in developing countries. The aim of this study was to evaluate the influence of gum Arabic (GA) and texturized soy protein (TSP) and their interactive effect on proximate, functional, and textural properties of the protein-rich snack stick produced from ground green maize, GA powder, and ground TSP. GA varied at 0%, 4%, 8%, and 12%, while TSP varied at 0%, 12%, 24% and 36%. The 5 cm long protein-rich snack sticks were made using a sausage stuffer and baked in an oven at 110˚C for 1 hr 30 minutes. The snack sticks were subjected to proximate, functional and textural analysis using the standard methods. Increasing GA resulted in a significant (p p < 0.05) increased the protein content (32.46%), Ash content (3.6%), fat (11.96%), and moisture content (16.25%) of protein-rich snack sticks. The interactive effect between GA and TSP led to a decrease in fibre and carbohydrates. Results from this study show GA and TSP significantly enhanced the physico-chemical properties of protein-rich snack sticks. A sample with 4% GA and 36% TSP is recommended for the best physico-chemical attributes of the protein-rich snack stick.展开更多
Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)...Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant datasets.However,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic cyberbullying.Given the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large dataset.Therefore,this study proposes a hybrid model using transformers trained on extensive Arabic datasets.It then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular Twitter.Additionally,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance accuracy.Experimental results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.展开更多
Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk o...Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.展开更多
BACKGROUND Arabic-speaking patients are underrepresented in orthopedic clinical studies,particularly in foot and ankle trauma research.The lack of validated Arabic language tools hinders their inclusion,creating a nee...BACKGROUND Arabic-speaking patients are underrepresented in orthopedic clinical studies,particularly in foot and ankle trauma research.The lack of validated Arabic language tools hinders their inclusion,creating a need for culturally and linguistically adapted instruments.The American Academy of Orthopedic Surgeons Foot and Ankle Outcomes Questionnaire(AAOS-FAOQ)is a widely used tool but has not been adapted for Arabic-speaking patients.AIM To translate,cross-culturally adapt,and validate the AAOS-FAOQ for Arabicspeaking patients with traumatic foot and ankle injuries.METHODS The cross-cultural adaptation followed established guidelines,involving forward and backward translations,expert review,and pre-testing.The final Arabic version was administered alongside the Arabic Short-Form 36(SF-36)to 100 patients for validity testing.Reliability was assessed through test-retest methods with 20 patients completing the questionnaire twice within 48 hours.Pearson correlation coefficients measured convergent and divergent validity with SF-36 subscales,while Cronbach's alpha and intraclass correlation coefficients(ICC)determined internal consistency and reliability.RESULTS Out of 100 patients,92 completed the first set of questionnaires.The Arabic AAOS-FAOQ showed strong correlations with the SF-36 subscales,particularly in physical function and bodily pain(r>0.6).Test-retest reliability was robust,with ICCs of 0.69 and 0.66 for the Global Foot and Ankle Scale and Shoe Comfort Scale,respectively.Cronbach's alpha for internal consistency ranged from 0.7 to 0.9.CONCLUSION The Arabic version of the AAOS-FAOQ demonstrated validity and reliability for use in Arabic-speaking patients with traumatic foot and ankle injuries.This adaptation will enhance the inclusion of this population in orthopedic clinical studies,improving the generalizability of research findings and patient care.展开更多
The environment-friendly and efficient selective separation of chalcopyrite and molybdenite poses a challenge in mineral pro-cessing.In this study,gum Arabic(GA)was initially proposed as a novel depressant for the sel...The environment-friendly and efficient selective separation of chalcopyrite and molybdenite poses a challenge in mineral pro-cessing.In this study,gum Arabic(GA)was initially proposed as a novel depressant for the selective separation of molybdenite from chalcopyrite during flotation.Microflotation results indicated that the inhibitory capacity of GA was stronger toward molybdenite than chalcopyrite.At pH 8.0 with 20 mg/L GA addition,the recovery rate of chalcopyrite in the concentrate obtained from mixed mineral flota-tion was 67.49%higher than that of molybdenite.Furthermore,the mechanism of GA was systematically investigated by various surface characterization techniques.Contact angle tests indicated that after GA treatment,the hydrophobicity of the molybdenite surface signifi-cantly decreased,but that of the chalcopyrite surface showed no apparent change.Fourier transform-infrared spectroscopy and X-ray photoelectron spectroscopy revealed a weak interaction force between GA and chalcopyrite.By contrast,GA was primarily adsorbed onto the molybdenite surface through chemical chelation,with possible contributions from hydrogen bonding and hydrophobic interactions.Pre-adsorbed GA could prevent butyl xanthate from being adsorbed onto molybdenite.Scanning electron microscopy–energy-dispersive spectrometry further indicated that GA was primarily adsorbed onto the“face”of molybdenite rather than the“edge.”Therefore,GA could be a promising molybdenite depressant for the flotation separation of Cu–Mo.展开更多
Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep ...Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.展开更多
Arabic Sign Language(ArSL)recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing(DHH)community.Researchers have proposed multiple methods for automated recognition of ArSL;howev...Arabic Sign Language(ArSL)recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing(DHH)community.Researchers have proposed multiple methods for automated recognition of ArSL;however,these methods face multiple challenges that include high gesture variability,occlusions,limited signer diversity,and the scarcity of large annotated datasets.Existing methods,often relying solely on either skeletal data or video-based features,struggle with generalization and robustness,especially in dynamic and real-world conditions.This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint distances and angles with temporal features extracted from RGB videos using the Inflated 3D ConvNet(I3D).By fusing these complementary modalities at the feature level and applying a majority-voting ensemble of XGBoost,Random Forest,and Support Vector Machine classifiers,the framework robustly captures both spatial configurations and motion dynamics of sign gestures.Feature selection using the Pearson Correlation Coefficient further enhances efficiency by reducing redundancy.Extensive experiments on the ArabSign dataset,which includes RGB videos and corresponding skeletal data,demonstrate that the proposed approach significantly outperforms state-of-the-art methods,achieving an average F1-score of 97%using a majority-voting ensemble of XGBoost,Random Forest,and SVM classifiers,and improving recognition accuracy by more than 7%over previous best methods.This work not only advances the technical stateof-the-art in ArSL recognition but also provides a scalable,real-time solution for practical deployment in educational,social,and assistive communication technologies.Even though this study is about Arabic Sign Language,the framework proposed here can be extended to different sign languages,creating possibilities for potentially worldwide applicability in sign language recognition tasks.展开更多
Finely divided silver nanoparticles were synthesized via the hydrothermal method. Arabic gum （AG） was used as both the reductant and steric stabilizer without any other surfactant. By adjusting the reaction temperat...Finely divided silver nanoparticles were synthesized via the hydrothermal method. Arabic gum （AG） was used as both the reductant and steric stabilizer without any other surfactant. By adjusting the reaction temperature, mass ratio of AG to AgNO3, and reaction time, silver nanoparticles with different morphological characteristics could be obtained. The products were characterized by UV-Vis, FTIR, TEM, SEM, and XRD measurements. It was found that temperature and AG played an important role in the synthesis of mono-disperse silver nanoparticles. Well dispersed and quasispherical silver nanoparticles were obtained under the optimal synthesis conditions of 10 mmol/L AgNO3, m（AG）/m（AgN03）= l：1, 160 ℃ and 3 h.展开更多
AIM: To develop an Arabic version of the ocular surface disease index(OSDI) and to assess its reliability and validity.METHODS: A cross sectional study was carried out to validate the Arabic version of the OSDI questi...AIM: To develop an Arabic version of the ocular surface disease index(OSDI) and to assess its reliability and validity.METHODS: A cross sectional study was carried out to validate the Arabic version of the OSDI questionnaire. Initially, forward-backward translation was used to translate the English version of OSDI into Arabic. The translated questionnaire was tested for equivalence and cultural adaptability. Totally 200 subjects were then recruited from a non-clinical population and asked to complete the Arabic version of the OSDI(ARB-OSDI). The reliability of the questionnaire was assessed using Cronbach’s-α. A subgroup of 30 participants was asked to complete the questionnaire on two occasions to test the test-retest reliability.RESULTS: A total of 200 participants were enrolled in the study. The average age of the study participants was 31.21±13.2 y and 57% were male. An acceptable internal consistency level for the ARB-OSDI questionnaire measured by Cronbach’s-α was revealed. All questions showed good internal consistency. Test-retest reliability analysis revealed good stability(interclass correlation coefficient, r=0.832, P<0.001). The construct validity for the questionnaire was also high.CONCLUSION: The ARB-OSDI questionnaire shows very good psychometric properties(acceptable internal consistency and test-retest reliability). That makes the questionnaire a valid potential tool to use in Arabic-speaking countries to monitor symptoms of dry eye disease in a larger population.展开更多
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.Howeve...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.展开更多
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...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.展开更多
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 lag...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%.展开更多
Recently,with the spread of online services involving websites,attack-ers have the opportunity to expose these services to malicious actions.To protect these services,A Completely Automated Public Turing Test to Tell ...Recently,with the spread of online services involving websites,attack-ers have the opportunity to expose these services to malicious actions.To protect these services,A Completely Automated Public Turing Test to Tell Computers and Humans Apart(CAPTCHA)is a proposed technique.Since many Arabic countries have developed their online services in Arabic,Arabic text-based CAPTCHA has been introduced to improve the usability for their users.More-over,there exist a visual cryptography(VC)technique which can be exploited in order to enhance the security of text-based CAPTCHA by encrypting a CAPTCHA image into two shares and decrypting it by asking the user to stack them on each other.However,as yet,the implementation of this technique with regard to Arabic text-based CAPTCHA has not been carried out.Therefore,this paper aims to implement an Arabic printed and handwritten text-based CAPTCHA scheme based on the VC technique.To evaluate this scheme,experi-mental studies are conducted,and the results show that the implemented scheme offers a reasonable security and usability levels with text-based CAPTCHA itself.展开更多
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 insc...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.展开更多
This paper explores the nature of the post-syntactic operations responsible for the representations of the linear order of terminal nodes.In particular,it argues in favor of a unified model of the morphosyntax and mor...This paper explores the nature of the post-syntactic operations responsible for the representations of the linear order of terminal nodes.In particular,it argues in favor of a unified model of the morphosyntax and morphophonology,wherein the theory of Distributed Morphology and Optimality Theory operate in a single module.The testing ground is an investigation of the formation of morphological causatives in Moroccan Arabic.Herein,the process of realizing causatives is morphological gemination,whereby the second consonant of the root is doubled.Investigating the question of what triggers the infixal process,I argue against the linearization algorithm suggested in Embick&Noyer(2001),Embick&Marantz(2008),and Embick(2006,2010).Instead,the claim I defend here is that the onus of the linearization process falls on the prosody in Arabic,the central assumption being that the morphosyntactic structure,the output of the syntactic derivation,is the input to OT morphophonological constraints.These constraints are responsible for the linearization of the terminal nodes of the syntactic derivation.I show that adopting one theory over others misses important generalizations about the language.展开更多
基金financed by the European Union-NextGenerationEU,through the National Recowery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001-C01.
文摘Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to significant challenges,one of the most pressing being cyberbullying.This issue has become a major concern in modern society,particularly due to its profound negative impacts on the mental health and well-being of its victims.In the Arab world,where social media usage is exceptionblly high,cyberbullying has become increasingly prevalent,necessitating urgent attention.Early detection of harmful online behavior is critical to fostering safer digital environments and mitigating the adverse efcts of cyberbullying.This underscores the importance of developing advanced tools and systems to identify and address such behavior efectively.This paper investigates the development of a robust cyberbullying detection and classifcation system tailored for Arabic comments on YouTube.The study explores the efectiveness of various deep learning models,including Bi-LSTM(Bidirectional Long Short Term Memory),LSTM(Long Short-Term Memory),CNN(Convolutional Neural Networks),and a hybrid CNN-LSTM,in classifying Arabic comments into binary classes(bullying or not)and multiclass categories.A comprehensive dataset of 20,000 Arabic YouTube comments was collected,preprocessed,and labeled to support these tasks.The results revealed that the CNN and hybrid CNN-LSTM models achieved the highest accuracy in binary classification,reaching an impressive 91.9%.For multiclass dlassification,the LSTM and Bi-LSTM models outperformed others,achieving an accuracy of 89.5%.These findings highlight the efctiveness of deep learning approaches in the mitigation of cyberbullying within Arabic online communities.
文摘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.
文摘Protein-energy malnutrition (PEM) as a result of poor nutrition, especially for deprived resourced households, is a big health concern in the world. According to the World Health Organisation, PEM accounts for 49% of the 10.4 million deaths of children under five that take place in developing countries. The aim of this study was to evaluate the influence of gum Arabic (GA) and texturized soy protein (TSP) and their interactive effect on proximate, functional, and textural properties of the protein-rich snack stick produced from ground green maize, GA powder, and ground TSP. GA varied at 0%, 4%, 8%, and 12%, while TSP varied at 0%, 12%, 24% and 36%. The 5 cm long protein-rich snack sticks were made using a sausage stuffer and baked in an oven at 110˚C for 1 hr 30 minutes. The snack sticks were subjected to proximate, functional and textural analysis using the standard methods. Increasing GA resulted in a significant (p p < 0.05) increased the protein content (32.46%), Ash content (3.6%), fat (11.96%), and moisture content (16.25%) of protein-rich snack sticks. The interactive effect between GA and TSP led to a decrease in fibre and carbohydrates. Results from this study show GA and TSP significantly enhanced the physico-chemical properties of protein-rich snack sticks. A sample with 4% GA and 36% TSP is recommended for the best physico-chemical attributes of the protein-rich snack stick.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,through the project number“NBU-FFR-2025-1197-01”.
文摘Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant datasets.However,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic cyberbullying.Given the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large dataset.Therefore,this study proposes a hybrid model using transformers trained on extensive Arabic datasets.It then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular Twitter.Additionally,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance accuracy.Experimental results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.
文摘Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.
文摘BACKGROUND Arabic-speaking patients are underrepresented in orthopedic clinical studies,particularly in foot and ankle trauma research.The lack of validated Arabic language tools hinders their inclusion,creating a need for culturally and linguistically adapted instruments.The American Academy of Orthopedic Surgeons Foot and Ankle Outcomes Questionnaire(AAOS-FAOQ)is a widely used tool but has not been adapted for Arabic-speaking patients.AIM To translate,cross-culturally adapt,and validate the AAOS-FAOQ for Arabicspeaking patients with traumatic foot and ankle injuries.METHODS The cross-cultural adaptation followed established guidelines,involving forward and backward translations,expert review,and pre-testing.The final Arabic version was administered alongside the Arabic Short-Form 36(SF-36)to 100 patients for validity testing.Reliability was assessed through test-retest methods with 20 patients completing the questionnaire twice within 48 hours.Pearson correlation coefficients measured convergent and divergent validity with SF-36 subscales,while Cronbach's alpha and intraclass correlation coefficients(ICC)determined internal consistency and reliability.RESULTS Out of 100 patients,92 completed the first set of questionnaires.The Arabic AAOS-FAOQ showed strong correlations with the SF-36 subscales,particularly in physical function and bodily pain(r>0.6).Test-retest reliability was robust,with ICCs of 0.69 and 0.66 for the Global Foot and Ankle Scale and Shoe Comfort Scale,respectively.Cronbach's alpha for internal consistency ranged from 0.7 to 0.9.CONCLUSION The Arabic version of the AAOS-FAOQ demonstrated validity and reliability for use in Arabic-speaking patients with traumatic foot and ankle injuries.This adaptation will enhance the inclusion of this population in orthopedic clinical studies,improving the generalizability of research findings and patient care.
基金supported by the National Key Research and Development Program of China(Nos.2022YFC2904502 and 2022YFC2904501)the Major Science and Technology Projects in Yunnan Province,China(No.202202AB080012).
文摘The environment-friendly and efficient selective separation of chalcopyrite and molybdenite poses a challenge in mineral pro-cessing.In this study,gum Arabic(GA)was initially proposed as a novel depressant for the selective separation of molybdenite from chalcopyrite during flotation.Microflotation results indicated that the inhibitory capacity of GA was stronger toward molybdenite than chalcopyrite.At pH 8.0 with 20 mg/L GA addition,the recovery rate of chalcopyrite in the concentrate obtained from mixed mineral flota-tion was 67.49%higher than that of molybdenite.Furthermore,the mechanism of GA was systematically investigated by various surface characterization techniques.Contact angle tests indicated that after GA treatment,the hydrophobicity of the molybdenite surface signifi-cantly decreased,but that of the chalcopyrite surface showed no apparent change.Fourier transform-infrared spectroscopy and X-ray photoelectron spectroscopy revealed a weak interaction force between GA and chalcopyrite.By contrast,GA was primarily adsorbed onto the molybdenite surface through chemical chelation,with possible contributions from hydrogen bonding and hydrophobic interactions.Pre-adsorbed GA could prevent butyl xanthate from being adsorbed onto molybdenite.Scanning electron microscopy–energy-dispersive spectrometry further indicated that GA was primarily adsorbed onto the“face”of molybdenite rather than the“edge.”Therefore,GA could be a promising molybdenite depressant for the flotation separation of Cu–Mo.
基金supported by the Deanship of Scientific Research at King Khalid University through Small Groups funding(Project Grant No.RGP1/243/45)The funding was awarded to Dr.Mohammed Abker.And Natural Science Foundation of China under Grant 61901388.
文摘Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.
基金funding this work through Research Group No.KS-2024-376.
文摘Arabic Sign Language(ArSL)recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing(DHH)community.Researchers have proposed multiple methods for automated recognition of ArSL;however,these methods face multiple challenges that include high gesture variability,occlusions,limited signer diversity,and the scarcity of large annotated datasets.Existing methods,often relying solely on either skeletal data or video-based features,struggle with generalization and robustness,especially in dynamic and real-world conditions.This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint distances and angles with temporal features extracted from RGB videos using the Inflated 3D ConvNet(I3D).By fusing these complementary modalities at the feature level and applying a majority-voting ensemble of XGBoost,Random Forest,and Support Vector Machine classifiers,the framework robustly captures both spatial configurations and motion dynamics of sign gestures.Feature selection using the Pearson Correlation Coefficient further enhances efficiency by reducing redundancy.Extensive experiments on the ArabSign dataset,which includes RGB videos and corresponding skeletal data,demonstrate that the proposed approach significantly outperforms state-of-the-art methods,achieving an average F1-score of 97%using a majority-voting ensemble of XGBoost,Random Forest,and SVM classifiers,and improving recognition accuracy by more than 7%over previous best methods.This work not only advances the technical stateof-the-art in ArSL recognition but also provides a scalable,real-time solution for practical deployment in educational,social,and assistive communication technologies.Even though this study is about Arabic Sign Language,the framework proposed here can be extended to different sign languages,creating possibilities for potentially worldwide applicability in sign language recognition tasks.
文摘Finely divided silver nanoparticles were synthesized via the hydrothermal method. Arabic gum （AG） was used as both the reductant and steric stabilizer without any other surfactant. By adjusting the reaction temperature, mass ratio of AG to AgNO3, and reaction time, silver nanoparticles with different morphological characteristics could be obtained. The products were characterized by UV-Vis, FTIR, TEM, SEM, and XRD measurements. It was found that temperature and AG played an important role in the synthesis of mono-disperse silver nanoparticles. Well dispersed and quasispherical silver nanoparticles were obtained under the optimal synthesis conditions of 10 mmol/L AgNO3, m（AG）/m（AgN03）= l：1, 160 ℃ and 3 h.
基金the Deanship of Research at Jordan University of Science and Technology。
文摘AIM: To develop an Arabic version of the ocular surface disease index(OSDI) and to assess its reliability and validity.METHODS: A cross sectional study was carried out to validate the Arabic version of the OSDI questionnaire. Initially, forward-backward translation was used to translate the English version of OSDI into Arabic. The translated questionnaire was tested for equivalence and cultural adaptability. Totally 200 subjects were then recruited from a non-clinical population and asked to complete the Arabic version of the OSDI(ARB-OSDI). The reliability of the questionnaire was assessed using Cronbach’s-α. A subgroup of 30 participants was asked to complete the questionnaire on two occasions to test the test-retest reliability.RESULTS: A total of 200 participants were enrolled in the study. The average age of the study participants was 31.21±13.2 y and 57% were male. An acceptable internal consistency level for the ARB-OSDI questionnaire measured by Cronbach’s-α was revealed. All questions showed good internal consistency. Test-retest reliability analysis revealed good stability(interclass correlation coefficient, r=0.832, P<0.001). The construct validity for the questionnaire was also high.CONCLUSION: The ARB-OSDI questionnaire shows very good psychometric properties(acceptable internal consistency and test-retest reliability). That makes the questionnaire a valid potential tool to use in Arabic-speaking countries to monitor symptoms of dry eye disease in a larger population.
文摘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.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR52。
文摘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.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Ara-bia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR38.
文摘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%.
文摘Recently,with the spread of online services involving websites,attack-ers have the opportunity to expose these services to malicious actions.To protect these services,A Completely Automated Public Turing Test to Tell Computers and Humans Apart(CAPTCHA)is a proposed technique.Since many Arabic countries have developed their online services in Arabic,Arabic text-based CAPTCHA has been introduced to improve the usability for their users.More-over,there exist a visual cryptography(VC)technique which can be exploited in order to enhance the security of text-based CAPTCHA by encrypting a CAPTCHA image into two shares and decrypting it by asking the user to stack them on each other.However,as yet,the implementation of this technique with regard to Arabic text-based CAPTCHA has not been carried out.Therefore,this paper aims to implement an Arabic printed and handwritten text-based CAPTCHA scheme based on the VC technique.To evaluate this scheme,experi-mental studies are conducted,and the results show that the implemented scheme offers a reasonable security and usability levels with text-based CAPTCHA itself.
文摘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.
文摘This paper explores the nature of the post-syntactic operations responsible for the representations of the linear order of terminal nodes.In particular,it argues in favor of a unified model of the morphosyntax and morphophonology,wherein the theory of Distributed Morphology and Optimality Theory operate in a single module.The testing ground is an investigation of the formation of morphological causatives in Moroccan Arabic.Herein,the process of realizing causatives is morphological gemination,whereby the second consonant of the root is doubled.Investigating the question of what triggers the infixal process,I argue against the linearization algorithm suggested in Embick&Noyer(2001),Embick&Marantz(2008),and Embick(2006,2010).Instead,the claim I defend here is that the onus of the linearization process falls on the prosody in Arabic,the central assumption being that the morphosyntactic structure,the output of the syntactic derivation,is the input to OT morphophonological constraints.These constraints are responsible for the linearization of the terminal nodes of the syntactic derivation.I show that adopting one theory over others misses important generalizations about the language.
