Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remain...Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.展开更多
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
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
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
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
Dingzhou eye ointment gained significant popularity from the late Qing dynasty to the Republican era.Its legacy persists through three prominent families:Bai Jingyu(白敬宇),Ma Yinglong(马应龙)and Zhang Qizhu(张齐珠).E...Dingzhou eye ointment gained significant popularity from the late Qing dynasty to the Republican era.Its legacy persists through three prominent families:Bai Jingyu(白敬宇),Ma Yinglong(马应龙)and Zhang Qizhu(张齐珠).Each family’s Dingzhou eye ointment is similar,with comparable ingredients,dosages,and usage methods,suggesting a common origin.While many well-established Dingzhou eye ointment brands claim their origins date back to the late Ming dynasty,the origins of its formulation are likely much older.In ancient Chinese medical texts,there were proto-formulations of similar eye ointments recorded during the Jin and Yuan dynasties,with comparable external and standardized eye medicine formulations appearing in the Ming and Qing dynasties.By the late Qing dynasty,Dingzhou eye ointment had become a popular mainstream topical eye treatment in traditional Chinese medicine.From a broader geographically perspective,the composition aligns with that of similar treatments used in Greco-Roman medicine,which combined minerals such as calamine with aromatic ingredients to create eye medicines.The principles behind the composition of Dingzhou eye ointment are in accord with the concept of the four humours in Arabian medicine.It is possible that the Hui ethnic group was responsible for the introduction of Dingzhou eye ointment to China,indicating cultural exchange between Chinese and foreign medicine practices.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
The paper gives an overview of language planning and policy( LPP) in Sudan. It mainly focuses on language problem in two different periods, namely, the colonial period and the post colonial one. The question of Arabic...The paper gives an overview of language planning and policy( LPP) in Sudan. It mainly focuses on language problem in two different periods, namely, the colonial period and the post colonial one. The question of Arabicization of Education in Sudan is also discussed, followed by a critical conclusion.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
The ultrafine silver powders were prepared by liquid reduction method using Arabic gum as dispersant.The effects of different dispersants,pH values,and temperature on the morphology and particle size of silver powders...The ultrafine silver powders were prepared by liquid reduction method using Arabic gum as dispersant.The effects of different dispersants,pH values,and temperature on the morphology and particle size of silver powders were investigated.It is found that Arabic gum can better adsorb on silver particles via chemical adsorption,and it shows the best dispersive effect among all the selected dispersants.The particle size of silver powders can be finely tuned from 0.34 to 4.09μm by adjusting pH values,while the morphology of silver powders can be tuned by changing the temperature.The silver powders with high tap density higher than 4.0 g/cm3 were successfully prepared in a wide temperature range of 21.8-70°C.Especially,the tap density is higher than 5.0 g/cm3 when the temperature is optimized to be 50°C.The facile process and high silver concentration of this method make it a promising way to prepare high quality silver powders for electronic paste.展开更多
AIM:To develop and test an Arabic version of the National Eye Institute Visual Function Questionnaire-25(NEI-VFQ-25).METHODS:NEI-VFQ-25 was translated into Arabic according to WHO translation guidelines. We enrolled a...AIM:To develop and test an Arabic version of the National Eye Institute Visual Function Questionnaire-25(NEI-VFQ-25).METHODS:NEI-VFQ-25 was translated into Arabic according to WHO translation guidelines. We enrolled adult consenting patients with bilateral chronic eye diseases who presented to 14 hospitals across Egypt from October to December 2012, and documented their clinical findings. Psychometric properties were then tested using STATA.RESULTS:We recruited 379 patients, whose mean age was(54.5±15)y. Of 46.2% were males, 227 had cataract,31 had glaucoma, 23 had retinal detachment, 37 had diabetic retinopathy, and 61 had miscellaneous visual defects. Non-response rate and the floor and ceiling numbers of the Arabic version(ARB-VFQ-25) were calculated. Internal consistency was high in all subscales(except general health), with Cronbach-α ranging from0.702-0.911. Test-retest reliability was high(intraclass correlation coefficient 0.79).CONCLUSION:RB-VFQ-25 isareliableandvalidtool for assessing visual functions of Arabic speaking patients. However, some questions had high non-response rates and should be substituted by available alternatives. Our results support the importance of including self-reported visual functions as part of routine ophthalmologic examination.展开更多
Named Entity Recognition(NER)is one of the fundamental tasks in Natural Language Processing(NLP),which aims to locate,extract,and classify named entities into a predefined category such as person,organization and loca...Named Entity Recognition(NER)is one of the fundamental tasks in Natural Language Processing(NLP),which aims to locate,extract,and classify named entities into a predefined category such as person,organization and location.Most of the earlier research for identifying named entities relied on using handcrafted features and very large knowledge resources,which is time consuming and not adequate for resource-scarce languages such as Arabic.Recently,deep learning achieved state-of-the-art performance on many NLP tasks including NER without requiring hand-crafted features.In addition,transfer learning has also proven its efficiency in several NLP tasks by exploiting pretrained language models that are used to transfer knowledge learned from large-scale datasets to domain-specific tasks.Bidirectional Encoder Representation from Transformer(BERT)is a contextual language model that generates the semantic vectors dynamically according to the context of the words.BERT architecture relay on multi-head attention that allows it to capture global dependencies between words.In this paper,we propose a deep learning-based model by fine-tuning BERT model to recognize and classify Arabic named entities.The pre-trained BERT context embeddings were used as input features to a Bidirectional Gated Recurrent Unit(BGRU)and were fine-tuned using two annotated Arabic Named Entity Recognition(ANER)datasets.Experimental results demonstrate that the proposed model outperformed state-of-the-art ANER models achieving 92.28%and 90.68%F-measure values on the ANERCorp dataset and the merged ANERCorp and AQMAR dataset,respectively.展开更多
The digital text media is the most common media transferred via the internet for various purposes and is very sensitive to transfer online with the possibility to be tampered illegally by the tampering attacks.Therefo...The digital text media is the most common media transferred via the internet for various purposes and is very sensitive to transfer online with the possibility to be tampered illegally by the tampering attacks.Therefore,improving the security and authenticity of the text when it is transferred via the internet has become one of the most difcult challenges that researchers face today.Arabic text is more sensitive than other languages due to Harakat’s existence in Arabic diacritics such as Kasra,and Damma in which making basic changes such as modifying diacritic arrangements can lead to change the text meaning.In this paper,an intelligent hybrid solution is proposed with highly sensitive detection for any tampering on Arabic text exchanged via the internet.Natural language processing,entropy,and watermarking techniques have been integrated into this method to improve the security and reliability of Arabic text without limitations in text nature or size,and type or volumes of tampering attack.The proposed scheme is implemented,simulated,and validated using four standard Arabic datasets of varying lengths under multiple random locations of insertion,reorder,and deletion attacks.The experimental and simulation results prove the accuracy of tampering detection of the proposed scheme against all kinds of tampering attacks.Comparison results show that the proposed approach outperforms all of the other baseline approaches in terms of tampering detection accuracy.展开更多
Acacia senegal, the gum arabic producing tree, is the most important tree species for the livelihood of the people in South Kordofan State, Sudan. The objective of this study was to determine the optimum tapping date ...Acacia senegal, the gum arabic producing tree, is the most important tree species for the livelihood of the people in South Kordofan State, Sudan. The objective of this study was to determine the optimum tapping date for gum arabic production in the study area. A randomized complete block design experiment with three replications was conducted at （Mead） area for two continuous growing seasons 2008/2009 - 2009/2010. The treatments comprised six tapping dates （1 Oct, 15 Oct, 1 Nov, 15 Nov, 1 Dec, and 15 Dec）. Results ishowed highly significant differences （p 〈 0.001） in gum arabic yield （g／tree） in all pickings and in the total gum yield between the tapping dates. The results also showed that tapping of trees on 15 October and 1 November gave a higher yield compared to the other dates. The highest gum yield of 1086.6 and 661.2 g/tree was recorded on 15 October and 15 November, while the lowest gum yield of 297.9 g/tree was recorded when the trees were tapped on 1 October. The two highest-yield dates of tapping （15 Oct and 1 Nov） are recommended as the best time for tapping for gum arabic production in South Kordofan State. These results can be used to increase gum arabic production and farmer income in South Kordofan State.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
Using Support Vector Machine(SVM)requires the selection of several parameters such as multi-class strategy type(one-against-all or one-against-one),the regularization parameter C,kernel function and their parameters.T...Using Support Vector Machine(SVM)requires the selection of several parameters such as multi-class strategy type(one-against-all or one-against-one),the regularization parameter C,kernel function and their parameters.The choice of these parameters has a great influence on the performance of the final classifier.This paper considers the grid search method and the particle swarm optimization(PSO)technique that have allowed to quickly select and scan a large space of SVM parameters.A comparative study of the SVM models is also presented to examine the convergence speed and the results of each model.SVM is applied to handwritten Arabic characters learning,with a database containing 4840 Arabic characters in their different positions(isolated,beginning,middle and end).Some very promising results have been achieved.展开更多
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 an...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.展开更多
基金This study is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001.
文摘Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.
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.
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.
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financed by the grant from the National Social Science Fund of China(No.22VJXG040).
文摘Dingzhou eye ointment gained significant popularity from the late Qing dynasty to the Republican era.Its legacy persists through three prominent families:Bai Jingyu(白敬宇),Ma Yinglong(马应龙)and Zhang Qizhu(张齐珠).Each family’s Dingzhou eye ointment is similar,with comparable ingredients,dosages,and usage methods,suggesting a common origin.While many well-established Dingzhou eye ointment brands claim their origins date back to the late Ming dynasty,the origins of its formulation are likely much older.In ancient Chinese medical texts,there were proto-formulations of similar eye ointments recorded during the Jin and Yuan dynasties,with comparable external and standardized eye medicine formulations appearing in the Ming and Qing dynasties.By the late Qing dynasty,Dingzhou eye ointment had become a popular mainstream topical eye treatment in traditional Chinese medicine.From a broader geographically perspective,the composition aligns with that of similar treatments used in Greco-Roman medicine,which combined minerals such as calamine with aromatic ingredients to create eye medicines.The principles behind the composition of Dingzhou eye ointment are in accord with the concept of the four humours in Arabian medicine.It is possible that the Hui ethnic group was responsible for the introduction of Dingzhou eye ointment to China,indicating cultural exchange between Chinese and foreign medicine practices.
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The paper gives an overview of language planning and policy( LPP) in Sudan. It mainly focuses on language problem in two different periods, namely, the colonial period and the post colonial one. The question of Arabicization of Education in Sudan is also discussed, followed by a critical conclusion.
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Project(2014DFA90520)supported by the International Cooperation Program of Ministry of Science and Technology of ChinaProject(2013A090100003)supported by the Production,Teaching and Research Program of Guangdong Province,ChinaProject(2013DY048)supported by the Science and Technology Cooperation Program of Daye Nonferrous Metals Group,China
文摘The ultrafine silver powders were prepared by liquid reduction method using Arabic gum as dispersant.The effects of different dispersants,pH values,and temperature on the morphology and particle size of silver powders were investigated.It is found that Arabic gum can better adsorb on silver particles via chemical adsorption,and it shows the best dispersive effect among all the selected dispersants.The particle size of silver powders can be finely tuned from 0.34 to 4.09μm by adjusting pH values,while the morphology of silver powders can be tuned by changing the temperature.The silver powders with high tap density higher than 4.0 g/cm3 were successfully prepared in a wide temperature range of 21.8-70°C.Especially,the tap density is higher than 5.0 g/cm3 when the temperature is optimized to be 50°C.The facile process and high silver concentration of this method make it a promising way to prepare high quality silver powders for electronic paste.
文摘AIM:To develop and test an Arabic version of the National Eye Institute Visual Function Questionnaire-25(NEI-VFQ-25).METHODS:NEI-VFQ-25 was translated into Arabic according to WHO translation guidelines. We enrolled adult consenting patients with bilateral chronic eye diseases who presented to 14 hospitals across Egypt from October to December 2012, and documented their clinical findings. Psychometric properties were then tested using STATA.RESULTS:We recruited 379 patients, whose mean age was(54.5±15)y. Of 46.2% were males, 227 had cataract,31 had glaucoma, 23 had retinal detachment, 37 had diabetic retinopathy, and 61 had miscellaneous visual defects. Non-response rate and the floor and ceiling numbers of the Arabic version(ARB-VFQ-25) were calculated. Internal consistency was high in all subscales(except general health), with Cronbach-α ranging from0.702-0.911. Test-retest reliability was high(intraclass correlation coefficient 0.79).CONCLUSION:RB-VFQ-25 isareliableandvalidtool for assessing visual functions of Arabic speaking patients. However, some questions had high non-response rates and should be substituted by available alternatives. Our results support the importance of including self-reported visual functions as part of routine ophthalmologic examination.
基金funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University through the Graduate Students Research Support Program.
文摘Named Entity Recognition(NER)is one of the fundamental tasks in Natural Language Processing(NLP),which aims to locate,extract,and classify named entities into a predefined category such as person,organization and location.Most of the earlier research for identifying named entities relied on using handcrafted features and very large knowledge resources,which is time consuming and not adequate for resource-scarce languages such as Arabic.Recently,deep learning achieved state-of-the-art performance on many NLP tasks including NER without requiring hand-crafted features.In addition,transfer learning has also proven its efficiency in several NLP tasks by exploiting pretrained language models that are used to transfer knowledge learned from large-scale datasets to domain-specific tasks.Bidirectional Encoder Representation from Transformer(BERT)is a contextual language model that generates the semantic vectors dynamically according to the context of the words.BERT architecture relay on multi-head attention that allows it to capture global dependencies between words.In this paper,we propose a deep learning-based model by fine-tuning BERT model to recognize and classify Arabic named entities.The pre-trained BERT context embeddings were used as input features to a Bidirectional Gated Recurrent Unit(BGRU)and were fine-tuned using two annotated Arabic Named Entity Recognition(ANER)datasets.Experimental results demonstrate that the proposed model outperformed state-of-the-art ANER models achieving 92.28%and 90.68%F-measure values on the ANERCorp dataset and the merged ANERCorp and AQMAR dataset,respectively.
基金The author extends his appreciation to the Deanship of Scientic Research at King Khalid University for funding this work under Grant Number(R.G.P.2/55/40/2019),Received by Fahd N.Al-Wesabi.www.kku.edu.sa。
文摘The digital text media is the most common media transferred via the internet for various purposes and is very sensitive to transfer online with the possibility to be tampered illegally by the tampering attacks.Therefore,improving the security and authenticity of the text when it is transferred via the internet has become one of the most difcult challenges that researchers face today.Arabic text is more sensitive than other languages due to Harakat’s existence in Arabic diacritics such as Kasra,and Damma in which making basic changes such as modifying diacritic arrangements can lead to change the text meaning.In this paper,an intelligent hybrid solution is proposed with highly sensitive detection for any tampering on Arabic text exchanged via the internet.Natural language processing,entropy,and watermarking techniques have been integrated into this method to improve the security and reliability of Arabic text without limitations in text nature or size,and type or volumes of tampering attack.The proposed scheme is implemented,simulated,and validated using four standard Arabic datasets of varying lengths under multiple random locations of insertion,reorder,and deletion attacks.The experimental and simulation results prove the accuracy of tampering detection of the proposed scheme against all kinds of tampering attacks.Comparison results show that the proposed approach outperforms all of the other baseline approaches in terms of tampering detection accuracy.
文摘Acacia senegal, the gum arabic producing tree, is the most important tree species for the livelihood of the people in South Kordofan State, Sudan. The objective of this study was to determine the optimum tapping date for gum arabic production in the study area. A randomized complete block design experiment with three replications was conducted at （Mead） area for two continuous growing seasons 2008/2009 - 2009/2010. The treatments comprised six tapping dates （1 Oct, 15 Oct, 1 Nov, 15 Nov, 1 Dec, and 15 Dec）. Results ishowed highly significant differences （p 〈 0.001） in gum arabic yield （g／tree） in all pickings and in the total gum yield between the tapping dates. The results also showed that tapping of trees on 15 October and 1 November gave a higher yield compared to the other dates. The highest gum yield of 1086.6 and 661.2 g/tree was recorded on 15 October and 15 November, while the lowest gum yield of 297.9 g/tree was recorded when the trees were tapped on 1 October. The two highest-yield dates of tapping （15 Oct and 1 Nov） are recommended as the best time for tapping for gum arabic production in South Kordofan State. These results can be used to increase gum arabic production and farmer income in South Kordofan State.
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Using Support Vector Machine(SVM)requires the selection of several parameters such as multi-class strategy type(one-against-all or one-against-one),the regularization parameter C,kernel function and their parameters.The choice of these parameters has a great influence on the performance of the final classifier.This paper considers the grid search method and the particle swarm optimization(PSO)technique that have allowed to quickly select and scan a large space of SVM parameters.A comparative study of the SVM models is also presented to examine the convergence speed and the results of each model.SVM is applied to handwritten Arabic characters learning,with a database containing 4840 Arabic characters in their different positions(isolated,beginning,middle and end).Some very promising results have been achieved.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(120/43)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:(22UQU4331004DSR32).
文摘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.
