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
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
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
Spices are defined as any aromatic condiment of plant origin used to alter the flavor and aroma of foods. Besides flavor and aroma, many spices have antioxidant activity, mainly related to the presence in cloves of ph...Spices are defined as any aromatic condiment of plant origin used to alter the flavor and aroma of foods. Besides flavor and aroma, many spices have antioxidant activity, mainly related to the presence in cloves of phenolic compounds, such as flavonoids, terpenoids and eugenol. In turn, the most common uses of gum arabic are in the form of powder for addition to soft drink syrups, cuisine and baked goods, specifically to stabilize the texture of products, increase the viscosity of liquids and promote the leavening of baked products (e.g., cakes). Both eugenol, extracted from cloves, and gum arabic, extracted from the hardened sap of two species of the Acacia tree, are dietary constituents routinely consumed virtually throughout the world. Both of them are also widely used medicinally to inhibit oxidative stress and genotoxicity. The prevention arm of the study included groups: Ia, IIa, IIIa, Iva, V, VI, VII, VIII. Once a week for 20 weeks, the controls received saline s.c. while the experimental groups received DMH at 20 mg/kg s.c. During the same period and for an additional 9 weeks, the animals received either water, 10% GA, EUG, or 10% GA + EUG by gavage. The treatment arm of the study included groups Ib, IIb, IIIb e IVb, IX, X, XI, XII). Once a week for 20 weeks, the controls received saline s.c. while the experimental groups received DMH at 20 mg/kg s.c. During the subsequent 9 weeks, the animals received either water, 10% GA, EUG or 10% GA + EUG by gavage. The novelty of this study is the investigation of their use alone and together for the prevention and treatment of experimental colorectal carcinogenesis induced by dimethylhydrazine. Our results show that the combined use of 10% gum arabic and eugenol was effective, with antioxidant action in the colon, as well as reducing oxidative stress in all colon segments and preventing and treating genotoxicity in all colon segments. Furthermore, their joint administration reduced the number of aberrant crypts and the number of aberrant crypt foci (ACF) in the dis展开更多
Dough improvers are substances with functional characteristics used in baking industry to enhance dough properties. Currently, the baking industry is faced with increasing demand for natural ingredients owing to incre...Dough improvers are substances with functional characteristics used in baking industry to enhance dough properties. Currently, the baking industry is faced with increasing demand for natural ingredients owing to increasing consumer awareness, thus contributing to the rising demand for natural hydrocolloids. Gum Arabic from Acacia senegal var. kerensis is a natural gum exhibiting excellent water binding and emulsification capacity. However, very little is reported on how it affects the rheological properties of wheat dough. The aim of this study was therefore, to determine the rheological properties of wheat dough with partial additions of gum Arabic as an improver. Six treatments were analyzed comprising of: flour-gum blends prepared by adding gum Arabic to wheat flour at different levels (1%, 2% and 3%), plain wheat flour (negative control), commercial bread flour and commercial chapati flour (positive controls). The rheological properties were determined using Brabender Farinograph, Brabender Extensograph and Brabender Viscograph. Results showed that addition of gum Arabic significantly (p chapati. These findings support the need to utilize gum Arabic from Acacia senegal var. kerensis as a dough improver.展开更多
Gum Arabic (GA) from Acacia senegal var. kerensis has been approved as an emulsifier, stabilizer, thickener, and encapsulator in food processing industry. Chia mucilage, on the other hand, has been approved to be used...Gum Arabic (GA) from Acacia senegal var. kerensis has been approved as an emulsifier, stabilizer, thickener, and encapsulator in food processing industry. Chia mucilage, on the other hand, has been approved to be used as a fat and egg yolk mimic. However, both chia mucilage and gum Arabic are underutilized locally in Kenya;thus, marginal reports have been published despite their potential to alter functional properties in food products. In this study, the potential use of chia mucilage and gum Arabic was evaluated in the development of an eggless fat-reduced mayonnaise (FRM). The mayonnaise substitute was prepared by replacing eggs and partially substituting sunflower oil with chia mucilage at 15%, 30%, 45%, and 60% levels and gum Arabic at 3% while reducing the oil levels to 15%, 30%, 45%, and 60%. The effect of different concentrations of oil and chia mucilage on the physicochemical properties, for example, pH, emulsion stability, moisture content, protein, carbohydrate, fats, calories, ash, and titratable acidity using AOAC methods and sensory properties for both consumer acceptability and quantitative descriptive analysis of mayonnaise were evaluated and compared to the control with eggs and 75% sunflower oil. The results indicated that all fat-reduced mayonnaises had significantly lower energy to 493 kcal/100g and 20% fat content but higher water content of 0.74 than the control with 784 Kcal/100g calories, 77% fat and 0.39 moisture. These differences increased with increasing substitution levels of chia mucilage, as impacted on pH, carbohydrate, and protein. There was no significant difference between ash content for both fat-reduced mayonnaise and control. Sensory evaluation demonstrated that mayonnaises substituted with chia seeds mucilage and gum Arabic were accepted. All the parameters are positively correlated to overall acceptability, with flavor having the strongest correlation of r = 0.78. Loadings from principal component analysis (PCA) of 16 sensory attributes of mayonnaise showed that approximat展开更多
Gender analysis of Twitter could reveal significant socio-cultural differ-ences between female and male users.Efforts had been made to analyze and auto-matically infer gender formerly for more commonly spoken language...Gender analysis of Twitter could reveal significant socio-cultural differ-ences between female and male users.Efforts had been made to analyze and auto-matically infer gender formerly for more commonly spoken languages’content,but,as we now know that limited work is being undertaken for Arabic.Most of the research works are done mainly for English and least amount of effort for non-English language.The study for Arabic demographic inference like gen-der is relatively uncommon for social networking users,especially for Twitter.Therefore,this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter(OMSDAE-GIAT)model.The presented OMSDAE-GIAR technique mainly concentrates on the identifica-tion and classification of gender exist in the Twitter data.To attain this,the OMS-DAE-GIAT model derives initial stages of data pre-processing and word embedding.Next,the MSDAE model is exploited for the identification of gender into two classes namely male and female.In the final stage,the OMSDAE-GIAT technique uses enhanced bat optimization algorithm(EBOA)for parameter tuning process,showing the novelty of our work.The performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct metrics.The comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches.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 comb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
The Arabic Dialect (AD) detection method involves analyzing the matching sound wave for various characteristics that identify the speaker’s dialect. Among these features are accent, intonation, stress, vowel length, ...The Arabic Dialect (AD) detection method involves analyzing the matching sound wave for various characteristics that identify the speaker’s dialect. Among these features are accent, intonation, stress, vowel length, vowel type, and other acoustic characteristics. Data from different speakers of different dialects is usually used in training machine learning algorithms. Based on this data, an algorithm is created to accurately identify the speaker’s dialect. Arabic dialects can be detected and classified using several models and techniques available in literature. Various models have been proposed from different perspectives. Therefore, this paper discussed different studies about AD for building an understanding of conceptual deep learning model to detect and classify Arabic dialects. The model captured the semantic, syntactic, and phonological characteristics of these dialects using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The proposed model consists of six stages: Natural Language Processing (NLP) stage, feature engineering techniques, neural networks, language models, optimization techniques, and evaluation techniques. Each stage of the proposed model has several techniques that can be used to detect and classify AD. The accuracy and capability of the proposed model will be performed in the future work.展开更多
Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.T...Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.展开更多
This study investigated the perceptions of English educators and supervisors in Jeddah Governorate regarding the process of teaching English to elementary students.A survey was conducted using a sample size of 94 educ...This study investigated the perceptions of English educators and supervisors in Jeddah Governorate regarding the process of teaching English to elementary students.A survey was conducted using a sample size of 94 educators and 10 supervisors.The data indicate that respondents considered English instruction at the elementary level essential for expanding kids’perspectives,improving academic performance,and promoting international involvement.The main advantages cited are the development of English language skills and the promotion of early education.Although not as easily noticeable,the disadvantages include potential negative impacts on an individual’s proficiency in Arabic and their sense of national identification.The highlighted challenges encompass insufficient teacher training,student reluctance towards English,limited resources,and school disparities.The proposed techniques focused on prioritizing English instructors’training,ensuring the use of appropriate content,utilizing technology,and promoting awareness of students and educators.The current research found different obstacles in teaching English at elementary stages.To overcome these obstacles,it will be essential to enhance teacher competencies,develop efficient teaching methods,get the backing of stakeholders,assign adequate resources,and carry out continuous evaluations.Further research can also contribute to a better understanding of how early English learning impacts on Arabic identity and proficiency.展开更多
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Spices are defined as any aromatic condiment of plant origin used to alter the flavor and aroma of foods. Besides flavor and aroma, many spices have antioxidant activity, mainly related to the presence in cloves of phenolic compounds, such as flavonoids, terpenoids and eugenol. In turn, the most common uses of gum arabic are in the form of powder for addition to soft drink syrups, cuisine and baked goods, specifically to stabilize the texture of products, increase the viscosity of liquids and promote the leavening of baked products (e.g., cakes). Both eugenol, extracted from cloves, and gum arabic, extracted from the hardened sap of two species of the Acacia tree, are dietary constituents routinely consumed virtually throughout the world. Both of them are also widely used medicinally to inhibit oxidative stress and genotoxicity. The prevention arm of the study included groups: Ia, IIa, IIIa, Iva, V, VI, VII, VIII. Once a week for 20 weeks, the controls received saline s.c. while the experimental groups received DMH at 20 mg/kg s.c. During the same period and for an additional 9 weeks, the animals received either water, 10% GA, EUG, or 10% GA + EUG by gavage. The treatment arm of the study included groups Ib, IIb, IIIb e IVb, IX, X, XI, XII). Once a week for 20 weeks, the controls received saline s.c. while the experimental groups received DMH at 20 mg/kg s.c. During the subsequent 9 weeks, the animals received either water, 10% GA, EUG or 10% GA + EUG by gavage. The novelty of this study is the investigation of their use alone and together for the prevention and treatment of experimental colorectal carcinogenesis induced by dimethylhydrazine. Our results show that the combined use of 10% gum arabic and eugenol was effective, with antioxidant action in the colon, as well as reducing oxidative stress in all colon segments and preventing and treating genotoxicity in all colon segments. Furthermore, their joint administration reduced the number of aberrant crypts and the number of aberrant crypt foci (ACF) in the dis
文摘Dough improvers are substances with functional characteristics used in baking industry to enhance dough properties. Currently, the baking industry is faced with increasing demand for natural ingredients owing to increasing consumer awareness, thus contributing to the rising demand for natural hydrocolloids. Gum Arabic from Acacia senegal var. kerensis is a natural gum exhibiting excellent water binding and emulsification capacity. However, very little is reported on how it affects the rheological properties of wheat dough. The aim of this study was therefore, to determine the rheological properties of wheat dough with partial additions of gum Arabic as an improver. Six treatments were analyzed comprising of: flour-gum blends prepared by adding gum Arabic to wheat flour at different levels (1%, 2% and 3%), plain wheat flour (negative control), commercial bread flour and commercial chapati flour (positive controls). The rheological properties were determined using Brabender Farinograph, Brabender Extensograph and Brabender Viscograph. Results showed that addition of gum Arabic significantly (p chapati. These findings support the need to utilize gum Arabic from Acacia senegal var. kerensis as a dough improver.
文摘Gum Arabic (GA) from Acacia senegal var. kerensis has been approved as an emulsifier, stabilizer, thickener, and encapsulator in food processing industry. Chia mucilage, on the other hand, has been approved to be used as a fat and egg yolk mimic. However, both chia mucilage and gum Arabic are underutilized locally in Kenya;thus, marginal reports have been published despite their potential to alter functional properties in food products. In this study, the potential use of chia mucilage and gum Arabic was evaluated in the development of an eggless fat-reduced mayonnaise (FRM). The mayonnaise substitute was prepared by replacing eggs and partially substituting sunflower oil with chia mucilage at 15%, 30%, 45%, and 60% levels and gum Arabic at 3% while reducing the oil levels to 15%, 30%, 45%, and 60%. The effect of different concentrations of oil and chia mucilage on the physicochemical properties, for example, pH, emulsion stability, moisture content, protein, carbohydrate, fats, calories, ash, and titratable acidity using AOAC methods and sensory properties for both consumer acceptability and quantitative descriptive analysis of mayonnaise were evaluated and compared to the control with eggs and 75% sunflower oil. The results indicated that all fat-reduced mayonnaises had significantly lower energy to 493 kcal/100g and 20% fat content but higher water content of 0.74 than the control with 784 Kcal/100g calories, 77% fat and 0.39 moisture. These differences increased with increasing substitution levels of chia mucilage, as impacted on pH, carbohydrate, and protein. There was no significant difference between ash content for both fat-reduced mayonnaise and control. Sensory evaluation demonstrated that mayonnaises substituted with chia seeds mucilage and gum Arabic were accepted. All the parameters are positively correlated to overall acceptability, with flavor having the strongest correlation of r = 0.78. Loadings from principal component analysis (PCA) of 16 sensory attributes of mayonnaise showed that approximat
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4310373DSR55.
文摘Gender analysis of Twitter could reveal significant socio-cultural differ-ences between female and male users.Efforts had been made to analyze and auto-matically infer gender formerly for more commonly spoken languages’content,but,as we now know that limited work is being undertaken for Arabic.Most of the research works are done mainly for English and least amount of effort for non-English language.The study for Arabic demographic inference like gen-der is relatively uncommon for social networking users,especially for Twitter.Therefore,this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter(OMSDAE-GIAT)model.The presented OMSDAE-GIAR technique mainly concentrates on the identifica-tion and classification of gender exist in the Twitter data.To attain this,the OMS-DAE-GIAT model derives initial stages of data pre-processing and word embedding.Next,the MSDAE model is exploited for the identification of gender into two classes namely male and female.In the final stage,the OMSDAE-GIAT technique uses enhanced bat optimization algorithm(EBOA)for parameter tuning process,showing the novelty of our work.The performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct metrics.The comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches.
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 comb
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The Arabic Dialect (AD) detection method involves analyzing the matching sound wave for various characteristics that identify the speaker’s dialect. Among these features are accent, intonation, stress, vowel length, vowel type, and other acoustic characteristics. Data from different speakers of different dialects is usually used in training machine learning algorithms. Based on this data, an algorithm is created to accurately identify the speaker’s dialect. Arabic dialects can be detected and classified using several models and techniques available in literature. Various models have been proposed from different perspectives. Therefore, this paper discussed different studies about AD for building an understanding of conceptual deep learning model to detect and classify Arabic dialects. The model captured the semantic, syntactic, and phonological characteristics of these dialects using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The proposed model consists of six stages: Natural Language Processing (NLP) stage, feature engineering techniques, neural networks, language models, optimization techniques, and evaluation techniques. Each stage of the proposed model has several techniques that can be used to detect and classify AD. The accuracy and capability of the proposed model will be performed in the future work.
文摘Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.
文摘This study investigated the perceptions of English educators and supervisors in Jeddah Governorate regarding the process of teaching English to elementary students.A survey was conducted using a sample size of 94 educators and 10 supervisors.The data indicate that respondents considered English instruction at the elementary level essential for expanding kids’perspectives,improving academic performance,and promoting international involvement.The main advantages cited are the development of English language skills and the promotion of early education.Although not as easily noticeable,the disadvantages include potential negative impacts on an individual’s proficiency in Arabic and their sense of national identification.The highlighted challenges encompass insufficient teacher training,student reluctance towards English,limited resources,and school disparities.The proposed techniques focused on prioritizing English instructors’training,ensuring the use of appropriate content,utilizing technology,and promoting awareness of students and educators.The current research found different obstacles in teaching English at elementary stages.To overcome these obstacles,it will be essential to enhance teacher competencies,develop efficient teaching methods,get the backing of stakeholders,assign adequate resources,and carry out continuous evaluations.Further research can also contribute to a better understanding of how early English learning impacts on Arabic identity and proficiency.
