• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.20-28
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• 2018

Background: As a result of the South Korean government's medical tourism strategies, the number of UAE inbound medical tourists have the highest annual increase rate among the Middle Eastern countries. The need for medical interpreters in such cases is inevitable, especially considering the languages spoken in both countries differs. The work of a medical interpreter affects patient satisfaction, quality of care, and the UAE patients' revisit intention. However, there is a lack of literature that focuses on the role of medical interpreters for the Arabic-speaking medical tourists in South Korea. The purpose of this study is to assess the satisfaction and the experience of UAE medical tourists in South Korea who have used medical interpretation services. Method: A descriptive exploratory design, including semi-structured interviews, was used in this study to examine factors influencing the satisfaction of UAE patients seeking medical attention in South Korea, who considered travelling exclusively for medical purposes. Eleven UAE medical tourists were selected for the interview using convenience sampling. Results: Two types of interpreters were revealed by the analysis: interpreters with Arabic as their mother tongue and interpreters with Korean as their mother tongue. Patients emphasized the origin of the interpreters and differentiated between the two types of interpreters' professionalism, abilities, and personal skills. However, due to the huge gap between the two cultures, languages, and religions, the UAE patients mainly preferred the Arabic medical interpreters. Further, each type had certain traits that impacted the medical care process and patients' satisfaction. Conclusion: This study shows that the South Korean government needs to focus on the training and assessment system for Arabic language interpreters. Additionally, cultural differences training for the interpreters may help to increase UEA patients' satisfaction.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.1-17
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• 2021

Pseudo relevance feedback (PRF) is a powerful query expansion (QE) technique that prepares queries using the top k pseudorelevant documents and choosing expansion elements. Traditional PRF frameworks have robustly handled vocabulary mismatch corresponding to user queries and pertinent documents; nevertheless, expansion elements are chosen, disregarding similarity to the original query's elements. Word embedding (WE) schemes comprise techniques of significant interest concerning QE, that falls within the information retrieval domain. Deep averaging networks (DANs) defines a framework relying on average word presence passed through multiple linear layers. The complete query is understandably represented using the average vector comprising the query terms. The vector may be employed for determining expansion elements pertinent to the entire query. In this study, we suggest a DANs-based technique that augments PRF frameworks by integrating WE similarities to facilitate Arabic information retrieval. The technique is based on the fundamental that the top pseudo-relevant document set is assessed to determine candidate element distribution and select expansion terms appropriately, considering their similarity to the average vector representing the initial query elements. The Word2Vec model is selected for executing the experiments on a standard Arabic TREC 2001/2002 set. The majority of the evaluations indicate that the PRF implementation in the present study offers a significant performance improvement compared to that of the baseline PRF frameworks.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.199-206
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• 2021

Rhetorical relations between two text fragments are essential information and support natural language processing applications such as Question - Answering (QA) system and automatic text summarization to produce an effective outcome. Question - Answering (QA) system facilitates users to retrieve a meaningful response. There is a demand for rhetorical relation based datasets to develop such a system to interpret and respond to user requests. There are a limited number of datasets for developing an Arabic QA system. Thus, there is a lack of an effective QA system in the Arabic language. Recent research works reveal that unsupervised learning can support the QA system to reply to users queries. In this study, researchers intend to develop a rhetorical relation based dataset for implementing unsupervised learning applications. A web crawler is developed to crawl Arabic content from the web. A discourse-annotated corpus is generated using the rhetorical structural theory. A Naïve Bayes based QA system is developed to evaluate the performance of datasets. The outcome shows that the performance of the QA system is improved with proposed dataset and able to answer user queries with an appropriate response. In addition, the results on fine-grained and coarse-grained relations reveal that the dataset is highly reliable.

• Journal of Preventive Medicine and Public Health
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• v.55 no.6
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• pp.578-585
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• 2022

Objectives: This study aimed to adapt the survey questionnaire designed by Moens et al. (2021) and determine the validity and reliability of the Arabic version of the survey in a sample of the Saudi population experiencing teleworking. Methods: The questionnaire includes 2 sections. The first consists of 13 items measuring the impact of extended telework during the coronavirus disease 2019 (COVID-19) crisis. The second section includes 6 items measuring the impact of the COVID-19 crisis on selfview of telework and digital meetings. The survey instrument was translated based on the guidelines for the cultural adaptation of self-administrated measures. Results: The reliability of the questionnaire responses was measured by Cronbach's alpha. The construct validity was checked through exploratory factor analysis followed by confirmatory factor analysis (CFA) to further assess the factor structure. CFA revealed that the model had excellent fit (root mean square error of approximation, 0.00; comparative fit index, 1.0; Tucker-Lewis index, 1; standardized root mean squared residual, 0.0). Conclusions: The Arabic version of the teleworking questionnaire had high reliability and good validity in assessing experiences and perceptions toward teleworking. While the validated survey examined perceptions and experiences during COVID-19, its use can be extended to capture experiences and perceptions during different crises.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.27 no.2
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• pp.137-140
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• 1982

Three levels of inoculum concentration from 10 to 30 percent, three kinds of adhesive materials, gum arabic, methyl cellulose and carboxy methyl cellulose, and five different pelleting materials including 4 different sources of lime and calcium carbonate were compared to investigate an optimum condition for seed inoculation by counting the number of viable rhizobium cells. For a peat-cultured Rhizobium inoculant, 10 per cent was found to be an optimum by showing 3.5 $\times$ 10$^9$ viable cells per seed. The highest number of viable cells were observed from gum arabic at 40 per cent, methyl cellulose at 5 per cent and carboxy methyl cellulose at 4 per cent. Among pelleting materials, a dental lime for investment originated from Ransom & Randolph Co. Ohio, U.S.A. resulted best as pelleting material.

• 한국생물공학회:학술대회논문집
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• 2000.11a
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• pp.769-772
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• 2000

Fish oil has abundant highlly unsaturated fatty acids such as EPA and DHA. Therefore, fish oil is very susceptible to peroxidation. Adding any antioxidants to fish oil is acceptable to prevent the peroxidation. However, nontoxic and strong naturally occurring antioxidants for fish oil was not developed. ${\alpha}-tocopherol$ is one of useful natural antioxidants, but it is not good to prevent the fish oil peroxidation. In this study, we examined the development of microencapsulated squid liver oil product to prevent effectively peroxidation of the fish oil. The acceptable materials for encapsuling the squid liver oil were gum arabic and gelatin. The ultimate encapsulation rate of squid liver oil was 45.5%.

• Food Science of Animal Resources
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• v.34 no.6
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• pp.808-814
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• 2014

This study investigated the effect of gum arabic (GA) combined with microbial transglutaminase (TG) on the functional properties of porcine myofibrillar protein (MP). As an indicator of functional property, heat-set gel and emulsion characteristics of MP treated with GA and/or TG were explored under varying NaCl concentrations (0.1-0.6 M). The GA improved thermal gelling ability of MP during thermal processing and after cooling, and concomitantly added TG assisted the formation of viscoelastic MP gel formation. Meanwhile, the addition of GA decreased cooking yield of MP gel at 0.6 M NaCl concentration, and the yield was further decreased by TG addition, mainly attributed by enhancement of protein-protein interactions. Emulsion characteristics indicated that GA had emulsifying ability and the addition of GA increased the emulsification activity index (EAI) of MP-stabilized emulsion. However, GA showed a negative effect on emulsion stability, particularly great drop in the emulsion stability index (ESI) was found in GA treatment at 0.6 M NaCl. Consequently, the results indicated that GA had a potential advantage to form a viscoelastic MP gel. For the practical aspect, the application of GA in meat processing had to be limited to the purposes of texture enhancer such as restructured products, but not low-salt products and emulsion-type meat products.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.503-522
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• 2014

In the cursive handwriting recognition process, script trajectory segmentation and modeling represent an important task for large or open lexicon context that becomes more complicated in multi-writer applications. In this paper, we will present a developed system of Arabic online handwriting modeling based on graphemes segmentation and the extraction of its geometric features. The main contribution consists of adapting the Fourier descriptors to model the open trajectory of the segmented graphemes. To segment the trajectory of the handwriting, the system proceeds by first detecting its baseline by checking combined geometric and logic conditions. Then, the detected baseline is used as a topologic reference for the extraction of particular points that delimit the graphemes' trajectories. Each segmented grapheme is then represented by a set of relevant geometric features that include the vector of the Fourier descriptors for trajectory shape modeling, normalized metric parameters that model the grapheme dimensions, its position in respect to the baseline, and codes for the description of its associated diacritics.

• Language and Information
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• v.8 no.1
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• pp.117-136
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• 2004

In this paper, we have proposed Auto- TSS, an automatic transcription module of three ambiguous symbols-period (．), colon (:) and slash (／)--using their linguistic contexts. Few previous studies have discussed the problems of ambiguities in reading those symbols into Korean alphabetic letters in order to improve the current Korean TTS (Text-To-Speech) systems. We have classified 9 different reading formulae of the three symbols, analyzed their left and right contexts, and investigated selection rules and distributions between the symbols and their contexts. Based on these linguistic features, 30 stereotyped patterns, 53 rules and 5 heuristics determining the types of reading formulae are investigated for Auto-TSS. This module works modularly in 4 steps. The pilot test was conducted with three test suites, which contain respectively 6,979, 3,491 and 2,450 morpheme clusters containing at least one of three ambiguous symbols and Arabic numeral(s). Encouraging results of 94.3%, 93.0%, 94.2% accuracy were obtained for the test suites. Our next phases are to develop a guessing routine for unknown contexts of the union symbols by using statistical information; to refine the proper nouns and terminology detecting module; and to apply Auto-TSS on a larger scale.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.317-323
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• 2021

Social media is increasingly becoming a part of our daily life for communicating each other. There are various tools and applications for communication and therefore, identity theft is a common issue among users of such application. A new style of identity theft occurs when cybercriminals break into WhatsApp account, pretend as real friends and demand money or blackmail emotionally. In order to prevent from such issues, data mining can be used for text classification (TC) in analysis authorship attribution (AA) to recognize original sender of the message. Arabic is one of the most spoken languages around the world with different variants. In this research, we built a machine learning model for mining and analyzing the Arabic messages to identify the author of the messages in Saudi dialect. Many points would be addressed regarding authorship attribution mining and analysis: collect Arabic messages in the Saudi dialect, filtration of the messages' tokens. The classification would use a cross-validation technique and different machine-learning algorithms (Naïve Baye, Support Vector Machine). Results of average accuracy for Naïve Baye and Support Vector Machine have been presented and suggestions for future work have been presented.