• 한국식품영양과학회지
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• 제32권8호
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• pp.1253-1261
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• 2003

본 연구에서 친수성콜로이드가 떡의 응고억제에 어떤 영향을 미치는지를 검토하였다. 조직검토에서 친수성콜로이드를 첨가한 떡은 대조구에 비해서 영향을 미쳤는데 그 중 arabic gum과 carrageenan이 떡의 응고억제 효과가 많이 있는 것으로 나타났다. 관능검사결과에서도 응집성은 친수성콜로이드를 첨가한 것이 대조구보다 높게 나타났다. 친수성콜로이드를 첨가했을 경우 표면 색깔의 변화가 적게 일어났다. 열특성 검토에서 호화개시온도는 arabic gum이 다소 낮은 것으로 나타났고 그 다음이 carrageenan, guar gum, gelatin, locust bean gum 순이었다. 떡의 용융엔탈피 (ΔH)의 변화에서는 대조구에 비해서 모두 낮은 값을 가지고 있었으며 그 범위는 12.8∼13.7 J/g이었다 Arabic gum이 가장 적은 값을 나타내었다. Arabic gum의 재결정도는 대조구와 비교할 때 크게 낮은 값을 나타내었다. Arabic gum과 carrageenan은 열에 대한 용융퍼짐성이 우수한 것으로 나타났다. Avrami equation의 검토에서 지수 n는 0.97 ∼ 1.12범위였다. 결론적으로 arabic gum과 carrageenan을 첨가하면 떡의 노화가 억제되는 것으로 나타났다.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.317-319
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• 2022

Rich morphology language such as Arabic needs more investigation and method to improve the sentiment analysis task. Using all document parts in the process of the sentiment analysis may add some unnecessary information to the classifier. Therefore, this paper shows the ongoing work to use sentence location as a feature with Arabic sentiment analysis. Our proposed method employs a supervised sentiment classification method by enriching the feature space model with some information from the document. The experiments and evaluations that were conducted in this work show that our proposed feature in the sentiment analysis for Arabic improves the performance of the classifier compared to the baseline model.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.359-367
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• 2022

Arabic skills are the tools by which children are prepared for the educational procedures on which their life depends. Deaf and hard of hearing students (DHH), must be able to grasp the same Arabic terms as hearing students and their different meanings in a context of different sentences less than what they are supposed to be due to their inability. However, problems arise in the same Arabic word and their different meanings in a context for (DHH) students since the way of comprehending such words does not meet the needs and circumstances of (DHH) students. Therefore, researchers introduce web-based method for Arabic words and their meanings in a context prototype that can overcome those problems. Methodology: The study sample consists of 30 (DHH) students at Al Amal City of Palestine, Gaza Region (GR). Those participants that agreed to take part in this study were recruited using a purposeful sampling method. Additionally, to examine the survey information descriptively, the Statistical Packages for social Sciences (SPSS) version 24.0 was used. A sign language teaching movie is utilized in the prototype to standardize the process and verify that Arabic vocabulary and their implications are comprehended. The Evolutionary Process Model of Prototype technique was utilized to create this system. Finding: The findings of this study show that the prototype built is workable and has the ability to help DHHS differentiate between phrases that have the same letters but distinct meanings. The findings of this study are expected to contribute to a better understanding and application of Development of Web-based Arabic Assessments for (DHH) Students in developing countries, which will help to increase the use of Development of Web-based Arabic for (HDD) students in those countries. The empirical models of Web-based Arabic for (DHH) students are established as a proof of concept for the proposed model. The results of this study are predicted to have a significant impact to the information system practitioners and to the body of knowledge.

• Asian Pacific Journal of Cancer Prevention
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• 제17권5호
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• pp.2619-2624
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• 2016

Background: Cancer is a worldwide health problem. Arabic countries are also concerned and the burden linked to the pain related to cancer is dsiquieting. The aim of this study is to set the panel of valid tools for assessing the multiple dimensions of pain in arabic speaking countries. Materials and Methods: A systematic review on PubMed, Scopus, and Science Direct databases was conducted using as key words cancer, pain and arabic speaking population. The content of 51 articles was studied and nine articles were retained for their relevance for the issue. Results: We founf eight different questionnaires. MSAS-Leb, EORTC-C30, EORTC-BR23, MDASI, FLIC, and COOP/WONCA are dedicated to physical and psychological dimensions of pain. BPI is centered on direct items for measuring pain accurately. ABQ-II is the unique tool focusing on barriers to cancer pain control. All tools are confirmed valid and reliable in the context studied for assessing pain and disconfort linked to cancer. Conclusions: This panel of questionnaires covers all relevant aims for assessing pain in diferent arabic speaking countries with the recommendation of a cultural adaptation to local arabic languages.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3701-3720
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• 2015

A system for the recognition of machine printed Arabic script is proposed. The Arabic script is shared by three languages i.e., Arabic, Urdu and Farsi. The three languages have a descent amount of vocabulary in common, thus compounding the problems for identification. Therefore, in an ideal scenario not only the script has to be differentiated from other scripts but also the language of the script has to be recognized. The recognition process involves the segregation of Arabic scripted documents from Latin, Han and other scripted documents using horizontal and vertical projection profiles, and the identification of the language. Identification mainly involves extracting connected components, which are subjected to Principle Component Analysis (PCA) transformation for extracting uncorrelated features. Later the traditional K-Nearest Neighbours (KNN) algorithm is used for recognition. Experiments were carried out by varying the number of principal components and connected components to be extracted per document to find a combination of both that would give the optimal accuracy. An accuracy of 100% is achieved for connected components >=18 and Principal components equals to 15. This proposed system would play a vital role in automatic archiving of multilingual documents and the selection of the appropriate Arabic script in multi lingual Optical Character Recognition (OCR) systems.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.120-128
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• 2023

In this work, we have presented ATSA, a hierarchical attention deep learning model for Arabic sentiment analysis. ATSA was proposed by addressing several challenges and limitations that arise when applying the classical models to perform opinion mining in Arabic. Arabic-specific challenges including the morphological complexity and language sparsity were addressed by modeling semantic composition at the Arabic morphological analysis after performing tokenization. ATSA proposed to perform phrase-chunks sentiment embedding to provide a broader set of features that cover syntactic, semantic, and sentiment information. We used phrase structure parser to generate syntactic parse trees that are used as a reference for ATSA. This allowed modeling semantic and sentiment composition following the natural order in which words and phrase-chunks are combined in a sentence. The proposed model was evaluated on three Arabic corpora that correspond to different genres (newswire, online comments, and tweets) and different writing styles (MSA and dialectal Arabic). Experiments showed that each of the proposed contributions in ATSA was able to achieve significant improvement. The combination of all contributions, which makes up for the complete ATSA model, was able to improve the classification accuracy by 3% and 2% on Tweets and Hotel reviews datasets, respectively, compared to the existing models.

• 수완나부미
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• 제3권1호
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• pp.47-66
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• 2011

Malay society is rooted on the Islamic concept. That Islam influenced every corner of that Malay society which had ever been an edge of the civilizations of the Indus and Ganges. Once the letters of that Hindu religion namely Sanscrit was adopted to this Malay society for the purpose of getting the Malay language, that is, Bahasa Melayu down to the practical literation but in vain. The Sanscrit was too complicated for Malay society to imitate and put it into practice in everyday life because it was totally different type of letters which has many of the similar allographs for a sound. In the end Malay society gave it up and just used the Malay language without using any letters for herself. After a few centuries Islam entered this Malay society with taking Arabic letters. It was not merely influencing Malay cultures, but to the religious life according to wide spread of that Islam. Finally Arabic letters was to the very means that Malay language was written by. It means that Arabic letters had been used for Arabic language in former times, but it became a similar form of letters for a new language which was named as Malay language. This Arabic letters for Arabic language has no problems whereas Arabic letters for Malay language has some of it. Naturally speaking, arabic letters was not designed for any other language but just for Arabic language itself. On account of this, there occurred a few problems in writing Malay consonants, just like p, ng, g, c, ny and v. These 6 letters could never be written down in Arabic letters. Those 6 ones were never known before in trying to pronounce by Arab people. Therefore, Malay society had only to modify a few new forms of letters for these 6 letters which had frequently been found in their own Malay sounds. As a result, pa was derived from fa, nga was derived from ain, ga was derived from kaf, ca was derived from jim, nya was derived from tha or ba, and va was derived from wau itself. Where must these 6 newly modified letters be put on this Arabic keyboard? This is the very core of this working paper. As a matter of course, these 6 letters were put on the place where 6 Arabic signs which were scarecely written in Malay language. Those 6 are found when they are used only in the 'shift-key-using-letters.' These newly designed 6 letters were put instead of the original places of fatha, kasra, damma, sukun, tanween and so on. The main differences between the 2 set of 6 letters are this: 6 in Arabic orginal keyboard are only signs for Arabic letters, on the other hand 6 Malay's are real letters. In others words, 6 newly modified Malay letters were substituted for unused 6 Arabic signs in Malay keyboard. This type of newly designed Malay Jawi Script keyboard is still used in Malaysia, Brunei and some other Malay countries. But this sort of keyboard also needs to go forward to find out another way of keyboard system which is in accordance with the alphabetically ordered keyboard system. It means that alif is going to be typed for A key, and zai shall be typed when Z key is pressed. This keyboard system is called 'Malay Jawi-English Rumi matching keyboard system', even though this system should probably be inconvenient for Malay Jawi experts who are good at Arabic 'alif-ba-ta'order.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.41-46
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• 2023

Because of the significant role that harakat plays in Arabic text, this paper used deep learning to extract Arabic text with its harakat from an image. Convolutional neural networks and recurrent neural network algorithms were applied to the dataset, which contained 110 images, each representing one word. The results showed the ability to extract some letters with harakat.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.1-6
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• 2023

Morphological analysis is a branch of natural language processing, is now a rapidly growing field. The fundamental tenet of morphological analysis is that it can establish the roots or stems of words and enable comparison to the original term. Arabic is a highly inflected and derivational language and it has a strong structure. Each root or stem can have a large number of affixes attached to it due to the non-concatenative nature of Arabic morphology, increasing the number of possible inflected words that can be created. Accurate verb recognition and extraction are necessary nearly all issues in well-known study topics include Web Search, Information Retrieval, Machine Translation, Question Answering and so forth. in this work we have designed and implemented an algorithm to detect and recognize Arbic Verbs from Arabic text.The suggested technique was created with "Python" and the "pyqt5" visual package, allowing for quick modification and easy addition of new patterns. We employed 17 alternative patterns to represent all verbs in terms of singular, plural, masculine, and feminine pronouns as well as past, present, and imperative verb tenses. All of the verbs that matched these patterns were used when a verb has a root, and the outcomes were reliable. The approach is able to recognize all verbs with the same structure without requiring any alterations to the code or design. The verbs that are not recognized by our method have no antecedents in the Arabic roots. According to our work, the strategy can rapidly and precisely identify verbs with roots, but it cannot be used to identify verbs that are not in the Arabic language. We advise employing a hybrid approach that combines many principles as a result.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.112-119
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• 2021

Social media has become a global means of communication in people's lives. Most people are using Twitter for communication purposes and its inappropriate use, which has negative effects on people's lives. One of the widely common misuses of Twitter is cyberbullying. As the resources of dialectal Arabic are rare, so for cyberbullying most people are using dialectal Arabic. For this reason, the ultimate goal of this study is to detect and classify cyberbullying on Twitter in the Arabic context in Saudi Arabia. To help in the detection and classification of tweets, Pointwise Mutual Information (PMI) to generate a lexicon, and Support Vector Machine (SVM) algorithms are used. The evaluation is performed on both methods in terms of the F1-score. However, the F1-score after applying the PMI is 50%, while after the SVM application on the resampling data it is 82%. The analysis of the results shows that the SVM algorithm outperforms better.