• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.1-12
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• 2023

With the rapid increase of user generated data on digital platforms, the task of categorizing and classifying theses huge data has become difficult. Topic modeling is an unsupervised machine learning technique that can be used to get a summary from a large collection of documents. Topic modeling has been widely used in English content, yet the application of topic modeling in Arabic language is limited. Therefore, the aim of this paper is to provide a systematic review of the application of topic modeling algorithms in Arabic content. Using a well-known and trusted databases including ScienceDirect, IEEE Xplore, Springer Link, and Google Scholar. Considering the publication date from 2012 to 2022, we got 60 papers. After refining the papers based on predefined criteria, we resulted in 32 papers. Our result show that unfortunately the application of topic modeling techniques in Arabic content is limited.

• Food Science and Preservation
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• v.12 no.6
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• pp.572-577
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• 2005

This study was conducted to investigate the effect of binding agents on the physicochemical characteristics of granule prepared by Lentinus edodes. The mushroom powder was mixed with com starch, lactose, gelatin, gum arabic, or dextrin(DE=23), and the mixtures were passed to granule sieve. Solubility of granule was in the following order, gum arabic>gelatin>lactose, dextrin>corn starch. L value was in the following order, com starch, lactose>gelatin, gum arabic, dextrin. a value and pH were not affected by the binding agents. b value was in following order, lactose, corn starch>gelatin, gum arabic>dextrin. Viscosity was in the following order, gelatin, corn starch>gum arabic, dextrin>lactose. Water absorption was in the following order, gelatin>lactose>corn starch>dextrin>gum arabic. Sugar content was in the following order, gum arabic>lactose, dextrin>corn starch, gelatin. Protein content was the highest in granule formed with gelatin. These results suggested that gum arabic can be utilized for improving solubility and lowering absorption of Lentinus edodes granule as binding agent.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.331-337
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• 2022

Smart learning is augmented with digital, context-aware, and adaptable technologies to encourage students to learn better and faster. To ensure that digital learning is successful and that implementation is efficient, it is critical that the dimensions of digital learning are arranged correctly and that interactions between the various elements are merged in an efficient and optimal manner. This paper builds and discusses a basic framework for smart learning in the digital age, aimed to improve students' abilities and performance in learning. The proposed framework consists of five dimensions: Teacher, Technology, Learner, Digital content, and Evaluation. The Teacher and Learner dimensions operate on two levels: (a) an abstract level to fit in knowledge and skills or interpersonal characteristics and (b) a concrete level in the form of digital devices used by teachers and learners. Moreover, this paper proposes asynchronous online course delivery model. An Arabic smart learning platform has been developed, based on these smart learning core dimensions and the asynchronous online course delivery model, because despite the official status of this language in many countries, there is a lack of Arabic platforms to teach Arabic. Moreover, many non-native Arabic speakers around the world have expressed an interest in learning it. The Arabic digital platform consists of over 70 lessons classified into three competence levels: beginner, intermediate, and advanced, delivered by Arabic experts and Arabic linguists from various Arab countries. The five dimensions are described for the Arabic platform in this paper. Learner dimension is the Arabic and non-Arabic speakers, Teacher dimension is Arabic experts and Arabic linguistics, Technology dimension consists of technology for Arabic platform that includes web design, cloud computing, big data, etc. The digital contents dimension consists of web-based video, records, etc. The evaluation dimension consists of Teachers rating, comments, and surveys.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.175-186
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• 2022

This research article develops an Arabic books' recommendation system, which is based on the content similarity that assists users to search for the right book and predict the appropriate and suitable books pertaining to their literary style. In fact, the system directs its users toward books, which can meet their needs from a large dataset of Information. Further, this system makes its predictions based on a set of data that is gathered from different books and converts it to vectors by using the TF-IDF system. After that, the recommendation algorithms such as the cosine similarity, the sequence matcher similarity, and the semantic similarity aggregate data to produce an efficient and effective recommendation. This approach is advantageous in recommending previously unrated books to users with unique interests. It is found to be proven from the obtained results that the results of the cosine similarity of the full content of books, the results of the sequence matcher similarity of Arabic titles of the books, and the results of the semantic similarity of English titles of the books are the best obtained results, and extremely close to the average of the result related to the human assigned/annotated similarity. Flask web application is developed with a simple interface to show the recommended Arabic books by using cosine similarity, sequence matcher similarity, and semantic similarity algorithms with all experiments that are conducted.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.5
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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.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.155-162
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• 2021

The expansion of the world wide web has led to a huge amount of user generated content over different forums and social media platforms, these rich data resources offer the opportunity to reflect, and track changing public sentiments and help to develop proactive reactions strategies for decision and policy makers. Analysis of public emotions and opinions towards events and sentimental trends can help to address unforeseen areas of public concerns. The need of developing systems to analyze these sentiments and the topics behind them has emerged tremendously. While most existing works reported in the literature have been carried out in English, this paper, in contrast, aims to review recent research works in Arabic language in the field of thematic sentiment analysis and which techniques they have utilized to accomplish this task. The findings show that the prevailing techniques in Arabic topic-based sentiment analysis are based on traditional approaches and machine learning methods. In addition, it has been found that considerably limited recent studies have utilized deep learning approaches to build high performance models.

• YAKHAK HOEJI
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• v.28 no.4
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• pp.223-229
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• 1984

Microcapsules of indomethacin were prepared by the complex coacervation technique using gelatin-gum arabic as the wall-forming material. The effects of varying drug-to-matrix ratios and formalization time, and hydroxy propyl cellulose (HPC) added on the release of drug from microcapsules were studied. As the amount of wall-forming material increased, the drug content in the microcapsules decreased and the release of drug from microcapsules was retarded. The drug content was lower in the HPC added microcapsules than that in the microcapsules was retarded. The drug content was lower in the HPC added microcapsules than that in the microcapsules without HPC and the microcapsules with 1:4 drug-to-matrix ratio showed the slowest release. The release rate of the drug from microcapsules with 1:2 drug-to-matrix was delayed according to the increase of formalization time and the microcapsules formalized for 24hr showed ratio the most retardation.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.61-71
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• 2024

The dynamic nature of Online Social Networks (OSNs), especially on platforms like Twitter, presents challenges in identifying and responding to community attacks, particularly within Arabic content. The proposed integrated system addresses these challenges by achieving 91% accuracy in detecting real-time community event attacks while efficiently managing computational costs. This is accomplished through the use of specialized integrated approach clustering to detect both major and minor attacks. Additionally, the system leverages clustering algorithms, temporal modules, and social network graphs to identify events, map communities, and analyze online dynamics. An extensive parameter sensitivity analysis was conducted to fine-tune the algorithm, and the system's effectiveness was validated using a benchmark dataset, demonstrating substantial improvements in event detection.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.9-16
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• 2023

Speech can actively elicit feelings and attitudes by using words. It is important for researchers to identify the emotional content contained in speech signals as well as the sort of emotion that resulted from the speech that was made. In this study, we studied the emotion recognition system using a database in Arabic, especially in the Saudi dialect, the database is from a YouTube channel called Telfaz11, The four emotions that were examined were anger, happiness, sadness, and neutral. In our experiments, we extracted features from audio signals, such as Mel Frequency Cepstral Coefficient (MFCC) and Zero-Crossing Rate (ZCR), then we classified emotions using many classification algorithms such as machine learning algorithms (Support Vector Machine (SVM) and K-Nearest Neighbor (KNN)) and deep learning algorithms such as (Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM)). Our Experiments showed that the MFCC feature extraction method and CNN model obtained the best accuracy result with 95%, proving the effectiveness of this classification system in recognizing Arabic spoken emotions.

• Korean Journal of Food Science and Technology
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• v.41 no.6
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• pp.657-661
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• 2009

Liquid microparticulated seaweed calcium was manufactured via a wet grinding process. Thereafter, different forming agents such as cyclodextrin, gum arabic, and Na-caseinate were added to the liquid calcium, which was then spray-dried to investigate the quality of the powdered calcium treatments. The moisture contents of samples were approximately 2%. It was also determined that the different kinds of forming agents did not affect the spray drying efficiency. In addition, calcium solubility was the highest in a solution of pH 2. In buffer solution and vinegar, the powdered calcium made with gum arabic showed the highest solubility among the treatments. The calcium contents of all the powdered microparticulate seaweed calcium samples were about 28%, and calcium content was not affected by the forming agents. The spray-dried calcium powder made by spray drying with gum arabic had the highest water vapor uptake, whereas the seaweed calcium was stable in terms of water adsorption. The results of SEM observations indicated that a portion of the spray-dried calcium powders were in nano-scale after wet-grinding. Among the treatments, the use of saccharides as a forming agent resulted in the most uniform particle distribution after spray-drying.