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  • Open Access


    Shallow Neural Network and Ontology-Based Novel Semantic Document Indexing for Information Retrieval

    Anil Sharma1,*, Suresh Kumar2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1989-2005, 2022, DOI:10.32604/iasc.2022.026095

    Abstract Information Retrieval (IR) systems are developed to fetch the most relevant content matching the user’s information needs from a pool of information. A user expects to get IR results based on the conceptual contents of the query rather than keywords. But traditional IR approaches index documents based on the terms that they contain and ignore semantic descriptions of document contents. This results in a vocabulary gap when queries and documents use different terms to describe the same concept. As a solution to this problem and to improve the performance of IR systems, we have designed… More >

  • Open Access


    Content Based Automated File Organization Using Machine Learning Approaches

    Syed Ali Raza1,2, Sagheer Abbas1, Taher M. Ghazal3,4, Muhammad Adnan Khan5,6, Munir Ahmad1, Hussam Al Hamadi7,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1927-1942, 2022, DOI:10.32604/cmc.2022.029400

    Abstract In the world of big data, it's quite a task to organize different files based on their similarities. Dealing with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer user. Much of file management related tasks will be solved if the files on any operating system are somehow categorized according to their similarities. Then, the browsing process can be performed quickly and easily. This research aims to design a system to automatically organize files based on their similarities in… More >

  • Open Access


    Contextual Text Mining Framework for Unstructured Textual Judicial Corpora through Ontologies

    Zubair Nabi1, Ramzan Talib1,*, Muhammad Kashif Hanif1, Muhammad Awais2

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1357-1374, 2022, DOI:10.32604/csse.2022.025712

    Abstract Digitalization has changed the way of information processing, and new techniques of legal data processing are evolving. Text mining helps to analyze and search different court cases available in the form of digital text documents to extract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reduced time. The rapid development of judicial ontologies seems to deliver interesting problem solving to legal knowledge formalization. Mining context information through ontologies from corpora is a challenging and interesting field. This research paper presents More >

  • Open Access


    Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning

    Seon Hong Lee1, Haein Lee1, Jang Hyun Kim2,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4983-4997, 2022, DOI:10.32604/cmc.2022.027943

    Abstract Metaverse is one of the main technologies in the daily lives of several people, such as education, tour systems, and mobile application services. Particularly, the number of users of mobile metaverse applications is increasing owing to the merit of accessibility everywhere. To provide an improved service, it is important to analyze online reviews that contain user satisfaction. Several previous studies have utilized traditional methods, such as the structural equation model (SEM) and technology acceptance method (TAM) for exploring user satisfaction, using limited survey data. These methods may not be appropriate for analyzing the users of… More >

  • Open Access


    XGBRS Framework Integrated with Word2Vec Sentiment Analysis for Augmented Drug Recommendation

    Shweta Paliwal1, Amit Kumar Mishra2,*, Ram Krishn Mishra3, Nishad Nawaz4, M. Senthilkumar5

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5345-5362, 2022, DOI:10.32604/cmc.2022.025858

    Abstract Machine Learning is revolutionizing the era day by day and the scope is no more limited to computer science as the advancements are evident in the field of healthcare. Disease diagnosis, personalized medicine, and Recommendation system (RS) are among the promising applications that are using Machine Learning (ML) at a higher level. A recommendation system helps inefficient decision-making and suggests personalized recommendations accordingly. Today people share their experiences through reviews and hence designing of recommendation system based on users’ sentiments is a challenge. The recommendation system has gained significant attention in different fields but considering More >

  • Open Access


    Automated Handwriting Recognition and Speech Synthesizer for Indigenous Language Processing

    Bassam A. Y. Alqaralleh1,*, Fahad Aldhaban1, Feras Mohammed A-Matarneh2, Esam A. AlQaralleh3

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3913-3927, 2022, DOI:10.32604/cmc.2022.026531

    Abstract In recent years, researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities. The recent developments of artificial intelligence (AI), natural language processing (NLP), and computational linguistics (CL) find useful in the analysis of regional low resource languages. Automatic lexical task participation might be elaborated to various applications in the NLP. It is apparent from the availability of effective machine recognition models and open access handwritten databases. Arabic language is a commonly spoken Semitic language, and it is written with the cursive Arabic alphabet from right to left. Arabic… More >

  • Open Access


    Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services

    Chao Ma1,*, Yinggang Sun1, Zhenguo Yang1, Hai Huang1, Dongyang Zhan2,3, Jiaxing Qu4

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6201-6217, 2022, DOI:10.32604/cmc.2022.022717

    Abstract The number of mobile application services is showing an explosive growth trend, which makes it difficult for users to determine which ones are of interest. Especially, the new mobile application services are emerge continuously, most of them have not be rated when they need to be recommended to users. This is the typical problem of cold start in the field of collaborative filtering recommendation. This problem may makes it difficult for users to locate and acquire the services that they actually want, and the accuracy and novelty of service recommendations are also difficult to satisfy… More >

  • Open Access


    Political Ideology Detection of News Articles Using Deep Neural Networks

    Khudran M. Alzhrani*

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 483-500, 2022, DOI:10.32604/iasc.2022.023914

    Abstract Individuals inadvertently allow emotions to drive their rational thoughts to predetermined conclusions regarding political partiality issues. Being well-informed about the subject in question mitigates emotions’ influence on humans’ cognitive reasoning, but it does not eliminate bias. By nature, humans tend to pick a side based on their beliefs, personal interests, and principles. Hence, journalists’ political leaning is defining factor in the rise of the polarity of political news coverage. Political bias studies usually align subjects or controversial topics of the news coverage to a particular ideology. However, politicians as private citizens or public officials are… More >

  • Open Access


    Insider Threat Detection Based on NLP Word Embedding and Machine Learning

    Mohd Anul Haq1, Mohd Abdul Rahim Khan1,*, Mohammed Alshehri2

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 619-635, 2022, DOI:10.32604/iasc.2022.021430

    Abstract The growth of edge computing, the Internet of Things (IoT), and cloud computing have been accompanied by new security issues evolving in the information security infrastructure. Recent studies suggest that the cost of insider attacks is higher than the external threats, making it an essential aspect of information security for organizations. Efficient insider threat detection requires state-of-the-art Artificial Intelligence models and utility. Although significant have been made to detect insider threats for more than a decade, there are many limitations, including a lack of real data, low accuracy, and a relatively low false alarm, which… More >

  • Open Access


    Arabic Fake News Detection Using Deep Learning

    Khaled M. Fouad1,3, Sahar F. Sabbeh1,2,*, Walaa Medhat1,3

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3647-3665, 2022, DOI:10.32604/cmc.2022.021449

    Abstract Nowadays, an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication. However, because user-generated content is unregulated, it may contain offensive content such as fake news, insults, and harassment phrases. The identification of fake news and rumors and their dissemination on social media has become a critical requirement. They have adverse effects on users, businesses, enterprises, and even political regimes and governments. State of the art has tackled the English language for news and used feature-based algorithms. This paper proposes a… More >

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