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  • Open Access

    ARTICLE

    A Novel Approach for Deciphering Big Data Value Using Dark Data

    Surbhi Bhatia*, Mohammed Alojail

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1261-1271, 2022, DOI:10.32604/iasc.2022.023501

    Abstract The last decade has seen a rapid increase in big data, which has led to a need for more tools that can help organizations in their data management and decision making. Business intelligence tools have removed many of the obstacles to data visibility, and numerous data mining technologies are playing an essential role in this visibility. However, the increase in big data has also led to an increase in ‘dark data’, data that does not have any predefined structure and is not generated intentionally. In this paper, we show how dark data can be mined… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    Pseudo NLP Joint Spam Classification Technique for Big Data Cluster

    WooHyun Park1, Nawab Muhammad Faseeh Qureshi2,*, Dong Ryeol Shin1

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 517-535, 2022, DOI:10.32604/cmc.2022.021421

    Abstract Spam mail classification considered complex and error-prone task in the distributed computing environment. There are various available spam mail classification approaches such as the naive Bayesian classifier, logistic regression and support vector machine and decision tree, recursive neural network, and long short-term memory algorithms. However, they do not consider the document when analyzing spam mail content. These approaches use the bag-of-words method, which analyzes a large amount of text data and classifies features with the help of term frequency-inverse document frequency. Because there are many words in a document, these approaches consume a massive amount… More >

  • Open Access

    ARTICLE

    Multilingual Sentiment Mining System to Prognosticate Governance

    Muhammad Shahid Bhatti1,*, Saman Azhar1, Abid Sohail1, Mohammad Hijji2, Hamna Ayemen1, Areesha Ramzan1

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 389-406, 2022, DOI:10.32604/cmc.2022.021384

    Abstract In the age of the internet, social media are connecting us all at the tip of our fingers. People are linkedthrough different social media. The social network, Twitter, allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights. This paper serves the purpose of text processing of a multilingual dataset including Urdu, English, and Roman Urdu. Explore machine learning solutions for sentiment analysis and train models, collect the data on government from Twitter, apply sentiment analysis, and provide a python More >

  • Open Access

    ARTICLE

    Applying Machine Learning Techniques for Religious Extremism Detection on Online User Contents

    Shynar Mussiraliyeva1, Batyrkhan Omarov1,*, Paul Yoo1,2, Milana Bolatbek1

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 915-934, 2022, DOI:10.32604/cmc.2022.019189

    Abstract In this research paper, we propose a corpus for the task of detecting religious extremism in social networks and open sources and compare various machine learning algorithms for the binary classification problem using a previously created corpus, thereby checking whether it is possible to detect extremist messages in the Kazakh language. To do this, the authors trained models using six classic machine-learning algorithms such as Support Vector Machine, Decision Tree, Random Forest, K Nearest Neighbors, Naive Bayes, and Logistic Regression. To increase the accuracy of detecting extremist texts, we used various characteristics such as Statistical More >

  • Open Access

    ARTICLE

    An Optimized English Text Watermarking Method Based on Natural Language Processing Techniques

    Fahd N. Al-Wesabi1,2,*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1519-1536, 2021, DOI:10.32604/cmc.2021.018202

    Abstract In this paper, the text analysis-based approach RTADZWA (Reliable Text Analysis and Digital Zero-Watermarking Approach) has been proposed for transferring and receiving authentic English text via the internet. Second level order of alphanumeric mechanism of hidden Markov model has been used in RTADZWA approach as a natural language processing to analyze the English text and extracts the features of the interrelationship between contexts of the text and utilizes the extracted features as watermark information and then validates it later with attacked English text to detect any tampering occurred on it. Text analysis and text zero-watermarking… More >

  • Open Access

    ARTICLE

    A Reliable NLP Scheme for English Text Watermarking Based on Contents Interrelationship

    Fahd N. Al-Wesabi1,2,*, Saleh Alzahrani3, Fuad Alyarimi3, Mohammed Abdul3, Nadhem Nemri3, Mohammed M. Almazah4

    Computer Systems Science and Engineering, Vol.37, No.3, pp. 297-311, 2021, DOI:10.32604/csse.2021.015915

    Abstract In this paper, a combined approach CAZWNLP (a combined approach of zero-watermarking and natural language processing) has been developed for the tampering detection of English text exchanged through the Internet. The third gram of alphanumeric of the Markov model has been used with text-watermarking technologies to improve the performance and accuracy of tampering detection issues which are limited by the existing works reviewed in the literature of this study. The third-grade level of the Markov model has been used in this method as natural language processing technology to analyze an English text and extract the… More >

  • Open Access

    ARTICLE

    Entropy-Based Watermarking Approach for Sensitive Tamper Detection of Arabic Text

    Fahd N. Al-Wesabi1,2,*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3635-3648, 2021, DOI:10.32604/cmc.2021.015865

    Abstract The digital text media is the most common media transferred via the internet for various purposes and is very sensitive to transfer online with the possibility to be tampered illegally by the tampering attacks. Therefore, improving the security and authenticity of the text when it is transferred via the internet has become one of the most difficult challenges that researchers face today. Arabic text is more sensitive than other languages due to Harakat’s existence in Arabic diacritics such as Kasra, and Damma in which making basic changes such as modifying diacritic arrangements can lead to… More >

  • Open Access

    RETRACTION

    RETRACTED: Recent Approaches for Text Summarization Using Machine Learning & LSTM0

    Neeraj Kumar Sirohi1,*, Mamta Bansal1, S. N. Rajan2

    Journal on Big Data, Vol.3, No.1, pp. 35-47, 2021, DOI:10.32604/jbd.2021.015954

    Abstract Nowadays, data is very rapidly increasing in every domain such as social media, news, education, banking, etc. Most of the data and information is in the form of text. Most of the text contains little invaluable information and knowledge with lots of unwanted contents. To fetch this valuable information out of the huge text document, we need summarizer which is capable to extract data automatically and at the same time capable to summarize the document, particularly textual text in novel document, without losing its any vital information. The summarization could be in the form of… More >

  • Open Access

    ARTICLE

    Tampering Detection Approach of Arabic-Text Based on Contents Interrelationship

    Fahd N. Al-Wesabi1, Abdelzahir Abdelmaboud2,*, Adnan A. Zain3, Mohammed M. Almazah4, Ammar Zahary5

    Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 483-498, 2021, DOI:10.32604/iasc.2021.014322

    Abstract Text information depends primarily on natural languages processing. Improving the security and usability of text information shared through the public internet has therefore been the most demanding problem facing researchers. In contact and knowledge sharing through the Internet, the authentication of content and the identification of digital content are becoming a key problem. Therefore, in this paper, a fragile approach of zero-watermarking based on natural language processing has been developed for authentication of content and prevention of misuse of Arabic texts distributed over the Internet. According to the proposed approach, watermark embedding, and identification was… More >

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