Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (25)
  • Open Access


    Phishing Website URL’s Detection Using NLP and Machine Learning Techniques

    Dinesh Kalla1,*, Sivaraju Kuraku2

    Journal on Artificial Intelligence, Vol.5, pp. 145-162, 2023, DOI:10.32604/jai.2023.043366

    Abstract Phishing websites present a severe cybersecurity risk since they can lead to financial losses, data breaches, and user privacy violations. This study uses machine learning approaches to solve the problem of phishing website detection. Using artificial intelligence, the project aims to provide efficient techniques for locating and thwarting these dangerous websites. The study goals were attained by performing a thorough literature analysis to investigate several models and methods often used in phishing website identification. Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Classifiers, Linear Support Vector Classifiers, and Naive Bayes were all used More >

  • Open Access


    Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review

    Batyrkhan Omarov1,*, Sergazi Narynov2, Zhandos Zhumanov2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5105-5122, 2023, DOI:10.32604/cmc.2023.034655

    Abstract Clinical applications of Artificial Intelligence (AI) for mental health care have experienced a meteoric rise in the past few years. AI-enabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals. Such initiatives, which range from “virtual psychiatrists” to “social robots” in mental health, strive to improve nursing performance and cost management, as well as meeting the mental health needs of vulnerable and underserved populations. Nevertheless, there is still a substantial gap between recent progress in AI mental health and the widespread use of… More >

  • Open Access


    A Review of Machine Learning Techniques in Cyberbullying Detection

    Daniyar Sultan1,2,*, Batyrkhan Omarov3, Zhazira Kozhamkulova4, Gulnur Kazbekova5, Laura Alimzhanova1, Aigul Dautbayeva6, Yernar Zholdassov1, Rustam Abdrakhmanov3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5625-5640, 2023, DOI:10.32604/cmc.2023.033682

    Abstract Automatic identification of cyberbullying is a problem that is gaining traction, especially in the Machine Learning areas. Not only is it complicated, but it has also become a pressing necessity, considering how social media has become an integral part of adolescents’ lives and how serious the impacts of cyberbullying and online harassment can be, particularly among teenagers. This paper contains a systematic literature review of modern strategies, machine learning methods, and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet. We undertake an in-depth review… More >

  • Open Access


    Evaluating Neural Dialogue Systems Using Deep Learning and Conversation History

    Inshirah Ali AlMutairi*, Ali Mustafa Qamar

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 155-165, 2022, DOI:10.32604/jai.2022.032390

    Abstract Neural talk models play a leading role in the growing popular building of conversational managers. A commonplace criticism of those systems is that they seldom understand or use the conversation data efficiently. The development of profound concentration on innovations has increased the use of neural models for a discussion display. In recent years, deep learning (DL) models have achieved significant success in various tasks, and many dialogue systems are also employing DL techniques. The primary issues involved in the generation of the dialogue system are acquiring perspectives into instinctual linguistics, comprehension provision, and conversation assessment.… More >

  • Open Access


    A Study of BERT-Based Classification Performance of Text-Based Health Counseling Data

    Yeol Woo Sung, Dae Seung Park, Cheong Ghil Kim*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 795-808, 2023, DOI:10.32604/cmes.2022.022465

    Abstract The entry into a hyper-connected society increases the generalization of communication using SNS. Therefore, research to analyze big data accumulated in SNS and extract meaningful information is being conducted in various fields. In particular, with the recent development of Deep Learning, the performance is rapidly improving by applying it to the field of Natural Language Processing, which is a language understanding technology to obtain accurate contextual information. In this paper, when a chatbot system is applied to the healthcare domain for counseling about diseases, the performance of NLP integrated with machine learning for the accurate More >

  • Open Access


    Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

    Daniyar Sultan1,2, Aigerim Toktarova3,*, Ainur Zhumadillayeva4, Sapargali Aldeshov5,6, Shynar Mussiraliyeva1, Gulbakhram Beissenova6,7, Abay Tursynbayev8, Gulmira Baenova4, Aigul Imanbayeva6

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2115-2131, 2023, DOI:10.32604/cmc.2023.032993

    Abstract Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms… More >

  • Open Access


    NLP-Based Subject with Emotions Joint Analytics for Epidemic Articles

    Woo Hyun Park1, Isma Farah Siddiqui2, Dong Ryeol Shin1, Nawab Muhammad Faseeh Qureshi3,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2985-3001, 2022, DOI:10.32604/cmc.2022.028241

    Abstract For the last couple years, governments and health authorities worldwide have been focused on addressing the Covid-19 pandemic; for example, governments have implemented countermeasures, such as quarantining, pushing vaccine shots to minimize local spread, investigating and analyzing the virus’ characteristics, and conducting epidemiological investigations through patient management and tracers. Therefore, researchers worldwide require funding to achieve these goals. Furthermore, there is a need for documentation to investigate and trace disease characteristics. However, it is time consuming and resource intensive to work with documents comprising many types of unstructured data. Therefore, in this study, natural language… More >

  • Open Access


    Deep-BERT: Transfer Learning for Classifying Multilingual Offensive Texts on Social Media

    Md. Anwar Hussen Wadud1, M. F. Mridha1, Jungpil Shin2,*, Kamruddin Nur3, Aloke Kumar Saha4

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.027841

    Abstract Offensive messages on social media, have recently been frequently used to harass and criticize people. In recent studies, many promising algorithms have been developed to identify offensive texts. Most algorithms analyze text in a unidirectional manner, where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences. In addition, there are many separate models for identifying offensive texts based on monolingual and multilingual, but there are a few models that can detect both monolingual and multilingual-based offensive texts. In this study, a detection system has been developed for both monolingual… More >

  • Open Access


    Impact of Data Quality on Question Answering System Performances

    Rachid Karra*, Abdelali Lasfar

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 335-349, 2023, DOI:10.32604/iasc.2023.026695

    Abstract In contrast with the research of new models, little attention has been paid to the impact of low or high-quality data feeding a dialogue system. The present paper makes the first attempt to fill this gap by extending our previous work on question-answering (QA) systems by investigating the effect of misspelling on QA agents and how context changes can enhance the responses. Instead of using large language models trained on huge datasets, we propose a method that enhances the model's score by modifying only the quality and structure of the data feed to the model.… 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 >

Displaying 1-10 on page 1 of 25. Per Page