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  • Open Access

    ARTICLE

    Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

    Milos Dobrojevic1,4, Luka Jovanovic1, Lepa Babic3, Miroslav Cajic5, Tamara Zivkovic6, Miodrag Zivkovic2, Suresh Muthusamy7, Milos Antonijevic2, Nebojsa Bacanin2,4,8,9,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4997-5027, 2024, DOI:10.32604/cmc.2024.054459 - 12 September 2024

    Abstract Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones, computers, or tablets. It can occur through various channels, such as social media, text messages, online forums, or gaming platforms. Cyberbullying involves using technology to intentionally harm, harass, or intimidate others and may take different forms, including exclusion, doxing, impersonation, harassment, and cyberstalking. Unfortunately, due to the rapid growth of malicious internet users, this social phenomenon is becoming more frequent, and there is a huge need to address this issue. Therefore, the main goal of the research… More >

  • Open Access

    REVIEW

    Unlocking the Potential: A Comprehensive Systematic Review of ChatGPT in Natural Language Processing Tasks

    Ebtesam Ahmad Alomari*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 43-85, 2024, DOI:10.32604/cmes.2024.052256 - 20 August 2024

    Abstract As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been a notable growth in research activity. This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain. This review paper systematically investigates the role of ChatGPT in diverse NLP tasks, including information extraction, Name Entity Recognition (NER), event extraction, relation extraction, Part of Speech (PoS) tagging, text classification, sentiment analysis, emotion recognition and text annotation. The novelty of this work lies in its… More >

  • Open Access

    ARTICLE

    A Machine Learning Approach to Cyberbullying Detection in Arabic Tweets

    Dhiaa Musleh1, Atta Rahman1,*, Mohammed Abbas Alkherallah1, Menhal Kamel Al-Bohassan1, Mustafa Mohammed Alawami1, Hayder Ali Alsebaa1, Jawad Ali Alnemer1, Ghazi Fayez Al-Mutairi1, May Issa Aldossary2, Dalal A. Aldowaihi1, Fahd Alhaidari3

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1033-1054, 2024, DOI:10.32604/cmc.2024.048003 - 18 July 2024

    Abstract With the rapid growth of internet usage, a new situation has been created that enables practicing bullying. Cyberbullying has increased over the past decade, and it has the same adverse effects as face-to-face bullying, like anger, sadness, anxiety, and fear. With the anonymity people get on the internet, they tend to be more aggressive and express their emotions freely without considering the effects, which can be a reason for the increase in cyberbullying and it is the main motive behind the current study. This study presents a thorough background of cyberbullying and the techniques used… More >

  • Open Access

    ARTICLE

    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 - 18 December 2023

    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

    REVIEW

    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 - 28 December 2022

    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

    REVIEW

    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 - 28 December 2022

    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

    ARTICLE

    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 - 29 September 2022

    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

    ARTICLE

    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 - 22 September 2022

    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

    ARTICLE

    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 - 15 June 2022

    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

    ARTICLE

    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 - 06 June 2022

    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 >

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