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Search Results (21)
  • 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

    Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

    Anwer Mustafa Hilal1,2,*, Aisha Hassan Abdalla Hashim1, Heba G. Mohamed3, Lubna A. Alharbi4, Mohamed K. Nour5, Abdullah Mohamed6, Ahmed S. Almasoud7, Abdelwahed Motwakel2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2033-2047, 2023, DOI:10.32604/csse.2023.031181 - 03 November 2022

    Abstract Recent developments on Internet and social networking have led to the growth of aggressive language and hate speech. Online provocation, abuses, and attacks are widely termed cyberbullying (CB). The massive quantity of user generated content makes it difficult to recognize CB. Current advancements in machine learning (ML), deep learning (DL), and natural language processing (NLP) tools enable to detect and classify CB in social networks. In this view, this study introduces a spotted hyena optimizer with deep learning driven cybersecurity (SHODLCS) model for OSN. The presented SHODLCS model intends to accomplish cybersecurity from the identification More >

  • Open Access

    ARTICLE

    Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

    Hanan Abdullah Mengash1, Jaber S. Alzahrani2, Majdy M. Eltahir3, Fahd N. Al-Wesabi4, Abdullah Mohamed5, Manar Ahmed Hamza6,*, Radwa Marzouk7

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1393-1407, 2023, DOI:10.32604/csse.2023.030328 - 03 November 2022

    Abstract Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, e-healthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for… 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

    Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

    Mesfer Al Duhayyim1,*, Heba G. Mohamed2, Saud S. Alotaibi3, Hany Mahgoub4,5, Abdullah Mohamed6, Abdelwahed Motwakel7, Abu Sarwar Zamani7, Mohamed I. Eldesouki8

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5011-5024, 2022, DOI:10.32604/cmc.2022.031096 - 28 July 2022

    Abstract Cyberbullying (CB) is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB. The recently developed deep learning (DL) models pave the way to design CB classifier models with maximum performance. At the same time, optimal hyperparameter tuning process plays a vital role to enhance overall results. This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification (TLGODL-CBC) model in Social Media. The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context. Initially, the input data is More >

  • Open Access

    ARTICLE

    The Association between Physical Education and Mental Health Indicators in Adolescents: A Cross-Sectional Study

    Xiaoqing Hu1, Yan Tang1,2,*

    International Journal of Mental Health Promotion, Vol.24, No.5, pp. 783-793, 2022, DOI:10.32604/ijmhp.2022.018332 - 27 July 2022

    Abstract Objective: To explore the associations between physical education attendance and mental health indicators. Methods: Using data from the Global Student Health Survey, the frequency of physical education attendance, suicidality-related indicators, loneliness, bullying, and anxiety were all assessed using a standardized self-reported questionnaire. Multivariable logistic regression was used to estimate the association between physical education attendance and mental health-related indicators. Results: The study included 276,169 participants from 71 countries (47.3% males, aged 11–18 years old). After controlling for sex, age, food insecurity, close friends, physical activity, sedentary time, others’ help, and parents’ understanding, physical education attendance was not… More >

  • Open Access

    ARTICLE

    Glowworm Optimization with Deep Learning Enabled Cybersecurity in Social Networks

    Ashit Kumar Dutta1,*, Basit Qureshi2, Yasser Albagory3, Majed Alsanea4, Anas Waleed AbulFaraj5, Abdul Rahaman Wahab Sait6

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 2097-2110, 2022, DOI:10.32604/iasc.2022.027500 - 25 May 2022

    Abstract Recently, the exponential utilization of Internet has posed several cybersecurity issues in social networks. Particularly, cyberbulling becomes a common threat to users in real time environment. Automated detection and classification of cyberbullying in social networks become an essential task, which can be derived by the use of machine learning (ML) and deep learning (DL) approaches. Since the hyperparameters of the DL model are important for optimal outcomes, appropriate tuning strategy becomes important by the use of metaheuristic optimization algorithms. In this study, an effective glowworm swarm optimization (GSO) with deep neural network (DNN) model named… More >

  • Open Access

    ARTICLE

    A Skeleton-based Approach for Campus Violence Detection

    Batyrkhan Omarov1,2,3,4,*, Sergazy Narynov1, Zhandos Zhumanov1,2, Aidana Gumar1,5, Mariyam Khassanova1,5

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 315-331, 2022, DOI:10.32604/cmc.2022.024566 - 24 February 2022

    Abstract In this paper, we propose a skeleton-based method to identify violence and aggressive behavior. The approach does not necessitate high-processing equipment and it can be quickly implemented. Our approach consists of two phases: feature extraction from image sequences to assess a human posture, followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence. A video violence dataset of 400 min comprising a single person's activities and 20 h of video data including physical violence and aggressive acts, and 13 classifications for distinguishing aggressor and victim behavior were More >

  • Open Access

    ARTICLE

    Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

    Amani Abdulrahman Albraikan1, Siwar Ben Haj Hassine2, Suliman Mohamed Fati3, Fahd N. Al-Wesabi2,4, Anwer Mustafa Hilal5,*, Abdelwahed Motwakel5, Manar Ahmed Hamza5, Mesfer Al Duhayyim6

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 907-923, 2022, DOI:10.32604/cmc.2022.024488 - 24 February 2022

    Abstract Cyberbullying (CB) is a distressing online behavior that disturbs mental health significantly. Earlier studies have employed statistical and Machine Learning (ML) techniques for CB detection. With this motivation, the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification (ODL-CDC) technique for CB detection in social networks. The proposed ODL-CDC technique involves different processes such as pre-processing, prediction, and hyperparameter optimization. In addition, GloVe approach is employed in the generation of word embedding. Besides, the pre-processed data is fed into Bidirectional Gated Recurrent Neural Network (BiGRNN) model for prediction. Moreover, hyperparameter tuning of BiGRNN More >

  • Open Access

    ARTICLE

    Consensus-Based Ensemble Model for Arabic Cyberbullying Detection

    Asma A. Alhashmi*, Abdulbasit A. Darem

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 241-254, 2022, DOI:10.32604/csse.2022.020023 - 08 October 2021

    Abstract Due to the proliferation of internet-enabled smartphones, many people, particularly young people in Arabic society, have widely adopted social media platforms as a primary means of communication, interaction and friendship making. The technological advances in smartphones and communication have enabled young people to keep in touch and form huge social networks from all over the world. However, such networks expose young people to cyberbullying and offensive content that puts their safety and emotional well-being at serious risk. Although, many solutions have been proposed to automatically detect cyberbullying, most of the existing solutions have been designed… More >

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