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  • Open Access


    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

    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 generated. Finally, the proposed method… More >

  • Open Access


    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

    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 model is carried out with… More >

  • Open Access


    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

    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 for English speaking consumers. The… More >

  • Open Access


    Integrated Approach to Detect Cyberbullying Text: Mobile Device Forensics Data

    G. Maria Jones1,*, S. Godfrey Winster2, P. Valarmathie3

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 963-978, 2022, DOI:10.32604/csse.2022.019483

    Abstract Mobile devices and social networks provide communication opportunities among the young generation, which increases vulnerability and cybercrimes activities. A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among youngsters. This paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics toolkit. We describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying detection. We use forensics techniques, Machine Learning (ML), and Deep Learning (DL) algorithms to exploit suspicious… More >

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