Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (14)
  • Open Access


    Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

    Khalid M. O. Nahar1,*, Mohammad Alauthman2, Saud Yonbawi3, Ammar Almomani4,5

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5307-5319, 2023, DOI:10.32604/cmc.2023.031848

    Abstract Social media networks are becoming essential to our daily activities, and many issues are due to this great involvement in our lives. Cyberbullying is a social media network issue, a global crisis affecting the victims and society as a whole. It results from a misunderstanding regarding freedom of speech. In this work, we proposed a methodology for detecting such behaviors (bullying, harassment, and hate-related texts) using supervised machine learning algorithms (SVM, Naïve Bayes, Logistic regression, and random forest) and for predicting a topic associated with these text data using unsupervised natural language processing, such as latent Dirichlet allocation. In addition,… More >

  • Open Access


    Methods Used to Reduce Bullying in Kindergarten from Teachers’ Perspectives

    Lina Bashatah*, Duaa Al-fifi

    International Journal of Mental Health Promotion, Vol.25, No.5, pp. 639-653, 2023, DOI:10.32604/ijmhp.2023.025878

    Abstract This study identified the methods used by kindergarten teachers to reduce bullying among their students in and out of the classroom and examined differences based on the teachers’ years of experience and the number of courses on bullying they had taken. A descriptive survey using a questionnaire tool collected responses from 208 public kindergarten teachers in Riyadh, Kingdom of Saudi Arabia. The participants agreed with using such methods to reduce bullying among children as responding to parents’ reports and following up on the reasons for a child’s absence. They also agreed that bullying in the classroom could be reduced by… More >

  • Open Access


    Firefly-CDDL: A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

    Monirah Al-Ajlan*, Mourad Ykhlef

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 19-34, 2023, DOI:10.32604/cmc.2023.033753

    Abstract There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community platforms. Among them is cyberbullying, which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react effectively. An alarmingly high percentage of people, especially teenagers, have reported being cyberbullied in recent years. A variety of approaches have been developed to detect cyberbullying, but they require time-consuming feature extraction and selection processes. Moreover, no approach to date has examined… 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 of 13 papers from four… More >

  • Open Access


    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

    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 of CB in the OSN.… More >

  • Open Access


    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

    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 online social networks, named SRO-MLCOSN… 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 and technological gadgets, conventional bullying… More >

  • Open Access


    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

    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 cleaned and pre-processed to make… More >

  • Open Access


    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

    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 signifi- cantly associated… More >

  • Open Access


    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

    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 EGSO-DNN is derived for cybersecurity… More >

Displaying 1-10 on page 1 of 14. Per Page