Image cyberbullying detection and recognition using transfer deep machine learning

Almomani, Ammar; Nahar, Khalid; Alauthman, Mohammad; Al-Betar, Mohammed Azmi; Yaseen, Qussai; Gupta, Brij B.

doi:10.1016/j.ijcce.2023.11.002

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…(2024) [17] proposed a method using a CNN to detect cyberbullying incidents on Instagram, demonstrating the capacity of deep learning models to discern intricate patterns in multimedia-rich content. Long Short-Term Memory (LSTM) networks, a type of RNN, have been employed for sequential modeling, enabling the understanding of temporal dynamics in cyberbullying conversations [18] .…”

Section: Existing Approaches To Cyberbullying Detectionmentioning

confidence: 99%

Attribute-specific Cyberbullying Detection Using Artificial Intelligence

Orelaja,

Ejiofor,

Sarpong

et al. 2024

j. of electron. & inf. syst.

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Cyberbullying, a pervasive issue in the digital age, poses threats to individuals’ well-being across various attributes such as religion, age, ethnicity, and gender. This research employs artificial intelligence to detect cyberbullying instances in Twitter data, utilizing both traditional and deep learning models. The study repurposes the Sentiment140 dataset, originally intended for sentiment analysis, for the nuanced task of cyberbullying detection. Ethical considerations guide the dataset transformation process, ensuring responsible AI development. The Naive Bayes algorithm demonstrates commendable precision, recall, and accuracy, showcasing its efficacy. The Bi-LSTM model, leveraging deep learning capabilities, exhibits nuanced cyberbullying detection. The study also underscores limitations, emphasizing the need for refined models and diverse datasets.

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Section: Existing Approaches To Cyberbullying Detectionmentioning

confidence: 99%

Attribute-specific Cyberbullying Detection Using Artificial Intelligence

Orelaja,

Ejiofor,

Sarpong

et al. 2024

j. of electron. & inf. syst.

View full text Add to dashboard Cite

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“…The use of semantic web technologies in AI chatbots facilitates the creation of a knowledge base that organizes information in a structured format, allowing for efficient retrieval and use of data (Gkinko & Elbanna, 2022;Almomani et al, 2024). This structured approach enables chatbots to deliver more personalized and contextually relevant responses to users, thereby enhancing the overall user experience and information delivery process.…”

mentioning

confidence: 99%

Examining the Influence of AI Chatbots on Semantic Web-Based Global Information Management in Various Industries

Xian,

Guomin,

Arya

et al. 2024

International Journal on Semantic Web and Information Systems

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This article presents a comprehensive analysis of the application and effect of ChatGPT, an advanced AI chatbot model, on global information management across various industries such as healthcare, industry, education, and more. Leveraging a dataset obtained from the Scopus database encompassing research papers from 2022 to 2023, this study investigates the influence of ChatGPT by examining publisher impact, authorship patterns based on Lotka's Law, country-specific scientific production, and keyword distribution. The analysis sheds light on prominent publishers, prolific authors, geographic distribution of research contributions, and prevailing research themes. By understanding the impact of ChatGPT in these sectors, this research contributes to the advancement of knowledge and facilitates informed decision-making regarding the responsible and effective utilization of artificial intelligence (AI) chatbot technologies in global information management.

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