Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This project introduces a hybrid LSTM-CNN model with Word2Vec embeddings to enhance cyberbullying detection. LSTM captures sequential patterns in user posts, while CNN extracts key features from text, enabling more accurate classification of bullying vs. non-bullying content.
Cyberbullying on digital platforms poses serious emotional and psychological risks. Traditional detection methods often miss subtle context and semantics in online text.
Experimental results show improved detection accuracy and efficiency, offering a robust solution to identify and mitigate harmful online behavior.
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Cite Article:
"Protective User From Online Harassment Through Automated Deletion System", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c672-c676, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505278.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator