AI for combating cyber bullying
| dc.contributor.author | CHANDAN R 1NH21AI019 GIRIDHARAN H 1ΝΗ21AI032 CHRIS JORDAN 1NH21CS062 SANJANA JELLA 1NHH21CS299 | |
| dc.date.accessioned | 2025-05-20T04:54:36Z | |
| dc.date.available | 2025-05-20T04:54:36Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Cyberbullying poses a significant threat to online safety, particularly impacting younger users who are more susceptible to toxic digital interactions. This research presents an innovative Al-based solution designed to automatically detect harmful behavior and toxic comments in online environments. By leveraging advanced deep learning models, specifically Bidirectional Long Short-Term Memory (Bi-LSTM) networks combined with Natural Language Processing (NLP) techniques, the system is trained on extensive datasets of user comments to identify both subtle and overt signs of cyberbullying. The approach incorporates effective preprocessing methods such as TextVectorization, along with caching, shuffling, and batching techniques to optimize performance. The Al system's ability to process and classify harmful content in real-time makes it a powerful tool for monitoring online communities, promoting safer interactions, and significantly reducing incidents of online harassment. Additionally, its adaptability across diverse platforms ensures scalability and flexibility in combating evolving cyberbullying tactics. By equipping digital platforms with this technology, the solution fosters healthier online environments, empowers moderators, and acts as a critical resource for organizations dedicated to enhancing user safety and well-being. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/18990 | |
| dc.language.iso | en | |
| dc.publisher | NHCE | |
| dc.title | AI for combating cyber bullying | |
| dc.type | Learning Object |