Mohammed Shaheen Alam Jony

Work place: Department of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh

E-mail: shaheenalamjony9@gmail.com

Website: https://orcid.org/0000-0003-3010-9130

Research Interests: Software Engineering, Computational Learning Theory

Biography

Md. Shaheen Alam Jony was born in the year 1999. Currently, he is pursuing a degree in Computer Science and Engineering at the American International University - Bangladesh, where he is enrolled as a student. His research focuses mostly on Deep Learning and Software Engineering as his key areas of study.

Author Articles
Machine Learning in Cyberbullying Detection from Social-Media Image or Screenshot with Optical Character Recognition

By Tofayet Sultan Nusrat Jahan Ritu Basak Mohammed Shaheen Alam Jony Rashidul Hasan Nabil

DOI: https://doi.org/10.5815/ijisa.2023.02.01, Pub. Date: 8 Apr. 2023

Along with the growth of the Internet, social media usage has drastically expanded. As people share their opinions and ideas more frequently on the Internet and through various social media platforms, there has been a notable rise in the number of consumer phrases that contain sentiment data. According to reports, cyberbullying frequently leads to severe emotional and physical suffering, especially in women and young children. In certain instances, it has even been reported that sufferers attempt suicide. The bully may occasionally attempt to destroy any proof they believe to be on their side. Even if the victim gets the evidence, it will still be a long time before they get justice at that point. This work used OCR, NLP, and machine learning to detect cyberbullying in photos in order to design and execute a practical method to recognize cyberbullying from images. Eight classifier techniques are used to compare the accuracy of these algorithms against the BoW Model and the TF-IDF, two key features. These classifiers are used to understand and recognize bullying behaviors. Based on testing the suggested method on the cyberbullying dataset, it was shown that linear SVC after OCR and logistic regression perform better and achieve the best accuracy of 96 percent. This study aid in providing a good outline that shapes the methods for detecting online bullying from a screenshot with design and implementation details.

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