Oleksiy Tverdokhlib

Work place: Lviv Polytechnic National University, Lviv, 79013, Ukraine

E-mail: oleksii.tverdokhlib.msaad.2023@lpnu.ua

Website: https://orcid.org/0009-0007-2471-6109

Research Interests:

Biography

Oleksiy Tverdokhlib is a master’s student specializing in System Analysis in the Department of Information Systems and Networks at Lviv Polytechnic National University. He is a budding researcher with a strong passion in the areas of Machine Learning, Deep Learning and Web Development.

Author Articles
Intelligent Processing Censoring Inappropriate Content in Images, News, Messages and Articles on Web Pages Based on Machine Learning

By Oleksiy Tverdokhlib Victoria Vysotska Olena Nagachevska Yuriy Ushenko Dmytro Uhryn Yurii Tomka

DOI: https://doi.org/10.5815/ijigsp.2025.01.08, Pub. Date: 8 Feb. 2025

This project aims to enhance online experiences quality by giving users greater control over the content they encounter daily. The proposed solution is particularly valuable for parents seeking to safeguard their children, educational institutions striving to foster a more conducive learning environment, and individuals prioritising ethical internet usage. It also supports users who wish to limit their exposure to misinformation, including fake news, propaganda, and disinformation. Through the implementation of a browser extension, this system will contribute to a safer internet, reducing users' vulnerability to harmful content and promoting a more positive and productive online environment. The primary objective of this work is to develop a browser extension that automatically detects and censors inappropriate text and images on web pages using artificial intelligence (AI) technologies. The extension will enable users to personalise censorship settings, including the ability to define custom prohibited words and toggle the filtering of text and images. Accuracy estimates for various classifiers such as Random Forest (0.879), Logistic Regression (0.904), Decision Tree (0.878), Naive Bayes (0.315), and KNN (0.832) were performed.

[...] Read more.
Other Articles