Work place: Lviv Polytechnic National University, Lviv, 79013, Ukraine
E-mail: olena.o.nahachevska@lpnu.ua
Website: https://orcid.org/0000-0002-5200-8085
Research Interests:
Biography
Olena Nagachevska is currently an Associate Professor in the Department of Foreign Languages for Engineering at Lviv Polytechnic National University (LPNU) in Lviv, Ukraine. She holds a PhD in Philology (2012) and the title of Associate Professor, conferred by the Ministry of Education, Youth, and Sports of Ukraine (2014). With a strong academic background and extensive experience in linguistics and education, her research spans a wide range of topics, focusing on innovative methods of teaching English, multicultural and intercultural communication, business English, and business communication. Since 2021, her research has increasingly shifted towards integrating IT technologies in education, including natural language processing (NLP), computational linguistics, machine learning, and cybersecurity. Olena has made significant academic contributions, with numerous publications in prestigious journals and edited volumes indexed in Web of Science and Scopus, covering topics such as foreign language communicative competence and linguistic realias. She is also actively engaged in academic platforms, including Google Scholar, ResearchGate, and IEEE.
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.
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