Faculty of Computer Science and Engineering

Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5

The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest faculties in that field in the region. The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.

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    COVID-19 Fake News Detection by Using BERT and RoBERTa models
    (IEEE, 2022-05-23)
    Pavlov, Tashko
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    We live in a world where COVID-19 news is an everyday occurrence with which we interact. We are receiving that information, either consciously or unconsciously, without fact-checking it. In this regard, it has become an enormous challenge to keep only true COVID-19 news relevant. People are exposed to these stories on a daily basis, and not all of them are true and fact-checked reports on the COVID-19 pandemic, which was the primary reason for our research. We accepted the challenge that fake news is extremely common and that some people take these news as they are. Knowing the true power of the most recent NLP achievements, in this research we focus on detecting fake news regarding COVID-19. Our approach includes using pre-trained BERT and RoBERTa models, which we then fine-tune on real and fake news about the COVID-19 pandemic. By using pre-trained BERT and RoBERTa models on tweet data, we explore their capabilities and compare them to previous research in regard to fine-tuned BERT models for this task in which we achieve better accuracy, recall and f1 score.
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    Review of Natural Language Processing in Pharmacology
    (American Society for Pharmacology & Experimental Therapeutics (ASPET), 2023-07)
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    Trajkovski, Vangel
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    Dimitrieva, Makedonka
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    Dobreva, Jovana
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    Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the past few years and now employs modern variants of deep neural networks to extract relevant patterns from large text corpora. The main objective of this work is to survey the recent use of NLP in the field of pharmacology. As our work shows, NLP is a highly relevant information extraction and processing approach for pharmacology. It has been used extensively, from intelligent searches through thousands of medical documents to finding traces of adversarial drug interactions in social media. We split our coverage into five categories to survey modern NLP: methodology, commonly addressed tasks, relevant textual data, knowledge bases, and useful programming libraries. We split each of the five categories into appropriate subcategories, describe their main properties and ideas, and summarize them in a tabular form. The resulting survey presents a comprehensive overview of the area, useful to practitioners and interested observers. SIGNIFICANCE STATEMENT: The main objective of this work is to survey the recent use of NLP in the field of pharmacology in order to provide a comprehensive overview of the current state in the area after the rapid developments that occurred in the past few years. The resulting survey will be useful to practitioners and interested observers in the domain.