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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Item type:Publication, RoBERTa for URL Classification: Enhancing Web Security and Content Filtering(ieee explore, 2023); Popovska-Mitrovikj, Aleksandra - Some of the metrics are blocked by yourconsent settings
Item type:Publication, StegYou: Model for Hiding, Retrieving and Detecting Digital Data in Images(Springer International Publishing, 2022-10-13) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Review of Natural Language Processing in Pharmacology(American Society for Pharmacology & Experimental Therapeutics (ASPET), 2023-07); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Big Data Analytics for HPC Environment(2022); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, MalDeWe: New Malware Website Detector Model based on Natural Language Processing using Balanced Dataset(IEEE, 2021-12) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Information Security Policies - A review on the challenges, effectiveness and successful usage(ICT-ACT, 2021); Popovska-Mitrovikj, Aleksandra - Some of the metrics are blocked by yourconsent settings
Item type:Publication, DD-RDL: Drug-Disease Relation Discovery and Labeling(Springer International Publishing, 2022-04-12); Drug repurposing, which is concerned with the study of the effectiveness of existing drugs on new diseases, has been growing in importance in the last few years. One of the core methodologies for drug repurposing is text-mining, where novel biological entity relationships are extracted from existing biomedical literature and publications, whose number skyrocketed in the last couple of years. This paper proposes an NLP approach for drug-disease relation discovery and labeling (DD-RDL), which employs a series of steps to analyze a corpus of abstracts of scientific biomedical research papers. The proposed ML pipeline restructures the free text from a set of words into drug-disease pairs using state-of-the-art text mining methodologies and natural language processing tools. The model’s output is a set of extracted triplets in the form (drug, verb, disease), where each triple describes a relationship between a drug and a disease detected in the corpus. We evaluate the model based on a gold standard dataset for drug-disease relationships, and we demonstrate that it is possible to achieve similar results without requiring a large amount of annotated biological data or predefined semantic rules. Additionally, as an experimental case, we analyze the research papers published as part of the COVID-19 Open Research Dataset (CORD-19) to extract and identify relations between drugs and diseases related to the ongoing pandemic. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Information Security Policies - A review on the challenges, effectiveness and successful usage(ICT-ACT, 2021); Popovska-Mitrovikj, AleksandraWe live in a society in which all implemented technologies are changing our lives forever and every sphere in the society is going through transitions. One of these spheres is the Information Technology (IT).We are overwhelmed with IT gadgets and applications in our every- day lives. We are used to absorbing the necessary information as easily as possible and the devices like desktops, laptops, mobile smartphones, tablets, etc. are our information resources. These recourses provide us with the necessities of knowledge and learning, and that is why they play a vital role in the modern society. However, most of these devices are commercial. This means when they are bought, they come with pre-de ned settings which, amongst other things, include Information Security. Each device comes with a mediocre level of protection and security for our sensitive data. This is the reason why we need to be careful when we pick our device's manufacturer, along with its service provider. We need to ask ourselves: What kind of data protection does this manufacturer o er? Can this service provider be compatible with the device's capabilities? To answer these questions, we need to be careful in choosing our device, service provider, and the way we handle sensitive data. Handling sensi- tive data is always left for the Information Security O cer, who creates the Information Security Policy. The proper Information Security Policy is very important for a person who is careful with his data. Most of the Information Security Policies cover the standard protocols and proce- dures, but if we analyze deeper, we will nd that there are weaknesses and failures that may cost a lot if Information Security Policies are not chosen properly. Picking a strong Information Security Policy is vital when we are willing to secure our data. The Information Security Policy is supposed to provide us and our organization a proper set of rules, which, if we respect and obey, we will get bene ts of it. This means that, as much as we follow protocols and procedures, which are well-de ned, structured, and capable of data protection, we will be safe. This paper is focused on the question how to comprehend Information Security Policies and what they are supposed to bring to us and our organization. It is an overview of Information Security Policy challenges, e ectiveness, and consequences of its non-adequate usage. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improving NER Performance by Applying Text Summarization on Pharmaceutical Articles(Springer International Publishing, 2020-10-30); Analyzing long text articles in the pharmaceutical domain, for the purpose of knowledge extraction and recognizing entities of interest, is a tedious task. In our previous research efforts, we were able to develop a platform which successfully extracts entities and facts from pharmaceutical texts and populates a knowledge graph with the extracted knowledge. However, one drawback of our approach was the processing time; the analysis of a single text source was not interactive enough, and the batch processing of entire article datasets took too long. In this paper, we propose a modified pipeline where the texts are summarized before the analysis begins. With this, the source articles is reduced significantly, to a compact version which contains only the most commonly encountered entities. We show that by reducing the text size, we get knowledge extraction results comparable to the full text analysis approach and, at the same time, we significantly reduce the processing time, which is essential for getting both real-time results on single text sources, and faster results when analyzing entire batches of collected articles from the domain. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of Machine Learning in DES Cryptanalysis(2020-09-24)