Trajanov, Dimitar

Preferred name

Trajanov, Dimitar

Official Name

Trajanov, Dimitar

Main Affiliation

dimitar.trajanov@finki.ukim.mk

Now showing 1 - 10 of 175

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Analysis of Long COVID Phenotypes and their Impact on Mental Health and Daily Functioning: Insights from Twitter
(Belgrade: Institute of molecular genetics and genetic engineering, 2023)
Markovikj, Marko
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Dobreva, Jovana
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Lucas, Mary
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Vodenska, Irena
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Chitkushev, Lou
In this study, we conducted an investigation into Long COVID from a user perspective, utilizing Twitter social media data. Prior to analysis, the data underwent preprocessing to obtain raw text per tweet. Our analysis commenced with basic statistical analysis and subsequently expanded to identify characteristic periods for the phenotypes based on dynamic timelines. We also explored the relationships between the phenotypes, as well as the interdependence between phenotypes and geolocation. In the context of this research, an analysis was conducted on a collection of tweets that encompassed the timeframe from March 2020 to March 2022. The dataset consisted of approximately 1.9 million tweets. In order to concentrate on word phrases, extraneous elements such as mentions, emoticons, links, and hashtags were eliminated. Subsequently, a process of lemmatization was performed. For the purpose of reducing the number of distinct phenotypes under investigation and facilitating the presentation of results, the collected data was categorized into five overarching groups: Cardiovascular, Respiratory, Daily Living, Neurological and Mental Health, and Other. The statistical data regarding the most commonly used words by individuals describing their experiences during the Long COVID period are as follows: “Ampicillin” was tweeted 125,295 times, “Death” was tweeted 121,156 times, “Suffer” was tweeted 125,113 times, and “Vaccine” was tweeted 108,968 times. We observe distinct patterns in the emergence of certain phenotypes during this period, particularly in relation to the quality of life. On August 1, 2020, the term “quality of life” was mentioned in only 223 tweets, whereas one year later, during the same month, this phenotype garnered 1,663 tweets. Our findings reveal that the occurrence of Long COVID phenotypes is influenced by both temporal and geographical factors. The analysis shows a clear and notable trend within the dataset. Specifically, it is observed that neurological symptoms, along with symptoms that impede individuals’ daily functioning, exhibit the highest prevalence, particularly during the latter half of the analyzed tweet period. This period corresponds to a time when an increasing number of individuals have recovered from COVID-19 and are reporting their experiences with Long COVID. Notably, fatigue, depression, stress, and anxiety emerge as the most prevalent phenotypes. This scientific investigation of the complex interactions between Long COVID phenotypes, mental health, and the manifestation of diverse symptoms is offering insights into the profound consequences on individuals’ lives. These findings shed light on the significant burden posed by Long COVID and its cascading effects on various aspects of individuals’ well-being and society at large.
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Using Centrality Measures to Extract Knowledge from Cryptocurrencies’ Interdependencies Networks
(Springer Nature, 2023)
Rusevski, Ivan
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Angelovski, Gorast
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Vodenska, Irena
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Chitkushev, Ljubomir
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Is the rising price of Bitcoin affected by Ethereum’s fall? Are cryptocurrencies interconnected and are shifts in prices a consequence of said influence, or maybe social media plays a more significant role? To answer these questions, we create 7 networks using different approaches, each of them representing the relationship between 18 most popular cryptocurrencies in a distinct way. Additionally, by calculating centrality measures on the networks, we discover the currency that will be the first to spread their influence onto others. Moreover, these measures detects a currency with a high influence over the entire network, as well as the one that have the most “important” neighbors. Our results show that cryptocurrencies are indeed interrelated, especially the more popular ones, which also happens to be the most affected by the social media platforms. Ethereum is one of the fastest to affect the others when change in price occur, while both Ethereum and Bitcoin have extensive reach in the networks.
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Inferring Cuisine - Drug Interactions Using the Linked Data Approach
(Springer Nature, 2015-03-20)
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Food - drug interactions are well studied, however much less is known about cuisine - drug interactions. Non-native cuisines are becoming increasingly more popular as they are available in (almost) all regions in the world. Here we address the problem of how known negative food - drug interactions are spread in different cuisines. We show that different drug categories have different distribution of the negative effects in different parts of the world. The effects certain ingredients have on different drug categories and in different cuisines are also analyzed. This analysis is aimed towards stressing out the importance of cuisine - drug interactions for patients which are being administered drugs with known negative food interactions. A patient being under a treatment with one such drug should be advised not only about the possible negative food - drug interactions, but also about the cuisines that could be avoided from the patient's diet.
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Methodology for food prices forecasting
(IEEE, 2023-12-15)
Peshevski, Dimitar
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Todorovska, Ana
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Trajkovikj, Filip
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Hristov, Nikola
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Trajanoska, Milena
Fluctuations in food prices play a pivotal role in maintaining economic equilibrium and influencing the very fabric of our everyday lives. This paper presents a comprehensive framework for modeling and analyzing food price trends in 12 select European countries, spanning from January 2013 to January 2023, utilizing advanced state-of-the-art Machine Learning techniques. To achieve this objective, historical price data and technical indicators are incorporated into the proposed XGBoost model alongside a baseline model. The model results are assessed using various measures, and a benchmark is established. Notably, the average achieved R2 for predicting food prices within the time frame from January 2020 to January 2022 is 0.85 and 0.64 from January 2021 to January 2023. The findings reveal the efficacy of the proposed model, providing valuable insights into food price forecasting model interpretability and laying the groundwork for further research, including exploration into areas such as food fraud, food sustainability, and other pertinent topics in food economics.
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Digital Social Innovation for Better Connected Government: The Case of Republic of Macedonia
(IGI Global, 2021)
Najdova Natalija
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Tasevska Belchovska Jasmina
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Social Innovation (SI) refers to new products, processes and methods that, in a creative and sustainable way, offer a better solution to social demands; which often requires changes in the practices of existing social systems. Digital Social Innovation (DSI) is ICT-based SI that uses digital technologies to invoke such changes. This chapter presents an insight into DSI in the Republic of Macedonia, and reports the results of a survey to show the level of understanding, awareness, and knowledge of DSI in the country. Although the idea of DSI is to bypass the governments, motivate people to self-organize, and solve their societal problems; results suggest that without a good strategy, enough funding, and suitable societal governance, it is difficult to tackle the challenges of raising the awareness of an individual or a community that it is they themselves who are the change-enablers as members of a social network.
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Temporal Authorization Graphs: Pros, Cons and Limits
(Springer International Publishing, 2022-01)
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Popovski, Ognen
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As more private data is entering the web, defining authorization about its access is crucial for privacy protection. This paper proposes a policy language that leverages SPARQL expressiveness and popularity for flexible access control management and enforces the protection using temporal graphs. The temporal graphs are created during the authentication phase and are cached for further usage. They enable design-time policy testing and debugging, which is necessary for correctness guarantee. The security never comes with convenience, and this paper examines the environments in which the temporal graphs are suitable. Based on the evaluation results, an approximated function is defined for suitability determination based on the expected temporal graph size.
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Using ML and Explainable AI to understand the interdependency networks between classical economic indicators and crypto-markets
(North-Holland, 2023-08-15)
Todorovska, Ana
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Peshov, Hristijan
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Rusevski, Ivan
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Vodenska, Irena
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Chitkushev, T. Lubomir
In a global world, no country, market, or economy is isolated. Interconnectivity is becoming a fundamental feature of economic systems, including macroeconomic trends, traditional financial markets, and digital markets. Cryptocurrencies, as a new digital asset, are becoming an integral part of the global economy. This study aims to explore the relationships between cryptocurrencies and traditional financial markets. We develop a methodology for analyzing the relationships between the largest cryptocurrencies and select global market-based economic indicators based on multimodal publicly available datasets incorporating structured numerical and unstructured news and social network data. To find the existence of directional associations, we develop an Explainable ML model that first learns the dependencies between different assets and then explains them in a form understandable by humans. We apply our methodology to analyze connectivity networks of seven cryptocurrencies (Bitcoin, Ethereum, Cardano, Chainlink, Litecoin, Stellar, and Ripple) and seven classical economic indicators, including five market indexes (BSE, Dow Jones, S&P500, FTSE, and Hang Seng) and two commodity prices (Oil and Gold).
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Learning Robust Food Ontology Alignment
(IEEE, 2022-12-17)
Mijalcheva, Viktorija
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Davcheva, Ana
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In today’s knowledge society, large number of information systems use many different individual schemes to represent data. Ontologies are a promising approach for formal knowledge representation and their number is growing rapidly. The semantic linking of these ontologies is a necessary prerequisite for establishing interoperability between the large number of services that structure the data with these ontologies. Consequently, the alignment of ontologies becomes a central issue when building a worldwide Semantic Web. There is a need to develop automatic or at least semi-automatic techniques to reduce the burden of manually creating and maintaining alignments. Ontologies are seen as a solution to data heterogeneity on the Web. However, the available ontologies are themselves a source of heterogeneity. On the Web, there are multiple ontologies that refer to the same domain, and with that comes the challenge of a given graph-based system using multiple ontologies whose taxonomy is different, but the semantics are the same. This can be overcome by aligning the ontologies or by finding the correspondence between their components.In this paper, we propose a method for indexing ontologies as a support to a solution for ontology alignment based on a neural network. In this process, for each semantic resource we combine the graph based representations from the RDF2vec model, together with the text representation from the BERT model in order to capture the semantic and structural features. This methodology is evaluated using the FoodOn and OntoFood ontologies, based on the Food Onto Map alignment dataset, which contains 155 unique and validly aligned resources. Using these limited resources, we managed to obtain accuracy of 74% and F1 score of 75% on the test set, which is a promising result that can be further improved in future. Furthermore, the methodology presented in this paper is both robust and ontology-agnostic. It can be applied to any ontology, regardless of the domain.
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Dynamic Load Balancing and Reactive Power Compensation Switch Embedded in Power Meters
(Institute of Electrical and Electronics Engineers (IEEE), 2017-04)
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Kocarev, Ljupco
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Impact of Community Structures on Ad Hoc Networks Performances
(Springer Berlin Heidelberg, 2010)
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Trajanov, Dimitar

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