Mirchev, Miroslav

Preferred name

Mirchev, Miroslav

Official Name

Mirchev, Miroslav

Main Affiliation

miroslav.mirchev@finki.ukim.mk

Now showing 1 - 10 of 32

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Optimization of self-emulsifying drug delivery system of cefuroxime axetil
(Macedonian Pharmaceutical Association, 2021)
Trajanovska, Eleonora
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Simonoska Crcarevska, Maja
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Jovanovikj, Frosina
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Atanasova, Ana
<jats:p>Abstract Overcoming solubility problems is the greatest challenge during formulation of poorly soluble active pharmaceutical ingredients (API’s) into oral solid dosage forms. Different formulation approaches were used to surpass this problem and enhance their solubility in the gastrointestinal (GI) fluids, in order to achieve a faster dissolution and better absorption, which will directly influence their therapeutic effect. In this paper, an evaluation of the potential of a self-emulsifying drug delivery system (SEDDS) to improve the solubility of the active ingredient cefuroxime axetil (CA) was done. Screening of the solubility of the API in different excipients was done, and Tween 80, PEG 400, and Olive oil as a surfactant, co-solvent, and oil, respectively, were chosen as the most convenient system constituents. An optimal self-emulsification and solubilization ability of this system was assessed using mixture experimental design statistical tools based on the response surface methodology (RSM). The prepared CA-SEDDS were evaluated for droplet size (d10, d50, d90 in µm), droplet size distribution (Span factor), and absorbance. As a complementary approach, for better representation of the non-linear relationship between the formulation compositions and the observed dispersion characteristics an artificial neural network (ANN) was used. Optimal formulation that consists of 10% (w/w) Tween 80 as surfactant, 80% (w/w) PEG 400 as co-solvent and 10% (w/w) Olive oil, was obtained. Both, mixture experimental design and ANN were combined for a comprehensive evaluation of CA-SEDDS and the obtained results suggested that formulation of SEDDS is a useful approach for improving the solubility of the CA. Keywords: self-emulsifying drug delivery systems (SEDDS), cefuroxime axetil, design of experiment, artificial neural network (ANN)</jats:p>
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Enhancing Portfolio Management Using Artificial Intelligence: Literature Review
(Frontiers, 2024-03-12)
Sutiene, Kristina
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Schwendner, Peter
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Sipos, Ciprian
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Lorenzo, Luis
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Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.
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Random walk with memory on complex networks
(American Physical Society, 2020-10-30)
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Kocarev, Ljupcho
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We study random walk on complex networks with transition probabilities which depend on the current and previously visited nodes. By using an absorbing Markov chain we derive an exact expression for the mean first passage time between pairs of nodes, for a random walk with a memory of one step. We have analyzed one particular model of random walk, where the transition probabilities depend on the number of paths to the second neighbors. The numerical experiments on paradigmatic complex networks verify the validity of the theoretical expressions, and also indicate that the flattening of the stationary occupation probability accompanies a nearly optimal random search.
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Classification of Companies using Graph Neural Networks
(IEEE, 2024-05-20)
Manchev, Jovan
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Classification of companies into GICS categories can be addressed using Graph Neural Networks (GNN), by utilizing the different types of relationship between companies such as customer, supplier, partner, competitor, and investor. We use the Relato business graph data and compare the performances of several GNNs and a large language model like BERT that is trained only on the descriptions of the companies. Our goal is company classification into its corresponding category within the four tiers of the GICS hierarchy. Several architectures of GNNs are explored such as GCN, GraphSAGE and GAT, but also RGCN and RGAT that consider the edge type, or relationship between the companies. The main purpose is to reveal what kind of relationship between the companies is most valuable when determining the category of the company. The findings indicate that Graph Neural Networks (GNNs) enhance both classification performance and the understanding of collaboration patterns among companies, providing valuable insights for determining the industry in which these companies operate. This contrasts with the classification based solely on company descriptions using BERT.
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Comparative Study of Random Walks with One-Step Memory on Complex Networks
(Springer, Cham, 2023-03-30)
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We investigate searching efficiency of different kinds of random walk on complex networks which rely on local information and one-step memory. For the studied navigation strategies we obtained theoretical and numerical values for the graph mean first passage times as an indicator for the searching efficiency. The experiments with generated and real networks show that biasing based on inverse degree, persistence and local two-hop paths can lead to smaller searching times. Moreover, these biasing approaches can be combined to achieve a more robust random search strategy. Our findings can be applied in the modeling and solution of various real-world problems.
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MEMRISTIVE NETWORKS OF CHUA’S CIRCUITS
(2014)
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Kocarev, Ljupco
Although envisioned in 1971 by L. Chua, memristors have attracted the attention of the research community recently by the promotion of their feasibility and a vast number of possible applications in non-volatile computer memory, pattern recognition and modelling neural networks. Synchronization is widely studied as a phenomenon in neural networks. This work provides synchronization analyses of two kinds of memristive networks of oscillators. First, we numerically examine networks of Chua’s circuits coupled by memristors that adapt according to the local state disagreements. As second, we employ the Master stability function (MSF) approach to study synchronization in networks of memristive Chua’s circuits coupled through simple resistors.
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Demographic analysis of music preferences in streaming service networks
(Springer, Cham, 2020-02-22)
Jovanovska, Lidija
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Evkoski, Bojan
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As Daniel J. Levitin noted, music is a cross-cultural phenomenon, a ubiquitous activity found in every known human culture. It is indeed, a living matter that flows through cultures, which makes it a complex system potentially holding valuable information. Therefore, we model country-to-country interactions to reveal macro-level music trends. The purpose of this paper is twofold. Firstly, we explore the way specific demographic characteristics, such as language and geographic location affect the global community structure in streaming service networks. Secondly, we examine whether a clear flow of musical trends exists in the world by identifying countries who are prominent leaders on the music streaming charts. The community analysis shows that there is strong support for the first claim. Next, we find that the flow of musical trends is not strongly directional globally, although we were still able to identify prominent leaders and followers within the communities. The obtained results can further lead to the development of more sophisticated music recommendation systems, kindle new cultural studies and bring discoveries in the field of musicology.
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Named Entity Recognition For Macedonian Language
(CIIT 2021, 2021-05-06)
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Ivan Krstev
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Fisnik Doko
Named Entity Recognition (NER), an outstanding technique for information extraction from unstructured texts, is lately becoming the central problem in the field of Natural Language Processing (NLP). In the last few years, multiple Python libraries, like SpaCy, NLTK and FLAIR, accomplished state-of-the-art performances for this problem. As NER is developing into a powerful technique, its real-live applications are becoming more and more numerous: from customer-message categorization to ease of document analysis in greater corporations. In this research, we use a ML-based system with the help of the FLAIR library in Python, which has already provided optimal results for NER in few world-class languages (English, German, Russian, French etc.), for financial entity recognition in financial texts written in Macedonian language. For the NER task on 13 distinct labels using our dataset in Macedonian language on the proposed ML model we have obtained F1-score of around 0.75.
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The Geographic Flow Of Music On Spotify
(2019)
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Jovanovska, Lidija
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As Daniel J. Levitin interestingly noted, No known human culture now or anytime in the recorded past lacked music. Therefore, the impetus behind this research paper is to model the interactions between countries in order to reveal music listening trends at a macro level. Subsequently, the framework for performing this analysis consists of techniques used in the multidisciplinary field known as Network Science. Throughout the past decade, the world has witnessed a gradual shift in the way music is listened to. In that respect, Spotify, an online music streaming service, has been the imperative giant with a user base of around 191 million. With the help of Spotify’s Application Programming Interface (API), a dataset was compiled, which contains the Weekly Top 40 streamed songs across 50 countries, in the year 2017. Through research, the team explored whether, and to which extent, do language, nationality and geographic distance influence the way global communities are formed. Furthermore, the project aimed to prove that there is a clear direction of leadership flow in the network. Until now, the acquired information supports the hypotheses that some countries do indeed follow the trends beset by others and that language and nationality play an essential role in the development of communities.
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Parallel Implementation of Random Walk Simulations with Different Movement Algorithms
(IEEE, 2021-11-23)
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Nasteski, Andrej
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This article contains a detailed explanation of the research, methodology and results of different searching strategies when traversing through an unknown area. We have been challenged by the ways to simulate and evaluate the effectiveness of various approaches in order to speed up the simulation using parallel computing. The goal is to compare the results from each combination of algorithms. The two categories of algorithms considered are direction based and step size based algorithms. In summary, the combination of exponential step size with backtracking and forward check direction algorithm produced the best results. We also concluded that using a parallel implementation resulted with a substantial speed up when compared to a sequential approach.

Mirchev, Miroslav

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