Kostadin Mishev

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Verified

Kostadin verified their affiliation via an institutional email.

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• Ph.D.
• Assistant Professor at Saints Cyril and Methodius University of Skopje

Using dataset analysis as a research method is becoming more popular among many researchers with diverse data collection and analysis backgrounds. This paper provides the first publicly available dataset consisting of audio segments and appropriate textual transcription in the Macedonian language. It is appropriately preprocessed and prepared for d...

Lexicon-based sentiment analysis in finance leverages specialized, manually annotated lexicons created by human experts to effectively extract sentiment from financial texts. Although lexicon-based methods are simple to implement and fast to operate on textual data, they require considerable manual annotation efforts to create, maintain, and update...

Lexicon-based sentiment analysis (SA) in finance leverages specialized, manually annotated lexicons created by human experts to extract sentiment from financial texts. Although lexicon-based methods are simple to implement and fast to operate on textual data, they require considerable manual annotation efforts to create, maintain, and update the le...

We explore the use of Wav2Vec 2.0, NeMo, and ESPNet models trained on a dataset in Macedonian language for the development of Automatic Speech Recognition (ASR) models for low-resource languages. The study aims to evaluate the performance of recent state-of-the-art models for speech recognition in low-resource languages, such as Macedonian, where t...

Using language models to detect or predict the presence of language phenomena in the text has become a mainstream research topic. With the rise of generative models, experiments using deep learning and transformer models trigger intense interest. Aspects like precision of predictions , portability to other languages or phenomena , scale have been c...

While the ethical principles of finance are well known in the literature, they are not sufficiently evaluated in the context of machine learning (ML). We use natural language processing (NLP) transformer models to quantitatively evaluate the relationships between the ethical principles of finance and the ethical principles of ML. To the best of our...

Even though named entity recognition (NER) has seen tremendous development in recent years, some domain-specific use-cases still require tagging of unique entities, which is not well handled by pre-trained models. Solutions based on enhancing pre-trained models or creating new ones are efficient, but creating reliable labeled training for them to l...

Contextualized language models are becoming omnipresent in the field of Natural Language Processing (NLP). Their learning representation capabilities show dominant results in almost all downstream NLP tasks. The main challenge that low-resource languages face is the lack of language-specific language models since their pre-training process requires...

Over the last decade, machine learning methods have revolutionized a large number of domains and provided solutions to many problems that people could hardly solve in the past. The availability of large amounts of data, powerful processing architectures, and easy-to-use software frameworks have made machine learning a popular, readily available, an...

The challenge of recognizing named entities in a given text has been a very dynamic field in recent years. This task is generally focused on tagging common entities, such as Person, Organization, Date, etc. However, many domain-specific use-cases exist which require tagging custom entities that are not part of the pre-trained models. This can be so...

This paper presents a technology4good initiative that integrates multiple breakthrough software modules with the aim to build a generic framework to aid the educational process for students with disabilities, such as, hearing and vision impairments, as well as various types of dyslexia. The purpose of the study is to apply various distinct research...

We propose a generic hierarchical clustering algorithm - named Blanket Clusterer, which allows researchers to examine their data and verify the results gained from other machine learning techniques. We also integrate a three-dimensional visualization plugin that provides better understanding of the clustering results. We verify the tool on a specif...

Rapid technological developments in the last decade have contributed to using machine learning (ML) in various economic sectors. Financial institutions have embraced technology and have applied ML algorithms in trading, portfolio management, and investment advising. Large-scale automation capabilities and cost savings make the ML algorithms attract...

Choosing optimal Deep Learning (DL) architecture and hyperparameters for a particular problem is still not a trivial task among researchers. The most common approach relies on popular architectures proven to work on specific problem domains led on the same experiment environment and setup. However, this limits the opportunity to choose or invent no...

This paper presents a technology4good initiative that integrates multiple breakthrough software modules with the aim to build a generic framework to aid the educational process for students with disabilities, such as, hearing and vision impairments, as well as various types of dyslexia. The purpose of the study is to apply various distinct research...

The challenge of recognizing named entities in a given text has been a very dynamic field in recent years. This is due to the advances in neural network architectures, increase of computing power and the availability of diverse labeled datasets, which deliver pre-trained, highly accurate models. These tasks are generally focused on tagging common e...

This paper presents MAKEDONKA, the first open-source Macedonian language synthesizer that is based on the Deep Learning approach. The paper provides an overview of the numerous attempts to achieve a human-like reproducible speech, which has unfortunately shown to be unsuccessful due to the work invisibility and lack of integration examples with rea...

Implementation of a smart parking system providing predictions about real-time parking occupancy is considered to be crucial when managing limited parking resources. In this study, we present a methodology based on machine-learning regression models for predicting parking availability. We use traffic congestion information and garage occupancy as i...

Financial and economic news is continuously monitored by financial market participants. According to the efficient market hypothesis, all past information is reflected in stock prices and new information is instantaneously absorbed in determining future stock prices. Hence, prompt extraction of positive or negative sentiments from news is very impo...

The academic disciplines and their interrelationships represent a backbone that organizes the enormous amount of documented human knowledge available today. Having an up-to-date overview of the established disciplines, the emerging ones, and their mutual interactions is essential to the academic institutions, publishers, and many other actors invol...

Nowadays, tremendous number of financial online articles are published every day. Numerous natural language processing (NLP) algorithms and methodologies have arose, not only for correct, but also for fast financial sentiment extraction. Currently, word and sentence encoders are popular topic in NLP field, due to their ability to represent them as...

The purpose of this paper is to predict the stock market direction by analyzing the newest financial news down-loaded from reliable sources, targeting big companies. The focus will be on lexicon-based and machine learning models, in order to calculate the polarity and subjectivity of news and explore the relationship between the specific features o...

A concept guided by the ISO 37120 standard for city services and quality of life is suggested as unified framework for smart city dashboards. The slow (annual, quarterly, or monthly) ISO 37120 indicators are enhanced and complemented with more detailed and person-centric indicators that can further accelerate the transition toward smart cities. The...

Publishing raw data as Linked Open Data gives an opportunity of data reusability and data understandability for the computer machines. Today, the air pollution problem is one of the biggest in the whole world. Republic of Macedonia, especially its capital Skopje, has big problems with the PM2.5 and PM10 particles in the air approved by several meas...