Mishev, Kostadin
Preferred name
Mishev, Kostadin
Official Name
Mishev, Kostadin
31 results
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Item type:Publication, Learning Translation Model to Translate Croatian Dialects to Modern Croatian Language(IEEE, 2023-05-22); Simjanoska, Monika - Some of the metrics are blocked by yourconsent settings
Item type:Publication, PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts using Transfer Learning(2021-02-25); ; ; 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 entities, but domain-specific use-cases require tagging custom entities which are not part of the pre-trained models. This can be solved by either fine-tuning the pre-trained models, or by training custom models. The main challenge lies in obtaining reliable labeled training and test datasets, and manual labeling would be a highly tedious task. In this paper we present PharmKE, a text analysis platform focused on the pharmaceutical domain, which applies deep learning through several stages for thorough semantic analysis of pharmaceutical articles. It performs text classification using state-of-the-art transfer learning models, and thoroughly integrates the results obtained through a proposed methodology. The methodology is used to create accurately labeled training and test datasets, which are then used to train models for custom entity labeling tasks, centered on the pharmaceutical domain. The obtained results are compared to the fine-tuned BERT and BioBERT models trained on the same dataset. Additionally, the PharmKE platform integrates the results obtained from named entity recognition tasks to resolve co-references of entities and analyze the semantic relations in every sentence, thus setting up a baseline for additional text analysis tasks, such as question answering and fact extraction. The recognized entities are also used to expand the knowledge graph generated by DBpedia Spotlight for a given pharmaceutical text. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, NLP-based Typo Correction Model for Croatian Language(IEEE, 2022-05-23); Simjanoska, MonikaSpelling correction plays an important role when applied in complex NLP-based applications and pipelines. Many of the existing models and techniques are developed to support the English language as it is the richest language in terms of resources available for training such models. The good occasion is that few of the methodologies provide the opportunity to adapt to other, low-resource languages. In this paper, we explore the power of the Neuspell Toolkit for training an original spelling correction model for the Croatian language. The toolkit itself comprises ten different models, but for the purposes of our work, we use the leverage of pre-trained transformer networks due to their experimentally proven spelling correction efficiency in the English language. The comparison is performed over different pre-trained Subword BERT architectures, including BERT Multilingual, DistilBERT, and XLM-RoBERTa, due to their subword representation support for the Croatian language. Furthermore, the training is done as a sequence labeling task on a newly created parallel Croatian dataset where the noisy examples are synthetically generated, and the misspelled words are labeled with their correct version. Finally, the model is tested in-vivo as part of our originally developed speech-to-text model for the Croatian language. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Unified Framework for Alzheimer’s Disease Knowledge Graphs: Architectures, Principles, and Clinical Translation(MDPI, 2025-05-19); ; This review paper synthesizes the application of knowledge graphs (KGs) in Alzheimer’s disease (AD) research, based on two basic questions, as follows: what types of input data are available to construct these knowledge graphs, and what purpose the knowledge graph is intended to fulfill. We synthesize results from existing works to illustrate how diverse knowledge graph structures behave in different data availability settings with distinct application targets in AD research. By comparative analysis, we define the best methodology practices by data type (literature, structured databases, neuroimaging, and clinical records) and application of interest (drug repurposing, disease classification, mechanism discovery, and clinical decision support). From this analysis, we recommend AD-KG 2.0, which is a new framework that coalesces best practices into a unifying architecture with well-defined decision pathways for implementation. Our key contributions are as follows: (1) a dynamic adaptation mechanism that adapts methodological elements automatically according to both data availability and application objectives, (2) a specialized semantic alignment layer that harmonizes terminologies across biological scales, and (3) a multi-constraint optimization approach for knowledge graph building. The framework accommodates a variety of applications, including drug repurposing, patient stratification for precision medicine, disease progression modeling, and clinical decision support. Our system, with a decision tree structured and pipeline layered architecture, offers research precise directions on how to use knowledge graphs in AD research by aligning methodological choice decisions with respective data availability and application goals. We provide precise component designs and adaptation processes that deliver optimal performance across varying research and clinical settings. We conclude by addressing implementation challenges and future directions for translating knowledge graph technologies from research tool to clinical use, with a specific focus on interpretability, workflow integration, and regulatory matters. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Performance Evaluation of Word and Sentence Embeddings for Finance Headlines Sentiment Analysis(Springer International Publishing, 2019); ; ; Vodenska, Irena - Some of the metrics are blocked by yourconsent settings
Item type:Publication, PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts Using Transfer Learning(MDPI, 2023-01-09); ; ; 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 learn on is still challenging. In this paper, we introduce PharmKE, a text analysis platform tailored to the pharmaceutical industry that uses deep learning at several stages to perform an in-depth semantic analysis of relevant publications. The proposed methodology is used to produce reliably labeled datasets leveraging cutting-edge transfer learning, which are later used to train models for specific entity labeling tasks. By building models for the well-known text-processing libraries spaCy and AllenNLP, this technique is used to find Pharmaceutical Organizations and Drugs in texts from the pharmaceutical domain. The PharmKE platform also incorporates the NER findings to resolve co-references of entities and examine the semantic linkages in each phrase, creating a foundation for further text analysis tasks, such as fact extraction and question answering. Additionally, the knowledge graph created by DBpedia Spotlight for a specific pharmaceutical text is expanded using the identified entities. The obtained results with the proposed methodology result in about a 96% F1-score on the NER tasks, which is up to 2% better than those of the fine-tuned BERT and BioBERT models developed using the same dataset. The ultimate benefits of the platform are that pharmaceutical domain specialists may more easily identify the knowledge extracted from the input texts thanks to the platform’s visualization of the model findings. Likewise, the proposed techniques can be integrated into mobile and pervasive systems to give patients more relevant and comprehensive information from scanned medication guides. Similarly, it can provide preliminary insights to patients and even medical personnel on whether a drug from a different vendor is compatible with the patient’s prescription medication. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, xAMR: Cross-lingual AMR End-to-End Pipeline(2022); Simjanoska, MonikaCreating multilingual end-to-end AMR models requires a large amount of cross-lingual data making the parsing and generating tasks exceptionally challenging when dealing with low-resource languages. To avoid this obstacle, this paper presents a cross-lingual AMR (xAMR) pipeline that incorporates the intuitive translation approach to and from the English language as a baseline for further utilization of the AMR parsing and generation models. The proposed pipeline has been evaluated via the cosine similarity of multiple state-of-the-art sentence embeddings used for representing the original and the output sentences generated by our xAMR approach. Also, BLEU and ROUGE scores were used to evaluate the preserved syntax and the word order. xAMR results were compared to multilingual AMR models’ performance for the languages experimented within this research. The results showed that our xAMR outperforms the multilingual approach for all the languages discussed in the paper and can be used as an alternative approach for abstract meaning representation of low-resource languages. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ISO-standardized smart city platform architecture and dashboard(IEEE, 2017-03-31); ; 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 architecture supports three tasks: acquire and manage data from heterogeneous sensors; process data originated from heterogeneous sources (sensors, OpenData, social data, blogs, news, and so on); and implement such collection and processing on the cloud. A prototype application based on the proposed architecture concept is developed for the city of Skopje, Macedonia. This article is part of a special issue on smart cities. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Embedded Deep Learning to Aid the Mobility of Individuals with Disabilities: A Solution for In-house Bus Line Recognition(2022); Simjanoska, MonikaThe mobility of individuals with visual impairments is a significant challenge as the cities are becoming more and more crowded each day. The technology is rapidly developing, offering novel high-tech smart white canes to aid the mobility of individuals with partial or total blindness. However, they are hardly affordable due to the high prices. Even more, they are impractical for in-vivo usage as they depend on thirdparty technologies and services, which require an Internet connection for data transfer and data processing on Cloud services. In this paper, we offer a novel methodology that aids the transportation of blind individuals, which is entirely integrated into the chip, thus avoiding the need for an Internet connection. Our methodology embeds three intelligent Deep learning models on a single smart mobile device, one model to localize the position of the bus line number approaching the individual, the second model to recognize the bus number, and the third is a text-to-speech model, which synthesizes speech to notify the individual in a pleasant and human-like manner about the number of the approaching bus. Our work presents one step closer to the completely independent embedded intelligent models that simplify the transportation of visually impaired persons using cutting-edge tools from AI. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Publishing Skopje Air Quality Data as Linked Data(Faculty of Computer Science and Engineering, Skopje, 2015-04);