Hadzieva, Elena

Preferred name
Hadzieva, Elena
Official Name
Hadzieva, Elena
Main Affiliation
Email
hadzieva@feit.ukim.edu.mk

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    On Complex Homogeneous Space of Vectors with Constraints
    (Prof. Marin Drinov Academic Publishing House, 2017)
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    null, null
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    Celakoska-Jordanova, Vesna
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    Introductory Survey on Challenges Encountered by University Teachers in Online Teaching of STEM Subjects During COVID-19 Lockdown
    (University of Debrecen/ Debreceni Egyetem, 2021-11-29)
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    Guncaga, Jan
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    Bose, Subash Chandra
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    Ivanoska, Kalina Sotiroska
    <jats:p>2020 will be remembered for COVID-19, a pandemic that forced the world to lock down and urged most educational providers to promptly implement e-learning solutions. In this paper, we point out some challenges faced by university teachers who had almost no earlier practice in online teaching. Nine lecturers working in different universities from Brazil, the Czech Republic, Estonia, Hungary, India, Macedonia, and Slovakia – all teaching science, technology, engineering, and mathematics (STEM) subjects – were interviewed to share their online teaching experiences during the COVID-19 lockdown. The aim of this introductory small-scale research paper is to provide a basis for future research regarding the influences that the COVID-19 situation has had on educational processes, as well as to assist educational providers in foreseeing and eliminating the possible problems of lecturers when establishing an online educational environment. Some conclusions are formulated from the interview survey, and possibilities for further research are described.</jats:p>
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    Introductory Survey on Challenges Encountered by University Teachers in Online Teaching of STEM Subjects During COVID-19 Lockdown
    (University of Debrecen/ Debreceni Egyetem, 2021-11-29)
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    Guncaga, Jan
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    Bose, Subash Chandra
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    <jats:p>2020 will be remembered for COVID-19, a pandemic that forced the world to lock down and urged most educational providers to promptly implement e-learning solutions. In this paper, we point out some challenges faced by university teachers who had almost no earlier practice in online teaching. Nine lecturers working in different universities from Brazil, the Czech Republic, Estonia, Hungary, India, Macedonia, and Slovakia – all teaching science, technology, engineering, and mathematics (STEM) subjects – were interviewed to share their online teaching experiences during the COVID-19 lockdown. The aim of this introductory small-scale research paper is to provide a basis for future research regarding the influences that the COVID-19 situation has had on educational processes, as well as to assist educational providers in foreseeing and eliminating the possible problems of lecturers when establishing an online educational environment. Some conclusions are formulated from the interview survey, and possibilities for further research are described.</jats:p>
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    Computer-aided detection of melanoma a case study
    (2018)
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    Trajkova, Vesna
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    Melanoma is the most dangerous form of skin cancer, and its detection at an early stage can allow timely treatment and prevention of fatal consequences. In this paper we present a case study of computer-aided diagnostics of melanoma using images of patients moles. The initial study was performed on two datasets: a benchmark dataset which is publicly available and a second one, containing images that were taken in hospitals in Macedonia. We present the obtained results and a short discussion of further directions for research. The results on the initial dataset were promising and showed 83% accuracy in the detection of the melanoma on the benchmark dataset. However, the same approach applied on the Macedonian dataset, the results could not be reproduced due to the low number of positive examples. The results showed that the performance of the classifiers did not benefit from under-sampling or oversampling techniques, nor did from feature selection. We can conclude that to build a reliable system for melanoma detection, a datasets of hundreds of images is not enough to train a machine-learning based model.