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RISS 인기검색어
- 검색키워드
- 저자 : Taras Panchenko (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
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A Comparison of Scene Change Localization Methods over the Open Video Scene Detection Dataset
Panchenko, Taras,Bieda, Igor International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.6
Scene change detection is an important topic because of the wide and growing range of its applications. Streaming services from many providers are increasing their capacity which causes the industry growth. The method for the scene change detection is described here and compared with the State-of-the-Art methods over the Open Video Scene Detection (OVSD) - an open dataset of Creative Commons licensed videos freely available for download and use to evaluate video scene detection algorithms. The proposed method is based on scene analysis using threshold values and smooth scene changes. A comparison of the presented method was conducted in this research. The obtained results demonstrated the high efficiency of the scene cut localization method proposed by authors, because its efficiency measured in terms of precision, recall, accuracy, and F-metrics score exceeds the best previously known results.
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Effective Methods for Heart Disease Detection via ECG Analyses
Yavorsky, Andrii,Panchenko, Taras International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.5
Generally developed for medical testing, electrocardiogram (ECG) recordings seizure the cardiac electrical signals from the surface of the body. ECG study can consequently be a vital first step to support analyze, comprehend, and expect cardiac ailments accountable for 31% of deaths globally. Different tools are used to analyze ECG signals based on computational methods, and explicitly machine learning method. In all abovementioned computational simulations are prevailing tools for cataloging and clustering. This review demonstrates the different effective methods for heart disease based on computational methods for ECG analysis. The accuracy in machine learning and three-dimensional computer simulations, among medical inferences and contributions to medical developments. In the first part the classification and the methods developed to get data and cataloging between standard and abnormal cardiac activity. The second part emphases on patient analysis from entire ECG recordings due to different kind of diseases present. The last part represents the application of wearable devices and interpretation of computer simulated results. Conclusively, the discussion part plans the challenges of ECG investigation and offers a serious valuation of the approaches offered. Different approaches described in this review are a sturdy asset for medicinal encounters and their transformation to the medical world can lead to auspicious developments.
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A Systematic Mapping Study on Artificial Intelligence Tools Used in Video Editing
Bieda, Igor,Panchenko, Taras International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.3
From the past two eras, artificial intelligence has gained the attention of researchers of all research areas. Video editing is a task in the list that starts leveraging the blessing of Artificial Intelligence (AI). Since AI promises to make technology better use of human life although video editing technology is not new yet it is adopting new technologies like AI to become more powerful and sophisticated for video editors as well as users. Like other technologies, video editing will also be facilitated by the majestic power of AI in near future. There has been a lot of research that uses AI in video editing, yet there is no comprehensive literature review that systematically finds all of this work on one page so that new researchers can find research gaps in that area. In this research we conducted a statically approach called, systematic mapping study, to find answers to pre-proposed research questions. The aim and objective of this research are to find research gaps in our topic under discussion.
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Methods of Automated Analysis of Curricula According to the Higher Education Standard
Liudmyla Omelchuk,Andrii Kryvolap,Taras Panchenko,Nataliia Rusina,Olena Shyshatska,Oleksii Tkachenko International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.11
The paper describes the new approaches to the automated analysis of curricula according to the higher education standard. The analysis process is proposed to carry out in two ways: (a) the analysis of completeness and sufficiency of curricula according to the standard of higher education; (b) the comparison of curricula of the same qualification and specialty. The problem of improving the quality of university students' training launches the process of monitoring and analyzing educational curricula and their correspondence to the higher education standard. We developed the rules and methods to compare curricula. In addition, we implemented the automated system of curricula comparison. The paper reveals the use of these methods based on the analysis of the curriculum bachelor level of higher education "Informatics", specialty "Computer science", at the Faculty of Computer Science and Cybernetics of the Taras Shevchenko National University of Kyiv. The findings put towards the idea that the implementation of developed methods as well as the automated system of curricula analysis will improve the educational services by higher education institutions.
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Neural Networks-Based Method for Electrocardiogram Classification
Maksym Kovalchuk,Viktoriia Kharchenko,Andrii Yavorskyi,Igor Bieda,Taras Panchenko International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.9
Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches.
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