Journal Issue: Научная визуализация
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Volume
2024-16
Number
5
Issue Date
Journal Title
Journal ISSN
2079-3537
Том журнала
Том журнала
Научная визуализация
(2024-16)
Статьи
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Visualizing the Impact of Machine Learning on Cardiovascular Disease Prediction: A Comprehensive Analysis of Research Trends
(НИЯУ МИФИ, 2024) Jeena, Joseph; Kartheeban, К.
Cardiovascular diseases (CVDs) continue to have a negative impact on global health, which highlights the need for accurate and efficient prediction methods. Machine learning (ML) techniques as tools for forecasting CVD has recently showed potential. This paper presents a comprehensive analysis of research trends in the field, focusing on visualizing the impact of ML in cardiovascular disease prediction. We used data visualization techniques to identify patterns and trends in an extensive database of scholarly publications on this subject that were published in Scopus between 1991 and 2023. The analysis reveals a substantial growth in research output, demonstrating the growing demand for ML-based CVD prediction. It reveals essential stakeholders and potential collaborators while highlighting the institutions and authors who have contributed most to this domain. The study also identifies high-impact journals that have published significant research in this domain, facilitating researchers in selecting appropriate outlets for dissemination. The study helps researchers identify the most critical areas for further research and fosters cooperation among subject-matter experts by offering insightful information about machine learning-based cardiovascular disease prediction development. The data is analyzed using the tools VOSviewer and Biblioshiny.
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Exploring the Research Landscape of Business Applications of Robotic Process Automation Through Bibliometric Analysis
(НИЯУ МИФИ, 2024) Shamini, James; Karthik, S; Binu, Thomas
The field of business process optimization and automation has seen the emergence of robotic process automation (RPA) as a disruptive technology. This research aims to give a systematic bibliometric analysis of the research ecosystem of robotic process automation in business to identify trends, patterns, and developments in this quickly developing area. Bibliometric methodologies, such as co-authorship analysis, keyword analysis, citation patterns, and publishing trends are performed in this work. Research papers from Scopus scientific databases are incorporated into the analysis through the identification of key writers, organizations, and nations that have made a substantial contribution to the growth of RPA literature. The report also explores the temporal evolution of RPA research, highlighting the development of research areas over time and identifying pockets of active research as well as prospective paradigm shifts. The research reveals key publications that have significantly influenced the course of RPA research by looking at citation networks.
The results of this bibliometric analysis enable scholars, practitioners, and policymakers to develop a more detailed grasp of the RPA research landscape in business. This study provides a roadmap for future research directions in robotic process automation by identifying research gaps and emerging trends in business management.
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Calculation of a parallaxpanoramogram in autostereoscopic systems with inconsistent monitor and lens raster parameters
(НИЯУ МИФИ, 2024) Kondratiev, N. V.; Ovechkis, Yu.N.; Vinokur, A. I.; Arsentiev, D. A.
A significant disadvantage of the multi-point of view autostereoscopic method is a drop in image resolution with an increase in the number of points of view. An effective means of increasing the resolution is the use of an inclined lens raster and vertical encoding the colors of the point of view. Algorithmically simple coding is obtained at optimal tilt angles, the tangent of which is 1 divided by a multiple of 3 (1/3, 1/6, etc.). This requirement imposes significant restrictions on the coordination of the geometric parameters of the equipment – the display panel and the lens raster. The approaches to spatial color coding proposed in this article and the algorithms implementing them make it possible to significantly expand the possibilities of creating autostereoscopic displays. The experimental work carried out convincingly confirms the theoretical conclusions. The main practical result was the developed software that allows fine-tuning of the angle of inclination of the raster and calculating a multi-point of view parallaxpanoramogram for a specific set of equipment.
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Visualization Metaphors in the Tasks of Exploratory Analysis of Heterogeneous Data
(НИЯУ МИФИ, 2024) Isaev, R. A.; Podvesovskii, A. G.; Zakharova, A. A.
The subject of the study is the construction and application of visual models using the concept of visualization metaphors in the context of exploratory analysis of heterogeneous data. This study considers improved variants of the previously proposed visualization metaphors that can be used as a basis for building visual models. A technology for exploratory analysis of heterogeneous data based on the joint use of different visualization metaphors is proposed. The process of visual data exploration at the stage of exploratory analysis using the proposed technology is demonstrated to be iterative and multiscenary, contingent upon the analysis goals. The software tool developed to implement the proposed technology is described, along with its additional functionality to calculate and export quantitative characteristics of the visual model. The software tool is then considered in the context of exploratory analysis of a synthetic data set. The future direction of the proposed approach to the construction of visual models, the technology of exploratory data analysis and the software tool for its support are determined.
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Visualization of Points of a Multidimensional Information Text Array on an Elastic Map for Assessing the Cluster Structure of Data
(НИЯУ МИФИ, 2024) Bondarev, A. E.
The article presents the results of computational experiments on displaying the points of the original multidimensional information array on the elastic map scan to assess the relative positions of semantic proximity areas in order to improve the processing of text information. Elastic maps are considered as a tool for providing analytical work with text information. As previous works show, in order to obtain the required distances corresponding to the cluster picture of the studied multidimensional volume, it is necessary to use the distances on the elastic map, which reflects the cluster portrait of the studied multidimensional data volume. The paper presents the cluster structures of points of the studied multidimensional volume obtained in this way on the elastic map scan in the plane of the first two principal components. An analysis of the relative positions of clusters of different configurations at different points in time is presented.