Ivo Bukovský

690 total citations
58 papers, 339 citations indexed

About

Ivo Bukovský is a scholar working on Artificial Intelligence, Control and Systems Engineering and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Ivo Bukovský has authored 58 papers receiving a total of 339 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Artificial Intelligence, 23 papers in Control and Systems Engineering and 10 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Ivo Bukovský's work include Neural Networks and Applications (23 papers), Fault Detection and Control Systems (14 papers) and Anomaly Detection Techniques and Applications (7 papers). Ivo Bukovský is often cited by papers focused on Neural Networks and Applications (23 papers), Fault Detection and Control Systems (14 papers) and Anomaly Detection Techniques and Applications (7 papers). Ivo Bukovský collaborates with scholars based in Czechia, Japan and Canada. Ivo Bukovský's co-authors include Noriyasu Homma, Jiří Bíla, Oldřich Vyšata, Martin Vališ, Kei Ichiji, Witold Kinsner, Makoto Yoshizawa, Madan M. Gupta, Xiaoyong Zhang and Ricardo Rodríguez Jorge and has published in prestigious journals such as Scientific Reports, IEEE Access and Physics in Medicine and Biology.

In The Last Decade

Ivo Bukovský

54 papers receiving 325 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ivo Bukovský Czechia 11 152 86 55 53 50 58 339
J.Y. Cheung United States 11 130 0.9× 114 1.3× 21 0.4× 25 0.5× 43 0.9× 58 364
Jiancheng Sun China 9 92 0.6× 32 0.4× 32 0.6× 16 0.3× 36 0.7× 34 316
Suranjana Samanta India 6 93 0.6× 18 0.2× 16 0.3× 34 0.6× 35 0.7× 22 284
Xiaolin Huang China 9 182 1.2× 64 0.7× 66 1.2× 13 0.2× 52 1.0× 31 448
Michael Tschannen Switzerland 11 198 1.3× 13 0.2× 8 0.1× 38 0.7× 82 1.6× 26 502
M.R. Varley United Kingdom 13 96 0.6× 16 0.2× 20 0.4× 25 0.5× 72 1.4× 39 366
Rahime Ceylan Türkiye 10 131 0.9× 33 0.4× 62 1.1× 45 0.8× 29 0.6× 20 333
Tal Ridnik Israel 6 255 1.7× 10 0.1× 20 0.4× 41 0.8× 23 0.5× 8 427
Zhenjie Yao China 10 65 0.4× 15 0.2× 22 0.4× 69 1.3× 20 0.4× 35 374
Henry Gouk United Kingdom 6 163 1.1× 19 0.2× 20 0.4× 31 0.6× 28 0.6× 14 300

Countries citing papers authored by Ivo Bukovský

Since Specialization
Citations

This map shows the geographic impact of Ivo Bukovský's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ivo Bukovský with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivo Bukovský more than expected).

Fields of papers citing papers by Ivo Bukovský

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ivo Bukovský. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ivo Bukovský. The network helps show where Ivo Bukovský may publish in the future.

Co-authorship network of co-authors of Ivo Bukovský

This figure shows the co-authorship network connecting the top 25 collaborators of Ivo Bukovský. A scholar is included among the top collaborators of Ivo Bukovský based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ivo Bukovský. Ivo Bukovský is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhang, Xiaoyong, Jiaoyang Wang, Akihito Usui, et al.. (2024). Inconsistency between Human Observation and Deep Learning Models: Assessing Validity of Postmortem Computed Tomography Diagnosis of Drowning. Journal of Imaging Informatics in Medicine. 37(3). 1–10. 1 indexed citations
2.
Zhang, Zhang, Xiaoyong Zhang, Kei Ichiji, Ivo Bukovský, & Noriyasu Homma. (2023). How intra-source imbalanced datasets impact the performance of deep learning for COVID-19 diagnosis using chest X-ray images. Scientific Reports. 13(1). 4 indexed citations
3.
Vyšata, Oldřich, et al.. (2021). Novelty detection-based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEG. Medical & Biological Engineering & Computing. 59(11-12). 2287–2296. 42 indexed citations
4.
Homma, Noriyasu, Xiaoyong Zhang, Kei Ichiji, et al.. (2020). Human ability enhancement for reading mammographic masses by a deep learning technique. 2962–2964. 2 indexed citations
5.
Bukovský, Ivo, et al.. (2019). Novelty detection based on learning entropy. Applied Stochastic Models in Business and Industry. 36(1). 178–183. 1 indexed citations
6.
Bukovský, Ivo, et al.. (2019). Lighting Pole Health Monitoring for Predictive Maintenance. Procedia Structural Integrity. 17. 799–805. 2 indexed citations
7.
Ichiji, Kei, Yusuke Yoshida, Noriyasu Homma, et al.. (2018). A key-point based real-time tracking of lung tumor in x-ray image sequence by using difference of Gaussians filtering and optical flow. Physics in Medicine and Biology. 63(18). 185007–185007. 7 indexed citations
8.
Kadoya, Noriyuki, Kei Ichiji, Y. Nakajima, et al.. (2017). Dosimetric evaluation of MLC-based dynamic tumor tracking radiotherapy using digital phantom: Desired setup margin for tracking radiotherapy. Medical dosimetry. 43(1). 74–81. 3 indexed citations
9.
Bukovský, Ivo, et al.. (2017). Influence of type and level of noise on the performance of an adaptive novelty detector. 373–377. 1 indexed citations
10.
Kukal, Jaromír, et al.. (2016). Feature selection via competitive levy flights. 44. 3731–3736. 1 indexed citations
11.
Zhang, Xiaoyong, Noriyasu Homma, Kei Ichiji, et al.. (2015). Tumor motion tracking using kV/MV X-ray fluoroscopy for adaptive radiation therapy. 1–4.
12.
13.
Bukovský, Ivo, et al.. (2015). A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications. BioMed Research International. 2015. 1–13. 24 indexed citations
14.
Bíla, Jiří, et al.. (2011). Qualitative modeling in the landscape development monitoring. International Conference on Systems. 35–41. 3 indexed citations
15.
Jorge, Ricardo Rodríguez, Ivo Bukovský, & Noriyasu Homma. (2011). Potentials of Quadratic Neural Unit for Applications. International Journal of Software Science and Computational Intelligence. 3(3). 1–12. 4 indexed citations
16.
Bukovský, Ivo, Kei Ichiji, Noriyasu Homma, Makoto Yoshizawa, & Ricardo Rodríguez Jorge. (2010). Testing potentials of dynamic quadratic neural unit for prediction of lung motion during respiration for tracking radiation therapy. 1–6. 8 indexed citations
17.
18.
Bíla, Jiří, et al.. (2009). Qualitative modeling and monitoring of the selected ecosystem violated with parasitic dehumidifying and dehydrating. 211–219. 3 indexed citations
19.
Bukovský, Ivo, Zeng‐Guang Hou, Jiří Bíla, & Madan M. Gupta. (2008). Foundations of Nonconventional Neural Units and their Classification. International Journal of Cognitive Informatics and Natural Intelligence. 2(4). 29–43. 4 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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