Dmytro Peleshko

599 total citations
38 papers, 369 citations indexed

About

Dmytro Peleshko is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Dmytro Peleshko has authored 38 papers receiving a total of 369 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 15 papers in Artificial Intelligence and 5 papers in Control and Systems Engineering. Recurrent topics in Dmytro Peleshko's work include Neural Networks and Applications (10 papers), Video Surveillance and Tracking Methods (7 papers) and Image and Signal Denoising Methods (6 papers). Dmytro Peleshko is often cited by papers focused on Neural Networks and Applications (10 papers), Video Surveillance and Tracking Methods (7 papers) and Image and Signal Denoising Methods (6 papers). Dmytro Peleshko collaborates with scholars based in Ukraine and Poland. Dmytro Peleshko's co-authors include Ivan Izonin, Olena Vynokurova, Yevgeniy Bodyanskiy, Nataliia Lotoshynska, Roman Tkachenko, Vasyl Lytvyn, Victoria Vysotska, Jörg Rainer Noennig and Т. А. Свиридова and has published in prestigious journals such as SHILAP Revista de lepidopterología, Neurocomputing and Data.

In The Last Decade

Dmytro Peleshko

33 papers receiving 312 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dmytro Peleshko Ukraine	12	125	115	92	70	42	38	369
Elena Troubitsynå Finland	11	36 0.3×	159 1.4×	37 0.4×	104 1.5×	92 2.2×	69	413
C. Raghavendra Rao India	11	36 0.3×	90 0.8×	26 0.3×	72 1.0×	19 0.5×	57	367
Yong‐Yi Fanjiang Taiwan	12	56 0.4×	153 1.3×	38 0.4×	210 3.0×	20 0.5×	60	384
Anatoly Sachenko Ukraine	9	30 0.2×	87 0.8×	34 0.4×	48 0.7×	4 0.1×	49	256
John Galletly United Kingdom	6	35 0.3×	106 0.9×	32 0.3×	31 0.4×	6 0.1×	20	347
Adnan Iftekhar China	10	184 1.5×	123 1.1×	23 0.3×	126 1.8×	3 0.1×	14	464
Werasak Kurutach Thailand	11	164 1.3×	115 1.0×	8 0.1×	85 1.2×	9 0.2×	55	439
Ye Hu China	9	52 0.4×	87 0.8×	25 0.3×	182 2.6×	6 0.1×	28	449
Frieder Ganz United Kingdom	10	122 1.0×	149 1.3×	13 0.1×	75 1.1×	4 0.1×	14	392

Countries citing papers authored by Dmytro Peleshko

Since Specialization

Citations

This map shows the geographic impact of Dmytro Peleshko'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 Dmytro Peleshko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dmytro Peleshko more than expected).

Fields of papers citing papers by Dmytro Peleshko

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dmytro Peleshko. 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 Dmytro Peleshko. The network helps show where Dmytro Peleshko may publish in the future.

Co-authorship network of co-authors of Dmytro Peleshko

This figure shows the co-authorship network connecting the top 25 collaborators of Dmytro Peleshko. A scholar is included among the top collaborators of Dmytro Peleshko 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 Dmytro Peleshko. Dmytro Peleshko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Lytvyn, Vasyl, et al.. (2020). Conceptual Model of Information System for Drone Monitoring of Trees' Condition.. 695–714. 3 indexed citations

Vynokurova, Olena, et al.. (2019). A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting. Data. 4(3). 126–126. 21 indexed citations

Vynokurova, Olena, et al.. (2018). A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction. Data. 3(4). 62–62. 6 indexed citations

Bodyanskiy, Yevgeniy, et al.. (2018). Hybrid fuzzy-clustering algorithm of unevenly and asynchronously spaced time series in computer engineering. 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET). 8. 930–935.

Bodyanskiy, Yevgeniy, et al.. (2018). The Autoencoder Based on Generalized Neo-Fuzzy Neuron and its Fast Learning for Deep Neural Networks. 3698. 113–118. 3 indexed citations

Vynokurova, Olena, et al.. (2018). Hybrid Multidimensional Wavelet-Neuro-System and its Learning Using Cross Entropy Cost Function in Pattern Recognition. 305–309. 4 indexed citations

Peleshko, Dmytro, et al.. (2017). Video-based Flame Detection using LBP-based Descriptor: Influences of Classifiers Variety on Detection Efficiency. International Journal of Intelligent Systems and Applications. 9(2). 42–48. 25 indexed citations

Bodyanskiy, Yevgeniy, et al.. (2017). Neo-Fuzzy Encoder and Its Adaptive Learning for Big Data Processing. SHILAP Revista de lepidopterología. 20(1). 2 indexed citations

Peleshko, Dmytro, et al.. (2016). Image Superresolution via Divergence Matrix and Automatic Detection of Crossover. International Journal of Intelligent Systems and Applications. 8(12). 1–8. 39 indexed citations

10.

Izonin, Ivan, et al.. (2015). Learning-based image super-resolution using weight coefficients of synaptic connections. 25–29. 26 indexed citations

11.

Bodyanskiy, Yevgeniy, et al.. (2015). Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms. 328–333. 10 indexed citations

12.

Peleshko, Dmytro, et al.. (2013). Research of usage of Haar-like features and AdaBoost algorithm in Viola-Jones method of object detection. 284–286. 14 indexed citations

13.

Peleshko, Dmytro, et al.. (2011). The use of disjunctive covering of images to increase strength of the RSA algorithm. 168–169. 1 indexed citations

14.

Peleshko, Dmytro, et al.. (2011). Analysis of invariant moments in tasks image processing. 263–264. 11 indexed citations

15.

Peleshko, Dmytro, et al.. (2011). Using of affine transformations for the encryption and decryption of two images. 348–349. 3 indexed citations

16.

Peleshko, Dmytro, et al.. (2011). Time series partitional clustering. 170–171. 1 indexed citations

17.

Peleshko, Dmytro, et al.. (2011). An application of combined detector algorithm to extract the interest points of foreground objects in videostreams. 262–262. 1 indexed citations

18.

Peleshko, Dmytro, et al.. (2011). Images protection by using projective and binary affine transformations with elements of RSA algorithm. 321–321. 3 indexed citations

19.

Peleshko, Dmytro, et al.. (2009). Stream modification of RSA algorithm for image coding with precize contoure extraction. 6 indexed citations

20.

Peleshko, Dmytro, et al.. (2009). Mathematical model of presenting an image and set of images. 98–100. 7 indexed citations

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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