Pavel Lyakhov

1.5k total citations · 1 hit paper
98 papers, 945 citations indexed

About

Pavel Lyakhov is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Pavel Lyakhov has authored 98 papers receiving a total of 945 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Computer Vision and Pattern Recognition, 34 papers in Artificial Intelligence and 29 papers in Information Systems. Recurrent topics in Pavel Lyakhov's work include Cryptography and Residue Arithmetic (22 papers), Advanced Image Fusion Techniques (18 papers) and Image and Signal Denoising Methods (18 papers). Pavel Lyakhov is often cited by papers focused on Cryptography and Residue Arithmetic (22 papers), Advanced Image Fusion Techniques (18 papers) and Image and Signal Denoising Methods (18 papers). Pavel Lyakhov collaborates with scholars based in Russia, Iran and Bangladesh. Pavel Lyakhov's co-authors include N.I. Chervyakov, Nikolay Nagornov, Maria Valueva, Georgii Valuev, Mikhail Babenko, Dmitrii Kaplun, Maxim Deryabin, Ali Newaz Bahar, Amir Sabbagh Molahosseini and Akira Otsuki and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Sustainability.

In The Last Decade

Pavel Lyakhov

83 papers receiving 888 citations

Hit Papers

Application of the residue number system to reduce hardwa... 2020 2026 2022 2024 2020 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Application of the residue number system to reduce hardware costs of the convolutional neural network implementation
    2020 330 citations Maria Valueva, Nikolay Nagornov et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pavel Lyakhov Russia 15 305 240 199 154 123 98 945
N.I. Chervyakov Russia 15 371 1.2× 259 1.1× 354 1.8× 104 0.7× 77 0.6× 73 945
Bin Gu China 8 414 1.4× 330 1.4× 89 0.4× 129 0.8× 71 0.6× 10 1000
Sultan Almotairi Saudi Arabia 14 266 0.9× 238 1.0× 86 0.4× 86 0.6× 60 0.5× 58 873
Mohamed Batouche Algeria 18 475 1.6× 367 1.5× 83 0.4× 85 0.6× 158 1.3× 116 1.2k
Nawal El‐Fishawy Egypt 21 486 1.6× 536 2.2× 216 1.1× 164 1.1× 69 0.6× 131 1.5k
S.S. Dlay United Kingdom 20 210 0.7× 432 1.8× 142 0.7× 110 0.7× 43 0.3× 141 1.4k
Yuanyuan Liu China 22 333 1.1× 428 1.8× 42 0.2× 137 0.9× 69 0.6× 85 1.3k
Lifeng Wang China 11 475 1.6× 276 1.1× 117 0.6× 65 0.4× 62 0.5× 25 1.2k

Countries citing papers authored by Pavel Lyakhov

Since Specialization
Citations

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

Fields of papers citing papers by Pavel Lyakhov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pavel Lyakhov

This figure shows the co-authorship network connecting the top 25 collaborators of Pavel Lyakhov. A scholar is included among the top collaborators of Pavel Lyakhov 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 Pavel Lyakhov. Pavel Lyakhov 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.
Lyakhov, Pavel, et al.. (2025). Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers. Mathematics. 13(5). 828–828. 1 indexed citations
2.
Lyakhov, Pavel, et al.. (2025). Analysis of video data from an unmanned aerial vehicle based on a structural similarity index. Computer Optics. 624–624.
3.
Lyakhov, Pavel, et al.. (2024). Efficient design and implementation of a reversible switched network in quantum cellular automata technology. Journal of King Saud University - Computer and Information Sciences. 36(1). 101910–101910. 6 indexed citations
4.
Lyakhov, Pavel, et al.. (2023). A novel QCA circuit-switched network with power dissipation analysis for nano communication applications. Nano Communication Networks. 35. 100438–100438. 15 indexed citations
5.
Lyakhov, Pavel, et al.. (2023). Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise. Applied Sciences. 13(3). 1585–1585. 5 indexed citations
6.
Lyakhov, Pavel, et al.. (2023). Multimodal Neural Network for Recognition of Cardiac Arrhythmias Based on 12-Load Electrocardiogram Signals. IEEE Access. 11. 133744–133754. 4 indexed citations
7.
Lyakhov, Pavel, et al.. (2023). Multimodal Analysis of Unbalanced Dermatological Data for Skin Cancer Recognition. IEEE Access. 11. 131487–131507. 10 indexed citations
8.
Lyakhov, Pavel, et al.. (2022). High-performance digital image filtering architectures in the residue number system based on the Winograd method. Computer Optics. 46(5). 3 indexed citations
9.
Lyakhov, Pavel. (2022). Advanced Information Processing Methods and Their Applications. Directory of Open access Books (OAPEN Foundation). 1 indexed citations
10.
11.
Lyakhov, Pavel, et al.. (2022). Novel Reversible Comparator Design in Quantum Dot-Cellular Automata with Power Dissipation Analysis. Applied Sciences. 12(15). 7846–7846. 6 indexed citations
12.
13.
Lyakhov, Pavel, et al.. (2020). Method for Determining Skin Lesions from Images Using Neural Network. 1–4. 2 indexed citations
14.
Chervyakov, N.I., et al.. (2020). 3D-generalization of impulse noise removal method for video data processing. Computer Optics. 44(1). 3 indexed citations
15.
Chervyakov, N.I., et al.. (2020). EEG Neuro - processing for the development of neurointerfaces. IOP Conference Series Materials Science and Engineering. 873(1). 12003–12003. 2 indexed citations
16.
Chervyakov, N.I., Pavel Lyakhov, Maxim Deryabin, et al.. (2020). Residue Number System-Based Solution for Reducing the Hardware Cost of a Convolutional Neural Network. Neurocomputing. 407. 439–453. 15 indexed citations
17.
Chervyakov, N.I., et al.. (2018). Area-Efficient FPGA Implementation of Minimalistic Convolutional Neural Network Using Residue Number System. SHILAP Revista de lepidopterología.
18.
Chervyakov, N.I., et al.. (2018). A new model to optimize the architecture of a fault-tolerant modular neurocomputer. Neurocomputing. 303. 37–46. 3 indexed citations
19.
Chervyakov, N.I., Amir Sabbagh Molahosseini, Pavel Lyakhov, Mikhail Babenko, & Maxim Deryabin. (2016). Residue-to-binary conversion for general moduli sets based on approximate Chinese remainder theorem. International Journal of Computer Mathematics. 94(9). 1833–1849. 37 indexed citations
20.
Chervyakov, N.I., Pavel Lyakhov, & Mikhail Babenko. (2014). Digital filtering of images in a residue number system using finite-field wavelets. Automatic Control and Computer Sciences. 48(3). 180–189. 20 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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