Georgii Valuev

605 total citations · 1 hit paper
14 papers, 412 citations indexed

About

Georgii Valuev is a scholar working on Information Systems, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Georgii Valuev has authored 14 papers receiving a total of 412 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Information Systems, 6 papers in Computer Vision and Pattern Recognition and 6 papers in Artificial Intelligence. Recurrent topics in Georgii Valuev's work include Cryptography and Residue Arithmetic (8 papers), Advanced Data Processing Techniques (4 papers) and Brain Tumor Detection and Classification (3 papers). Georgii Valuev is often cited by papers focused on Cryptography and Residue Arithmetic (8 papers), Advanced Data Processing Techniques (4 papers) and Brain Tumor Detection and Classification (3 papers). Georgii Valuev collaborates with scholars based in Russia, Mexico and Germany. Georgii Valuev's co-authors include Maria Valueva, Pavel Lyakhov, Nikolay Nagornov, N.I. Chervyakov, Dmitrii Kaplun, Maxim Deryabin, Arutyun Avetisyan, Tatiana Ermakova, Andrei Tchernykh and Mikhail Babenko and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Neurocomputing.

In The Last Decade

Georgii Valuev

11 papers receiving 390 citations

Hit Papers

align trajectories

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.

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. Mathematics and Computers in Simulation profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Georgii Valuev Russia	7	129	94	56	50	44	14	412
Maria Valueva Russia	7	143 1.1×	116 1.2×	72 1.3×	66 1.3×	46 1.0×	26	462
Nikolay Nagornov Russia	8	157 1.2×	139 1.5×	53 0.9×	63 1.3×	59 1.3×	30	543
Sourav Mishra India	7	150 1.2×	127 1.4×	36 0.6×	39 0.8×	70 1.6×	21	534
Pooja Asopa India	4	113 0.9×	101 1.1×	33 0.6×	40 0.8×	60 1.4×	5	458
Sakshi Indolia India	4	109 0.8×	119 1.3×	27 0.5×	34 0.7×	60 1.4×	7	465
T Anjali India	14	94 0.7×	131 1.4×	55 1.0×	102 2.0×	26 0.6×	117	554
Saleh Albelwi Saudi Arabia	8	152 1.2×	146 1.6×	24 0.4×	26 0.5×	45 1.0×	15	413
Sonain Jamil South Korea	12	81 0.6×	128 1.4×	24 0.4×	62 1.2×	35 0.8×	35	472
Jyoti Prakash Sahoo India	6	171 1.3×	86 0.9×	24 0.4×	35 0.7×	27 0.6×	14	361
Kang Li China	10	169 1.3×	94 1.0×	33 0.6×	21 0.4×	49 1.1×	47	391

Countries citing papers authored by Georgii Valuev

Since Specialization

Citations

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

Fields of papers citing papers by Georgii Valuev

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Georgii Valuev

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

All Works

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14 of 14 papers shown

Valuev, Georgii, Maria Valueva, Mikhail Babenko, & Andrei Tchernykh. (2023). Digital Filter Architecture Based on Modified Winograd Method F(2× 2, 5× 5) and Residue Number System. IEEE Access. 11. 26807–26819.

Babenko, Mikhail, et al.. (2023). A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption. Big Data and Cognitive Computing. 7(2). 84–84. 6 indexed citations

Valueva, Maria, Georgii Valuev, Mikhail Babenko, Andrei Tchernykh, & Jorge M. Cortés-Mendoza. (2022). Method for Convolutional Neural Network Hardware Implementation Based on a Residue Number System. Proceedings of the Institute for System Programming of RAS. 34(3). 61–74. 1 indexed citations

Valueva, Maria, Georgii Valuev, Mikhail Babenko, Andrei Tchernykh, & Jorge M. Cortés-Mendoza. (2022). Method for Convolutional Neural Network Hardware Implementation Based on a Residue Number System. Programming and Computer Software. 48(8). 735–744. 1 indexed citations

Lyakhov, Pavel, Georgii Valuev, Maria Valueva, Dmitrii Kaplun, & Aleksandr Sinitca. (2021). Single Image Super-Resolution Method Based on Bilinear Interpolation and U-Net Combination. 1–4. 2 indexed citations

Valueva, Maria, Pavel Lyakhov, Georgii Valuev, & Nikolay Nagornov. (2021). Digital Filter Architecture With Calculations in the Residue Number System by Winograd Method F (2 × 2, 2 × 2). IEEE Access. 9. 143331–143340. 5 indexed citations

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

Lyakhov, Pavel, Maria Valueva, Georgii Valuev, & Nikolay Nagornov. (2020). High-Performance Digital Filtering on Truncated Multiply-Accumulate Units in the Residue Number System. IEEE Access. 8. 209181–209190. 16 indexed citations

Valueva, Maria, Nikolay Nagornov, Pavel Lyakhov, Georgii Valuev, & N.I. Chervyakov. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation. 177. 232–243. 330 indexed citations breakdown →

10.

Lyakhov, Pavel, Maria Valueva, Georgii Valuev, & Nikolay Nagornov. (2020). A Method of Increasing Digital Filter Performance Based on Truncated Multiply-Accumulate Units. Applied Sciences. 10(24). 9052–9052. 13 indexed citations

11.

Chervyakov, N.I., Pavel Lyakhov, Nikolay Nagornov, Maria Valueva, & Georgii Valuev. (2019). Hardware implementation of a convolutional neural network using calculations in the residue number system. Computer Optics. 43(5). 7 indexed citations

12.

Valueva, Maria, et al.. (2019). Construction of Residue Number System Using Hardware Efficient Diagonal Function. Electronics. 8(6). 694–694. 10 indexed citations

13.

Chervyakov, N.I., et al.. (2018). Area-Efficient FPGA Implementation of Minimalistic Convolutional Neural Network Using Residue Number System. SHILAP Revista de lepidopterología.

14.

Chervyakov, N.I., et al.. (2018). Area-Efficient FPGA Implementation of Minimalistic Convolutional Neural Network Using Residue Number System. 112–118. 6 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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