Kazuyuki Murase

7.6k total citations · 1 hit paper
192 papers, 5.7k citations indexed

About

Kazuyuki Murase is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cellular and Molecular Neuroscience. According to data from OpenAlex, Kazuyuki Murase has authored 192 papers receiving a total of 5.7k indexed citations (citations by other indexed papers that have themselves been cited), including 83 papers in Artificial Intelligence, 47 papers in Computer Vision and Pattern Recognition and 45 papers in Cellular and Molecular Neuroscience. Recurrent topics in Kazuyuki Murase's work include Neural Networks and Applications (56 papers), Pain Mechanisms and Treatments (30 papers) and Face and Expression Recognition (28 papers). Kazuyuki Murase is often cited by papers focused on Neural Networks and Applications (56 papers), Pain Mechanisms and Treatments (30 papers) and Face and Expression Recognition (28 papers). Kazuyuki Murase collaborates with scholars based in Japan, Bangladesh and United States. Kazuyuki Murase's co-authors include Mirjana Randić, Xin Yao, Pan Dong Ryu, Md. Monirul Islam, Sukarna Barua, Md. Monirul Islam, Md. Monirul Kabir, Hiroshi Ikeda, Md. Shahjahan and Md. Faijul Amin and has published in prestigious journals such as Science, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

Kazuyuki Murase

181 papers receiving 5.4k citations

Hit Papers

MWMOTE--Majority Weighted Minority Oversampling Technique... 2012 2026 2016 2021 2012 250 500 750
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. MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
    2012 828 citations Kazuyuki Murase 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
Kazuyuki Murase Japan 36 2.1k 1.8k 1.4k 1.2k 643 192 5.7k
Jiang Wang China 45 1.2k 0.6× 1.4k 0.8× 718 0.5× 415 0.4× 539 0.8× 573 9.7k
John H. Reynolds United States 32 1.6k 0.8× 2.6k 1.4× 729 0.5× 157 0.1× 919 1.4× 128 11.9k
P. S. Sastry India 35 1.8k 0.8× 215 0.1× 1.3k 0.9× 299 0.3× 592 0.9× 129 5.5k
Abdülhamit Subaşı Saudi Arabia 51 2.2k 1.0× 969 0.5× 503 0.4× 144 0.1× 863 1.3× 178 10.4k
Chih‐Wei Hsu Taiwan 24 1.3k 0.6× 166 0.1× 364 0.3× 190 0.2× 988 1.5× 170 5.5k
Alok Sharma Australia 44 669 0.3× 307 0.2× 3.3k 2.4× 109 0.1× 468 0.7× 197 5.7k
Bing Huang China 47 745 0.4× 387 0.2× 1.6k 1.2× 572 0.5× 135 0.2× 279 7.4k
Yuanjie Zheng China 34 1.6k 0.7× 246 0.1× 408 0.3× 252 0.2× 2.4k 3.7× 244 8.9k
Concha Bielza Spain 32 1.8k 0.8× 184 0.1× 789 0.6× 87 0.1× 405 0.6× 180 4.4k
T.M. McGinnity United Kingdom 37 1.6k 0.8× 726 0.4× 259 0.2× 65 0.1× 566 0.9× 308 5.5k

Countries citing papers authored by Kazuyuki Murase

Since Specialization
Citations

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

Fields of papers citing papers by Kazuyuki Murase

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kazuyuki Murase

This figure shows the co-authorship network connecting the top 25 collaborators of Kazuyuki Murase. A scholar is included among the top collaborators of Kazuyuki Murase 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 Kazuyuki Murase. Kazuyuki Murase 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
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Akhand, M. A. H., et al.. (2018). Capacitated Vehicle Routing Problem Solving through Adaptive Sweep Based Clustering plus Swarm Intelligence based Route Optimization. Oriental journal of computer science and technology. 11(2). 88–102. 3 indexed citations
3.
Murase, Kazuyuki, H. T. Kim, Yutaka Kawano, et al.. (2014). Increased mitochondrial apoptotic priming of human regulatory T cells after allogeneic hematopoietic stem cell transplantation. Haematologica. 99(9). 1499–1508. 16 indexed citations
4.
Alnajjar, Fady, et al.. (2010). A novel dynamic edge detection inspired from mammalian retina toward better robot vision. World Automation Congress. 1–6. 1 indexed citations
5.
Murase, Kazuyuki, et al.. (2009). 不安行動に対する匂いの効果,およびc-Fos染色で明らかとなった関連脳領域. The Journal of Physiological Sciences. 59. 482. 1 indexed citations
6.
Iqbal, Asif, et al.. (2008). A Novel Algorithm for Translation, Rotation and Scale Invariant Character Recognition. 2008. 1367–1372. 4 indexed citations
7.
Kabir, Md. Monirul, Md. Monirul Islam, & Kazuyuki Murase. (2008). A New Wrapper Feature Selection Approach using Neural Network. 2008. 1953–1958. 14 indexed citations
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Islam, Md. Monirul, et al.. (2006). A New Algorithm to Design Multiple Hidden Layered Artificial Neural Networks. 2006. 463–468. 3 indexed citations
10.
Hyder, Md Mashud, Md. Monirul Islam, M. A. H. Akhand, & Kazuyuki Murase. (2006). "A Novel Two-Stage Approach for Translation, Rotation and Scale Invariant Character Recognition". 2006. 1352–1357. 1 indexed citations
11.
Alam, Mohammad Shafiul, Md. Monirul Islam, M. A. H. Akhand, & Kazuyuki Murase. (2006). A Recurring Multi-Stage Evolutionary System for Balancing Exploitation and Exploration. 2006. 1111–1116. 1 indexed citations
12.
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Ikeda, Hiroshi, et al.. (2005). Optical Analyses of GABAergic Presynaptic Inhibition in Spinal Dorsal Horn. 1 indexed citations
14.
Shahjahan, Md & Kazuyuki Murase. (2003). A Dynamic node decaying method for pruning artificial neural networks. IEICE Transactions on Information and Systems. 86(4). 736–751. 1 indexed citations
15.
Ikeda, Hiroshi, et al.. (2003). Optical imaging with retrogradely transported voltage-sensitive dye from projection neurons in marginal layer of the spinal dorsal horn. Society of Instrument and Control Engineers of Japan. 3. 2421–2425.
16.
Islam, Md. Monirul & Kazuyuki Murase. (2001). A New Crossover Operator and Its Application to Artificial Neural Networks Evolution. IEICE Transactions on Information and Systems. 84(9). 1144–1154. 2 indexed citations
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Shimizu‐Sasamata, Masao, Sachiko Kawasaki‐Yatsugi, Masamichí Okada, et al.. (1996). YM90K: pharmacological characterization as a selective and potent alpha-amino-3-hydroxy-5-methylisoxazole-4-propionate/kainate receptor antagonist.. Journal of Pharmacology and Experimental Therapeutics. 276(1). 84–92. 77 indexed citations
19.
Park, Jin Bong, et al.. (1994). Possible presence of the ATP-sensitive K+ channel in isolated spinal dorsal horn neurons of the rat. Neuroscience Letters. 170(2). 208–212. 15 indexed citations
20.
Arancio, Ottavio, Megumu Yoshimura, Kazuyuki Murase, & Amy B. MacDermott. (1993). The distribution of excitatory amino acid receptors on acutely dissociated dorsal horn neurons from postnatal rats. Neuroscience. 52(1). 159–167. 16 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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