Ikuko Nishikawa

1.1k total citations
51 papers, 779 citations indexed

About

Ikuko Nishikawa is a scholar working on Industrial and Manufacturing Engineering, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Ikuko Nishikawa has authored 51 papers receiving a total of 779 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Industrial and Manufacturing Engineering, 11 papers in Artificial Intelligence and 9 papers in Molecular Biology. Recurrent topics in Ikuko Nishikawa's work include Nonlinear Dynamics and Pattern Formation (8 papers), Machine Learning in Bioinformatics (7 papers) and Transportation and Mobility Innovations (7 papers). Ikuko Nishikawa is often cited by papers focused on Nonlinear Dynamics and Pattern Formation (8 papers), Machine Learning in Bioinformatics (7 papers) and Transportation and Mobility Innovations (7 papers). Ikuko Nishikawa collaborates with scholars based in Japan, United States and China. Ikuko Nishikawa's co-authors include Yoshiki Kuramoto, Hideki Takayasu, Hal Tasaki, Toshio Aoyagi, Koji Okuda, Masahiro Ito, Satoshi Fukuchi, Ken Nishikawa, Yukiko Nakajima and Keiichi Homma and has published in prestigious journals such as Brain Research, International Journal of Molecular Sciences and Journal of Statistical Physics.

In The Last Decade

Ikuko Nishikawa

45 papers receiving 733 citations

Peers

Ikuko Nishikawa

CNC

SNP

CMP

Daniele Avitabile United Kingdom

Marco Thiel United Kingdom

Márton Pósfai United States

Toru Ohira Japan

Mathias Bode Germany

Yanmei Kang China

Chris Langton United States

Zi‐Gang Huang China

Jens Christian Claussen Germany

Leonardo Parisi Italy

Daniele Avitabile United Kingdom

Ikuko Nishikawa

375 ×1.2

CNC

311 ×1.5

SNP

224 ×1.2

56 ×0.5

CMP

57 ×0.6

Citations per year, relative to Ikuko Nishikawa Ikuko Nishikawa (= 1×) peers Daniele Avitabile

Countries citing papers authored by Ikuko Nishikawa

Since Specialization

Citations

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

Fields of papers citing papers by Ikuko Nishikawa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ikuko Nishikawa

This figure shows the co-authorship network connecting the top 25 collaborators of Ikuko Nishikawa. A scholar is included among the top collaborators of Ikuko Nishikawa 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 Ikuko Nishikawa. Ikuko Nishikawa 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

Nishimura, Masaki, et al.. (2024). Two-Stage Diffusion Model for 3D Medical Image Segmentation. 1–7.

Kobayashi, Ryota, Shigehiro Namiki, Ryohei Kanzaki, et al.. (2013). Population coding is essential for rapid information processing in the moth antennal lobe. Brain Research. 1536. 88–96. 2 indexed citations

Shibata, Tomohiro, et al.. (2010). A comparison of the brain activity between experts and non-experts in a basic drumming using NIRS. IEICE Technical Report; IEICE Tech. Rep.. 110(295). 31–36. 1 indexed citations

Nishikawa, Ikuko, et al.. (2010). Prediction of the protein O-glycosylation by machine learning and statistical characters around the glycosylation sites. International Conference on Software Engineering. 671–674. 1 indexed citations

Nishikawa, Ikuko, et al.. (2010). Simulated annealing method based on recursive problem decomposition for vehicle routing problems. Society of Instrument and Control Engineers of Japan. 1016–1020.

Taniguchi, Tadahiro, Yusuke Takahashi, & Ikuko Nishikawa. (2010). Multi-agent simulation on relationship between individuals' travel behavior and residential choice behavior. Society of Instrument and Control Engineers of Japan. 1728–1733. 1 indexed citations

Nishikawa, Ikuko, et al.. (2009). Decomposition model of vehicle routing problem and hybrid approach mixing branch-and-bound techniques and meta-heuristics. 2009 ICCAS-SICE. 759–763.

Taniguchi, Tadahiro, et al.. (2009). Design of Autonomous Trading Agent using Natural Actor-Critic Method to Realize a Locally Produced and Consumed Electric Energy Network. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics. 21(6). 1078–1091. 1 indexed citations

Han, Xian‐Hua, et al.. (2009). Principal Component Analysis for Prediction of O-Linked Glycosylation Sites in Protein by Multi-Layered Neural Networks. 1193–1196. 3 indexed citations

10.

Watanabe, Shinya, et al.. (2006). 2B2 HIERARCHICAL APPROACH WITH INFORMATIONAL FEEDBACK FOR PICKUP AND DELIVERY PROBLEMS(Technical session 2B: Vehicle scheduling and communication). 2006. 48–53. 2 indexed citations

11.

Nishikawa, Ikuko, et al.. (2006). Improvements of the Traffic Signal Control by Complex-Valued Hopfield Networks. The 2006 IEEE International Joint Conference on Neural Network Proceedings. 459–464. 11 indexed citations

12.

Nishikawa, Ikuko, et al.. (2006). 2 types of complex-valued Hopfield networks and the application to a traffic signal control. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.. 2. 782–787. 3 indexed citations

13.

Nishikawa, Ikuko, et al.. (2003). Area-wide Control of Traffic Signals by Phase Model. Transactions of the Society of Instrument and Control Engineers. 39(2). 199–208. 9 indexed citations

14.

Nishikawa, Ikuko, et al.. (2002). Hebbian Learning of a Behavior Network and its Application to a Collision Avoidance by an Autonomous Agent. Transactions of the Institute of Systems Control and Information Engineers. 15(7). 350–358. 2 indexed citations

15.

Nishikawa, Ikuko & Shuji Uchida. (2001). A Stochastic Unsupervised Learning Algorithm and Its Application to an Autonomous Agent. Transactions of the Society of Instrument and Control Engineers. 37(12). 1169–1177.

16.

Nishikawa, Ikuko, et al.. (1993). Line Balancing Using a Hopfield Network. 제어로봇시스템학회 국내학술대회 논문집. 1(2). 391–394. 2 indexed citations

17.

Aoyagi, Toshio, et al.. (1993). A model for feature linking via collective oscillations in the primary visual cortex. Biological Cybernetics. 68(6). 483–490. 43 indexed citations

18.

Aoyagi, Toshio, et al.. (1992). Neural Network Model Carrying Phase Information with Application to Collective Dynamics. Progress of Theoretical Physics. 87(5). 1119–1126. 9 indexed citations

19.

Kuramoto, Yoshio, et al.. (1992). Neural Network Model Carrying Phase Information with Application to Collective Dynamics. Progress of Theoretical Physics. 87(5). 1119–1126. 16 indexed citations

20.

Kuramoto, Yoshiki & Ikuko Nishikawa. (1987). Statistical macrodynamics of large dynamical systems. Case of a phase transition in oscillator communities. Journal of Statistical Physics. 49(3-4). 569–605. 289 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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