Hiroki Suyari

749 total citations
44 papers, 477 citations indexed

About

Hiroki Suyari is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Economics and Econometrics. According to data from OpenAlex, Hiroki Suyari has authored 44 papers receiving a total of 477 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Statistical and Nonlinear Physics, 12 papers in Artificial Intelligence and 11 papers in Economics and Econometrics. Recurrent topics in Hiroki Suyari's work include Statistical Mechanics and Entropy (22 papers), Complex Systems and Time Series Analysis (11 papers) and Advanced Thermodynamics and Statistical Mechanics (8 papers). Hiroki Suyari is often cited by papers focused on Statistical Mechanics and Entropy (22 papers), Complex Systems and Time Series Analysis (11 papers) and Advanced Thermodynamics and Statistical Mechanics (8 papers). Hiroki Suyari collaborates with scholars based in Japan, Italy and Australia. Hiroki Suyari's co-authors include Tatsuaki Wada, Makoto Tsukada, Masanori Ohya, Hajime Yokota, Robert K. Niven, Takashi Uno, Takuro Horikoshi, A.M. Scarfone, Luigi Accardi and Kazuhide Inage and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Information Theory and Spine.

In The Last Decade

Hiroki Suyari

41 papers receiving 462 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Hiroki Suyari Japan	11	285	117	102	90	63	44	477
Pamela Burrage Australia	17	149 0.5×	21 0.2×	35 0.3×	35 0.4×	156 2.5×	59	1.2k
Xavier Warin France	11	163 0.6×	92 0.8×	90 0.9×	55 0.6×	57 0.9×	37	781
Joanna Janczura Poland	16	184 0.6×	234 2.0×	31 0.3×	33 0.4×	203 3.2×	39	871
Grzegorz Sikora Poland	15	188 0.7×	188 1.6×	37 0.4×	31 0.3×	235 3.7×	45	609
Antoine Ayache France	16	72 0.3×	358 3.1×	22 0.2×	45 0.5×	52 0.8×	55	841
Marjorie G. Hahn United States	15	76 0.3×	64 0.5×	205 2.0×	71 0.8×	99 1.6×	44	610
Jacques Istas France	13	36 0.1×	309 2.6×	75 0.7×	55 0.6×	32 0.5×	34	754
Pierre Vallois France	13	82 0.3×	108 0.9×	109 1.1×	17 0.2×	61 1.0×	58	643
Mireille Bossy France	12	93 0.3×	43 0.4×	64 0.6×	24 0.3×	78 1.2×	47	587
Marie-Thérèse Wolfram United Kingdom	15	243 0.9×	122 1.0×	16 0.2×	21 0.2×	119 1.9×	45	847

Countries citing papers authored by Hiroki Suyari

Since Specialization

Citations

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

Fields of papers citing papers by Hiroki Suyari

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hiroki Suyari

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

Maeda, Akio, et al.. (2025). Reliable and efficient automated short-answer scoring for a large dataset using active learning and deep learning. Interactive Learning Environments. 33(6). 3776–3787.

Mori, Shinichiro, et al.. (2024). Improving respiratory signal prediction with a deep neural network and simple changes to the input and output data format. Physics in Medicine and Biology. 69(8). 85023–85023. 1 indexed citations

Mori, Shinichiro, et al.. (2023). Optimizing 3DCT image registration for interfractional changes in carbon-ion prostate radiotherapy. Scientific Reports. 13(1). 7448–7448. 2 indexed citations

Takahashi, Manami, Hiroyuki Takaoka, Hajime Yokota, et al.. (2023). Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery stenosis. Heart and Vessels. 38(11). 1318–1328. 5 indexed citations

Mori, Shinichiro, et al.. (2023). Real-time deep neural network-based automatic bowel gas segmentation on X-ray images for particle beam treatment. Physical and Engineering Sciences in Medicine. 46(2). 659–668. 1 indexed citations

Suyari, Hiroki, et al.. (2023). Spatiotemporal forecasting of vertical track alignment with exogenous factors. Scientific Reports. 13(1). 2354–2354. 3 indexed citations

Yokota, Hajime, Takashi Takeuchi, Hiroki Mukai, et al.. (2022). Multidimensional Deep Learning Reduces False-Positives in the Automated Detection of Cerebral Aneurysms on Time-Of-Flight Magnetic Resonance Angiography: A Multi-Center Study. Frontiers in Neurology. 12. 742126–742126. 21 indexed citations

Yokota, Hajime, Takuro Horikoshi, Takuma Hashimoto, et al.. (2021). Development of attenuation correction methods using deep learning in brain‐perfusion single‐photon emission computed tomography. Medical Physics. 48(8). 4177–4190. 10 indexed citations

Yokota, Hajime, et al.. (2021). Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer. Scientific Reports. 11(1). 16521–16521. 10 indexed citations

10.

Suyari, Hiroki & A.M. Scarfone. (2014). α-divergence derived as the generalized rate function in a power-law system. arXiv (Cornell University). 130–134. 1 indexed citations

11.

Suyari, Hiroki, Atsumi Ohara, & Tatsuaki Wada. (2010). Mathematical Aspects of Generalized Entropies and their Applications. Journal of Physics Conference Series. 201. 11001–11001. 1 indexed citations

12.

Scarfone, A.M., Hiroki Suyari, & Tatsuaki Wada. (2009). Gauss’ law of error revisited in the framework of Sharma-Taneja-Mittal information measure. Open Physics. 7(3). 414–420. 5 indexed citations

13.

Niven, Robert K. & Hiroki Suyari. (2009). The q-gamma and (q,q)-polygamma functions of Tsallis statistics. Physica A Statistical Mechanics and its Applications. 388(19). 4045–4060. 6 indexed citations

14.

Wada, Tatsuaki & Hiroki Suyari. (2007). A two-parameter generalization of Shannon–Khinchin axioms and the uniqueness theorem. Physics Letters A. 368(3-4). 199–205. 35 indexed citations

15.

Suyari, Hiroki, et al.. (2006). A Proposal of algorithm that reduces computational complexity for Online Profit Sharing. IEICE Technical Report; IEICE Tech. Rep.. 105(657). 103–108. 1 indexed citations

16.

Suyari, Hiroki. (2005). Refined formalism of the maximum entropy principle in Tsallis statistics. arXiv (Cornell University). 2 indexed citations

17.

Suyari, Hiroki. (2003). Introduction to Bayesian Network(1). 21(4). 315–318. 43 indexed citations

18.

Suyari, Hiroki. (1999). New information theoretical approach to the storage capacity of neural networks with binary weights. 1999. 431–436. 1 indexed citations

19.

Suyari, Hiroki, et al.. (1999). Information theoretical approach to the storage capacity of neural networks with binary weights. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 60(4). 4576–4579. 3 indexed citations

20.

Ohya, Masanori & Hiroki Suyari. (1995). An application of lifting theory to optical communication processes. Reports on Mathematical Physics. 36(2-3). 403–420. 4 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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