Hiroto Saigo

Scopus著者プロファイル

ウェブサイトhttps://hyoka.ofc.kyushu-u.ac.jp/html/100021288_ja.html

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年別の研究成果

フィンガープリント

Hiroto Saigoが活動している研究トピックを掘り下げます。このトピックラベルは、この研究者の研究成果に基づきます。これらがまとまってユニークなフィンガープリントを構成します。

1 類似のプロファイル

26 学術誌
11 会議への寄与
3 章
1 コメント/討論

Multi-level PEnet: A Robust Three-Stage Model for Parameter Estimation in Non-Gaussian Noise-Driven Stochastic Differential Equations
Li, S., Cheng, Y., Ruan, Y. & Saigo, H., 2025, (印刷中) In: Nonlinear Dynamics. 107933.
研究成果: ジャーナルへの寄稿 › 学術誌 › 査読
A Branch-and-Bound Approach to Efficient Classification and Retrieval of Documents
Ii, K., Saigo, H. & Tabei, Y., 2024, Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. Castrillon-Santana, M., De Marsico, M. & Fred, A. (eds.). Science and Technology Publications, Lda, p. 205-214 10 p. (International Conference on Pattern Recognition Applications and Methods; vol. 1).
研究成果: 書籍/レポートタイプへの寄稿 › 会議への寄与

Open Access
Benchmarking a Wide Range of Unsupervised Learning Methods for Detecting Anomaly in Blast Furnace
Itakura, K., Bahadur, D. & Saigo, H., 2024, Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. Castrillon-Santana, M., De Marsico, M. & Fred, A. (eds.). Science and Technology Publications, Lda, p. 641-650 10 p. (International Conference on Pattern Recognition Applications and Methods; vol. 1).
研究成果: 書籍/レポートタイプへの寄稿 › 会議への寄与
pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model
Pratyush, P., Pokharel, S., Saigo, H. & Kc, D. B., 12月 2023, In: BMC bioinformatics. 24, 1, 41.
研究成果: ジャーナルへの寄稿 › 学術誌 › 査読

Open Access
14 被引用数 (Scopus)
Correction: DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction (BMC Bioinformatics, (2020), 21, S3, (63), 10.1186/s12859-020-3342-z)
Thapa, N., Chaudhari, M., McManus, S., Roy, K., Newman, R. H., Saigo, H. & Kc, D. B., 12月 2022, In: BMC bioinformatics. 23, 1, 349.
研究成果: ジャーナルへの寄稿 › コメント/討論 › 査読

Open Access
1 被引用数 (Scopus)

西郷浩人

フィンガープリント

過去5年の共同研究と上位研究分野

詳細を開く

Multi-level PEnet: A Robust Three-Stage Model for Parameter Estimation in Non-Gaussian Noise-Driven Stochastic Differential Equations

A Branch-and-Bound Approach to Efficient Classification and Retrieval of Documents

Benchmarking a Wide Range of Unsupervised Learning Methods for Detecting Anomaly in Blast Furnace

pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model

Correction: DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction (BMC Bioinformatics, (2020), 21, S3, (63), 10.1186/s12859-020-3342-z)

西郷 浩人

フィンガープリント

過去5年の共同研究と上位研究分野

研究成果

Multi-level PEnet: A Robust Three-Stage Model for Parameter Estimation in Non-Gaussian Noise-Driven Stochastic Differential Equations

A Branch-and-Bound Approach to Efficient Classification and Retrieval of Documents

Benchmarking a Wide Range of Unsupervised Learning Methods for Detecting Anomaly in Blast Furnace

pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model

Correction: DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction (BMC Bioinformatics, (2020), 21, S3, (63), 10.1186/s12859-020-3342-z)

西郷浩人