2025年度 / Apr. 2025 〜 Mar. 2026 の研究に関わる業績（研究発表・論文・外部資金・受賞等）です。
The list shows published papers, grants, and awards in 2025-2026 season.

  • 修士論文 / Master’s Thesis
    • 西尾直樹，「形状情報と属性情報を統合した類似図面検索システム」
    • 遠藤陽季，「リソース制約のあるエッジデバイス上で動作する音響イベント分類モデル」 ※優秀発表賞受賞
    • 藤本蓮，「棚栽培ブドウ圃場におけるマップ統合型自律移動システム」
    • 島津亮輔，「非公開（特許対応）」
  • 卒業論文 / Graduate Thesis
    • 藤森颯翔，「感情強度制御を用いたトラブル・クレーム応対訓練用LLM模擬顧客システム」
    • 荒井秀太，「3Dグラフニューラルネットワークを用いた環化反応経路エネルギー予測」
    • 堀川萌樹，「カメラ・LiDAR併用によるブドウ収穫用ロボットの環境認識」 ※優秀発表賞
    • 鈴木裕真，「切断位置検出と障害物回避を行うブドウ収穫用ロボットアームの動作制御」
    • 矢崎幸明，「シャインマスカット収穫用エンドエフェクタの開発」
    • 今井勇太，「深層強化学習を用いたブドウ収穫用ロボットアーム制御」
  • 雑誌論文 (Journal Papers): 7 papers
      • Masayuki Karasawa, Chee Siang Leow, Hideaki Yajima, Shuta Arai, Hiromitsu Nishizaki, Tohru Terada, Hajime Sato, “ColabReaction: Accelerating Transition State Searches with Machine Learning Potentials on Google Colaboratory”, Journal of Chemical Information and Modeling, Vol.65, No.21, pp.11908-11914, Nov./2025. IF:5.6, DOI: 10.1021/acs.jcim.5c02398
        Abstract: We have developed a rapid and automated transition state (TS) search method for chemical reactions by combining the double-ended method, Direct MaxFlux (DMF), with machine learning (ML) potentials. Compared to conventional quantum mechanical (QM) scan-based approaches, this method achieves approximately 2 orders of magnitude speedup, typically locating TS structures within 10 min. To promote broad accessibility, this method is implemented on Google Colaboratory (Colab), leveraging its cloud-based GPU environment to eliminate the need for local computational resources. We named this implementation as ColabReaction. A modified panel-based graphical user interface is also provided, allowing users to perform TS searches through a web-based interface without writing code. This platform offers a cost-free, user-friendly solution for reaction pathway exploration and mechanistic analysis, particularly for experimental researchers and students without prior experience in computational chemistry. ColabReaction is open-source and freely available at https://ColabReaction.net and https://github.com/BILAB/ColabReaction.
      • Yikang Wang, Xingming Wang, Chee Siang Leow, Qishan Zhang, Ming Li, Hiromitsu Nishizaki, “Enhancing the Robustness of Speech Anti-spoofing Countermeasures through Joint Optimization and Transfer Learning”, Vol.E108-D, No.12, pp.1594-1604, Dec./2025. IF:0.8, DOI: 10.1587/transinf.2025EDP7044
        Abstract: Currently, research in deepfake speech detection focuses on the generalization of detection systems towards different spoofing methods, mainly for noise-free clean speech. However, the performance of speech anti-spoofing countermeasure (CM) systems often does not work well in more complicated scenarios, such as those involving noise and reverberation. To address the problem of enhancing the robustness of CM systems, we propose a transfer learning-based hybrid approach with Speech Enhancement front-end and Counter Measure back-end Joint optimization (SECM-Joint), investigating its effectiveness in improving robustness against noise and reverberation. Experimental results show that our SECM-Joint method reduces EER by 19.11% to 64.05% relatively in most noisy conditions and 23.23% to 30.67% relatively in reverberant environments compared to a Conformer-based CM baseline system without pre-training. Additionally, our dual-path U-Net (DUMENet) further enhances the robustness for real-world applications. These results demonstrate that the proposed method effectively enhances the robustness of CM systems in noisy and reverberant conditions. Codes and experimental data supporting this work are publicly available at: https://github.com/ikou-austin/SECM-Joint
      • Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Latifah Munirah Kamarudin, “Optimizing mRNA Vaccine Degradation Prediction via Penalized Dropout Approaches,” IEEE Access, vol. 13, pp. 137561-137578, 2025, ISSN: 2169-3536, DOI: 10.1109/ACCESS.2025.3595155, IF: 3.6
        Abstract: Predicting mRNA vaccine degradation rates with precision is essential for ensuring stability, efficacy, and optimal deployment strategies, particularly given the unique challenges posed by their rapid degradation. This study introduces a comprehensive approach that integrates bioinformatic insights with advanced computational methodologies to address these challenges. A novel tetramer-label encoding approach (4-mer-lbA) was proposed, integrating biological relevance with data-driven analysis to enhance predictive accuracy. To further optimize model performance, two advanced hyperparameter optimization (HPO) techniques—Dropout-Enhanced Technique (DEet) and Hyperparameter Optimization Algorithm Penalizer (HOPeR)—are proposed to mitigate overfitting, address inefficiencies in conventional HPO algorithms (HPOAs), and accelerate model convergence. The methodologies were validated on mRNA degradation datasets using deep neural network (DNN) architectures, with particular attention to the comparative performance of sequential and wrapped architectural designs. Results demonstrate that sequential architectures outperform wrapped models by reducing overfitting and computational demands. The integration of DEet and HOPeR further optimized hyperparameter exploration, with DEet enhancing model robustness through dropout regularization and HOPeR introducing adaptive penalties to systematically eliminate suboptimal configurations. The experimental outcomes highlight significant advancements in convergence rates and error reduction, particularly in complex models like 3-layer-wrapped Bidirectional Long Short-Term Memory (3wBiLSTM). By the 100th epoch, training and validation losses reached 0.0023 and 0.0029, respectively, indicating a substantial improvement over baseline models. These methodologies extend beyond mRNA vaccine research, demonstrating versatility across diverse machine learning domains. By addressing critical challenges in HPO and predictive modeling, the study offers scalable and robust solutions for advancing biotechnology and interdisciplinary research.
      • Chee Siang Leow, Tomoki Kitagawa, Hideaki Yajima, Hiromitsu Nishizaki, “Handwritten Character Image Generation for Effective Data Augmentation,” IEICE Transactions on Information and Systems, Vol. E108-D, No. 8, pp.1-10, Aug. 2025, DOI: 10.1587/transinf.2024EDP7201, IF: 0.8
        Abstract: This study introduces data augmentation techniques to enhance training datasets for a Japanese handwritten character classification model, addressing the high cost of collecting extensive handwritten character data. A novel method is proposed to automatically generate a largescale dataset of handwritten characters from a smaller dataset, utilizing a style transformation approach, particularly Adaptive Instance Normalization (AdaIN). Additionally, the study presents an innovative technique to improve character structural information by integrating features from the Contrastive Language-Image Pre-training (CLIP) text encoder. This approach enables the creation of diverse handwritten character images, including Kanji, by merging content and style elements. The effectiveness of our approach is demonstrated by evaluating a handwritten character classification model using an expanded dataset, which includes Japanese hiragana, katakana, and Kanji from the ETL Character Database. The character classification model’s macro F1 score improved from 0.9733 with the original dataset to 0.9861 using the augmented dataset by the proposed approach. This result indicated that our proposed character generation model was able to generate new character images that were not included in the original dataset and that they effectively contributed to training the handwritten character classification model.
      • Ryosuke Shimazu, Chee Siang Leow, Prawit Buayai, Xiaoyang Mao, Wan-Young Chung, Hiromitsu Nishizaki, “Non-invasive estimation of Shine Muscat grape color and sensory evaluation from standard camera images,” The Visual Computer, pp.1-16, May 2025, DOI: 10.1007/s00371-025-03925-6, IF: 3.0, (international co-authored paper)
        Abstract: This study proposes a non-invasive method to estimate both color and sensory attributes of Shine Muscat grapes from standard camera images. First, we focus on color estimation by integrating a Vision Transformer (ViT) feature extractor with interquartile range (IQR)-based outlier removal. Experimental results show that our approach achieves 97.2% accuracy, significantly outperforming Convolutional Neural Network (CNN) models. This improvement underscores the importance of capturing global contextual information to differentiate subtle color variations in grape ripeness. Second, we address human sensory evaluation by collecting questionnaire responses on 13 attributes (e.g., “Sweetness,” “Overall taste rating”), each rated on a five-point scale. Because these ratings tend to cluster around midrange values (labels “2,” “3,” and “4”), we initially limit the dataset to the extreme labels “1” (“lowest grade”) and “5” (“highest grade”) for binary classification. Three attributes—“Overall color,” “Sweetness,” and “Overall taste rating”—exhibit relatively high classification accuracies of 79.9%, 75.1%, and 75.7%, respectively. By contrast, the other 10 attributes reach only 50%–66%, suggesting that subjective variations and limited visual cues pose significant challenges. Overall, the proposed approach demonstrates the feasibility of an image-based system that integrates color estimation and sensory evaluation to support more objective, data-driven harvest timing decisions for Shine Muscat grapes.
      • Taoqi Bao, Jiangnan Ye, Zhankong Bao, Chee Siang Leow, Haoji Hu, Jianfeng Lu, Issei Fujishiro, Jiayi Xu, “L2H-NeRF: low- to high-frequency-guided NeRF for 3D reconstruction with a few input scenes,” The Visual Computer, pp.1-12, May 2025, DOI: 10.1007/s00371-025-03974-x, IF: 3.0, (international co-authored paper)
        Abstract: Nowadays, three-dimensional (3D) reconstruction techniques are becoming increasingly important in the fields of architecture, game development, movie production, and more. Due to common issues in the reconstruction process, such as perspective distortion and occlusion, traditional 3D reconstruction methods face significant challenges in achieving high-precision results, even when dense data are used as inputs. With the advent of neural radiance field (NeRF) technology, high-fidelity 3D reconstruction results are now possible. However, high computational resources are usually required for NeRF computations. Recently, a few data inputs are used to ensure the highest quality. In this paper, we propose an innovative low- to high-frequency-guided NeRF (L2H-NeRF) framework that decomposes scene reconstruction into coarse and fine stages. For the first stage, a low-frequency enhancement network based on a vision transformer is proposed, where the low-frequency-based globally coherent geometric structure is recovered, with the dense depth restored in a depth completion way. In the second stage, a high-frequency enhancement network is incorporated, where the high-frequency-related detail is compensated by robust feature alignment across adjacent views using a plug-and-play feature extraction and matching module. Experiments demonstrate that both the accuracy of the geometric structure and the feature detail of the proposed L2H-NeRF outperforms state-of-the-art methods.
      • Ziying Li, Haifeng Zhao, Hiromitsu Nishizaki, Chee Siang Leow, Xingfa Shen, “Chinese Character Recognition based on Swin Transformer-Encoder” Digital Signal Processing, Vol. 161, No. C, 105080, pp.1-10, May 2025, DOI:https://doi.org/10.1016/j.dsp.2025.105080, IF: 2.9, ※international co-authored paper
        Abstract: Optical Character Recognition (OCR) technology, which converts printed or handwritten text into machine-readable text, holds significant application and research value in document digitization, information automation, and multilingual support. However, existing methods predominantly focus on English text recognition and often struggle with addressing the complexities of Chinese characters. This study proposes a Chinese text recognition model based on the Swin Transformer encoder, demonstrating its remarkable adaptability to Chinese character recognition. In the image preprocessing stage, we introduced an overlapping segmentation technique that enables the encoder to effectively capture the complex structural relationships between individual strokes in lengthy Chinese texts. Additionally, by incorporating a mapping layer between the encoder and decoder, we enhanced the Swin Transformer’s adaptability to small image scenarios, thereby improving its feasibility for Chinese text recognition tasks. Experimental results indicate that this model outperforms classical models such as CRNN and ASTER on handwritten and web-based datasets, validating its robustness and reliability.
    • 国際会議論文 (Reviewed conference papers): 9 papers
      • Naoki Muto, Chee Siang Leow, Junichi Hoshino, Takehito Utsuro, Hiromitsu Nishizaki, “Speech-Content-Driven Highlighting of Translated Lecture Slides for Foreign Language Lecture Understanding”, Proceedings of 2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2025), Singapore, Oct./2025, pp.831-836, DOI: 10.1109/APSIPAASC65261.2025.11249109
      • Ryota Uematsu, Chee Siang Leow, Norihide Kitaoka, Hiromitsu Nishizaki, “Improving Automatic Speech Recognition Model for Super-Elderly Voice Using Speech Synthesis Model”, Proceedings of 2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2025), Singapore, Oct./2025, pp.986-991, DOI: 10.1109/APSIPAASC65261.2025.11248972
      • Junkang Yang, Hongqing Liu, Liming Shi, Lu Gan, Hiromitsu Nishizaki, Chee Siang Leow, “A Semi-Supervised Acoustic Scene Classification Network Based on Multi-Modal Information Fusion”, Proceedings of 2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2025), Singapore, Oct./2025, pp.177-181, DOI: 10.1109/APSIPAASC65261.2025.11249027
      • Haruki Endo, Hideaki Yajima, Chee Siang Leow, Tsutomu Tanzawa, Koji Makino, Kazuyoshi Ishida, Hiromitsu Nishizaki, “Design and Training of a Sound Classification Model on a Resource-Constrained Edge Device for Fruit Theft Prevention”, Proceedings of IECON 2025 – 51st Annual Conference of the IEEE Industrial Electronics Society, Madrid, Spain, Oct./2025, pp.1-6, DOI: 10.1109/IECON58223.2025.11221896
      • Koki Fujita, Chee Siang Leow, Hiromitsu Nishizaki, “Handwritten Character Generation Based on Multi-Character Inputs for Character Recognition Model”, Proceedings of 2025 IEEE 14th Global Conference on Consumer Electronics (GCCE 2025), Osaka, Japan, Sep./2025, pp.675-679, DOI: 10.1109/GCCE65946.2025.11275224
      • Hideaki Yajima, Chee Siang Leow, Hiromitsu Nishizaki, “Text Detection and Recognition in Document Images Using Large-scale Vision Language Model”, Proceedings of 2025 IEEE 14th Global Conference on Consumer Electronics (GCCE 2025), Osaka, Japan, Sep./2025, pp.1009-1013, DOI: 10.1109/GCCE65946.2025.11275607
      • Yuxi Wang, Yikang Wang, Qishan Zhang, Hiromitsu Nishizaki, Ming Li, “VCapAV: A Video-Caption Based Audio-Visual Deepfake Detection Dataset”, Proceedings of INTERSPEECH 2025, pp. 3908-3912, Aug/2025, Presented in Rotterdam, The Netherlands, DOI:10.21437/Interspeech.2025-1713
      • Koji Makino, Sota Fujiwara, Ziwei Song, Prawit Buayai, Daisuke Inoue, Kazuyoshi Ishida, Hiromitsu Nishizaki, Xiaoyang Mao, Hidetsugu Terada, “Development of an Onion Sorting Machine with a Detachable Mechanism Using AI”, Proceeings of the IFToMM International Conference on Mechanisms, Transmissions and Applicationsin, New Advances in Mechanisms, Transmissions and Applications, Springer Nature Switzerland, Cham, 2025, pp.270-279, Taoyuan, Taiwan, DOI: 10.1007/978-3-032-05466-1_28
      • Po Lun Huang, Prawit Buayai, Hidetsugu Terada, Hiromitsu Nishizaki, Xiaoyang Mao, Koji Makino, “A Development of a Robotic Handling System for Automated Grape Thinning”, Proceedings of Jc-IFToMM International Symposium, Vol.8, 2025, pp.112-119, Nagoya, Japan, DOI: 10.57272/jciftomm.8.0_112
    • 口頭発表 (Domestic conference, not reviewed) 1 papers
      • 高橋空大，花一傑，長谷川遼，宇津呂武仁，星野准一，西崎博光，「状態遷移モデルおよび大規模言語モデルを用いた複数顧客接客訓練対話のモデル化」，人工知能学会全国大会論文集，Vol.JSAI2025，pp.2D1GS903，2025年，DOI: 10.11517/pjsai.JSAI2025.0_2D1GS903
    • 招待講演 (Invited Talks)
      • 【基調講演】西崎博光，「Digital Artisan’s Eye: How AI Masters the Art of Agricultural Innovation」，International Joint Conference on AI-DRIVEN Digital Twin 2025 (AIDT 2025) “Net-Zero Horizons: Transforming Smart Industries”，World Trade Centre (WTC) Kuala Lumpur，Malaysia，2025年9月10日～11日．
      • 【招待講演】西﨑博光，「Initiatives of Internal Quality Assurance in the “Asia Real Problem Solving-Driven AI Education Program” in Partnership with Four Asian Universities」，文部科学省「大学の世界展開力強化事業」国際質保証制度設計業務成果発信シンポジウム「国境を越えた高等教育の質保証：実践・視点・地域的アプローチの共有」，主催：大学改革支援・学位授与機構（NIAD-QE），於：学術総合センター，2025年11月4日（火）
    • 書籍・雑誌記事 (Books, Magazines)
      • 佐藤玄，唐澤昌之，寺田透，レオ・チー・シャン，矢島英明，荒井秀太，西﨑博光，「ColabReaction：反応の核心に、誰でも飛び込める時代へ」，酵素工学ニュース（酵素工学研究会誌），第94号，pp.33-37, 2025年10月，酵素工学研究会．
    • 学外授業・セミナー
      • 【セミナー講師】西崎博光：電子情報通信学会ネットワークシステム研究会シュミレーションスクール「深層学習ハンズオン」，2025年5月17日（土）9:00-16:00，オンライン
      • 【公開授業】西﨑博光，「見て考える！AI入門」，山梨大学 工学部 公開授業（2025年度高大連携・山梨県内高校生向け），於：山梨大学甲府キャンパス（工－1），2025年8月7日（木）9:00～10:30．
      • 【講師】西﨑博光，「Pythonスキルアップ講座① ～データ操作編～」，AI・データ利活用スペシャリスト養成講座2025，主催：山梨県（DX課），協力：山梨大学・（一社）山梨県情報通信業協会，山梨大学甲府東キャンパス 情報メディア館 2階 第3実習室，2025年10月2日（木）15:00～17:30．
      • 【講師】西﨑博光，「Pythonスキルアップ講座② ～API活用編～」，AI・データ利活用スペシャリスト養成講座2025，主催：山梨県（DX課），協力：山梨大学・（一社）山梨県情報通信業協会，山梨大学甲府東キャンパス 情報メディア館 2階 第3実習室，2025年10月9日（木）15:00～17:30．
      • 【講師】西﨑博光，「生成AI入門セミナー① ～文章生成AI編～」，AI・データ利活用スペシャリスト養成講座2025，主催：山梨県（DX課），協力：山梨大学・（一社）山梨県情報通信業協会，山梨大学甲府東キャンパス 情報メディア館 2階 第3実習室，2025年11月6日（木）15:00～17:30．
      • 【講師】西﨑博光，「生成AI入門セミナー② ～画像生成AI編～」，AI・データ利活用スペシャリスト養成講座2025，主催：山梨県（DX課），協力：山梨大学・（一社）山梨県情報通信業協会，山梨大学甲府東キャンパス 情報メディア館 2階 第3実習室，2025年11月13日（木）15:00～17:30．
    • 表彰・報道等
      • 【報道】テレビ朝日「未来をここから」SDGs企画として放送された ANNnewsCH ニュース「ぶどう栽培の自動化を目指して　AIロボットが熟練の技を再現【SDGs】」において、山梨大学などの研究グループによるブドウ栽培自動化の研究（AIロボットによる摘粒・収穫作業の自動化）が紹介された．放送・配信日：2026年2月27日，配信：ANNnewsCH（YouTube）．動画URL：https://www.youtube.com/watch?v=GKsVzgSiUw0