データ化学工学研究室(金子研究室)の著書・論文

2020.08.22

●著書

  1. 金子 弘昌, 化学のためのPythonによるデータ解析・機械学習入門(改訂2版), オーム社, 2023年8月30日 内容
  2. 金子 弘昌, 化学・化学工学のための実践データサイエンス―Pythonによるデータ解析・機械学習―, 朝倉書店, 2022年10月5日 内容
  3. 金子 弘昌, Pythonで学ぶ実験計画法入門 ベイズ最適化によるデータ解析, 講談社, 2021年6月3日 内容
  4. 金子 弘昌, Pythonで気軽に化学・化学工学, 丸善出版, 2021年5月1日 内容
  5. [改訂2版があります！] 金子 弘昌, 化学のためのPythonによるデータ解析・機械学習入門, オーム社, 2019年10月23日 内容

●論文

  1. Ayami Ohkuma, Yoshihito Yamauchi, Nobuhito Yamada, Satoshi Ooyama, Hiromasa Kaneko, Cloud point prediction model for polyvinyl alcohol production plants considering process dynamics, Results in Engineering, 103475, 2024. 内容 論文URL
  2. Yuya Shiraki, Yuki Nakayama, Satoshi Natori, Kazuya Suda, Yuki Ono, Hiromasa Kaneko, Adaptive Soft Sensor Considering Process State in Film Manufacturing Process and Identification of Critical Process Variables, Results in Chemistry, 9, 101677, 2024. 内容 論文URL
  3. Yuki Nakayama, Saki Morishita, Hayato Doi, Tatsuya Hirano, Hiromasa Kaneko, Molecular Design of Novel Herbicide and Insecticide Seed Compounds with Machine Learning, ACS Omega, 9(16), 18488–18494, 2024. 内容 論文URL
  4. Ayano Yamamoto, Shota Horikawa, Kitaru Suzuki, Mamoru Aizawa, Hiromasa Kaneko, Prediction of bone formation rate of bioceramics using machine learning and image analysis, New Journal of Chemistry, 48, 5599-5604, 2024. 内容 論文URL
  5. Hiromasa Kaneko, Evaluation and Optimization Methods for Applicability Domain Methods and Their Hyperparameters, Considering the Prediction Performance of Machine Learning Models, considering the prediction performance of machine learning models, ACS Omega, 9(10), 11453–11458, 2024. 内容 論文URL
  6. Hiromasa Kaneko, Clustering method for the construction of machine learning model with high predictive ability, Chemometrics and Intelligent Laboratory Systems, 246, 105084, 2024. 内容 論文URL
  7. Shuto Yamakage, Kazutoshi Terauchi, Fumiya Hamada, Toshinori Yamaji, Hiromasa Kaneko, Predicting product quality and optimising process design using dynamic time warping in batch processes with varying batch times, Case Studies in Chemical and Environmental Engineering, 9, 100655, 2024. 内容 論文URL
  8. Shota Horikawa, Kitaru Suzuki, Kohei Motojima, Kazuaki Nakano, Masaki Nagaya, Hiroshi Nagashima, Hiromasa Kaneko, Mamoru Aizawa, Material Design of Porous Hydroxyapatite Ceramics via Inverse Analysis of an Estimation Model for Bone-Forming Ability Based on Machine Learning and Experimental Validation of Biological Hard Tissue Responses, Materials, 17(3), 571, 2024. 内容 論文URL
  9. Yuki Nakayama, Hiromasa Kaneko, Development of New Molecular Descriptors Based on Flare Software Considering Three-Dimensional Chemical Structures, Industrial & Engineering Chemistry Research, 63(1), 49–551, 2024. 内容 論文URL 表紙絵
  10. Shunsuke Yuyama, Hiromasa Kaneko, Simultaneous Design of Gas Separation Membranes and Schemes through Combined Process and Materials Informatics, Industrial & Engineering Chemistry Research, 62(44), 18541–18551, 2023. 内容 論文URL 表紙絵
  11. Daisuke Sugizaki, Hiromasa Kaneko, Design of Ammonia Borane Dehydrogenation Catalysts Using Previous Study Data, Public Data, and Machine Learning, Industrial & Engineering Chemistry Research, 62(43), 17849–17856, 2023. 内容 論文URL 表紙絵
  12. Tetsuya Yamada, Hiromasa Kaneko, Fumitaka Hayashi, Tatsuya Doi, Michihisa Koyama, Katsuya Teshima, Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba5Nb4O15 Crystals, Crystal Growth & Design, 23(12), 8678–8693, 2023. 論文URL
  13. Kohei Motojima, Abhijit Sen, Yoichi M. A. Yamada, Hiromasa Kaneko, Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions, Journal of Chemical Information and Modeling, 63(18), 5764–5772, 2023. 内容 論文URL 表紙絵
  14. Hiromasa Kaneko, T-Gen: Time series data generator for inverse analysis of machine learning models, Case Studies in Chemical and Environmental Engineering, 8, 100475, 2023. 内容 論文URL
  15. Toshiharu Morishita, Hiromasa Kaneko, Enhancing Search Performance of Bayesian Optimization by Creating Different Descriptor Datasets Using Density Functional Theory, ACS Omega, 8(36), 33032–33038, 2023. 内容 論文URL
  16. Yuki Nakayama, Hiromasa Kaneko, Robust Design of a Dimethyl Ether Production Process Using Process Simulation and Robust Bayesian Optimization, ACS Omega, 8(32), 29161–29168, 2023. 内容 論文URL 表紙絵
  17. Shigeyoshi Samizo, Hiromasa Kaneko, Predictive Modeling of HMG-CoA Reductase Inhibitory Activity and Design of New HMG-CoA Reductase Inhibitors, ACS Omega, 8(30), 27247–27255, 2023. 内容 論文URL 表紙絵
  18. Hiromasa Kaneko, Interpretation of machine learning models for datasets with many features using feature importance, ACS Omega, 8(25), 23218–23225, 2023. 内容 論文URL
  19. Rahul Sasikumar, Miriam C. Rodríguez González, Shingo Hirose, Hiromasa Kaneko, Kazukuni Tahara, Nanoscale Chemical Patterning of Graphite at Different Length Scales, Nanoscale, 15, 10295-10305, 2023. 論文URL
  20. Hiromasa Kaneko, Molecular Descriptors, Structure Generation, and Inverse QSAR/QSPR based on SELFIES, ACS Omega, 8(24), 21781–21786, 2023. 内容 論文URL 表紙絵
  21. Hiromasa Kaneko, Defect rate prediction and failure-cause diagnosis in a mass-production process for precision electric components, Analytical Science Advances, 4(9-10), 312-318, 2023. 内容 論文URL
  22. Sota Aoi, Shingo Hirose, Wakana Soeda, Hiromasa Kaneko, Kunal Mali, Steven De Feyter, Kazukuni Tahara, Spatially Controlled Aryl Radical Grafting of Graphite Surfaces Guided by Self-Assembled Molecular Networks of Linear Alkane Derivatives: The Importance of Conformational Dynamics, Langmuir, 39(17), 5986–5994, 2023. 論文URL
  23. Kohei Motojima, Rina Shiratsuchi, Kitaru Suzuki, Mamoru Aizawa, Hiromasa Kaneko, Machine learning model for predicting the material properties and bone formation rate and direct inverse analysis of the model for new synthesis conditions of bioceramics, Industrial & Engineering Chemistry Research, 62(14), 5898–5906, 2023. 内容 論文URL 表紙絵
  24. Hiroaki Taniwaki, Hiromasa Kaneko, Retrosynthetic and synthetic reaction prediction model based on sequence-to-sequence with attention for polymer designs, Macromolecular Theory and Simulations, 32(4), 2300011, 2023. 内容 論文URL
  25. Shuto Yamakage, Hiromasa Kaneko, Design of batch process with machine learning, feature extraction, and direct inverse analysis, Case Studies in Chemical and Environmental Engineering, 7, 100308, 2023. 内容 論文URL
  26. Noriko Nakamura, Risa Hamada, Hiromasa Kaneko, Seiichi Ohta, Selecting optimum miRNA panel for miRNA signature-based companion diagnostic model to predict the response of R-CHOP treatment in diffuse large B-cell lymphoma, Journal of Bioscience and Bioengineering, 135(4), 341-347, 2023. 内容 論文URL
  27. Kohei Nemoto, Hiromasa Kaneko, De Novo Direct Inverse QSPR/QSAR: Chemical Variational Autoencoder and Gaussian Mixture Regression Models, Journal of Chemical Information and Modeling, 63(3), 794-805, 2023. 内容 論文URL 表紙絵
  28. Toshiharu Morishita, Hiromasa Kaneko, Initial Sample Selection in Bayesian Optimization for Combinatorial Optimization of Chemical Compounds, ACS Omega, 8(2), 2001–2009, 2023. [PMCID# PMC9850731]  内容 論文URL
  29. Hiromasa Kaneko, Local interpretation of nonlinear regression model with k-nearest neighbors, Digital Chemical Engineering, 6, 100078, 2023. 内容 論文URL
  30. Daigo Kaneko, Hiromasa Kaneko, Fumitaka Hayashi, Kohei Fukaishi, Tetsuya Yamada, Katsuya Teshima, Process-Informatics-Assisted Preparation of Lithium Titanate Crystals with Various Sizes and Morphologies, Industrial & Engineering Chemistry Research, 62(1), 511–518, 2023. 内容 論文URL
  31. Hiromasa Kaneko, Direct prediction of the batch time and process variable profiles using batch process data based on different batch times, Computers & Chemical Engineering, 169, 108072, 2023. 内容 論文URL
  32. Ryo Iwama, Hiromasa Kaneko, Integration of Materials and Process Informatics: Metal Oxide and Process Design for CO2 Reduction, ACS Omega, 7(50), 46922–46934, 2022 [PMCID# PMC9773958]. 内容 論文URL 表紙絵
  33. Hiromasa Kaneko, Cross-validated Permutation Feature Importance Considering Correlation between Features, Analytical Science Advances, 3(9-10), 278-287, 2022. 内容 論文URL
  34. Tatsuhito Ando, Naoto Shimizu, Norihisa Yamamoto, Nobuyuki Matsuzawa, Hiroyuki Maeshima, Hiromasa Kaneko, Design of Molecules with Low Hole and Electron Reorganization Energy Using DFT Calculations and Bayesian Optimization, The Journal of Physical Chemistry A, 126(36), 6336–6347, 2022. 内容 論文URL
  35. Shuto Yamakage, Hiromasa Kaneko, Design of adaptive soft sensor based on Bayesian optimization, Case Studies in Chemical and Environmental Engineering, 6, 100237, 2022. 内容 論文URL
  36. Shingo Hashimoto, Hiromasa Kaneko, Steven De Feyter, Yoshito Tobe, Kazukuni Tahara, Symmetry and spacing controls in periodic covalent functionalization of graphite surfaces templated by self-assembled molecular networks, Nanoscale, 14(35), 12595-12609, 2022. 論文URL
  37. Hiroaki Taniwaki, Hiromasa Kaneko, Molecular Design of Monomers by Considering the Dielectric Constant and Stability of the Polymer, Polymer Engineering and Science, 62(9), 2750-2756, 2022. 内容 論文URL
  38. Nobuhito Yamada, Hiromasa Kaneko, Adaptive Soft Sensor Based on Transfer Learning and Ensemble Learning for Multiple Process States, Analytical Science Advances, 3(5-6), 205-211, 2022. 内容 論文URL
  39. Ryo Iwama, Koji Takizawa, Kenichi Shinmei, Eisuke Baba, Noritoshi Yagihashi, Hiromasa Kaneko, Design and Analysis of Metal Oxides for CO2 Reduction Using Machine Learning, Transfer learning, and Bayesian Optimization, ACS Omega, 7(12), 10709–10717, 2022 [PMCID# PMC8973119]. 内容 論文URL
  40. Hiromasa Kaneko, Genetic Algorithm-based Partial Least Squares with Only the First Component (GA-PLSFC) for Model Interpretation, ACS Omega, 7(10), 8968–8979, 2022 [PMCID# PMC8928558]. 内容 論文URL
  41. Yasuhiro Kanno, Hiromasa Kaneko, Deep Convolutional Neural Network with Deconvolution and a Deep Autoencoder for Fault Detection and Diagnosis, ACS Omega, 7(2), 2458-2466, 2022 [PMCID# PMC8772318]. 内容 論文URL
  42. Hiromasa Kaneko, True Gaussian Mixture Regression and Genetic Algorithm-based Optimization with Constraints for Direct Inverse Analysis, Science and Technology of Advanced Materials: Methods, 2(1), 14-22, 2022. 内容 論文URL
  43. Toshiharu Morishita, Hiromasa Kaneko, Development of Prediction Models for the Self-Accelerating Decomposition Temperature of Organic Peroxides, ACS Omega, 7(2), 2429-2437, 2022 [PMCID# PMC8771957]. 内容 論文URL
  44. Shunsuke Yuyama, Hiromasa Kaneko, Correlation between the Metal and Organic Components, Structure Property, and Gas-Adsorption Capacity of Metal–Organic Frameworks, Journal of Chemical Information and Modeling, 61(12), 5785–5792, 2021. 内容 論文URL
  45. Nobuhito Yamada, Hiromasa Kaneko, Adaptive Soft Sensor Ensemble for Selecting Both Process Variables and Dynamics for Multiple Process States, Chemometrics and Intelligent Laboratory Systems, 219, 104443, 2021. 内容 論文URL
  46. Hiromasa Kaneko, Lifting the Limitations of Gaussian Mixture Regression through Coupling with Principal Component Analysis and Deep Autoencoding, Chemometrics and Intelligent Laboratory Systems, 218, 104437, 2021. 内容 論文URL
  47. Tomoya Ebi, Abhijit Sen, Raghu N. Dhital, Yoichi M. A. Yamada, Hiromasa Kaneko, Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts, ACS Omega, 6(41), 27578–27586, 2021 [PMCID# PMC8529890]. 内容 論文URL
  48. Hiromasa Kaneko, Examining Variable Selection Methods for the Predictive Performance of Regression Models and the Proportion of Selected Variables and Selected Random Variables, Heliyon, 7(6), e07356, 2021. 内容 論文URL
  49. Hiromasa Kaneko, Estimating the Reliability of Predictions in Locally Weighted Partial Least-Squares Modeling, Journal of Chemometrics, 35(9), e3364, 2021. 内容 論文URL
  50. Hiromasa Kaneko, Extended Gaussian Mixture Regression for Forward and Inverse Analysis, Chemometrics and Intelligent Laboratory Systems, 213, 104325, 2021. 内容 論文URL
  51. Ryo Iwama, Hiromasa Kaneko, Design of Ethylene Oxide Production Process Based on Adaptive Design of Experiments and Bayesian Optimization, Journal of Advanced Manufacturing and Processing, 3(3), e10085, 2021. 内容 論文URL
  52. Hiromasa Kaneko, Shunsuke Kono, Akihiro Nojima, Takuya Kambayashi, Transfer Learning and Wavelength Selection Method in NIR Spectroscopy to Predict Glucose and Lactate Concentrations in Culture Media Using VIP-Boruta, Analytical Science Advances, 2(9-10), 470-479, 2021. 内容 論文URL
  53. Kaina Shibata, Hiromasa Kaneko, Prediction of Spin–Spin Coupling Constants with Machine Learning in NMR, Analytical Science Advances, 2(9-10), 464-469, 2021. 内容 論文URL
  54. Hiromasa Kaneko, Estimation and Visualization of Process States Using Latent Variable Models Based on Gaussian Process, Analytical Science Advances, 2(5-6), 326-333, 2021. 内容 論文URL
  55. Hiromasa Kaneko, Adaptive Design of Experiments Based on Gaussian Mixture Regression, Chemometrics and Intelligent Laboratory Systems, 208, 104226, 2021. 内容 論文URL
  56. Hiromasa Kaneko, Support Vector Regression That Takes into Consideration the Importance of Explanatory Variables, Journal of Chemometrics, 35(4), e3327, 2021. 内容 論文URL
  57. Hiroki Yoshihama, Hiromasa Kaneko, Design of Thermoelectric Materials with High Electrical Conductivity, High Seebeck Coefficient, and Low Thermal Conductivity, Analytical Science Advances, 2(5-6), 289-294, 2021. 内容 論文URL
  58. Naoto Shimizu, Hiromasa Kaneko, Constructing Regression Models with High Prediction Accuracy and Interpretability Based on Decision Tree and Random Forests, Journal of Computer Chemistry, Japan, 20, 71-87, 2021. 内容 論文URL
  59. Fumika Nitta, Hiromasa Kaneko, Two‐ and Three‐Dimensional Quantitative Structure‐Activity Relationship Models Based on Conformer Structures, Molecular Informatics, 40(3), 2000123, 2021. 内容 論文URL
  60. Naoto Shimizu, Hiromasa Kaneko, Direct Inverse Analysis Based on Gaussian Mixture Regression for Multiple Objective Variables in Material Design, Materials & Design, 196, 109168, 2020. 内容 論文URL
  61. Yasuhiro Kanno, Hiromasa Kaneko, Ensemble Just-in-time Model Based on Gaussian Process Dynamical Models for Nonlinear and Dynamic Processes, Chemometrics and Intelligent Laboratory Systems, 203, 104061, 2020. 内容 論文URL
  62. Kazukuni Tahara, Yuki Kubo, Shingo Hashimoto, Toru Ishikawa, Hiromasa Kaneko, Anton Brown, Brandon Hirsch, Steven De Feyter, Yoshito Tobe, Porous Self-Assembled Molecular Networks as Templates for Chiral Position-Controlled Chemical Functionalization of Graphitic Surfaces, Journal of the American Chemical Society,142(16), 7699-7708, 2020. 論文URL
  63. 佐藤 圭悟, 金子 弘昌, モデルの適用範囲の考慮したアンサンブル学習法の開発, Journal of Computer Chemistry, Japan, 18(4), 187-193, 2019. 内容 論文URL
  64. 高野 森乃介, 金子 弘昌, 高屈折率および高ガラス転移温度をもつ高分子材料のモノマー設計, Journal of Computer Chemistry, Japan, 18(2), 115-121, 2019. 内容 論文URL
  65. Hiromasa Kaneko, Estimation of Predictive Performance for Test Data in Applicability Domains Using y-randomization, Journal of Chemometrics, 33(9), e3171, 2019. 内容 論文URL
  66. Yasuhiro Kanno, Hiromasa Kaneko, Improvement of Predictive Accuracy in Semi-Supervised Regression Analysis by Selecting Unlabeled Chemical Structures, Chemometrics and Intelligent Laboratory Systems, 191, 82-87, 2019. 内容 論文URL
  67. Hiromasa Kaneko, Beware of r2 even for Test Datasets: Using the Latest Measured y-values (r2LM) in Time Series Data Analysis, Journal of Chemometrics, 33(2), e3093, 2019. 内容 論文URL
  68. Hiromasa Kaneko, Data Visualization, Regression, Applicability Domains and Inverse Analysis Based on Generative Topographic Mapping, Molecular Informatics, 38(3), 1800088, 2019. 内容 論文URL
  69. Hiromasa Kaneko, Sparse Generative Topographic Mapping for Both Data Visualization and Clustering, Journal of Chemical Information and Modeling, 58(12), 2528-2535, 2018. 内容 論文URL
  70. Hiromasa Kaneko, Illustration of Merits of Semi-supervised Learning in Regression Analysis, Chemometrics and Intelligent Laboratory Systems, 182, 47-56, 2018. 内容 論文URL
  71. Hiromasa Kaneko, Automatic Outlier Sample Detection Based on Regression Analysis and Repeated Ensemble Learning, Chemometrics and Intelligent Laboratory Systems, 177, 74-82, 2018. 内容 論文URL
  72. Hiromasa Kaneko, K-Nearest Neighbor Normalized Error for Visualization and Reconstruction – A New Measure for Data Visualization Performance, Chemometrics and Intelligent Laboratory Systems, 176, 22-33, 2018. 内容 論文URL
  73. Hiromasa Kaneko, Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Sub-Models, Journal of Chemical Information and Modeling, 58(2), 480–489, 2018. 内容 論文URL
  74. Hiromasa Kaneko, A New Measure of Regression Model Accuracy that Considers Applicability Domains, Chemometrics and Intelligent Laboratory Systems, 171, 1-8, 2017. 内容 論文URL

* 金子が明治大学に異動する前の研究成果 (査読付き論文 68 報など) について興味のある方は別途ご連絡ください。

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