Development of a New Regression Analysis Method Using Independent Component Analysis. - Hiromasa Kaneko; Masamoto Arakawa; Kimito Funatsu (J. Chem. Inf. Model., 2008)

Improvement and Estimation of Prediction Accuracy of Soft Sensor Models Based on Time Difference. - Hiromasa Kaneko; Kimito Funatsu (IEA/AIE (1), 2011)

Novel soft sensor method for detecting completion of transition in industrial polymer processes. - Hiromasa Kaneko; Masamoto Arakawa; Kimito Funatsu (Comput. Chem. Eng., 2011)

Adaptive Soft Sensor Model Using Online Support Vector Regression with the Time Variable and Discussion on Appropriate Parameter Settings. - Hiromasa Kaneko; Kimito Funatsu (KES, 2013)

Adaptive soft sensor model using online support vector regression with time variable and discussion of appropriate hyperparameter settings and window size. - Hiromasa Kaneko; Kimito Funatsu (Comput. Chem. Eng., 2013)

Erratum for "Development of a New Regression Analysis Method Using Independent Component Analysis". - Hiromasa Kaneko; Masamoto Arakawa; Kimito Funatsu (J. Chem. Inf. Model., 2013)

Criterion for Evaluating the Predictive Ability of Nonlinear Regression Models without Cross-Validation. - Hiromasa Kaneko; Kimito Funatsu (J. Chem. Inf. Model., 2013)

Automatic Database Monitoring for Process Control Systems. - Hiromasa Kaneko; Kimito Funatsu (IEA/AIE (1), 2014)

Applicability Domain Based on Ensemble Learning in Classification and Regression Analyses. - Hiromasa Kaneko; Kimito Funatsu (J. Chem. Inf. Model., 2014)

Ring system-based chemical graph generation for de novo molecular design. - Tomoyuki Miyao; Hiromasa Kaneko; Kimito Funatsu (J. Comput. Aided Mol. Des., 2016)

Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from y to x). - Tomoyuki Miyao; Hiromasa Kaneko; Kimito Funatsu (J. Chem. Inf. Model., 2016)

Chemical-Space-Based de Novo Design Method To Generate Drug-Like Molecules. - Shunichi Takeda; Hiromasa Kaneko; Kimito Funatsu (J. Chem. Inf. Model., 2016)

On Generative Topographic Mapping and Graph Theory combined approach for unsupervised non-linear data visualization and fault identification. - Matheus S. Escobar; Hiromasa Kaneko; Kimito Funatsu (Comput. Chem. Eng., 2017)

Formulation of the excess absorption in infrared spectra by numerical decomposition for effective process monitoring. - Shojiro Shibayama; Hiromasa Kaneko; Kimito Funatsu (Comput. Chem. Eng., 2018)

Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Submodels. - Hiromasa Kaneko (J. Chem. Inf. Model., 2018)

Sparse Generative Topographic Mapping for Both Data Visualization and Clustering. - Hiromasa Kaneko (J. Chem. Inf. Model., 2018)

Correlation between the Metal and Organic Components, Structure Property, and Gas-Adsorption Capacity of Metal-Organic Frameworks. - Shunsuke Yuyama; Hiromasa Kaneko (J. Chem. Inf. Model., 2021)

Direct prediction of the batch time and process variable profiles using batch process data based on different batch times. - Hiromasa Kaneko (Comput. Chem. Eng., 2023)

De Novo Direct Inverse QSPR/QSAR: Chemical Variational Autoencoder and Gaussian Mixture Regression Models. - Kohei Nemoto; Hiromasa Kaneko (J. Chem. Inf. Model., 2023)

Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions. - Kohei Motojima; Abhijit Sen; Yoichi M. A. Yamada; Hiromasa Kaneko (J. Chem. Inf. Model., 2023)