Hiromasa Kaneko

Orcid: 0000-0001-8367-6476

According to our database1, Hiromasa Kaneko authored at least 20 papers between 2008 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2023
Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions.
J. Chem. Inf. Model., September, 2023

De Novo Direct Inverse QSPR/QSAR: Chemical Variational Autoencoder and Gaussian Mixture Regression Models.
J. Chem. Inf. Model., February, 2023

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

2021
Correlation between the Metal and Organic Components, Structure Property, and Gas-Adsorption Capacity of Metal-Organic Frameworks.
J. Chem. Inf. Model., 2021

2018
Sparse Generative Topographic Mapping for Both Data Visualization and Clustering.
J. Chem. Inf. Model., 2018

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

Formulation of the excess absorption in infrared spectra by numerical decomposition for effective process monitoring.
Comput. Chem. Eng., 2018

2017
On Generative Topographic Mapping and Graph Theory combined approach for unsupervised non-linear data visualization and fault identification.
Comput. Chem. Eng., 2017

2016
Chemical-Space-Based de Novo Design Method To Generate Drug-Like Molecules.
J. Chem. Inf. Model., 2016

Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from <i>y</i> to x).
J. Chem. Inf. Model., 2016

Ring system-based chemical graph generation for de novo molecular design.
J. Comput. Aided Mol. Des., 2016

2014
Applicability Domain Based on Ensemble Learning in Classification and Regression Analyses.
J. Chem. Inf. Model., 2014

Automatic Database Monitoring for Process Control Systems.
Proceedings of the Modern Advances in Applied Intelligence, 2014

2013
Criterion for Evaluating the Predictive Ability of Nonlinear Regression Models without Cross-Validation.
J. Chem. Inf. Model., 2013

Erratum for "Development of a New Regression Analysis Method Using Independent Component Analysis".
J. Chem. Inf. Model., 2013

Adaptive soft sensor model using online support vector regression with time variable and discussion of appropriate hyperparameter settings and window size.
Comput. Chem. Eng., 2013

Adaptive Soft Sensor Model Using Online Support Vector Regression with the Time Variable and Discussion on Appropriate Parameter Settings.
Proceedings of the 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, 2013

2011
Novel soft sensor method for detecting completion of transition in industrial polymer processes.
Comput. Chem. Eng., 2011

Improvement and Estimation of Prediction Accuracy of Soft Sensor Models Based on Time Difference.
Proceedings of the Modern Approaches in Applied Intelligence, 2011

2008
Development of a New Regression Analysis Method Using Independent Component Analysis.
J. Chem. Inf. Model., 2008


  Loading...