Kaneko Hiromasa - Program search result - SCEJ 90th Annual Meeting (Tokyo, 2025)

`SCEJ 90th Annual Meeting (Tokyo, 2025)`

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Program search result : Kaneko Hiromasa : 14 programs

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Authors field exact matches “Kaneko Hiromasa”; 14 programs are found.
The search results are sorted by the start time.

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
Day 1 13:40– 14:00	I115	Machine learning to develop extraction solvent for Ga(III) and separation of Ga(III) and In(III) by multi-stage solvation extraction (U. Miyazaki) *(Reg)Oshima Tatsuya, Nakatamari Shoma, (Reg)Ohe Kaoru, (Reg)Inada Asuka, (Meiji U.) (Reg)Kaneko Hiromasa	Solvation Extraction Gallium Indium	4-f	325
Day 2 13:20– 15:20	PC201	Construction of a model for predicting the activity of BiVO₄ photocatalyst based on published articles and design of synthesis conditions using a genetic algorithm (Meiji U.) *(Stu)Takami Yuta, Iwase Akihide, (Reg)Kaneko Hiromasa	Bismuth Vanadate Synthesis conditions Genetic algorithm	6-e	132
Day 2 13:20– 15:20	PC205	Construction of property prediction model and inverse analysis of the model in carbon material manufacturing process with different batch times (Meiji U.) *(Stu)Matsubara Masayoshi, (Mitsubishi Chemical) (Cor)Sasaki Ryo, (Cor)Takahara Jun, (Cor)Moritake Shinji, (Cor)Harada Yasuyuki, (Meiji U.) (Reg)Kaneko Hiromasa	Machine learning Batch time	6-e	240
Day 2 13:20– 15:20	PC209	Prediction of Ionic Conductivity in Solid Electrolytes Using a Machine Learning Model (Meiji U.) *(Stu)Ishikawa Eri, (Reg)Kaneko Hiromasa	Solid Electrolyte Machine Learning Materials Informatics	6-f	244
Day 2 13:20– 15:20	PC211	Design of New Catalysts for Suzuki-Miyaura Type Cross-Coupling Reactions Using Polymeric Nickel Catalyst Structure by Bayesian Optimization (Meiji U.) *(Stu)Takaoka Sho, (Riken) Zhang Zhenzhong, Yamada Yoichi M. A., (Meiji U.) (Reg)Kaneko Hiromasa	Bayesian Optimization Polymeric Ni Catalysts Chemical Reactions	6-f	630
Day 2 13:20– 15:20	PC213	Development of machine learning models to predict gas permeability from monomer structures and properties of polymer materials (Meiji U.) *(Stu)Ochiai Haruki, Nagai Kazukiyo, (Reg)Kaneko Hiromasa	Machine Learning Polymer Permeability	6-g	349
Day 2 13:20– 15:20	PC216	Design of new acetylcholinesterase inhibitors using a Generative Adversarial Network (Meiji U.) *(Stu)Ando Ruka, (Reg)Kaneko Hiromasa	acetylcholinesterase Generative Adversarial Network machine learning	6-g	43
Day 2 13:20– 15:20	PC218	Development and improvement of an odor prediction model based on molecular structure using olfactory receptor information (Meiji U.) *(Stu)Wakutsu Yuta, (Reg)Kaneko Hiromasa	Odor Protein Machine Learning	6-g	93
Day 2 13:20– 15:20	PC220	Development of machine learning model-based scores to evaluate pesticide-likeness (Meiji U.) *(Stu)Sakai Yuta, (Reg)Kaneko Hiromasa	Machine Learning Quantitative Structure-Activity Relationship Pesticide	6-g	120
Day 2 13:20– 15:20	PC221	Exploration of Candidate Molecules for Organic Semiconductor Materials Using Generative Models (Meiji U.) *(Stu)Nakanishi Yamato, (Panasonic Ind.) Matsuzawa Nobuyuki N, Maeshima Hiroyuki, Ando Tatsuhito, (Meiji U.) (Reg)Kaneko Hiromasa	Machine learning Organic semiconductor Hierarchical Variational Autoencoder	6-f	242
Day 2 13:20– 15:20	PC223	Development of a method for predicting drug-drug interactions considering negative data mixed with positive data (Meiji U.) *(Stu)Kosakai Soma, (Reg)Kaneko Hiromasa	Drug-drug interaction Machine learning Positive-unlabeled learning	6-f	119
Day 2 13:20– 15:20	PC229	Development of machine learning models for predicting the degradation activity and thermostability of plastic-degrading enzymes (Meiji U.) *(Stu)Ohkuma Ayami, (Reg)Kaneko Hiromasa	machine learning bioinformatics plastics-degrading enzymes	6-g	378
Day 2 13:20– 15:20	PC234	Building machine learning models to suggest new drug candidates for schizophrenia (Meiji U.) *(Stu)Kimura Shoei, (Reg)Kaneko Hiromasa	Machine learning Drug design Schizophrenia	6-g	373
Day 3 9:00– 10:00	Chair: Kaneko Hiromasa, Yamaki Takehiro
H301	On Modeling Arbitrary Boundary Deformations for Granular Flow Simulations (U. Tokyo) *(Reg)Li Shuo, (Reg)Sakai Mikio	Discrete element method Signed distance function Boundary deformation	6-c	81
H302	Optimizing Water Intake for Run-of-River Hydropower Using Chemical Plant Control Technology (Resonac) *(Cor)Kurauchi Yuji, (Cor)Yoshida Katsuhisa, (Cor)Okuno Yoshishige, (Cor)Yoshihara Kazuki, (Cor)Aoyagi Tatsuya, (Cor)Takei Masahiro, (Cor)Kondo Takumi, (Cor)Kihara Hiroyuki, (Cor)Komuta Daiki, (Cor)Takinami Akitoshi	Hydropower Model Predictive Control process control	6-d	251
H303	An approximate model of a complex reaction process for controlling a product property. (ENEOS) *(Reg)Daiguji Masaharu, (TUAT) (Reg)Yamashita Yoshiyuki	process control	6-d	557

Time

Paper
ID

Title / Authors

Keywords

Topic code

Ack.
number

Day 1
13:40– 14:00

I115

Machine learning to develop extraction solvent for Ga(III) and separation of Ga(III) and In(III) by multi-stage solvation extraction

(U. Miyazaki) *(Reg)Oshima Tatsuya, Nakatamari Shoma, (Reg)Ohe Kaoru, (Reg)Inada Asuka, (Meiji U.) (Reg)Kaneko Hiromasa

Solvation Extraction
Gallium
Indium

4-f

325

Day 2
13:20– 15:20

PC201

Construction of a model for predicting the activity of BiVO₄ photocatalyst based on published articles and design of synthesis conditions using a genetic algorithm

(Meiji U.) *(Stu)Takami Yuta, Iwase Akihide, (Reg)Kaneko Hiromasa

Bismuth Vanadate
Synthesis conditions
Genetic algorithm

6-e

132

Day 2
13:20– 15:20

PC205

Construction of property prediction model and inverse analysis of the model in carbon material manufacturing process with different batch times

(Meiji U.) *(Stu)Matsubara Masayoshi, (Mitsubishi Chemical) (Cor)Sasaki Ryo, (Cor)Takahara Jun, (Cor)Moritake Shinji, (Cor)Harada Yasuyuki, (Meiji U.) (Reg)Kaneko Hiromasa

Machine learning
Batch time

6-e

240

Day 2
13:20– 15:20

PC209

Prediction of Ionic Conductivity in Solid Electrolytes Using a Machine Learning Model

(Meiji U.) *(Stu)Ishikawa Eri, (Reg)Kaneko Hiromasa

Solid Electrolyte
Machine Learning
Materials Informatics

6-f

244

Day 2
13:20– 15:20

PC211

Design of New Catalysts for Suzuki-Miyaura Type Cross-Coupling Reactions Using Polymeric Nickel Catalyst Structure by Bayesian Optimization

(Meiji U.) *(Stu)Takaoka Sho, (Riken) Zhang Zhenzhong, Yamada Yoichi M. A., (Meiji U.) (Reg)Kaneko Hiromasa

Bayesian Optimization
Polymeric Ni Catalysts
Chemical Reactions

6-f

630

Day 2
13:20– 15:20

PC213

Development of machine learning models to predict gas permeability from monomer structures and properties of polymer materials

(Meiji U.) *(Stu)Ochiai Haruki, Nagai Kazukiyo, (Reg)Kaneko Hiromasa

Machine Learning
Polymer
Permeability

6-g

349

Day 2
13:20– 15:20

PC216

Design of new acetylcholinesterase inhibitors using a Generative Adversarial Network

(Meiji U.) *(Stu)Ando Ruka, (Reg)Kaneko Hiromasa

acetylcholinesterase
Generative Adversarial Network
machine learning

6-g

Day 2
13:20– 15:20

PC218

Development and improvement of an odor prediction model based on molecular structure using olfactory receptor information

(Meiji U.) *(Stu)Wakutsu Yuta, (Reg)Kaneko Hiromasa

Odor
Protein
Machine Learning

6-g

Day 2
13:20– 15:20

PC220

Development of machine learning model-based scores to evaluate pesticide-likeness

(Meiji U.) *(Stu)Sakai Yuta, (Reg)Kaneko Hiromasa

Machine Learning
Quantitative Structure-Activity Relationship
Pesticide

6-g

120

Day 2
13:20– 15:20

PC221

Exploration of Candidate Molecules for Organic Semiconductor Materials Using Generative Models

(Meiji U.) *(Stu)Nakanishi Yamato, (Panasonic Ind.) Matsuzawa Nobuyuki N, Maeshima Hiroyuki, Ando Tatsuhito, (Meiji U.) (Reg)Kaneko Hiromasa

Machine learning
Organic semiconductor
Hierarchical Variational Autoencoder

6-f

242

Day 2
13:20– 15:20

PC223

Development of a method for predicting drug-drug interactions considering negative data mixed with positive data

(Meiji U.) *(Stu)Kosakai Soma, (Reg)Kaneko Hiromasa

Drug-drug interaction
Machine learning
Positive-unlabeled learning

6-f

119

Day 2
13:20– 15:20

PC229

Development of machine learning models for predicting the degradation activity and thermostability of plastic-degrading enzymes

(Meiji U.) *(Stu)Ohkuma Ayami, (Reg)Kaneko Hiromasa

machine learning
bioinformatics
plastics-degrading enzymes

6-g

378

Day 2
13:20– 15:20

PC234

Building machine learning models to suggest new drug candidates for schizophrenia

(Meiji U.) *(Stu)Kimura Shoei, (Reg)Kaneko Hiromasa

Machine learning
Drug design
Schizophrenia

6-g

373

Day 3
9:00– 10:00

Chair: Kaneko Hiromasa, Yamaki Takehiro

H301

On Modeling Arbitrary Boundary Deformations for Granular Flow Simulations

(U. Tokyo) *(Reg)Li Shuo, (Reg)Sakai Mikio

Discrete element method
Signed distance function
Boundary deformation

6-c

H302

Optimizing Water Intake for Run-of-River Hydropower Using Chemical Plant Control Technology

(Resonac) *(Cor)Kurauchi Yuji, (Cor)Yoshida Katsuhisa, (Cor)Okuno Yoshishige, (Cor)Yoshihara Kazuki, (Cor)Aoyagi Tatsuya, (Cor)Takei Masahiro, (Cor)Kondo Takumi, (Cor)Kihara Hiroyuki, (Cor)Komuta Daiki, (Cor)Takinami Akitoshi

Hydropower
Model Predictive Control
process control

6-d

251

H303

An approximate model of a complex reaction process for controlling a product property.

(ENEOS) *(Reg)Daiguji Masaharu, (TUAT) (Reg)Yamashita Yoshiyuki

process control

6-d

557

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