SCEJ 55th Autumn Meeting (Sapporo, 2024)
Program search result : 機械学習 : 25 programs
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Title (J) field includes “機械学習”; 25 programs are found. (“Poster with Flash” presentations are double-counted.)
The search results are sorted by the start time.
| Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
|---|---|---|---|---|---|
| Day 1 | M101 | Machine learning model for predicting three-dimensional flow field inside a face mask obtained from X-ray CT | face mask numerical simulation machine learning | SY-52 | 527 |
| Day 1 | YB130 | Construction of Prediction Model for the Yield of Suzuki-Miyaura Type Cross-Coupling Reactions Using Polymeric Ni Catalysts and Proposal of Novel Polymeric Ni Catalysts | Machine Learning Polymeric Ni Catalysts Chemical Reactions | SY-63 | 271 |
| Day 1 | YB132 | Application of Machine Learning in Designing the Width of Permeable Reactive Barriers Based on Reduction Mechanisms | Machine learning Permeable reactive barrier LUMO | SY-63 | 388 |
| Day 1 | YB158 | Energy-saving design of ethylbenzene production process using machine learning and Bayesian optimization | Machine learning Bayesian optimization Process design | SY-63 | 76 |
| Day 1 | M102 | Data analysis for unsteady flow under low oscillating Reynolds number conditions using machine learning | oscillatory flow machine learning PIV analysis | SY-52 | 618 |
| Day 1 | N102 | Rapid measurement of emulsion viscosity using machine learning | Emulsion Viscosity Machine Learning | SY-53 | 129 |
| Day 1 | M104 | Prediction of flow patterns in liquid-liquid two-phase flow within capillaries using machine learning | liquid-liquid flow slug flow machine learning | SY-52 | 1071 |
| Day 1 | L113 | Machine learning model estimating blood glucose monitoring from mid-infrared spectra measured by the photothermal monitoring method | mid-infrared spectra noninvasive blood glucose monitoring machine learning | SY-66 | 102 |
| Day 1 | Q113 | [Invited lecture] Physical property prediction based on machine learning and ab initio calculation | Machine learning Physical property Transfer learning | SY-73 | 537 |
| Day 1 | S116 | Construction of a machine learning model for predicting the characteristics from the fuel cell electrode catalyst layer preparation process | Fuel Cell Electrode Machine learning | ST-23 | 489 |
| Day 1 | YB130 | Construction of Prediction Model for the Yield of Suzuki-Miyaura Type Cross-Coupling Reactions Using Polymeric Ni Catalysts and Proposal of Novel Polymeric Ni Catalysts | Machine Learning Polymeric Ni Catalysts Chemical Reactions | SY-63 | 271 |
| Day 1 | YB132 | Application of Machine Learning in Designing the Width of Permeable Reactive Barriers Based on Reduction Mechanisms | Machine learning Permeable reactive barrier LUMO | SY-63 | 388 |
| Day 1 | YB158 | Energy-saving design of ethylbenzene production process using machine learning and Bayesian optimization | Machine learning Bayesian optimization Process design | SY-63 | 76 |
| Day 2 | YA206 | Constructing model for predicting pesticide activity using scaffolds by machine learning and visualizing the basis for prediction | Machine Learning Quantitative Structure-Activity Relationship Pesticide | SY-78 | 86 |
| Day 2 | YA232 | Construction of predictive model for biodegradability as an alternative to biodegradability test using machine learning | Machine Learning Biodegradation plastic | SY-78 | 90 |
| Day 2 | YA266 | Measurement of adsorption equilibrium of organic compounds on poly(N-isopropylacrylamide) hydrogel and prediction using machine learning | poly(N-isopropylacrylamide) adsorption machine learning | SY-78 | 296 |
| Day 2 | YA223 | Research to Improving the Accuracy of Machine Learning Models for Predicting Reorganization Energy | Machine learning Organic semiconductor Graph Convolutional Network | SY-78 | 272 |
| Day 2 | K209 | Estimation of lower explosive limit/limiting oxygen concentration of flammable gases and vapors using machine learning | Machine Learning Lower Explosive Limit Limiting Oxygen Concentration | SY-76 | 226 |
| Day 2 | R213 | [Review lecture] Property prediction with machine learning models and designs of molecules, materials and processes through inverse analysis of models | Machine learning Material design Process design | SY-51 | 431 |
| Day 2 | YB245 | Machine learning model predicting properties from monomer structures of alkyl sulfonated polyimides | alkyl sulfonated polyimide machine learning fuel cell | SY-75 | 82 |
| Day 2 | YB249 | Prediction of cell characteristics of PEFC by machine learning | Fuel cell Catalyst layer Machine learning | SY-75 | 1021 |
| Day 2 | R215 | Searching for the optimal machine learning prediction model in carbon dioxide expansion fluid density | machine learning carbon dioxide density | SY-51 | 403 |
| Day 2 | E218 | [The Outstanding Paper Award] Optimization of metal nanoparticle synthesis conditions using automated flow system and machine learning | Nanoparticle Microreactor Machine Learning | ST-22 | 448 |
| Day 3 | W305 | Machine learning-based cancer diagnosis using size-dependent blood residence time of nanoparticles | machine learning cancer diagnosis blood residence time | SY-72 | 624 |
| Day 3 | W308 | [Featured presentation] Predictive Modeling of Cell Viability in Extrusion-Based 3D Bioprinting using Machine Learning | Extrusion-based 3D bioprinting Cell viability Machine learning | SY-72 | 394 |
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SCEJ 55th Autumn Meeting (Sapporo, 2024)
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