SCEJ 55th Autumn Meeting (Sapporo, 2024)
Program search result : 予測 : 34 programs
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Title (J) field includes “予測”; 34 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 | YB128 | Construction of yield prediction model in catalytic cracking of vegetable oils considering fatty acid composition | catalytic cracking vegetable oil machine learning | SY-63 | 697 |
| 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 | YA150 | Proposal for a method to predict amino acid residues determining enzyme substrate specificity | enzyme engineering substrate specificity logistic regression | SY-68 | 377 |
| 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 | YA189 | Investigation of the use of information from olfactory receptors for molecular odor prediction | Odor Protein Machine Learning | SY-68 | 165 |
| Day 1 | YA189 | Investigation of the use of information from olfactory receptors for molecular odor prediction | Odor Protein Machine Learning | SY-68 | 165 |
| 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 | YA150 | Proposal for a method to predict amino acid residues determining enzyme substrate specificity | enzyme engineering substrate specificity logistic regression | SY-68 | 377 |
| Day 1 | H122 | Highly accurate prediction of transport phenomena in Si single crystal growth process using Hybrid-PINNs | Physics Informed Neural Networks Machine learning Crystal growth | SY-56 | 525 |
| Day 1 | S122 | Optimization of operation conditions of fuel cell systems based on material properties using model predictive control | Fuel Cell Systems Model Predictive Control | ST-23 | 696 |
| Day 1 | L124 | [Featured presentation] Wavelength-weighting concentration prediction robust against spectra-unknown components using few samples | spectroscopic analysis | SY-66 | 209 |
| Day 1 | YB128 | Construction of yield prediction model in catalytic cracking of vegetable oils considering fatty acid composition | catalytic cracking vegetable oil machine learning | SY-63 | 697 |
| 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 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 | YA258 | Evaluation of optical properties of light shielding gel using temperature-responsive polymer and construction of a model for predicting transmittance | temperature-responsive polymer light shielding material gel | SY-78 | 659 |
| 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 | V203 | Prediction and prevention of the surface melting and puffing of freeze-dried products | Freeze-drying Surface melting Puffing | SY-70 | 208 |
| Day 2 | H204 | [The Outstanding Paper Award] Accuracy and improvement of prediction of detailed kinetic mechanisms for co-pyrolysis | detailed kinetic mechanism co-pyrolysis hydrocarbon | SY-56 | 174 |
| 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 | L213 | Optimization of property prediction model in dynamic manufacturing process for carbon materials using a genetic algorithm | Machine learning Genetic-algorithm-based process variables and dynamics selection Multi-objective optimization | SY-66 | 273 |
| 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 | E214 | Prediction model for real-time power generation of solar cells with shadow simulation based on module structure and irradiance fraction | Solar cell Energy system Prediction model | ST-22 | 865 |
| 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 | L216 | Deep Learning Prediction Model for Solubility of Nanoparticles From Perspective of Similarity | Nanoparticle solubility Deep learning | SY-66 | 677 |
| Day 2 | L219 | Molecular design with direct inverse analysis of autoencoder-based QSAR/QSPR model | Machine learning Inverse analysis Molecular design | SY-66 | 140 |
| Day 2 | E220 | Development of a product composition prediction model for catalytic cracking reactions and its application to the prediction of reactions with unknown feedstocks | physics-informed machine learning catalytic cracking reaction prediction | ST-22 | 1128 |
| Day 3 | Q304 | [Review lecture] Future of the reactions in supercritical water and high-temperature water -Plastics development based on recycling & reaction prediction using natural language processing- | Plastic recycling Machine learning Hydrothermal | SY-73 | 1044 |
| 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 |
| Day 3 | I309 | Prediction of heating value of biochar during pyrolysis processes of biomass | Reaction kinetic model Elemental composition Differential scanning calorimeter | ST-24 | 818 |
| Day 3 | I324 | Constructing a Model for Predicting the Growth of Microalgae | Microalgae Nannochloropsis Growth Simulation | ST-24 | 858 |
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SCEJ 55th Autumn Meeting (Sapporo, 2024)
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