SCEJ 54th Autumn Meeting (Fukuoka, 2023)
Program search result : 学習 : 23 programs
The preprints(abstracts) are now open (Aug. 28). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants and invited persons are required.
Title (J) field includes “学習”; 23 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 | H101 | Development of machine learning model for CO2 absorption performance of blended amine solutions | CO2 absorption machine learning amine | ST-21 | 445 |
| Day 1 | PA117 | Machine learning model for predicting jet fuel fraction yield in catalytic cracking of vegetable oils | catalytic cracking sustainable aviation fuel machine learning | SY-62 | 563 |
| Day 1 | PA120 | Construction of yield prediction model in catalytic cracking of vegetable oils using transfer learning between different catalysts | catalytic cracking transfer learning vegetable oil | SY-62 | 647 |
| Day 1 | PB163 | Construction of a machine-learning model to predict optimal mevalonate pathway gene expression levels for efficient production of carotenoids in yeast | Carotenoid Machine-learning Metabolic engineering | SY-67 | 143 |
| Day 1 | PB165 | Improvement of hyaluronic acid production by culture medium optimization using deep learning | Streptococcus zooepidemicus Hyaluronic acid Deep learning | SY-67 | 407 |
| Day 1 | PB183 | Analyzing the effect of amino acid composition on the growth of lactic acid bacteria by deep neural networks | Lactic Acid Bacteria Deep Neural Network Amino Acid | SY-67 | 681 |
| Day 1 | PA117 | Machine learning model for predicting jet fuel fraction yield in catalytic cracking of vegetable oils | catalytic cracking sustainable aviation fuel machine learning | SY-62 | 563 |
| Day 1 | PA120 | Construction of yield prediction model in catalytic cracking of vegetable oils using transfer learning between different catalysts | catalytic cracking transfer learning vegetable oil | SY-62 | 647 |
| Day 1 | H117 | Deep learning model for predicting all protein-protein interactions from sequence data | Cross attention deep learning prediction | ST-21 | 33 |
| Day 1 | PB163 | Construction of a machine-learning model to predict optimal mevalonate pathway gene expression levels for efficient production of carotenoids in yeast | Carotenoid Machine-learning Metabolic engineering | SY-67 | 143 |
| Day 1 | PB165 | Improvement of hyaluronic acid production by culture medium optimization using deep learning | Streptococcus zooepidemicus Hyaluronic acid Deep learning | SY-67 | 407 |
| Day 1 | PB183 | Analyzing the effect of amino acid composition on the growth of lactic acid bacteria by deep neural networks | Lactic Acid Bacteria Deep Neural Network Amino Acid | SY-67 | 681 |
| Day 1 | H121 | Machine learning guided enzyme’s molecular recognition specificity conversion | enzyme design machine learning | ST-21 | 235 |
| Day 2 | PA203 | Prediction of the polymer gel-solvent interaction parameter χ using support vector regression | polymer gel interaction parameter machine learning | SY-79 | 178 |
| Day 2 | K203 | Design of high selective membranes for CO2 separation by machine learning and computational chemistry | Molecular simulation Machine learning Porous membrane | SY-61 | 972 |
| Day 2 | PA246 | Energy prediction by neural network with self-supervised learning for catalyst | Neural networks self-supervised learning catalyst energy prediction | SY-79 | 559 |
| Day 2 | PB214 | Investigation of reactin and mass transport performance of fuel cell catalyst layers using machine learning | Fuel cell Catalyst layer Machine learning | SY-75 | 469 |
| Day 2 | J220 | Study of Scaling factor optimization based on accuracy in training data to improve MSPC accuracy | Continuous Pharmaceutical Manufacturing Wet Granulation Multivariate Statistical Process Control | SY-65 | 781 |
| Day 2 | J222 | Simulation Evaluation of Quality Improvement by Deep Reinforcement Learning Method for Semi-Batch Process | Reinforcement Learning Semi-Batch Process | SY-65 | 724 |
| Day 3 | T301 | New discoveries emerged from machine learning-assisted directed evolution of enzymes | Directed evolution enzyme Machine learning | SY-71 | 1027 |
| Day 3 | PB301 | [Requested talk] High-speed computing for powder mixing process using machine learning | Powder mixing High-speed computing Machine learning | HQ-14 | 67 |
| Day 3 | Y321 | Prediction of Physical Properties by Deep Learning | Physical properties Prediction Deep learning | SY-51 | 687 |
| Day 3 | T322 | [Requested talk] Machine learning screening of intestine deliverable bioactive peptides | Bioactive peptide Silica gel Edible protein | SY-72 | 2 |
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SCEJ 54th Autumn Meeting (Fukuoka, 2023)
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