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SCEJ 54th Autumn Meeting (Fukuoka, 2023)

Program search result : 機械学習 : 12 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 “機械学習”; 12 programs are found. (“Poster with Flash” presentations are double-counted.)
The search results are sorted by the start time.

TimePaper
ID
Title / AuthorsKeywordsTopic codeAck.
number
Day 1
9:009:20
H101Development of machine learning model for CO2 absorption performance of blended amine solutions
(AIST) *(Reg)Fujii Tatsuya, (Reg)Kohno Yuki, (Reg)Makino Takashi, (Tokyo Tech) Sako Masami, Ishihama Keisuke, Yasuo Nobuaki, Kawauchi Susumu
CO2 absorption
machine learning
amine
ST-21445
Day 1
9:0010:40
PA117Machine learning model for predicting jet fuel fraction yield in catalytic cracking of vegetable oils
(Shinshu U.) *(Stu)Katayama Yuzuki, (Reg)Shimada Iori
catalytic cracking
sustainable aviation fuel
machine learning
SY-62563
Day 1
10:2010:30
PB163Construction of a machine-learning model to predict optimal mevalonate pathway gene expression levels for efficient production of carotenoids in yeast
(Osaka Metro. U.) *(Stu)Shimazaki Shun, (Reg)Yamada Ryosuke, Yamamoto Yoshiki, (Reg)Matsumoto Takuya, (Reg)Ogino Hiroyasu
Carotenoid
Machine-learning
Metabolic engineering
SY-67143
Day 1
12:4013:40
PA117Machine learning model for predicting jet fuel fraction yield in catalytic cracking of vegetable oils
(Shinshu U.) *(Stu)Katayama Yuzuki, (Reg)Shimada Iori
catalytic cracking
sustainable aviation fuel
machine learning
SY-62563
Day 1
15:0016:20
PB163Construction of a machine-learning model to predict optimal mevalonate pathway gene expression levels for efficient production of carotenoids in yeast
(Osaka Metro. U.) *(Stu)Shimazaki Shun, (Reg)Yamada Ryosuke, Yamamoto Yoshiki, (Reg)Matsumoto Takuya, (Reg)Ogino Hiroyasu
Carotenoid
Machine-learning
Metabolic engineering
SY-67143
Day 1
15:4016:00
H121Machine learning guided enzyme’s molecular recognition specificity conversion
(Osaka U.) *(Reg)Niide Teppei, Sugiki Sou, Mori Seiya, (Reg)Toya Yoshihiro, (Reg)Shimizu Hiroshi
enzyme design
machine learning
ST-21235
Day 2
9:3011:00
PA203Prediction of the polymer gel-solvent interaction parameter χ using support vector regression
(TUAT) *(Stu·PCEF)Kamikawa Yuna, (Reg)Kitajima Teiji, (Reg)Tokuyama Hideaki
polymer gel
interaction parameter
machine learning
SY-79178
Day 2
9:4010:00
K203Design of high selective membranes for CO2 separation by machine learning and computational chemistry
(Kogakuin U.) *(Reg)Takaba Hiromitsu, Morisaki Kazuki, (Reg)Miyagawa Masaya
Molecular simulation
Machine learning
Porous membrane
SY-61972
Day 2
14:3016:00
PB214Investigation of reactin and mass transport performance of fuel cell catalyst layers using machine learning
(Kyushu U.) *(Stu)Kondo A., (Stu)Saito Y., Permatasari A., (Stu)Nakano K., (Reg)Inoue G.
Fuel cell
Catalyst layer
Machine learning
SY-75469
Day 3
9:009:20
T301New discoveries emerged from machine learning-assisted directed evolution of enzymes
(Tohoku U.) *(Reg)Nakazawa Hikaru, (Reg)Ito Tomoyuki, (AIST/U. Tokyo/Kitasato U.) Saito Yutaka, (Tohoku U.) Oikawa Misaki, Sato Takumi, (RevolKa) Kataoka Shiro, (Tohoku U./Riken) (Reg)Umetsu Mitsuo
Directed evolution
enzyme
Machine learning
SY-711027
Day 3
13:2014:20
PB301[Requested talk] High-speed computing for powder mixing process using machine learning
(Osaka Metro. U.) (Stu)Kishida Naoki
Powder mixing
High-speed computing
Machine learning
HQ-1467
Day 3
16:0016:20
T322[Requested talk] Machine learning screening of intestine deliverable bioactive peptides
(Nagoya U.) (Reg)Honda Hiroyuki
Bioactive peptide
Silica gel
Edible protein
SY-722

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SCEJ 54th Autumn Meeting (Fukuoka, 2023)


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