Kitami Inst. Tech. - Program search result - SCEJ 54th Autumn Meeting (Fukuoka, 2023)

`SCEJ 54th Autumn Meeting (Fukuoka, 2023)`

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Program search result : Kitami Inst. Tech. : 5 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.

Affillations field begins with “Kitami Inst. Tech.”, “Kitami Tech”; 5 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 10:30– 10:40	PB165	Improvement of hyaluronic acid production by culture medium optimization using deep learning (Kitami Inst. Tech.) *(Stu)Watanabe Kazuki, Kawai Yoshizumi, Chiou Tai-Ying, (Reg)Konishi Masaaki	Streptococcus zooepidemicus Hyaluronic acid Deep learning	SY-67	407
Day 1 11:00– 11:10	PB183	Analyzing the effect of amino acid composition on the growth of lactic acid bacteria by deep neural networks (Kitami Inst. Tech./Hokkaido Sugar) *(Stu)Kobayashi Yoshimi, (Kitami Inst. Tech.) Chiou Tai-Ying, (Reg)Konishi Masaaki	Lactic Acid Bacteria Deep Neural Network Amino Acid	SY-67	681
Day 1 15:00– 16:20	PB165	Improvement of hyaluronic acid production by culture medium optimization using deep learning (Kitami Inst. Tech.) *(Stu)Watanabe Kazuki, Kawai Yoshizumi, Chiou Tai-Ying, (Reg)Konishi Masaaki	Streptococcus zooepidemicus Hyaluronic acid Deep learning	SY-67	407
Day 1 15:00– 16:20	PB183	Analyzing the effect of amino acid composition on the growth of lactic acid bacteria by deep neural networks (Kitami Inst. Tech./Hokkaido Sugar) *(Stu)Kobayashi Yoshimi, (Kitami Inst. Tech.) Chiou Tai-Ying, (Reg)Konishi Masaaki	Lactic Acid Bacteria Deep Neural Network Amino Acid	SY-67	681
Day 3 10:40– 11:00	T306	[Featured presentation] New proposal for the design of microbial media by profiling of media components (Kitami Inst. Tech.) (Reg)Konishi Masaaki	midium profiling design	SY-71	233

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SCEJ 54th Autumn Meeting (Fukuoka, 2023)

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