Online edition: ISSN 2432-6380
[TOP] | [2016] | [2017] | [2018] | [2019] | [2020] | [2021] | [2022] | [Japanese] / [English]
AI2019-42
Elucidation of factors supporting co-creative consensus building and proposal of the communication environment
Sae Kondo (UT), Taichi Miyamae, Masaaki Kuzuya, Karen Sasagawa, Hiroyuki Sekikawa (Itoki), Takashi Numata (Hitachi), Kotaro Onishi, Hiroko Ohno (HF), Masako Maeda, Takuo Inoue, Chikako Goto, Hideki Koizumi (UT)
pp. 1 - 6
AI2019-43
About Data Consortium in Marunouchi
Hiroyuki Okuyama, Aya Komatsubara (Mitsubishi Estate)
pp. 7 - 8
AI2019-44
Necessity of distribution and accumulation of municipal data on childcare support measures and fertility rate
Yoshiko Matsuga, Teruyoshi Hishiki (Toho Univ.)
pp. 9 - 13
AI2019-45
Estimation of Target Plasma Concentration of Propofol by Decision Tree Learning and Polynomial Modeling
Kei Ten, Eisaka Toshio (Kitami Inst. of Tech.)
pp. 15 - 16
AI2019-46
Utilizing Job Stress Questionnaire data by Data Analysis Method
Keiya Okada, Keisuke Onishi, Masahiro Akimoto (KKE.Inc)
pp. 17 - 22
AI2019-47
Quine's Philosophy concerning Analysis and Synthesis of Data
-- Reflecting "Two Dogmas of Empiricism" from a Modern Perspective --
Makoto Koike (MK Microwave)
pp. 23 - 31
AI2019-48
Visualization of Data Value Coupling based on Data Jacket Logic
Yukio Ohsawa, Teruaki Hayashi, Gensei Ishimura, Sae Kondo (UT), Tokuhisa Shiromizu (DJWG)
pp. 33 - 38
AI2019-49
Creative Data Value Chain Description Model
Teruaki Hayashi, Gensei Ishimura (UT), Tokuhisa Shiromizu (DJWG), Yukio Ohsawa (UT)
pp. 39 - 43
AI2019-50
Proposal for a novel method of science communication utilizing IMDJ(Innovators Marketplace on Data Jackets)
Gensei Ishimura (Tokyo Tech), Teruaki Hayashi (Univ. of Tokyo), Tokuhisa Shiromizu (DJWG), Yukio Ohsawa (Univ. of Tokyo)
pp. 45 - 48
AI2019-51
(See Japanese page.)
pp. 49 - 53
AI2019-52
A Data Fusion Method Assuming Latent Proxy Variables for Target Variables
Yoshihide Nishio, Yasuo Tanida (Synergy Marketing)
pp. 55 - 60
AI2019-53
Customer Analysis Based on Benefit Segmentation Using a t-SNE
Fumiaki Saitoh (CIT)
pp. 61 - 66
Note: Each article is a technical report without peer review, and its polished version will be published elsewhere.