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Paper Abstract and Keywords
Presentation 2024-11-13 15:35
Predicting and Visualizing Demand for Stylish Cars Using Decision Tree Analysis and Generative AI
Miho Hamabe, Kazuya Suzuki, Takayasu Yamaguchi (Akita Prefectural Univ.) IA2024-47
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, Internet sales of automobiles have accelerated overseas. The Model Y sold by Tesla, which has no dealers, topped the world sales ranking in 2023, surpassing Toyota's Corolla, the top seller for 20 years. By shifting its marketing to online, centred on social networking services, Tesla realizes the cars consumers want with zero advertising costs.

Producers pursuing profit cannot afford to produce products for which demand is not expected. Larger companies tend to target the masses because they want to avoid risk, but if they continue to avoid risky challenges, they would eventually decline.

We, therefore, identify the conditions for profitable designs using a demand model with Decision Tree Analysis and simulate the market response before costly production by visualizing cars using generative AI with Generated Knowledge Prompting.

Through experiments to predict sales by learning a demand model using five years of car sales data, we obtained a coefficient of determination of over 0.9 and visualized new car design options that can expect to get a large number of sales.

By clarifying the relationship between demand and design through Decision Tree Analysis, and obtaining users' feedback before production while driving users' desire to purchase through visualization with generative AI, it could spread exciting cars worldwide, transcending national and racial boundaries.
Keyword (in Japanese) (See Japanese page) 
(in English) Decision Tree Analysis / generative AI / demand forecasting / visualization / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 254, IA2024-47, pp. 20-27, Nov. 2024.
Paper # IA2024-47 
Date of Issue 2024-11-06 (IA) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IA2024-47

Conference Information
Committee IA  
Conference Date 2024-11-13 - 2024-11-13 
Place (in Japanese) (See Japanese page) 
Place (in English) Akita Atorion (Akita Pref.) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Student Sessions, etc. (cosponsored by Committee on Internet Technology) 
Paper Information
Registration To IA 
Conference Code 2024-11-IA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Predicting and Visualizing Demand for Stylish Cars Using Decision Tree Analysis and Generative AI 
Sub Title (in English)  
Keyword(1) Decision Tree Analysis  
Keyword(2) generative AI  
Keyword(3) demand forecasting  
Keyword(4) visualization  
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1st Author's Name Miho Hamabe  
1st Author's Affiliation Akita Prefectural University (Akita Prefectural Univ.)
2nd Author's Name Kazuya Suzuki  
2nd Author's Affiliation Akita Prefectural University (Akita Prefectural Univ.)
3rd Author's Name Takayasu Yamaguchi  
3rd Author's Affiliation Akita Prefectural University (Akita Prefectural Univ.)
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Speaker Author-1 
Date Time 2024-11-13 15:35:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # IA2024-47 
Volume (vol) vol.124 
Number (no) no.254 
Page pp.20-27 
#Pages 8 
Date of Issue 2024-11-06 (IA) 

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