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Paper Abstract and Keywords
Presentation 2025-07-03 09:00
[Poster Presentation] Spatio-Temporal State Estimation and Prediction Using Graph Neural Networks
Mayu Inoue, Keisuke Ishibashi (International Christian Univ.) CQ2025-27
Abstract (in Japanese) (See Japanese page) 
(in English) Honeybees are vital in maintaining social, economic, and environmental health. However, the apiculture industry faces operational challenges and unpredictability , which has made apiculture less attractive to newcomers. While advancements have improved hive monitoring, most existing approaches are reactive, alerting beekeepers after changes have occurred. To enable more proactive hive management, this study proposes a novel approach to forecast honeybee comb cells using Spatio-Temporal Graph Convolutional Networks (STGCNs). The approach combines Convolutional Neural Networks (CNNs) for feature extraction from hive images with STGCNs to account for both spatial and temporal contexts. The model achieved 43% forecasting accuracy, demonstrating the potential of predictive modeling for hive state estimation.
Keyword (in Japanese) (See Japanese page) 
(in English) Beekeeping / STGCN / Forecasting / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 94, CQ2025-27, pp. 58-61, July 2025.
Paper # CQ2025-27 
Date of Issue 2025-06-25 (CQ) 
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 CQ2025-27

Conference Information
Committee CQ  
Conference Date 2025-07-02 - 2025-07-04 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) network performance, virtual environment/metaverse, graph analysis, game theory, machine learning, interaction, social media, etc. 
Paper Information
Registration To CQ 
Conference Code 2025-07-CQ 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Spatio-Temporal State Estimation and Prediction Using Graph Neural Networks 
Sub Title (in English)  
Keyword(1) Beekeeping  
Keyword(2) STGCN  
Keyword(3) Forecasting  
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1st Author's Name Mayu Inoue  
1st Author's Affiliation International Christian University (International Christian Univ.)
2nd Author's Name Keisuke Ishibashi  
2nd Author's Affiliation International Christian University (International Christian Univ.)
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Speaker Author-1 
Date Time 2025-07-03 09:00:00 
Presentation Time 75 minutes 
Registration for CQ 
Paper # CQ2025-27 
Volume (vol) vol.125 
Number (no) no.94 
Page pp.58-61 
#Pages 4 
Date of Issue 2025-06-25 (CQ) 

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