Updated on 2026/02/27

写真a

 
ZETTSU Koji
 
Organization
Graduate School of Informatics Professor
Graduate School
Graduate School of Informatics
Undergraduate School
School of Informatics Department of Computer Science
Title
Professor
External link

Degree 1

  1. Doctor (Informatics) （ 2005.3   Kyoto University ） 

To the head of Degree.▲

Research Interests 3

  1. データベース

  2. AI

  3. 情報検索

To the head of Research Interests.▲

Research Areas 3

  1. Informatics / Intelligent informatics

  2. Informatics / Database science

  3. Informatics / Web and service informatics

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Research History 3

  1. Nagoya University   Graduate School of Informatics   Professor

    2025.4

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  2. National Institute of Information and Communications Technology   Big Data Integration Research Center   Director General

    2018.4

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  3. National Institute of Information and Communications Technology   Director

    2011.4 - 2021.3

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To the head of Research History.▲

Education 2

  1. Kyoto University

    2002.4 - 2005.3

  2. Tokyo Institute of Technology

    1998.4 - 1992.3

To the head of Education.▲

Professional Memberships 4

  1. 日本データベース学会

  2. 情報処理学会

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  3. 電子情報通信学会

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  4. ACM

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Papers 62

  1. Periodic-confidence: a null-invariant measure to discover partial periodic patterns in non-uniform temporal databases.

    Rage Uday Kiran, Vipul Chhabra, Saideep Chennupati, Krishna Reddy Polipalli, Minh-Son Dao, Koji Zettsu

    International Journal of Data Science and Analytics   Vol. 20 ( 2 ) page： 727 - 749   2025.8

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1007/s41060-023-00462-0

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  2. Revealing spatiotemporal variations in areas potentially linked to COVID-19 spread using fine-grained population data Open Access

    Ishida, N; Toyoda, M; Umemoto, K; Zettsu, K

    SCIENTIFIC REPORTS   Vol. 15 ( 1 )   2025.7

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    Publisher：Scientific Reports  

    The COVID-19 pandemic has highlighted the need to better understand the dynamics of disease spread in cities in order to develop efficient and effective epidemiological strategies. In this study, we utilise fine-grained spatiotemporal population data obtained from mobile devices to identify areas and time of day that may contribute to COVID-19 spread, and investigate how they change throughout different waves of the pandemic. To evaluate the potential risk to city residents, we analyse the correlation between the effective reproduction number and population dynamics at locations regularly visited by these residents. Our case study of Tokyo identifies highly-correlated areas at a fine-grained level, revealing shifts in these areas within cities and across urban and suburban regions as the pandemic progresses. We also explore the characteristics of the potential areas of concern through the lenses of points of interest and population dynamics. Our findings have implications for comprehensively understanding the spatiotemporal dynamics of COVID-19 and offer insights into public health interventions for managing pandemics.

    DOI： 10.1038/s41598-025-06658-7

    Open Access

    Web of Science

    Scopus

    PubMed

  3. Smart Driving Assistance with Real-Time Risk Assessment and Personalized Driving Coaching to Enhance Road Safety. Reviewed

    Wenbin Gan, Minh-Son Dao, Koji Zettsu

    MMM (5)     page： 210 - 217   2025

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1007/978-981-96-2074-6_24

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    Other Link： https://dblp.uni-trier.de/db/conf/mmm/mmm2025-5.html#GanDZ25

  4. TOU: A Truncated-factorized reduction for a lightweight fine-tuning method. Open Access

    Phuong Thi Mai Nguyen, Koji Zettsu

    Proceedings of the 6th Workshop on Intelligent Cross-Data Analysis and Retrieval(ICDAR@ICMR)     page： 38 - 45   2025

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    Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

    DOI： 10.1145/3733566.3734432

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    Other Link： https://dblp.uni-trier.de/db/conf/icdar2/icdar2025.html#NguyenZ25

  5. Simulated Insight, Real-World Impact: Enhancing Driving Safety with CARLA-Simulated Personalized Lessons and Eye-Tracking Risk Coaching. Open Access

    Wenbin Gan, Minh-Son Dao, Koji Zettsu

    ICMI     page： 769 - 771   2025

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1145/3716553.3757087

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    Other Link： https://dblp.uni-trier.de/db/conf/icmi/icmi2025.html#GanDZ25

  6. FSBridge: Bridging Federated and Split Learning for Next-Generation Edge AI.

    Tran Anh Khoa, Minh-Son Dao, Koji Zettsu

    IJCNN     page： 1 - 8   2025

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/IJCNN64981.2025.11229149

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    Other Link： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2025.html#KhoaDZ25

  7. Efficient Federated Split Learning on Android Smartphones via Adaptive Offloading Point Mechanism. Open Access

    Pham Duy Thanh, Koji Zettsu

    Proceedings of the 6th Workshop on Intelligent Cross-Data Analysis and Retrieval(ICDAR@ICMR)     page： 20 - 26   2025

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    Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

    DOI： 10.1145/3733566.3734434

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    Other Link： https://dblp.uni-trier.de/db/conf/icdar2/icdar2025.html#ThanhZ25

  8. Bridging Video and Symbols: A Hybrid AI for Edge Traffic-Risk Reasoning. Open Access

    Minh-Son Dao, Phuong Thi Mai Nguyen, Swe Nwe Nwe Htun, Koji Zettsu

    ICMI Companion     page： 48 - 52   2025

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1145/3747327.3763041

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    Other Link： https://dblp.uni-trier.de/db/conf/icmi/icmi2025c.html#DaoNHZ25

  9. A Novel Depth-First Search Algorithm for Partial Periodic-Frequent Pattern Mining in Temporal Databases. Open Access

    Pamalla Veena, Vanitha Kattumuri, Yutaka Watanobe, Rage Uday Kiran, So Nakamura, Palla Likhitha, Koji Zettsu

    IEEE Access   Vol. 13   page： 109840 - 109853   2025

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1109/ACCESS.2025.3581769

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  10. Scalable Federated Split Learning for Smart Mobile and IoT Devices Open Access

    Thanh, PD; Khoa, TA; Dao, MS; Zettsu, K

    PROCEEDINGS OF THE 2025 FEDERATED LEARNING AND EDGE AI FOR PRIVACY AND MOBILITY, FLEDGE-AI 2025     page： 70 - 76   2025

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    Publisher：Fledge AI 2025 Proceedings of the 2025 Federated Learning and Edge AI for Privacy and Mobility  

    This paper introduces a virtual platform for federated split learning (FedSL) that supports on-device training using virtual Android smartphones as edge clients. To optimize model partitioning under both synchronous and asynchronous scenarios, we employ an adaptive offloading point (AOP) framework integrated with reinforcement learning and GMM clustering. The proposed virtualization framework enables diverse virtual Android instances to participate in training, enabling scalability beyond physical device limitations. Experimental results on both real (Google Pixel 8) and virtual devices validate the feasibility and effectiveness of AOP-based FedSL in both synchronous and asynchronous settings. Compared to traditional edge modules like Toradex Apalis, smartphones (both physical and virtual) demonstrate competitive performance for scalable, low-latency federated learning (FL) system. Furthermore, the virtual Android platform shows efficacy in supporting reproducible experiments using various FL algorithms.

    DOI： 10.1145/3737899.3768526

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  11. 3P-ECLAT: mining partial periodic patterns in columnar temporal databases. Reviewed

    Pamalla Veena, Rage Uday Kiran, Penugonda Ravikumar, Likhitha Palla, Yutaka Watanobe, Sadanori Ito, Koji Zettsu, Masashi Toyoda, Bathala Venus Vikranth Raj

    Applied Intelligence   Vol. 54 ( 11-12 ) page： 657 - 679   2024.1

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1007/s10489-023-05172-5

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  12. Clustering-Enhanced Reinforcement Learning for Adaptive Offloading in Resource-Constrained Devices. Reviewed

    Tran Anh Khoa, Minh-Son Dao, Do-Van Nguyen, Koji Zettsu

    IEEE International Conference on Smart Computing(SMARTCOMP)     page： 133 - 140   2024

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/SMARTCOMP61445.2024.00039

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    Other Link： https://dblp.uni-trier.de/db/conf/smartcomp/smartcomp2024.html#KhoaDNZ24

  13. Spatial-temporal Graph Transformer Network for Spatial-temporal Forecasting. Reviewed

    Minh-Son Dao, Koji Zettsu, Duy-Tang Hoang

    IEEE Big Data     page： 1276 - 1281   2024

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/BigData62323.2024.10825469

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    Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2024.html#DaoZH24

  14. SimLLM: Detecting Sentences Generated by Large Language Models Using Similarity between the Generation and its Re-generation. Reviewed

    Hoang-Quoc Nguyen-Son, Minh-Son Dao, Koji Zettsu

    Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing(EMNLP)     page： 22340 - 22352   2024

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    Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computational Linguistics  

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    Other Link： https://dblp.uni-trier.de/rec/conf/emnlp/2024

  15. Near-Miss Accident Prediction on the Edge: A Real-Time System for Safer Driving. Reviewed

    Minh-Son Dao, Koji Zettsu

    Proceedings of the 2024 International Conference on Multimedia Retrieval(ICMR)     page： 1165 - 1169   2024

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    Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

    DOI： 10.1145/3652583.3657623

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    Other Link： https://dblp.uni-trier.de/db/conf/mir/icmr2024.html#DaoZ24

  16. Enhancing Smart Service Development: Embedding Image Recognition Capabilities within the xDataAPI. Reviewed

    Sadanori Ito, Koji Zettsu

    The Fifth Workshop on Intelligent Cross-Data Analysis and Retrieval(ICDAR@ICMR)     page： 5 - 10   2024

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    Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

    DOI： 10.1145/3643488.3660296

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    Other Link： https://dblp.uni-trier.de/rec/conf/icdar2/2024

  17. Drive-CLIP: Cross-Modal Contrastive Safety-Critical Driving Scenario Representation Learning and Zero-Shot Driving Risk Analysis. Reviewed

    Wenbin Gan, Minh-Son Dao, Koji Zettsu

    MultiMedia Modeling - 30th International Conference     page： 82 - 97   2024

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    Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

    DOI： 10.1007/978-3-031-53308-2_7

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    Other Link： https://dblp.uni-trier.de/db/conf/mmm/mmm2024-2.html#GanDZ24

  18. Digital Twin Orchestration: Framework and Smart City Applications. Reviewed

    Do-Van Nguyen, Minh-Son Dao, Koji Zettsu

    The Second Workshop on AI for Digital Twins and Cyber-Physical Applications in conjunction with 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024)(AI4DT&CP@IJCAI)     page： 21 - 40   2024

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    Publishing type：Research paper (international conference proceedings)   Publisher：CEUR-WS.org  

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    Other Link： https://dblp.uni-trier.de/rec/conf/ai4dt/2024

  19. A fundamental approach to discover closed periodic-frequent patterns in very large temporal databases.

    Pamalla Veena, Rage Uday Kiran, Penugonda Ravikumar, Likhitha Palla, Yuto Hayamizu, Kazuo Goda, Masashi Toyoda, Koji Zettsu, Sourabh Shrivastava

    Applied Intelligence   Vol. 53 ( 22 ) page： 27344 - 27373   2023.11

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1007/s10489-023-04811-1

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  20. HDSHUI-miner: a novel algorithm for discovering spatial high-utility itemsets in high-dimensional spatiotemporal databases.

    Rage Uday Kiran, Pamalla Veena, Penugonda Ravikumar, Bathala Venus Vikranth Raj, Minh-Son Dao, Koji Zettsu, Sai Chithra Bommisetti

    Applied Intelligence   Vol. 53 ( 8 ) page： 8536 - 8561   2023.4

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1007/s10489-022-04436-w

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  21. AOP: Towards Adaptive Offloading Point Approach in a Federated Learning Framework for Edge AI Applications.

    Tran Anh Khoa, Do-Van Nguyen, Minh-Son Dao, Koji Zettsu

    29th IEEE International Conference on Parallel and Distributed Systems(ICPADS)     page： 2846 - 2847   2023

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/ICPADS60453.2023.00403

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    Other Link： https://dblp.uni-trier.de/db/conf/icpads/icpads2023.html#KhoaNDZ23

  22. Procedural Driving Skill Coaching from More Skilled Drivers to Safer Drivers: A Survey.

    Wenbin Gan, Minh-Son Dao, Koji Zettsu

    Proceedings of the 4th ACM Workshop on Intelligent Cross-Data Analysis and Retrieval(ICDAR@ICMR)     page： 10 - 18   2023

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    Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

    DOI： 10.1145/3592571.3592973

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    Other Link： https://dblp.uni-trier.de/db/conf/icdar2/icdar2023.html#GanDZ23

  23. MM-TrafficRisk: A Video-based Fleet Management Application for Traffic Risk Prediction, Prevention, and Querying.

    Minh-Son Dao, Muhamad Hilmil Muchtar Aditya Pradana, Koji Zettsu

    IEEE International Conference on Big Data     page： 1697 - 1706   2023

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/BigData59044.2023.10386866

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    Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2023.html#DaoPZ23

  24. Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements. Open Access

    Pamalla Veena, Sreepada Tarun, Rage Uday Kiran, Minh-Son Dao, Koji Zettsu, Yutaka Watanobe, Ji Zhang 0001

    IEEE Access   Vol. 11   page： 118676 - 118688   2023

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1109/ACCESS.2023.3326419

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  25. Leveraging Knowledge Graphs for CheapFakes Detection: Beyond Dataset Evaluation.

    Minh-Son Dao, Koji Zettsu

    IEEE International Conference on Multimedia and Expo Workshops     page： 99 - 104   2023

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/ICMEW59549.2023.00024

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    Other Link： https://dblp.uni-trier.de/db/conf/icmcs/icmew2023.html#DaoZ23

  26. Fostering Innovation in Urban Transportation Risk Management: A Multi-Sector Collaborative Benchmarking Platform.

    Minh-Son Dao, Huy Quang Ung, Sadanori Ito, Shinya Wada, Koji Zettsu

    IEEE International Conference on Big Data     page： 1903 - 1907   2023

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/BigData59044.2023.10386827

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    Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2023.html#DaoUIWZ23

  27. Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases.

    Palla Likhitha, Pamalla Veena, Rage Uday Kiran, Koji Zettsu

    Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining   Vol. 13937   page： 29 - 41   2023

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    Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

    DOI： 10.1007/978-3-031-33380-4_3

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    Other Link： https://dblp.uni-trier.de/db/conf/pakdd/pakdd2023-3.html#LikhithaVKZ23

  28. Discovering Fuzzy Partial Periodic Patterns in Quantitative Irregular Multiple Time Series.

    Pamalla Veena, Palla Likhitha, R. Uday Kiran, José María Luna, Philippe Fournier-Viger, Koji Zettsu

    IEEE International Conference on Fuzzy Systems(FUZZ)     page： 1 - 7   2023

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/FUZZ52849.2023.10309773

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    Other Link： https://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2023.html#VeenaLKLFZ23

  29. Augmenting Ego-Vehicle for Traffic Near-Miss and Accident Classification Dataset using Manipulating Conditional Style Translation.

    Hilmil Pradana, Minh-Son Dao, Koji Zettsu

    CoRR   Vol. abs/2301.02726   2023

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.48550/arXiv.2301.02726

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  30. Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases Open Access

    Kiran, RU; Veena, P; Ravikumar, P; Saideep, C; Zettsu, K; Shang, HC; Toyoda, M; Kitsuregawa, M; Reddy, PK

    ELECTRONICS   Vol. 11 ( 10 )   2022.5

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    Publisher：Electronics Switzerland  

    Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a database. With this motivation, this paper proposes a novel model for finding partial periodic patterns that may exist in temporal databases. An efficient pattern-growth algorithm, called Partial Periodic Pattern-growth (3P-growth), is also presented, which can effectively find all desired patterns within a database. Substantial experiments on both real-world and synthetic databases showed that our algorithm is not only efficient in terms of memory and runtime, but is also highly scalable. Finally, the effectiveness of our patterns is demonstrated using two case studies. In the first case study, our model was employed to identify the highly polluted areas in Japan. In the second case study, our model was employed to identify the road segments on which people regularly face traffic congestion.

    DOI： 10.3390/electronics11101523

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  31. A Novel Null-Invariant Temporal Measure to Discover Partial Periodic Patterns in Non-uniform Temporal Databases.

    R. Uday Kiran, Vipul Chhabra, Saideep Chennupati, P. Krishna Reddy, Minh-Son Dao, Koji Zettsu

    Database Systems for Advanced Applications - 27th International Conference     page： 569 - 577   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

    DOI： 10.1007/978-3-031-00123-9_45

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    Other Link： https://dblp.uni-trier.de/db/conf/dasfaa/dasfaa2022-1.html#KiranCCRDZ22

  32. UPFP-growth++: An Efficient Algorithm to Find Periodic-Frequent Patterns in Uncertain Temporal Databases.

    Palla Likhitha, Rage Veena, Rage Uday Kiran, Koji Zettsu, Masashi Toyoda, Philippe Fournier-Viger

    Neural Information Processing - 29th International Conference   Vol. 1792   page： 182 - 194   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

    DOI： 10.1007/978-981-99-1642-9_16

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    Other Link： https://dblp.uni-trier.de/db/conf/iconip/iconip2022-5.html#LikhithaVKZTF22

  33. Towards Intellectual Property Rights Protection in Big Data.

    Rafik Hamza, Minh-Son Dao, Sadanori Ito, Koji Zettsu

    ICDAR@ICMR 2022: Proceedings of the 3rd ACM Workshop on Intelligent Cross-Data Analysis and Retrieval(ICDAR@ICMR)     page： 50 - 57   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

    DOI： 10.1145/3512731.3534211

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    Other Link： https://dblp.uni-trier.de/rec/conf/mir/2022icdar

  34. Towards Efficient Discovery of Periodic-Frequent Patterns in Dense Temporal Databases Using Complements.

    Pamalla Veena, Sreepada Tarun, R. Uday Kiran, Minh-Son Dao, Koji Zettsu, Yutaka Watanobe, Ji Zhang 0001

    Database and Expert Systems Applications - 33rd International Conference   Vol. 13427   page： 204 - 215   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

    DOI： 10.1007/978-3-031-12426-6_16

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    Other Link： https://dblp.uni-trier.de/db/conf/dexa/dexa2022-2.html#VeenaTKDZWZ22

  35. Towards Efficient Discovery of Partial Periodic Patterns in Columnar Temporal Databases.

    Penugonda Ravikumar, Bathala Venus Vikranth Raj, Palla Likhitha, Rage Uday Kiran, Yutaka Watanobe, Sadanori Ito, Koji Zettsu, Masashi Toyoda

    Intelligent Information and Database Systems - 14th Asian Conference   Vol. 13758   page： 141 - 154   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

    DOI： 10.1007/978-3-031-21967-2_12

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    Other Link： https://dblp.uni-trier.de/db/conf/aciids/aciids2022-2.html#RavikumarRLKWIZ22

  36. splitDyn: Federated Split Neural Network for Distributed Edge AI Applications.

    Tran Anh Khoa, Do-Van Nguyen, Minh-Son Dao, Koji Zettsu

    IEEE International Conference on Big Data     page： 6066 - 6073   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/BigData55660.2022.10020803

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    Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#KhoaNDZ22

  37. Monitoring and Improving Personalized Sleep Quality from Long-Term Lifelogs.

    Wenbin Gan, Minh-Son Dao, Koji Zettsu

    IEEE International Conference on Big Data     page： 4356 - 4364   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/BigData55660.2022.10020829

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    Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#GanDZ22a

  38. MM-AQI: A Novel Framework to Understand the Associations Between Urban Traffic, Visual Pollution, and Air Pollution.

    Kazuki Tejima, Minh-Son Dao, Koji Zettsu

    Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems(IEA/AIE)   Vol. 13343   page： 597 - 608   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

    DOI： 10.1007/978-3-031-08530-7_50

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    Other Link： https://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2022.html#TejimaDZ22

  39. IoT-based Multimodal Analysis for Smart Education: Current Status, Challenges and Opportunities.

    Wenbin Gan, Minh-Son Dao, Koji Zettsu, Yuan Sun 0006

    ICDAR@ICMR 2022: Proceedings of the 3rd ACM Workshop on Intelligent Cross-Data Analysis and Retrieval(ICDAR@ICMR)     page： 32 - 40   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

    DOI： 10.1145/3512731.3534208

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    Other Link： https://dblp.uni-trier.de/db/conf/mir/icdar2022.html#GanDZS22

  40. FedProb: An Aggregation Method Based on Feature Probability Distribution for Federated Learning on Non-IID Data.

    Do-Van Nguyen, Anh-Khoa Tran, Koji Zettsu

    IEEE International Conference on Big Data     page： 2875 - 2881   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/BigData55660.2022.10020923

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    Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#NguyenTZ22

  41. FedMCRNN: Federated Learning using Multiple Convolutional Recurrent Neural Networks for Sleep Quality Prediction.

    Tran Anh Khoa, Do-Van Nguyen, Phuoc Van Nguyen Thi, Koji Zettsu

    ICDAR@ICMR 2022: Proceedings of the 3rd ACM Workshop on Intelligent Cross-Data Analysis and Retrieval(ICDAR@ICMR)     page： 63 - 69   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

    DOI： 10.1145/3512731.3534207

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    Other Link： https://dblp.uni-trier.de/db/conf/mir/icdar2022.html#KhoaNTZ22

  42. Discovering Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases.

    Penugonda Ravikumar, R. Uday Kiran, Palla Likhitha, T. Chandrasekhar, Yutaka Watanobe, Koji Zettsu

    9th IEEE International Conference on Data Science and Advanced Analytics(DSAA)     page： 1 - 10   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/DSAA54385.2022.10032391

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    Other Link： https://dblp.uni-trier.de/db/conf/dsaa/dsaa2022.html#RavikumarKLCWZ22

  43. Discovering Fuzzy Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases.

    Pamalla Veena, Penugonda Ravikumar, Kundai Kwangwari, R. Uday Kiran, Kazuo Goda, Yutaka Watanobe, Koji Zettsu

    IEEE International Conference on Fuzzy Systems(FUZZ-IEEE)     page： 1 - 8   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/FUZZ-IEEE55066.2022.9882785

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    Other Link： https://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2022.html#VeenaRKKGWZ22

  44. An Open Case-based Reasoning Framework for Personalized On-board Driving Assistance in Risk Scenarios.

    Wenbin Gan, Minh-Son Dao, Koji Zettsu

    IEEE International Conference on Big Data     page： 1822 - 1829   2022

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/BigData55660.2022.10020284

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    Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#GanDZ22

  45. An information provision method for visualized traffic risks Open Access

    Ito S., Zettsu K.

    Aip Conference Proceedings   Vol. 2409   2021.12

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    Publisher：Aip Conference Proceedings  

    We have conducted a fundamental study on an information provision method for visualized traffic risks in situations where a driver recognizes the risks and makes a route-selection decision. Experiments were performed to compare the effects of the standard map expression with a simplified-route expression of risk data based on situational recognition. We found that simplification did not affect the correctness of the selection task but did influence the accuracy of situation recognition. This result suggests that to reduce unintended communication errors in the vehicle, it is necessary to condense the information in advance.

    DOI： 10.1063/5.0068764

    Scopus

  46. Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

    Ravikumar, P; Likhitha, P; Raj, BVV; Kiran, RU; Watanobe, Y; Zettsu, K

    ELECTRONICS   Vol. 10 ( 12 )   2021.6

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  47. Extracting areas potentially spreading COVID-19 by focusing on correlation between mobile phone population statistics and the number of new positive cases

    ISHIDA Nobumasa, TOYODA Masashi, UMEMOTO Kazutoshi, SHANG Haichuan, ZETTSU Koji

    Proceedings of the Annual Conference of JSAI   Vol. JSAI2021 ( 0 ) page： 1J3GS10e03 - 1J3GS10e03   2021

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    Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

    <p>We propose a method to extract areas potentially spreading COVID-19 by focusing on correlation between mobile phone population statistics and the number of new positive cases. Our experiment showed that our method can successfully extract areas that are consistent with the government's views on infection sources.</p>

    DOI： 10.11517/pjsai.jsai2021.0_1j3gs10e03

    CiNii Research

  48. A Unified Framework to Discover Partial Periodic-Frequent Patterns in Row and Columnar Temporal Databases.

    Pamalla Veena, So Nakamura, Palla Likhitha, R. Uday Kiran, Yutaka Watanobe, Koji Zettsu

    2021 International Conference on Data Mining     page： 607 - 614   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/ICDMW53433.2021.00080

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    Other Link： https://dblp.uni-trier.de/db/conf/incdm/incdm2021w.html#VeenaNLKWZ21

  49. Towards Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

    Penugonda, R; Palla, L; Rage, UK; Watanobe, Y; Zettsu, K

    ADVANCES AND TRENDS IN ARTIFICIAL INTELLIGENCE. ARTIFICIAL INTELLIGENCE PRACTICES, IEA/AIE 2021, PT I   Vol. 12798   page： 28 - 40   2021

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    Publisher：Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics  

    Finding periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Most previous studies focused on finding these patterns in row temporal databases. To the best of our knowledge, there exists no study that aims to find periodic-frequent patterns in columnar temporal databases. One cannot ignore the importance of the knowledge that exists in very large columnar temporal databases. It is because the real-world big data is widely stored in columnar temporal databases. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world databases demonstrate that PF-ECLAT is not only memory and runtime efficient but also highly scalable. Finally, we present the usefulness of PF-ECLAT with a case study on air pollution analytics.

    DOI： 10.1007/978-3-030-79457-6_3

    Web of Science

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  50. Spatially-distributed Federated Learning of Convolutional Recurrent Neural Networks for Air Pollution Prediction.

    Do Van Nguyen, Koji Zettsu

    2021 IEEE International Conference on Big Data (Big Data)     page： 3601 - 3608   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/BigData52589.2021.9671336

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    Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#NguyenZ21

  51. Models to Predict Sleeping Quality from Activities and Environment: Current Status, Challenges and Opportunities.

    Thi Phuoc Van Nguyen, Do Van Nguyen, Koji Zettsu

    ICDAR@ICMR 2021: Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval(ICDAR@ICMR)     page： 52 - 56   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

    DOI： 10.1145/3463944.3469268

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    Other Link： https://dblp.uni-trier.de/db/conf/mir/icdar2021.html#NguyenNZ21

  52. MM-trafficEvent: An Interactive Incident Retrieval System for First-view Travel-log Data.

    Minh-Son Dao, Dinh-Duy Pham, Manh-Phu Nguyen, Thanh-Binh Nguyen, Koji Zettsu

    2021 IEEE International Conference on Big Data (Big Data)     page： 4842 - 4851   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/BigData52589.2021.9671724

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    Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#DaoPNNZ21

  53. Investigation on Privacy-Preserving Techniques For Personal Data.

    Rafik Hamza, Koji Zettsu

    ICDAR@ICMR 2021: Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval(ICDAR@ICMR)     page： 62 - 66   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

    DOI： 10.1145/3463944.3469267

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    Other Link： https://dblp.uni-trier.de/db/conf/mir/icdar2021.html#HamzaZ21

  54. Improving the Awareness of Sustainable Smart Cities by Analyzing Lifelog Images and IoT Air Pollution Data.

    Tuan-Vinh La, Minh-Son Dao, Kazuki Tejima, Rage Uday Kiran, Koji Zettsu

    2021 IEEE International Conference on Big Data (Big Data)     page： 3589 - 3594   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/BigData52589.2021.9671403

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  55. IMAGE-2-AQI: Aware of the Surrounding Air Qualification by a Few Images.

    Minh-Son Dao, Koji Zettsu, Rage Uday Kiran

    Advances and Trends in Artificial Intelligence. From Theory to Practice - 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems   Vol. 12799   page： 335 - 346   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

    DOI： 10.1007/978-3-030-79463-7_28

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    Other Link： https://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2021-2.html#DaoZK21

  56. Fed xData: A Federated Learning Framework for Enabling Contextual Health Monitoring in a Cloud-Edge Network.

    Tran Anh Khoa, Do-Van Nguyen, Minh-Son Dao, Koji Zettsu

    2021 IEEE International Conference on Big Data (Big Data)     page： 4979 - 4988   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/BigData52589.2021.9671536

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    Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#KhoaNDZ21

  57. Efficient Discovery of Partial Periodic-Frequent Patterns in Temporal Databases.

    So Nakamura, R. Uday Kiran, Palla Likhitha, Penugonda Ravikumar, Yutaka Watanobe, Minh-Son Dao, Koji Zettsu, Masashi Toyoda

    Database and Expert Systems Applications - 32nd International Conference   Vol. 12923   page： 221 - 227   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

    DOI： 10.1007/978-3-030-86472-9_20

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    Other Link： https://dblp.uni-trier.de/db/conf/dexa/dexa2021-1.html#NakamuraKPRWDZT21

  58. Discovering Top-k Spatial High Utility Itemsets in Very Large Quantitative Spatiotemporal databases.

    Pradeep Pallikila, Pamalla Veena, R. Uday Kiran, Ram Avatar, Sadanori Ito, Koji Zettsu, P. Krishna Reddy

    2021 IEEE International Conference on Big Data (Big Data)     page： 4925 - 4935   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/BigData52589.2021.9671912

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    Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#PallikilaVKAIZR21

  59. Discovering Spatial High Utility Itemsets in High-Dimensional Spatiotemporal Databases.

    Sai Chithra Bommisetty, Penugonda Ravikumar, Rage Uday Kiran, Minh-Son Dao, Koji Zettsu

    Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices - 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems   Vol. 12798   page： 53 - 65   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

    DOI： 10.1007/978-3-030-79457-6_5

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    Other Link： https://dblp.uni-trier.de/db/conf/ieaaie/ieaaie2021-1.html#BommisettyRKDZ21

  60. Discovering Periodic-Frequent Patterns in Uncertain Temporal Databases.

    R. Uday Kiran, Palla Likhitha, Minh-Son Dao, Koji Zettsu, Ji Zhang 0001

    Neural Information Processing - 28th International Conference   Vol. 1516   page： 710 - 718   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

    DOI： 10.1007/978-3-030-92307-5_83

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    Other Link： https://dblp.uni-trier.de/db/conf/iconip/iconip2021-5.html#KiranLDZZ21

  61. Discovering Maximal Partial Periodic Patterns in Very Large Temporal Databases.

    Palla Likhitha, Pamalla Veena, R. Uday Kiran, Yutaka Watanobe, Koji Zettsu

    2021 IEEE International Conference on Big Data (Big Data)     page： 1460 - 1469   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/BigData52589.2021.9671556

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    Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2021.html#LikithaVKWZ21

  62. Discovering Fuzzy Frequent Spatial Patterns in Large Quantitative Spatiotemporal databases.

    Pamalla Veena, Sai Chithra Bommisetty, R. Uday Kiran, Sonali Agarwal, Koji Zettsu

    30th IEEE International Conference on Fuzzy Systems(FUZZ-IEEE)     page： 1 - 8   2021

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    Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

    DOI： 10.1109/FUZZ45933.2021.9494594

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    Other Link： https://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2021.html#VeenaBKAZ21

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Books 2

  1. Insights for Urban Road Safety: A New Fusion-3DCNN-PFP Model to Anticipate Future Congestion from Urban Sensing Data

    Dao M.S., Kiran R.U., Zettsu K.

    Periodic Pattern Mining Theory Algorithms and Applications  2021.1  （ ISBN:9789811639630, 9789811639647 ）

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    Traffic congestion is a significant challenge that cities worldwide have to tackle as it poses many potential risks. Building a predictive system to anticipate future congestion would alleviate them. Furthermore, if the system can discover future frequent traffic congestion patterns, authorities can build reaction plans to deal with congestion more effectively. Unfortunately, other works have failed to achieve it. This study proposes a novel dynamic system to address the mentioned problem. It integrates a traffic congestion prediction model and a periodic-frequent pattern discovery algorithm. In particular, we utilize our novel Fusion-3DCNN deep learning model and a periodic-frequent pattern discovery algorithm in the system. The former predicts long-term traffic congestion on citywide mesh codes using multi-modal urban sensing data, while the latter identifies sets of mesh codes that are regularly predicted to have heavy traffic congestion. Experimental results on a real-world dataset collected in Kobe City, Japan, from 2014 to 2015 show that our framework is efficient in terms of accuracy and time.

    DOI： 10.1007/978-981-16-3964-7_14

    Scopus

  2. Real-World Applications of Periodic Patterns

    Kiran R.U., Toyoda M., Zettsu K.

    Periodic Pattern Mining Theory Algorithms and Applications  2021.1  （ ISBN:9789811639630, 9789811639647 ）

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    Previous chapters of this textbook have mainly focused on introducing different types of periodic patterns and their mining algorithms. Some chapters have also focused on evaluating the algorithms. In this chapter, we will present three real-world applications of periodic patterns. The first case study is traffic congestion analytics, where periodic-frequent pattern mining was employed to identify the road segments in which users have regularly encountered traffic congestion in the transportation network. The second case study is flight incidents data analytics, where partial periodic pattern mining was employed to identify factors that are regularly causing flight incidents in the data. The third case study is air pollution analytics, where fuzzy periodic pattern mining was employed to identify the geographical regions where people were exposed to harmful levels of air pollution.

    DOI： 10.1007/978-981-16-3964-7_13

    Scopus

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MISC 1

  1. 特集「2025年度人工知能学会全国大会（第39回）」KS-32「ドメイン特化生成AI の共創・協調に向けて」

    是津 耕司, 黒川 茂莉

    人工知能   Vol. 40 ( 6 ) page： 895 - 895   2025.11

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    Authorship：Lead author   Language：Japanese   Publishing type：Meeting report  

    DOI： 10.11517/jjsai.40.6_890

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Research Project for Joint Research, Competitive Funding, etc. 1

  1. 安全なデータ連携による最適化AI技術の研究開発

    Grant number：23811358  2023.4 - 2026.3

    総務省  情報通信技術の研究開発  

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    Authorship：Coinvestigator(s) 

    Grant amount：\658657609 （ Direct Cost: \506659700 、 Indirect Cost：\151997909 ）

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KAKENHI (Grants-in-Aid for Scientific Research) 4

  1. AI基盤モデル循環進化フレームワークの研究

    Grant number：25K15256  2025.4 - 2028.3

    日本学術振興会  科学研究費助成事業  基盤研究(C)

    是津 耕司

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  2. Exploring Novel Mathematical Models and Efficient Algorithms to Discover Periodic Spatial Patterns in Irregular Spatiotemporal Big Data

    Grant number：21K12034  2021.4 - 2025.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (C)

    RAGE Uday Kiran

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    Authorship：Coinvestigator(s) 

    This research aims to develop a mathematical and computational framework to discover periodic spatial itemsets-groups of locations and pollutants that repeatedly co-occur over time-in irregular spatiotemporal air pollution data. Real-world air quality data is often incomplete and noisy, making traditional mining techniques ineffective. To address this, we will (1) design a model that captures approximate periodic patterns, (2) propose novel pruning techniques to reduce the exponential search space of itemsets, (3) develop efficient sequential and distributed algorithms based on Apache Spark, and (4) validate the approach using real air pollution datasets in Japan. The outcomes will support timely environmental insights and open-source tools for large-scale air quality analysis.

  3. 偏在性に着目したユビキタスコンテンツ利活用技術の研究開発

    Grant number：21013050  2009.4 - 2011.3

    日本学術振興会  科学研究費助成事業  特定領域研究

    是津 耕司

    　 More details

    Authorship：Coinvestigator(s) 

    Grant amount：\5000000 （ Direct Cost: \5000000 ）

  4. 偏在性に着目したユビキタスコンテンツ利活用技術の研究開発

    Grant number：19024073  2007.4 - 2008.3

    日本学術振興会  科学研究費助成事業  特定領域研究

    是津 耕司

    　 More details

    Authorship：Coinvestigator(s) 

    Grant amount：\5800000 （ Direct Cost: \5800000 ）

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Industrial property rights 1

  1. 警告信号生成装置、警告信号生成方法、および、プログラム

    ダオ ミン ソン, プラダナ ムハマド ヒルミル ムクタ アディチャ, 是津 耕司

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    Applicant：国立研究開発法人情報通信研究機構

    Application no：特願2022-149761  Date applied：2022.9

    Announcement no：特開2024-044308  Date announced：2024.4

    J-GLOBAL

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Social Contribution 1

  1. Recommendation ITU-T H.770.1: Service scenarios and high-level requirements for metaverse cross-platform interoperability

    Role(s)：Editer

    International Telecommunication Union  2025.12

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