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NICT論文一覧へ

2021年度

主な論文・著書・特許等

  1. La, T.V., Dao, M.S., Tejima, K., Kiran, R.U., Zettsu, K.: Improving the Awareness of Sustainable Smart Cities by Analyzing Lifelog Images and IoT Air Pollution Data, Fourth International Workshop on the Internet of Things Data Analytics (IoTDA)(IEEE BigData 2021), online, pp.3589-3594 (December 2021).
  2. Tran, A.K., Nguyen, D.V., Dao, M.S., Zettsu, K.: Fed xData: A Federated Learning Framework for Enabling Contextual Health Monitoring in a Cloud-Edge Network, 2021 IEEE International Conference on Big Data (IEEE BigData 2021), online, pp.4979-4988 (December 2021).
  3. Pallikila, P., Veena, P., Kiran, R.U., Avatar, R., Ito, S., Zettsu, K., Reddy, P.K.: Discovering Top-k Spatial High Utility Itemsets in Very Large Quantitative Spatiotemporal databases, 2021 IEEE International Conference on Big Data (IEEE BigData 2021), online, pp.4925-4935 (December 2021).
  4. Nguyen, D.V., Zettsu, K.: Spatially-distributed Federated Learning of Convolutional Recurrent Neural Networks for Air Pollution Prediction, IEEE International Conference on Big Data 2021 (IEEE BigData 2021), online, pp.3601-3608 (December 2021).
  5. Likitha, P., Veena, P., Kiran, R.U., Watanobe, Y., Zettsu, K.: Discovering Maximal Partial Periodic Patterns in Very Large Temporal Databases,2021 IEEE International Conference on Big Data (IEEE BigData 2021), online, pp.1460-1469 (December 2021).
  6. Dao, M.S., Pham, D.P., Nguyen, M.P., Nguyen, T.B., Zettsu, K.: MM-trafficEvent: An Interactive Incident Retrieval System for First-view Travel-log Data, 2021 IEEE International Conference on Big Data (IEEE BigData 2021), online, pp.4842-4851 (December 2021).
  7. Kiran, R.U., Likhitha, P., Dao, M.S., Zettsu, K., Zhang, J.: Discovering Periodic-Frequent Patterns in Very Large Uncertain Temporal Databases, The 28th International Conference on Neural Information Processing (ICONIP2021), online, pp.710-718 (December 2021).
  8. Veena, P., Nakamura, S., Likhitha, P., Kiran, R.U., Watanobe, Y., Zettsu, K.: A Unified Framework to Discover Partial Periodic-Frequent Patterns in Row and Columnar Temporal Databases, 2021 International Conference on Data Mining Workshops (ICDMW), online, pp.607-614 (December 2021).
  9. Nguyen, T.P.V., Dao, M.S., Zettsu, K.: A Personalized Adaptive Algorithm for Sleep Quality Prediction using Physiological and Environmental Sensing Data, NAFOSTED Conference on Information and Computer Science (NICS 2021), online, pp.113-119 (December 2021).
  10. Dao, M.S., Kiran, R.U., Zettsu, K.: Insights for Urban Road Safety: A new Fusion-3DCNN-PFP Model to Anticipate Future Congestion from Urban Sensing Data, Periodic Pattern Mining: Theory, Algorithms, and Applications, Vol. 8, pp.237-263 (December 2021).
  11. Hamza, R., Zettsu, K.: Investigation on Privacy-Preserving Techniques For Personal Data, ICDAR '21: Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR'21), online, pp.62-66 (November 2021).
  12. Nguyen, T.P.V., Nguyen, D.V., Zettsu, K.: Models to Predict Sleeping Quality from Activities and Environment: Current Status, Challenges and Opportunities, ACM International Conference on Multimedia Retrieval (ICMR'21), online, pp.52-56 (November 2021).
  13. Rauber, A., Gobwein, B., Zwolf, C.M., Schubert, C., Worister, F., Duncan, J., Flicker, K., Zettsu, K., Meixner, K., McIntosh, L.D., Jenkyns, R., Proll, S., Miksa, T., Parsons, M.A.: Precisely and Persistently Identifying and Citing Arbitrary Subsets of Dynamic Data,Harvard Data Science Review, Vol. 3, (November 2021). DOI: 10.1162/99608f92.be565013
  14. Kiran, R.U., Toyoda, M., Zettsu, K.: Real-World Applications of Periodic Patterns, Periodic Pattern Mining, pp.229-235 (October 2021).
  15. Nakamura, S., Kiran, R.U., Likhitha, P., Ravikumar, P., Watanobe, Y., Dao, M.S., Zettsu, K., Toyoda, M.: Efficient Discovery of Partial Periodic-Frequent Patterns in Temporal Databases, The 32nd International Conference on Database and Expert Systems Applications (DEXA2021), online, pp.221-227 (September 2021).
  16. Duong, D.Q., Le, Q.M., Nguyen, T.T.L., Nguyen, H.D., Dao, M.S., Nguyen, T.B.: An Effective AQI Estimation Using Sensor Data and Stacking Mechanism, The 20th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SOMET 2021), online, pp.405-418 (September 2021).
  17. Veena, P., Sai, C.B., Kiran, R.U., Aggrawal, S., Zettsu, K.: Discovering Fuzzy Frequent Spatial Patterns in Large Quantitative Spatiotemporal databases, IEEE International Conference on Fuzzy Systems 2021 (FUZZ-IEEE 2021), online, pp.1-8 (July 2021).
  18. Chithra, B.S., Ravikumar, P., Kiran, R.U., Dao, M.S., Zettsu, K.: Discovering Spatial High Utility Itemsets in High-Dimensional Spatiotemporal Databases, 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2021), online, Vol. 12798, pp.53-65 (July 2021).
  19. Ravikumar, P., Likhitha, P., Kiran, R.U., Watanobe, Y., Zettsu, K.: Towards Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases, 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems(IEA/AIE 2021), online, Vol. 12798, pp.28-40 (July 2021).
  20. Dao, M.S., Zettsu, K., Kiran, R.U.: IMAGE-2-AQI: Aware of the Surrounding Air Qualification by a few Images, 34th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2021), online, pp.335-346 (July 2021).
  21. Ravikumar, P., Likhitha, P., Raj, B.V.V., Kiran,R.U., Watanobe, Y., Zettsu, K.: Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases, Electronics, Vol. 10, pp.1478 (June 2021).
  22. Nguyen, H.D., Nguyen, T.T.L., Nguyen, M.T., Nguyen, T.B., Dao, M.S.: MNR-Air: An Economic and Dynamic Crowdsourcing Mechanism to Collect Personal Lifelog and Surrounding Environment Dataset. A Case Study in Ho Chi Minh City, Vietnam, the 27th International Conference on Multimedia Modeling (MMM), online, Vol. 12573, pp.206-217 (June 2021).