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中西 崇文/Takafumi Nakanishi

Takafumi Nakanishi

  • SENIOR FELLOW

Associate Professor, Head of Data Science Department, Musashino University; Senior Researcher, Center for Global Communications, International University of Japan; Visiting Professor, Graduate School, Digital Hollywood University

Areas of Expertise

  • Data mining
  • sensitivity information processing
  • media content analysis

Research Program

Nudge Analysis and Generation Mechanisms Based on Responsible AI and the Development of Data Scientists for Their Social Implementation

Bio

Received his PhD from the Graduate School of Systems and Information Engineering, University of Tsukuba in March 2006. From 2006, he became engaged in research and development of knowledge cluster systems at the National Institute of Information and Communications Technology (NICT). In 2014, he became associate professor and senior research fellow at the Center for Global Communications, International University of Japan, engaged in research and development of mining and data mining methods. In April 2018, he joined the Faculty of Engineering, Musashino University, as associate professor, and has been an associate professor in the university’s Faculty of Data Science since April 2019. He has also been a member of the Study Group on the Utilization of Information in the Distribution and Logistics Field, Ministry of Economy, Trade, and Industry (2015–16); a member of the study group on the impact assessment of ICT intelligent systems, Ministry of Internal Affairs and Communications (2016); and a member of the AI Network Society Promotion Council, Ministry of Internal Affairs and Communications (2016–).


Select Publications

  1. Kato, T. Nakanishi, B. Ahsan, H. Shimauchi, “Time-series topic analysis using singular spectrum transformation for detecting political business cycles,” Journal of Cloud Computing: Advances, Systems and Applications, 10, 21 (2021).
  2. Nakanishi, “Semantic Waveform Model for Similarity Measure by Time-series Variation in Meaning,” in Proceedings of the 3rd IEEE/IIAI International Congress on Applied Information Technology, pp. 382-387 (2021).
  3. Hakii, K. Shimada, T. Nakanishi, R. Okada, K. Matsuda, R. Onishi, K. Takahashi, “Weather Map Prediction Using RGB Metaphorical Feature Extraction for Atmospheric Pressure Patterns,” in Proceedings of 20th IEEE/ACIS International Conference on Computer and Information Science, pp. 21-27 (2021).
  4. Nitta, S. Hagimoto, A. Yanase, T. Nakanishi, R. Okada, V. Sornlertlamvanich, “Finger Character Recognition in Sign Language Using Finger Feature Knowledge Base for Similarity Measure,” in Proceedings of the 3rd IEEE/IIAI International Congress on Applied Information Technology (2020).
  5. Nakanishi, R. Okada, R. Nakahodo, “Kansei Transition Analysis by Time-series Change of Media Content,” in Proceedings of 2020 9th International Congress on Advanced Applied Informatics pp. 422-427 (2020).
  6. Kato, T. Nakanishi, H. Shimauchi, B. Ahsan, “Topic Variation Detection Method for Detecting Political Business Cycles,” in Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, ACM, New York, NY, USA, 85-93 (2019).
  7. Ahsan, S. Kato, T. Nakanishi, H. Shimauchi, “Augmenting Political Data through Generative Adversarial Networks (GANs),” paper presented at the 115th American Political Science Association's Annual Meeting (2019).
  8. Kato, H. Shimauchi, T. Nakanishi, and B. Ahsan, “Detecting Political Business Cycles through the Prime Minister’s Daily Schedule,” Paper presented at the 77th Annual Meeting of the Midwest Political Science Association (2019).
  9. Matsumoto, T. Nakanishi, T. Sakawa, K. Onodera, S. Orimo, H. Kobayashi, “AI-Josyu: Thinking Support System in Class by Real-time Speech Recognition and Keyword Extraction,” EMITTER International Journal of Engineering Technology, vol. 7, no. 1 (2019).
  10. Nakanishi, K. Matsumoto, T. Sakawa, K. Onodera, S. Orimo, H. Kobayashi, “A Class Content Summary Method based on Media-driven Real-time Content Management Framework,” in Proceedings of 2019 8th International Congress on Advanced Applied Informatics, pp. 795-798 (2019).
  11. Okada, T. Nakanishi, Y. Tanaka, Y. Ogasawara, K. Ohashi, “A Time Series Structure Analysis Method of a Meeting Using Text Data and a Visualization Method of State Transitions,” New Generation Computing, vol. 37, pp. 113–137 (2019).
  12. Nakanishi, K. Matsumoto, T. Sakawa, K. Onodera, S. Orimo, H. Kobayashi, “Media-driven Real-time Content Management Framework and its Application to In-Class Thinking Support System,” in Proceedings of 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (2018).
  13. Matsumoto, T. Nakanishi, T. Kitagawa, “Semantic-Dependent Access Log Analysis for Predicting the Demographic Data,” in Proceedings of the 28th International Conference on Information Modelling and Knowledge Bases, Riga, Latvia, pp. 110-129 (2018).
  14. Nakanishi, R. Okada, Y. Tanaka, Y. Ogasawara, K. Ohashi, “A Topic Extraction Method on the Flow of Conversation in Meetings,” in Proceedings of 6th International Congress on Advanced Applied Informatics, pp. 350–355 (2017).
  15. Nakanishi, “A Feature Selection for Comparison among Each Concept and Its Visualization,” ACIS International Journal of Computer & Information Science, vol.17, no.1 (2016).

By This Author

Working Paper Japanese Politics

Time-Series Topic Analysis Using Singular Spectrum Transformation for Detecting Pol...

Sota Kato / Takafumi Nakanishi / Hirokazu Shimauchi / Budrul Ahsan

December 14, 2020

Publications

[ワーキングペーパー] Time-Series Topic Analysis Using Singular Spectrum Transformation for Detecting Political Business Cycles

Time-Series Topic Analysis Using Singular Spectrum Transformation for Detecting Political Business Cycles

By Sota Kato

By Takafumi Nakanishi

By Hirokazu Shimauchi

By Budrul Ahsan

By

Tokyo Foundation for Policy Research

December, 2020

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