Kyushu University [Daisuke Ikeda (Associate Professor) Faculty of Information Science and Electrical Engineering, Department of Informatics]

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Daisuke Ikeda
Associate Professor
Intelligence Science
Department of Informatics
Faculty of Information Science and Electrical Engineering
Last modified date：2024.04.25

Graduate School

Department of Information Science and Technology
Graduate School of Information Science and Electrical Engineering

Department of Library Science
Graduate School of Integrated Frontier Sciences

Department of informatics
Graduate School of Information Science and Electrical Engineering

Department of Informatics
Graduate School of Information Science and Electrical Engineering

Undergraduate School

Department of Electrical Engineering and Computer Science
School of Engineering

Department of Physics
School of Sciences

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Library
International Center for Space and Planetary Environmental Science
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Country of degree conferring institution (Overseas)

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ORCID(Open Researcher and Contributor ID)

Total Priod of education and research career in the foreign country

Outline Activities

Research

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1.	Daisuke Ikeda, Kun Qian, Kengo Nawata , A Theoretical Framework for Disruptive Changes Based on Information Dissemination , IIAI Letters on Institutional Research , 2023.08.
2.	Daisuke Ikeda, Einoshin Suzuki, Finding Peculiar Compositions of Two Frequent Strings with Background Texts, Journal of Knowledge and Information Systems, 10.1007/s10115-013-0688-9, 2013.09.
3.	Daisuke Ikeda, Mining Infrequent Patterns of Two Frequent Substrings from a Single Set of Biological Sequences, Proceedings of the 2013 International Conference on Parallel and Distributed Processing Techniques and Applications, 1, 136-142, 2013.07.
4.	Daisuke Ikeda, Osamu Maruyama, Satoru Kuhara, Infrequent, Unexpected, and Contrast Pattern Discovery from Bacterial Genomes by Genome-wide Comparat ive Analysis, 4th International Conference on Bioinformatics Models, Methods and Algorithms, 308-311, 2013.02.
5.	Daisuke Ikeda, Kota Sakoda, Tetsuya Oishi, Sozo Inoue, Requirement Analysis for Systems Supporting Research Communications in Various Disciplines, 2010.07, [URL].
6.	Daisuke Ikeda and Einoshin Suzuki, Mining Peculiar Compositions of Frequent Substrings from Sparse Text Data Using Background Texts, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) Part I Lecture Notes in Artificial Intelligence 5781, 596-611, 2009.09.
7.	Daisuke Ikeda and Sozo Inoue, Access Flows to a Repository from Other Services, The 4th International Conference on Open Repositories, 2009.05.
8.	Daisuke Ikeda and Sozo Inoue, A Sustainable Model based on the Social Network Service to Support the Research Cycle, The 3rd International Conference on Open Repositories,, 2008.04.
9.	Daisuke Ikeda and Sozo Inoue, A New, Sustainable Model for the Institutional Repository: A CSI Project "Integration and Presentation of Diverse Information Resources", DRF International Conference Open Access and Institutional Repository in Asia-Pacific, p58, 2008.01.

1.	Linshuo Yang, Daisuke Ikeda, The Impact of Language Properties in Multilingual Datasets on Sarcasm Detection, 16th International Conference on E-Service and Knowledge Management, 2022.09.
2.	Daisuke Ikeda , A Theoretical Background for DX Based on Information Dissemination, 2nd International Symposium on Applied Informatics and Innovations, 2022.09.
3.	Daisuke Ikeda, A STEM WORKSHOP FOR KIDS IN EARLY ELEMENTARY SCHOOL GRADES ABOUT SAMPLING THEOREM OF INFORMATION THEORY , 18th annual International Technology, Education and Development Conference (INTED2024), 2019.03.
4.	Daisuke Ikeda, Text Mining with Variety and Frequency, 第28回MRS日本年次大会国際ワークショップ「先端プラズマ技術が拓くナノマテリアルズフロンティア」, 2018.12, The recent progress of information technologies enables researchers to use such a technology in their disciplines. In particular, machine learning have attracted great attention on account of recent great successes of deep learning. Because major machine learning frameworks assume definite criteria for learning, they are useful for well-defined problems. On the other hand, typical data mining approaches try to find interesting patterns in data, and thus it is useful to exploratively find a hypothesis. Compared to machine learning, which has its solid theoretical background, data mining does not have a unified framework and many heuristics methods have been developed depending on target problems. For example, counting of target data, such as words in texts, is a basic method, but it is difficult to find interesting patterns because frequent ones are obvious and infrequent are abundant. In this talk, a simple framework is proposed, where some variety is introduced in addition to frequency of data, and it can lead finding interesting patterns, such as regions related to horizontal gene transfer in DNA sequences without knowledge of this domain..
5.	Daisuke Ikeda, EXTEND LECTURES FOR KIDS ABOUT INFORMATION INTO SAMPLING THEOREM AND THE FOURIER TRANSFORM , 13th annual International Technology, Education and Development Conference (INTED2019), 2019.03.
6.	Daisuke Ikeda, Toward Improving Research Processes using Big Data, 2nd Asia-Pacific Conference on Plasma Physics, 2018.11, マテリアル科学において、様々な条件で実験を行い、最適なパラメータを決定する必要がある。しかし、一回の実験には多大なコストと時間がかかるため、ICTを用いてこれをサポートすることが望まれている。本発表では、ガウス過程と呼ばれる非線形回帰の手法を用いて、最適な実験パラメータを見つける手段を説明する。.
7.	Daisuke Ikeda, LESSONS ON INFORMATION IN TERMS OF THE FOURIER TRANSFORM FOR PRIMARY-AGED STUDENTS, 12th annual International Technology, Education and Development Conference (INTED2019), 2019.03.
8.	Daisuke Ikeda, Mining Infrequent Patterns of Two Frequent Substrings from a Single Set of Biological Sequences, 2013 International Conference on Parallel and Distributed Processing Techniques and Applications, 2013.07.
9.	Daisuke Ikeda, Osamu Maruyama, Satoru Kuhara, Infrequent, Unexpected, and Contrast Pattern Discovery from Bacterial Genomes by Genome-wide Comparat ive Analysis, 4th International Conference on Bioinformatics Models, Methods and Algorithms, 2013.02.