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RESEARCH PAPER · Chemical Physics Letters · 2019

Data analysis of multi-dimensional thermophysical properties of liquid substances based on clustering approach of machine learning

Gota Kikugawa, Yuta Nishimura, Koji Shimoyama, Taku Ohara, Tomonaga Okabe, Fumio S. Ohuchi

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42 direct citing works

Data analysis of multi-dimensional thermophysical properties of liquid substances based on clustering approach of machine learningOriginalIntroducing self-organized maps (SOM) as a visualization tool for materials research and education55Correlation Clustering with Overlaps. (c2020)0Crystalline structure and grain boundary identification in nanocrystalline aluminum using K -means clustering7A Processor Selection Method based on Execution Time Estimation for Machine Learning Programs3An Externally-Constrained Ising Clustering Method for Material Informatics5Working Towards an AI-Based Clustering of Airports, in the Effort of Improving Humanitarian Disaster Preparedness2Analysis and Research on the Rehabilitation Effect of Physical Exercise on College Students’ Mental Depression Based on Multidimensional Data Mining4Evaluations of atomic-resolution strain fields using molecular dynamics simulations combined with corrected smoothed particle hydrodynamics18Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence13A comparative study of machine learning approaches for predicting viscosity in Sacran/CNF solutions4Extraction of molecular information from experimental data on liquids using manifold learning2A machine learning approach to predicting resonance frequency in soft magnetic composites2A Causal EWT-LSTM Framework for Anomaly Detection and Localized Reconstruction of Indoor Temperature Time Series in District Heating Buildings1

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  1. Introducing self-organized maps (SOM) as a visualization tool for materials research and education

    Results in Materials · 2019-11-03

    Jimin Qian, Nam Phuong Nguyen, Yutaka Oya, Gota Kikugawa, Tomonaga Okabe, Yue Huang, Fumio S. Ohuchi

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  2. Correlation Clustering with Overlaps. (c2020)

    Unknown journal · 2020-05-27

    Amin Fakhereldine

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  3. Crystalline structure and grain boundary identification in nanocrystalline aluminum using K -means clustering

    Modelling and Simulation in Materials Science and Engineering · 2020-06-17

    Nicolás Amigó

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  4. A Processor Selection Method based on Execution Time Estimation for Machine Learning Programs

    IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) · 2021-06-01

    Kou Murakami, Kazuhiko Komatsu, Masayuki Sato, Hiroaki Kobayashi

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  5. An Externally-Constrained Ising Clustering Method for Material Informatics

    International Symposium on Computing and Networking Workshops (CANDARW) · 2021-11-01

    Kazuhiko Komatsu, Masahito Kumagai, Ji Qi, Masayuki Sato, Hiroaki Kobayashi

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  6. Working Towards an AI-Based Clustering of Airports, in the Effort of Improving Humanitarian Disaster Preparedness

    Lecture notes in networks and systems · 2022-01-01

    Maria Browarska, Karla Saldaña Ochoa

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  7. Analysis and Research on the Rehabilitation Effect of Physical Exercise on College Students’ Mental Depression Based on Multidimensional Data Mining

    Occupational Therapy International · 2022-07-13

    Wen Zou

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  8. Evaluations of atomic-resolution strain fields using molecular dynamics simulations combined with corrected smoothed particle hydrodynamics

    Computational Materials Science · 2023-06-22

    Yoshiaki Kawagoe, Tomonaga Okabe

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  9. Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence

    Trees Forests and People · 2023-09-21

    Gianmarco Goycochea Casas, Juan Rodrigo Baselly Villanueva, Mathaus Messias Coimbra Limeira, Carlos Moreira Miquelino Eleto Torres, Hélio Garcia Leite

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  10. A comparative study of machine learning approaches for predicting viscosity in Sacran/CNF solutions

    Chemical Physics Letters · 2023-12-12

    Tarik Sadat

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  11. Extraction of molecular information from experimental data on liquids using manifold learning

    Journal of Molecular Liquids · 2024-10-15

    Shota Arai, Gota Kikugawa, Takashi Yoshidome

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  12. A machine learning approach to predicting resonance frequency in soft magnetic composites

    Applied Physics A · 2025-12-04

    Tarik Sadat

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  13. A Causal EWT-LSTM Framework for Anomaly Detection and Localized Reconstruction of Indoor Temperature Time Series in District Heating Buildings

    Buildings · 2026-05-23

    Enze Zhou, Minjia Du, Yaning Liu, Yan Wu, Wenxiao Xu

    Read original paper ↗ ·
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