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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

This tutorial describes a simple method to construct a deep learning algorithm for performing 2-class sequence classification of metagenomic...

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

We present a protocol for a behavioral analysis of adults (ages 18 to 70-year-old) engaged in learning processes, undertaking tasks designed for Self-Regulated Learning (SRL). The participants, university teachers and students, and adults from the University of Experience, were monitored with eye-tracking devices and the data were analyzed with data-mining...

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Here, we present a protocol for the behavioral analysis of a project-based learning methodology for health sciences students (20-56 years old). The protocol facilitates the comparison of the participants' performance in e-Learning versus blended-Learning (b-Learning) through a monitoring tool. The results are analyzed using Educational Data Mining and qualitative...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to...

Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation

Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation

Electrochemical impedance spectroscopy (EIS) of species that undergo reversible oxidation or reduction in solution was used for determination of rate constants of oxidation or...

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

An object segmentation protocol for orbital computed tomography (CT) images is introduced. The methods of labeling the ground truth of orbital structures by using super-resolution, extracting the volume of interest from CT images, and modeling multi-label segmentation using 2D sequential U-Net for orbital CT images are explained for supervised...

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Updated: Jan 20, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

機械学習支援型2次元材料研究における歴史的データマイニングの深掘り：電気化学的応用

Krittapong Deshsorn^1,2, Panwad Chavalekvirat^1,2, Somrudee Deepaisarn^3,2

¹School of Bio-Chemical Engineering and Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand.

ACS materials Au

|January 19, 2026

まとめ

機械学習は、エネルギー応用のための2次元材料の発見と最適化を加速します。このレビューは、トレンドを分析し、機械学習、密度汎関数理論、実験が材料科学をどのように進歩させるかを強調しています。

キーワード:

2次元材料 KDD 変換データマイニングデータサイエンス電気化学エネルギー機械学習ストレージ

さらに関連する動画

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

関連する実験動画

Last Updated: Jan 20, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

科学分野:

材料科学
計算化学
電気化学

背景:

機械学習（ML）は2次元材料の設計に革命をもたらしています。
MLは、発見、最適化、スクリーニングプロセスを加速します。
電気化学的エネルギー応用における2次元材料へのMLの統合は、成長分野です。

研究の目的:

電気化学的エネルギー応用における2次元材料へのMLの歴史的および進行中の統合をレビューすること。
知識発見データベース（KDD）アプローチを使用してトレンドと洞察を分析すること。
ML、密度汎関数理論（DFT）、および実験の相乗効果を強調すること。

主な方法:

Scopusデータベースからのデータマイニング。
引用、キーワード、トレンドの分析。
マクロおよびミクロスコープの洞察を得るための文献レポートのコンピュータ分析（ヒートマップ、ネットワークグラフ）。

主要な成果:

大規模な文献分析から主要な洞察を特定しました。
重要な材料特性を特定するためのML技術を紹介しました。
ML、DFT、実験による材料科学の共同進歩を実証しました。

結論:

ML、DFT、および従来の実験は、エネルギー応用のための2次元材料の進歩を共同で推進しています。
このレビューは、バッテリー、燃料電池、スーパーキャパシタ、および合成におけるMLアプリケーションの包括的な分析を提供します。
MLは、不可欠な材料特性の同定を強化するために不可欠です。