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Classification of CoCr-based magnetic thin films via GLCM texture features extracted from EFTEM images and machine learning
Classification of CoCr-based magnetic thin films via GLCM texture features extracted from EFTEM images and machine learning
(DOI: https://doi.org/10.1063/5.0232442)
Image Preprocessing - Splitting into Training and Testing Images
Construction of Gray-level Co-occurrence Matrix (GLCM)
Generate GLCM Texture Features
Train and Test ML models
Data-driven design of Sn-based perovskite solar cells using Machine Learning
Data-driven design of Sn-based perovskite solar cells using Machine Learning
(DOI: https://doi.org/10.1063/5.0177271)
Data pre-processing
Remove duplicate entries
Screen out potential outliers
Feature Encoding
Feature Scaling
Training Machine Learning Regression Models
Evaluating models - MAE, MSE, RMSE, and R2 Score.
Hyper-parameter tuning
RandomizedSearchCV
GridSearchCV
Generating new combination of features
Predict using best performing ML models.
Optical image analysis for graphene layer detection: Enhanced green channel methodology
Optical image analysis for graphene layer detection: Enhanced green channel methodology
(DOI: https://doi.org/10.1016/j.commatsci.2024.113198)
Image pre-processing
Apply filters
Remove background effects
Extract pixel values from Graphene flakes
Analysis of Green channel pixel values
Develop threshold and segment image
Optical image analysis of WSe2 − thresholding for layer detection
Optical image analysis of WSe2 − thresholding for layer detection
(DOI: https://doi.org/10.1016/j.commatsci.2025.113888)
Statistical Analysis on RGB pixel values in WSe2 flakes in optical images
Develop thresholds for mono, bi, tri-layer flakes.
Compare IoU accuracy among red, green, and blue color channels.
Choose the best performing color channel and segment images.
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