Abdul Hamid Rumman

Abdul Hamid Rumman

PhD Student at the Department of Chemical Engineering,Texas A&M University

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Optical image analysis of WSe2 − thresholding for layer detection

Published in Computational Materials Science, 2025

  • Thresholds via GUI: Tuned using RGB channels for different WSe2 flake layers.
  • Red Channel Analysis: Distinctive range and median values noted.
  • Accuracy: Red channel highest at 99 % IoU; green also strong at 94 %.
  • Blue Channel Issue: Overlapping values reduced segmentation effectiveness.

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Classification of CoCr-based magnetic thin films via GLCM texture features extracted from EFTEM images and machine learning

Published in AIP Advances, 2024

  • GLCM (Gray-level Co-occurrence Matrix) based texture features effectively classify CoCr-based magnetic thin films using EFTEM images.
  • LightGBM and ANN models achieved classification accuracies exceeding 85%, with ANN reaching 100% accuracy
  • Image segmentation strategy significantly impacts model performance, with 18 segments proving optimal for the ANN model.
  • SHAP analysis revealed feature importance, highlighting the ANN's ability to utilize a broader range of features for pattern identification.

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Optical image analysis for graphene layer detection: Enhanced green channel methodology

Published in Computational Materials Science, 2024

  • Enhanced green channel thresholding for effective segmentation of graphene layers in optical images with variable lighting.
  • The thresholding can be fine-tuned and adjusted according to the lighting condition.
  • It can detect the subtle variation in contrast at the layer edges.
  • Image analysis shows a universal pattern in the green channel pixel median value.
  • Intersection over union metric (IoU) yielded 94 %, and 89 % accuracy for monolayer and bilayer respectively.

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Data-driven design for enhanced efficiency of Sn-based perovskite solar cells using machine learning

Published in APL Machine Learning, 2023

  • A novel three-step machine learning methodology was developed using 26,000 experimental records to predict perovskite solar cell performance.
  • Random Forest model achieved the best performance (R2 score of 0.70 for PCE) and was used to optimize non-toxic Sn-based perovskite devices.
  • The methodology led to significant PCE improvements in Sn-based devices, increasing by up to 28.35% through architecture and deposition optimizations.
  • Key features identified for optimization included device architecture, dimensionality, and deposition procedures for essential layers.
  • This data-driven approach offers an efficient alternative to traditional methods for designing high-efficiency, sustainable Sn-based perovskite solar cells.

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