Mohammad Kaosain Akbar

Overview

I am currently pursuing PhD in Computer Science at the University of Calgary, where I conduct research in software engineering under the supervision of Prof. Mahmoud Alfadel.

Before moving into software engineering, my research was primarily focused on artificial intelligence, machine learning, and deep learning for energy systems, particularly energy disaggregation. My work has involved supervised and semi-supervised learning, time-series modelling, electric-vehicle charging data, forecasting, data imputation, and other applied AI methods.

I completed my master's degree in Systems Engineering at Concordia University in Montreal, Canada, under the supervision of Prof. Nizar Bouguila and Prof. Manar Amayri. I previously earned my bachelor's degree in Computer Science and Engineering from North South University in Bangladesh, where my final capstone project was supervised by Prof. Nova Ahmed.


Selected Publications

  1. ResiDualNet: A novel electric vehicle charging data imputation technique to enhance load forecasting accuracy B. M. Fahim, M. K. Akbar, M. Amayri · Building Simulation, 2025
  2. GAF-TCN NILM: A novel approach to non-intrusive load monitoring using image analysis with gramian angular field and temporal convolutional networks M. K. Akbar, M. Amayri, N. Bouguila · IEEE ISIE, 2025
  3. Energy Disaggregation Using Radial Basis Function Neural Networks based on Deep Co Training Architecture M. K. Akbar, M. Amayri, N. Bouguila · IEEE International Conference on Pattern Recognition Systems, 2025
  4. Short-term EV load forecasting using Kolmogorov Arnold Networks B. M. Fahim, M. K. Akbar, M. Amayri · IEEE ISIE, 2025
  5. A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid M. K. Akbar, M. Amayri, N. Bouguila · Building Simulation, 2024
  6. Evaluation of regression models and Bayes-Ensemble Regressor technique for non-intrusive load monitoring M. K. Akbar, M. Amayri, N. Bouguila, B. Delinchant, F. Wurtz · Sustainable Energy, Grids and Networks, 2024

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