Mahmudul Hasan Abid

Work place: Electronics and Communication Engineering Discipline, Khulna University, Khulna-9208, Bangladesh



Research Interests: Microwave Technology, Microwave Photonics, Microwave Measurements, Optical Communication, Wireless Communication, Computer systems and computational processes, Computational Learning Theory


Mahmudul Hasan Abid is a student of B.Sc. in Electronics and Communication Engineering, Khulna University, Khulna, Bangladesh. He is currently in the final year of his undergraduate. His research interests include machine learning, IOT, smart grid, microwave communication and digital communication.

Author Articles
Home Occupancy Classification Using Machine Learning Techniques along with Feature Selection

By Abdullah-Al Nahid Niloy Sikder Mahmudul Hasan Abid Rafia Nishat Toma Iffat Ara Talin Lasker Ershad Ali

DOI:, Pub. Date: 8 Jun. 2022

Monitoring systems for electrical appliances have gained massive popularity nowadays. These frameworks can provide consumers with helpful information for energy consumption. Non-intrusive load monitoring (NILM) is the most common method for monitoring a household’s energy profile. This research presents an optimized approach for identifying load needs and improving the identification of NILM occupancy surveillance. Our study suggested implementing a dimensionality reduction algorithm, popularly known as genetic algorithm (GA) along with XGBoost, for optimized occupancy monitoring. This exclusive model can masterly anticipate the usage of appliances with a significantly reduced number of voltage-current characteristics. The proposed NILM approach pre-processed the collected data and validated the anticipation performance by comparing the outcomes with the raw dataset’s performance metrics. While reducing dimensionality from 480 to 238 features, our GA-based NILM approach accomplished the same performance score in terms of accuracy (73%), recall (81%), ROC-AUC Score (0.81), and PR-AUC Score (0.81) like the original dataset. This study demonstrates that introducing GA in NILM techniques can contribute remarkably to reduce computational complexity without compromising performance.

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