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Galib Muhammad Shahriar Himel

Work place: School of Computer Sciences, Universiti Sains Malaysia, 11800 USM Penang, Malaysia

E-mail: galib.muhammad.shahriar@gmail.com

Website: https://orcid.org/0000-0002-2257-6751

Research Interests: Artificial Intelligence, Computer Vision, Machine Learning

Biography

Galib Muhammad Shahriar Himel received his 1st BSc degree in Computer Science & Engineering from Ahsanullah University of Science and Technology (AUST) in the year 2016. Then he received his 1st MSc degree in Computer Science & Engineering from United International University (UIU) in the year 2018. Then he received his 2nd BSc degree in Computing from the University of Greenwich (UoG), UK in 2021. After that, he received his 2nd MSc degree in Computer Science specializing in Intelligent Systems from American International University-Bangladesh (AIUB) in the year 2022. He has completed his 3rd MSc degree in Applied Physics and Electronics from Jahangirnagar University (JU) in the year 2023. He has worked as a researcher at the Bangladesh University of Business and Technology (BUBT) and also as a part-time researcher at the Independent University, of Bangladesh (IUB). He is also involved in several types of research related to Bio-medical image processing using machine learning. His research interest includes Artificial Intelligence, Machine Learning, Bioinformatics, Bio-medical image analysis & Computer Vision. Currently, He is pursuing his PhD degree at Universiti Sains Malaysia.

Author Articles
Enhanced Surgical Mask Recognition Using EfficientNet Architecture

By Galib Muhammad Shahriar Himel Md Masudul Islam

DOI: https://doi.org/10.5815/ijigsp.2024.05.03, Pub. Date: 8 Oct. 2024

The research article presents a robust solution to detect surgical masks using a combination of deep learning techniques. The proposed method utilizes the SAM to detect the presence of masks in images, while EfficientNet is employed for feature extraction and classification of mask type. The compound scaling method is used to distinguish between surgical and normal masks in the data set of 2000 facial photos, divided into 60% training, 20% validation, and 20% testing sets. The machine learning model is trained on the data set to learn the discriminative characteristics of each class and achieve high accuracy in mask detection. To handle the variability of mask types, the study applies various versions of EfficientNet, and the highest accuracy of 97.5% is achieved using EfficientNetV2L, demonstrating the effectiveness of the proposed method in detecting masks of different complexities and designs.

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