MS. Asiya Batool

Lab Engineer
  • Department of Computer Science

Asiya Batool is a highly accomplished computer science professional who earned her Master of Science degree in Computer Science from the prestigious Pakistan Institute of Engineering and Applied Sciences (PIEAS) in 2021, following the completion of her Bachelor's degree in Software Engineering from the University of Sargodha in 2017. Asiya's industry experience includes working as a Web Developer at INFOSYS Institute of Information Technology in Sargodha in 2015, as well as serving as a graphic designer at QodIT, where she was responsible for creating compelling visual compositions. In 2017, she began her career in academia, teaching at G.C.W. Mianwali for two years before joining the University of Mianwali as a Visiting Lecturer in Computer Science in 2021. Currently, Asiya works as a Lab Engineer in the Department of Computer Science at Namal Mianwali, where she brings her considerable expertise to bear in support of cutting-edge research and development initiatives.

Academic Background
MS Computer Science (Identity Based 3D Face Reconstruction ) PIEAS, Islamabad 2021
BS Software Engineering (3D Game Development ) University of Sargodha, Sargodha 2017
Visiting Lecturer CS University of Mianwali 22-Nov-2021 - 06-Mar-2023
Journal Publications
A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron 13-Feb-2022 The Coronavirus (COVID-19) outbreak in December 2019 has become an ongoing threat to humans worldwide, creating a health crisis that infected millions of lives, as well as devastating the global economy. Deep learning (DL) techniques have proved helpful in analysis and delineation of infectious regions in radiological images in a timely manner. This paper makes an in-depth survey of DL techniques and draws a taxonomy based on diagnostic strategies and learning approaches. DL techniques are systematically categorized into classification, segmentation, and multi-stage approaches for COVID-19 diagnosis at image and region level analysis. Each category includes pre-trained and custom-made Convolutional Neural Network architectures for detecting COVID-19 infection in radiographic imaging modalities; X-Ray, and Computer Tomography (CT). Furthermore, a discussion is made on challenges in developing diagnostic techniques in pandemic, cross-platform interoperability, and examining imaging modality, in addition to reviewing methodologies and performance measures used in these techniques. This survey provides an insight into promising areas of research in DL for analyzing radiographic images and thus, may further accelerate the research in designing of customized DL based diagnostic tools for effectively dealing with new variants of COVID-19 and emerging challenges.