SONAIN JAMIL

Mr. SONAIN JAMIL

University of Technology Sydney, Sydney, New South Wales, Australia
orcid

Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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25 April 2023 in Drones
Verified
Journal Article
Open access
PDF file
SJSONAIN JAMIL
SONAIN JAMIL
Md. Jalil PiranMd. Jalil Piran
Md. Jalil Piran
OKOh-Jin Kwon
Oh-Jin Kwon
As a special type of transformer, vision transformers (ViTs) can be used for various computer vision (CV) applications. Convolutional neural networks (CNNs) have several potential problems that can be resolved with ViTs. For image coding tasks such as compression, super-resolution, segmentation, and denoising, different variants of ViTs are used. In our survey, we determined the many CV applications to which ViTs are applicable. CV applications reviewed included image classification, object detection, image segmentation, image compression, image super-resolution, image denoising, anomaly detection, and drone imagery. We reviewed the state of the-art and compiled a list of available models and discussed the pros and cons of each model.


21 July 2023 in BioMedInformatics
Verified
Journal Article
Open access
PDF file
MTMuhammad Turab
Muhammad Turab
SJSONAIN JAMIL
SONAIN JAMIL
Digital twins (DTs) are becoming increasingly popular in various industries, and their potential for healthcare in the metaverse continues to attract attention. The metaverse is a virtual world where individuals interact with digital replicas of themselves and the environment. This paper focuses on personalized and precise medicine and examines the current application of DTs in healthcare within the metaverse. Healthcare practitioners may use immersive virtual worlds to replicate medical scenarios, improve teaching experiences, and provide personalized care to patients. However, the integration of DTs in the metaverse poses technical, regulatory, and ethical challenges that need to be addressed, including data privacy, standards, and accessibility. Through this examination, we aim to provide insights into the transformative potential of DTs in healthcare within the metaverse and encourage further research and development in this exciting domain.

10 May 2023 in Engineering Applications of Artificial Intelligence
Verified
Journal Article
Closed access
SJSONAIN JAMIL
SONAIN JAMIL
Md. Jalil PiranMd. Jalil Piran
Md. Jalil Piran
MRMuhibUr Rahman
MuhibUr Rahman
OKOh-Jin Kwon
Oh-Jin Kwon

13 June 2022 in Applied System Innovation
Verified
Journal Article
Open access
PDF file
SJSONAIN JAMIL
SONAIN JAMIL
MRMuhibUr Rahman
MuhibUr Rahman
FFFawad Fawad
Fawad Fawad
As a result of the advancement in the fourth industrial revolution and communication technology, the use of digital twins (DT) and federated learning (FL) in the industrial Internet of Things (IIoT), the Internet of Vehicles (IoV), and the Internet of Drones (IoD) is increasing. However, the deployment of DT and FL for IoV is challenging. In this survey, we focus on DT and FL for IIoT, IoV, and IoD. Initially, we analyzed the existing surveys. In this paper, we present the applications of DT and FL in IIoT, IoV, and IoD. We also present the open research issues and future directions.

15 July 2020 in Sensors
Verified
Journal Article
Open access
PDF file
SJSONAIN JAMIL
SONAIN JAMIL
FFFawad Fawad
Fawad Fawad
MRMuhibUr Rahman
MuhibUr Rahman
AUAmin Ullah
Amin Ullah
SBSalman Badnava
Salman Badnava
SMSeyed Sajad Mirjavadi
Seyed Sajad Mirjavadi
Unmanned aerial vehicles (UAVs) have become popular in surveillance, security, and remote monitoring. However, they also pose serious security threats to public privacy. The timely detection of a malicious drone is currently an open research issue for security provisioning companies. Recently, the problem has been addressed by a plethora of schemes. However, each plan has a limitation, such as extreme weather conditions and huge dataset requirements. In this paper, we propose a novel framework consisting of the hybrid handcrafted and deep feature to detect and localize malicious drones from their sound and image information. The respective datasets include sounds and occluded images of birds, airplanes, and thunderstorms, with variations in resolution and illumination. Various kernels of the support vector machine (SVM) are applied to classify the features. Experimental results validate the improved performance of the proposed scheme compared to other related methods.