All Outputs (12)

UAVs and Blockchain Synergy: Enabling Secure Reputation-based Federated Learning in Smart Cities (2024)
Journal Article
Aqleem Abbas, S. M., Khan, M. A., Boulila, W., Kouba, A., Shahbaz Khan, M., & Ahmad, J. (2024). UAVs and Blockchain Synergy: Enabling Secure Reputation-based Federated Learning in Smart Cities. IEEE Access, 12, 154035-154053. https://doi.org/10.1109/access.2024.3432610

Unmanned aerial vehicles (UAVs) can be used as drones’ edge Intelligence to assist with data collection, training models, and communication over wireless networks. UAV use for smart cities is rapidly growing in various industries, including tracking... Read More about UAVs and Blockchain Synergy: Enabling Secure Reputation-based Federated Learning in Smart Cities.

Chaotic Quantum Encryption to Secure Image Data in Post Quantum Consumer Technology (2024)
Journal Article
Khan, M. S., Ahmad, J., Al-Dubai, A., Pitropakis, N., Ghaleb, B., Ullah, A., Khan, M. A., & Buchanan, W. J. (2024). Chaotic Quantum Encryption to Secure Image Data in Post Quantum Consumer Technology. IEEE Transactions on Consumer Electronics, 70(4), 7087 - 7101. https://doi.org/10.1109/tce.2024.3415411

The rapid advancement in consumer technology has led to an exponential increase in the connected devices, resulting in an enormous and continuous flow of data, particularly the image data. This data needs to be processed, managed, and secured efficie... Read More about Chaotic Quantum Encryption to Secure Image Data in Post Quantum Consumer Technology.

A novel medical image data protection scheme for smart healthcare system (2024)
Journal Article
Rehman, M. U., Shafique, A., Khan, M. S., Driss, M., Boulila, W., Ghadi, Y. Y., Changalasetty, S. B., Alhaisoni, M., & Ahmad, J. (2024). A novel medical image data protection scheme for smart healthcare system. CAAI Transactions on Intelligence Technology, 9(4), 821-836. https://doi.org/10.1049/cit2.12292

The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the... Read More about A novel medical image data protection scheme for smart healthcare system.

A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm (2023)
Journal Article
Yasin Ghadi, Y., Alshehri, M. S., Almakdi, S., Saidani, O., Alturki, N., Masood, F., & Shahbaz Khan, M. (2023). A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm. Computers, Materials & Continua, 77(1), 781-797. https://doi.org/10.32604/cmc.2023.042777

Securing digital image data is a key concern in today’s information-driven society. Effective encryption techniques are required to protect sensitive image data, with the Substitution-box (S-box) often playing a pivotal role in many symmetric encrypt... Read More about A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm.

FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices (2023)
Journal Article
Ahmad, K., Khan, M. S., Ahmed, F., Driss, M., Boulila, W., Alazeb, A., Alsulami, M., Alshehri, M. S., Ghadi, Y. Y., & Ahmad, J. (2023). FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices. Fire Ecology, 19, Article 54. https://doi.org/10.1186/s42408-023-00216-0

Background: Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, b... Read More about FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices.

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants (2023)
Journal Article
Tahir, H., Shahbaz Khan, M., Ahmed, F., M. Albarrak, A., Noman Qasem, S., & Ahmad, J. (2023). Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants. Computers, Materials & Continua, 75(2), 3517-3535. https://doi.org/10.32604/cmc.2023.035410

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent dec... Read More about Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants.

A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder (2023)
Journal Article
Ahmed, F., Rehman, M. U., Ahmad, J., Khan, M. S., Boulila, W., Srivastava, G., Lin, J. C.-W., & Buchanan, W. J. (2023). A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder. ACM transactions on multimedia computing communications and applications, 19(3s), Article 128. https://doi.org/10.1145/3570165

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally... Read More about A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder.

Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques (2022)
Journal Article
Aamir, S., Rahim, A., Aamir, Z., Abbasi, S. F., Khan, M. S., Alhaisoni, M., Khan, M. A., Khan, K., & Ahmad, J. (2022). Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques. Computational and Mathematical Methods in Medicine, 2022, Article 5869529. https://doi.org/10.1155/2022/5869529

Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various com... Read More about Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques.

Machine Learning-Based Prediction of Node Localization Accuracy in IIoT-Based MI-UWSNs and Design of a TD Coil for Omnidirectional Communication (2022)
Journal Article
Gang, Q., Muhammad, A., Khan, Z. U., Khan, M. S., Ahmed, F., & Ahmad, J. (2022). Machine Learning-Based Prediction of Node Localization Accuracy in IIoT-Based MI-UWSNs and Design of a TD Coil for Omnidirectional Communication. Sustainability, 14(15), Article 9683. https://doi.org/10.3390/su14159683

This study aims to realize Sustainable Development Goals (SDGs), i.e., SDG 9: Industry Innovation and Infrastructure and SDG 14: Life below Water, through the improvement of localization estimation accuracy in magneto-inductive underwater wireless se... Read More about Machine Learning-Based Prediction of Node Localization Accuracy in IIoT-Based MI-UWSNs and Design of a TD Coil for Omnidirectional Communication.

Addressing the Directionality Challenge through RSSI-Based Multilateration Technique, to Localize Nodes in Underwater WSNs by Using Magneto-Inductive Communication (2022)
Journal Article
Qiao, G., Muhammad, A., Muzzammil, M., Shoaib Khan, M., Tariq, M. O., & Khan, M. S. (2022). Addressing the Directionality Challenge through RSSI-Based Multilateration Technique, to Localize Nodes in Underwater WSNs by Using Magneto-Inductive Communication. Journal of marine science and engineering, 10(4), Article 530. https://doi.org/10.3390/jmse10040530

The deployment and efficient use of wireless sensor networks (WSNs) in underwater and underground environments persists to be a difficult task. In addition, the localization of a sensor Rx node in WSNs is an important aspect for the successful commun... Read More about Addressing the Directionality Challenge through RSSI-Based Multilateration Technique, to Localize Nodes in Underwater WSNs by Using Magneto-Inductive Communication.

Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset (2021)
Journal Article
Umair, M., Khan, M. S., Ahmed, F., Baothman, F., Alqahtani, F., Alian, M., & Ahmad, J. (2021). Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset. Sensors, 21(17), Article 5813. https://doi.org/10.3390/s21175813

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of... Read More about Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset.

MEMS Sensors for Diagnostics and Treatment in the Fight Against COVID-19 and Other Pandemics (2021)
Journal Article
Khan, M. S., Tariq, M. O., Nawaz, M., & Ahmed, J. (2021). MEMS Sensors for Diagnostics and Treatment in the Fight Against COVID-19 and Other Pandemics. IEEE Access, 9, 61123-61149. https://doi.org/10.1109/access.2021.3073958

As the world is going through an existential global health crisis, i.e., the outbreak of novel coronavirus-caused respiratory disease (Covid-19), the healthcare systems of all the countries require readily available, low cost and highly precise equip... Read More about MEMS Sensors for Diagnostics and Treatment in the Fight Against COVID-19 and Other Pandemics.