All Outputs (8)

Improving Generalization for Hyperspectral Image Classification: The Impact of Disjoint Sampling on Deep Models (2024)
Journal Article
Ahmad, M., Mazzara, M., Distefano, S., Khan, A. M., & Altuwaijri, H. A. (2024). Improving Generalization for Hyperspectral Image Classification: The Impact of Disjoint Sampling on Deep Models. Computers, Materials & Continua, 81(1), 503-532. https://doi.org/10.32604/cmc.2024.056318

Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art (SOTA) models e.g., Attention Graph and Vision Transformer. When training, validation, and test sets overlap or share data, it introduces a bias that inflates perf... Read More about Improving Generalization for Hyperspectral Image Classification: The Impact of Disjoint Sampling on Deep Models.

Leveraging Deep Reinforcement Learning and Healthcare Devices for Active Travelling in Smart Cities (2024)
Journal Article
Kazmi, S. M. A., Khan, Z., Khan, A., Mazzara, M., & Khattak, A. M. (online). Leveraging Deep Reinforcement Learning and Healthcare Devices for Active Travelling in Smart Cities. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/tce.2024.3470978

Smart cities are increasingly challenged by population growth and the environmental emissions of urban transportation systems, necessitating sustainable urban planning to improve public health, environmental quality, and overall urban livability. A n... Read More about Leveraging Deep Reinforcement Learning and Healthcare Devices for Active Travelling in Smart Cities.

A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study (2024)
Journal Article
Azam, M. M. B., Anwaar, F., Khan, A. M., Anwar, M., Ghani, H. B. A., Eisa, T. A. E., & Abdelmaboud, A. (2024). A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study. Egyptian Informatics Journal, 27, Article 100508. https://doi.org/10.1016/j.eij.2024.100508

Infectious disease is a particular type of disorder triggered by organisms and transmitted directly or indirectly from an infected one like COVID-19. The global economy and public health are immensely affected by COVID-19, a recently emerging infecti... Read More about A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study.

Global Knowledge, Local Impact: Domain Adaptation and Classification for Obesity in the UAE (2024)
Presentation / Conference Contribution
Raza, M., Khattak, A., Abbas, W., & Khan, A. (2024, June). Global Knowledge, Local Impact: Domain Adaptation and Classification for Obesity in the UAE. Presented at 37th IEEE International Symposium on Computer-Based Medical Systems (CBMS), Guadalajara, Mexico

Obesity, a global public health concern, is escalating rapidly, especially in the Middle East, with the United Arab Emirates (UAE) witnessing one of the highest prevalence rates among adults and children. This multifactorial health issue is influence... Read More about Global Knowledge, Local Impact: Domain Adaptation and Classification for Obesity in the UAE.

Spatial Spectral Transformer with Conditional Position Encoding for Hyperspectral Image Classification (2024)
Journal Article
Ahmad, M., Usama, M., Khan, A. M., Distefano, S., Altuwaijri, H. A., & Mazzara, M. (2024). Spatial Spectral Transformer with Conditional Position Encoding for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 1-1. https://doi.org/10.1109/lgrs.2024.3431188

In Transformer-based Hyperspectral Image Classification (HSIC), predefined positional encodings (PEs) are crucial for capturing the order of each input token. However, their typical representation as fixed-dimension learnable vectors makes it challen... Read More about Spatial Spectral Transformer with Conditional Position Encoding for Hyperspectral Image Classification.

Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks (2024)
Journal Article
Garaev, R., Rasheed, B., & Khan, A. M. (2024). Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks. Algorithms, 17, Article 162. https://doi.org/10.3390/a17040162

Deep neural networks (DNNs) have gained prominence in various applications, but remain vulnerable to adversarial attacks that manipulate data to mislead a DNN. This paper aims to challenge the efficacy and transferability of two contemporary defense... Read More about Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks.

Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks (2024)
Journal Article
Rasheed, B., Abdelhamid, M., Khan, A., Menezes, I., & Masood Khatak, A. (2024). Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks. IEEE Access, 12, 131323-131335. https://doi.org/10.1109/ACCESS.2024.3457784

Deep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate intermediate high-level concepts into the model architecture, prom... Read More about Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks.

Pyramid Hierarchical Spatial-Spectral Transformer for Hyperspectral Image Classification (2024)
Journal Article
Ahmad, M., Butt, M. H. F., Mazzara, M., Distefano, S., Khan, A. M., & Altuwaijri, H. A. (2024). Pyramid Hierarchical Spatial-Spectral Transformer for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 17681-17689. https://doi.org/10.1109/jstars.2024.3461851

The Transformer model encounters challenges with variable-length input sequences, leading to efficiency and scalability concerns. To overcome this, we propose a pyramid-based hierarchical Spatial-Spectral Transformer (PyFormer). This innovative appro... Read More about Pyramid Hierarchical Spatial-Spectral Transformer for Hyperspectral Image Classification.