Outputs (23)

A comprehensive survey for Hyperspectral Image Classification: The evolution from conventional to transformers and Mamba models (2025)
Journal Article
Ahmad, M., Distefano, S., Khan, A. M., Mazzara, M., Li, C., Li, H., Aryal, J., Ding, Y., Vivone, G., & Hong, D. (2025). A comprehensive survey for Hyperspectral Image Classification: The evolution from conventional to transformers and Mamba models. Neurocomputing, 644, Article 130428. https://doi.org/10.1016/j.neucom.2025.130428

Hyperspectral Image Classification (HSIC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often enc... Read More about A comprehensive survey for Hyperspectral Image Classification: The evolution from conventional to transformers and Mamba models.

DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification (2025)
Journal Article
Ahmad, M., Mazzara, M., Distefano, S., Khan, A. M., & Ullo, S. L. (in press). DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 10419-10428. https://doi.org/10.1109/JSTARS.2025.3558889

Hyperspectral image classification (HSIC) presents significant challenges due to spectral redundancy and spatial discontinuity, both of which can negatively impact classification performance. To mitigate these issues, this work proposes the Different... Read More about DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification.

Spatial-spectral morphological mamba for hyperspectral image classification (2025)
Journal Article
Ahmad, M., Butt, M. H. F., Khan, A. M., Mazzara, M., Distefano, S., Usama, M., Roy, S. K., Chanussot, J., & Hong, D. (online). Spatial-spectral morphological mamba for hyperspectral image classification. Neurocomputing, Article 129995. https://doi.org/10.1016/j.neucom.2025.129995

Recent advancements in transformers, specifically self-attention mechanisms, have significantly improved hyperspectral image (HSI) classification. However, these models often have inefficiencies, as their computational complexity scales quadratically... Read More about Spatial-spectral morphological mamba for hyperspectral image classification.

TOA and TDOA Based Asynchronous Self-Localization: Three Stage Framework for Simultaneous Localization of Microphones and Audio Sources (2025)
Thesis
Cao, F. (2025). TOA and TDOA Based Asynchronous Self-Localization: Three Stage Framework for Simultaneous Localization of Microphones and Audio Sources. (Thesis). University of Hull. https://hull-repository.worktribe.com/output/5086296

Self-localization, a pivotal aspect explored in this research, holds significant relevance across various applications, including human-robot interaction and surveillance for aging individuals. Traditional localization methods relying on GPS signals... Read More about TOA and TDOA Based Asynchronous Self-Localization: Three Stage Framework for Simultaneous Localization of Microphones and Audio Sources.

Multi-head spatial-spectral mamba for hyperspectral image classification (2025)
Journal Article
Ahmad, A., Butt, M. H. F., Usama, M., Altuwaijri, H. A., Mazzara, M., Distefano, S., & Khan, A. M. (2025). Multi-head spatial-spectral mamba for hyperspectral image classification. Remote Sensing Letters, 16(4), 15-29. https://doi.org/10.1080/2150704X.2025.2461330

Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing the limitations of transformers. However, traditional Mamba models often overlook the rich spectral information in hyperspectral images (H... Read More about Multi-head spatial-spectral mamba for hyperspectral image classification.

LLM Based Cross Modality Retrieval to Improve Recommendation Performance (2024)
Presentation / Conference Contribution
Anwaar, F., Khan, A. M., & Khalid, M. (2024, August). LLM Based Cross Modality Retrieval to Improve Recommendation Performance. Presented at 2024 29th International Conference on Automation and Computing (ICAC), Sunderland, UK

The metadata of items and users play an important role in improving the decision-making process in the Recom-mender System. In recent times, web scraping-based techniques have been widely utilized to extract explicit user and item meta-data from diff... Read More about LLM Based Cross Modality Retrieval to Improve Recommendation Performance.

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.