All Outputs (21)

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.

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.

Structure Estimation of Adversarial Distributions for Enhancing Model Robustness: A Clustering-Based Approach (2023)
Journal Article
Rasheed, B., Khan, A., & Masood Khattak, A. (2023). Structure Estimation of Adversarial Distributions for Enhancing Model Robustness: A Clustering-Based Approach. Applied Sciences, 13(19), Article 10972. https://doi.org/10.3390/app131910972

In this paper, we propose an advanced method for adversarial training that focuses on leveraging the underlying structure of adversarial perturbation distributions. Unlike conventional adversarial training techniques that consider adversarial example... Read More about Structure Estimation of Adversarial Distributions for Enhancing Model Robustness: A Clustering-Based Approach.

Overhead Based Cluster Scheduling of Mixed Criticality Systems on Multicore Platform (2023)
Journal Article
Ali, A., Khattak, A. M., Iqbal, S., Alfandi, O., Hayat, B., Siddiqi, M. H., & Khan, A. (2023). Overhead Based Cluster Scheduling of Mixed Criticality Systems on Multicore Platform. IEEE Access, 11, 142341-142359. https://doi.org/10.1109/ACCESS.2023.3330973

The cluster-based technique is gaining focus for scheduling tasks of mixed-criticality (MC) real-time multicore systems. In this technique, the cores of the MC system are distributed in groups known as clusters. When all cores are distributed in clus... Read More about Overhead Based Cluster Scheduling of Mixed Criticality Systems on Multicore Platform.

A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification (2022)
Journal Article
Ahmad, M., Ghous, U., Hong, D., Khan, A. M., Yao, J., Wang, S., & Chanussot, J. (2022). A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification. IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society, 60, 1-16. https://doi.org/10.1109/TGRS.2022.3209182

Convolutional neural networks (CNNs) have been extensively studied for hyperspectral image classification (HSIC). However, CNNs are critically attributed to a large number of labeled training samples, which outlays high costs in terms of time and res... Read More about A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification.

Computing on Wheels: A Deep Reinforcement Learning-Based Approach (2022)
Journal Article
Ahsan Kazmi, S. M., Ho, T. M., Nguyen, T. T., Fahim, M., Khan, A., Piran, M. J., & Baye, G. (2022). Computing on Wheels: A Deep Reinforcement Learning-Based Approach. IEEE Transactions on Intelligent Transportation Systems, 23(11), 22535-22548. https://doi.org/10.1109/TITS.2022.3165662

Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation... Read More about Computing on Wheels: A Deep Reinforcement Learning-Based Approach.

Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification (2022)
Journal Article
Ahmad, M., Khan, A. M., Mazzara, M., Distefano, S., Roy, S. K., & Wu, X. (2022). Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 3948-3957. https://doi.org/10.1109/JSTARS.2022.3171586

The nonlinear relation between the spectral information and the corresponding objects (complex physiognomies) makes pixelwise classification challenging for conventional methods. To deal with nonlinearity issues in hyperspectral image classification... Read More about Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification.

Hyperspectral Image Classification - Traditional to Deep Models: A Survey for Future Prospects (2021)
Journal Article
Ahmad, M., Shabbir, S., Roy, S. K., Hong, D., Wu, X., Yao, J., …Chanussot, J. (2022). Hyperspectral Image Classification - Traditional to Deep Models: A Survey for Future Prospects. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 968-999. https://doi.org/10.1109/JSTARS.2021.3133021

Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics, i.e., the nonlinear relation among the... Read More about Hyperspectral Image Classification - Traditional to Deep Models: A Survey for Future Prospects.

Using Proximity Graph Cut for Fast and Robust Instance-Based Classification in Large Datasets (2021)
Journal Article
Protasov, S., & Khan, A. M. (2021). Using Proximity Graph Cut for Fast and Robust Instance-Based Classification in Large Datasets. Complexity, 2021, Article 2011738. https://doi.org/10.1155/2021/2011738

K-nearest neighbours (kNN) is a very popular instance-based classifier due to its simplicity and good empirical performance. However, large-scale datasets are a big problem for building fast and compact neighbourhood-based classifiers. This work pres... Read More about Using Proximity Graph Cut for Fast and Robust Instance-Based Classification in Large Datasets.

A Novel Contract Theory-Based Incentive Mechanism for Cooperative Task-Offloading in Electrical Vehicular Networks (2021)
Journal Article
Kazmi, S. M., Dang, T. N., Yaqoob, I., Manzoor, A., Hussain, R., Khan, A., Hong, C. S., & Salah, K. (2022). A Novel Contract Theory-Based Incentive Mechanism for Cooperative Task-Offloading in Electrical Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems, 23(7), 8380-8395. https://doi.org/10.1109/TITS.2021.3078913

The proliferation of compute-intensive services in next-generation vehicular networks will impose an unprecedented computation demand to meet stringent latency and resource requirements. Vehicular edge or fog computing has been a widely adopted solut... Read More about A Novel Contract Theory-Based Incentive Mechanism for Cooperative Task-Offloading in Electrical Vehicular Networks.

Adversarial Reconstruction Loss for Domain Generalization (2021)
Journal Article
Bekkouch, I. E. I., Nicolae, D. C., Khan, A., Kazmi, S. M., Khattak, A. M., & Ibragimov, B. (2021). Adversarial Reconstruction Loss for Domain Generalization. IEEE Access, 9, 42424-42437. https://doi.org/10.1109/ACCESS.2021.3066041

The biggest fear when deploying machine learning models to the real world is their ability to handle the new data. This problem is significant especially in medicine, where models trained on rich high-quality data extracted from large hospitals do no... Read More about Adversarial Reconstruction Loss for Domain Generalization.

A Fast and Compact 3-D CNN for Hyperspectral Image Classification (2020)
Journal Article
Ahmad, M., Khan, A. M., Mazzara, M., Distefano, S., Ali, M., & Sarfraz, M. S. (2022). A Fast and Compact 3-D CNN for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. https://doi.org/10.1109/LGRS.2020.3043710

Hyperspectral images (HSIs) are used in a large number of real-world applications. HSI classification (HSIC) is a challenging task due to high interclass similarity, high intraclass variability, overlapping, and nested regions. The 2-D convolutional... Read More about A Fast and Compact 3-D CNN for Hyperspectral Image Classification.

Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation (2020)
Journal Article
Ramirez Rivera, A., Khan, A., Bekkouch, I. E. I., & Sheikh, T. S. (2022). Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation. IEEE Transactions on Neural Networks and Learning Systems, 33(1), 281-291. https://doi.org/10.1109/TNNLS.2020.3027667

Anomaly detection suffers from unbalanced data since anomalies are quite rare. Synthetically generated anomalies are a solution to such ill or not fully defined data. However, synthesis requires an expressive representation to guarantee the quality o... Read More about Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation.

Multiclass non-randomized spectral-spatial active learning for hyperspectral image classification (2020)
Journal Article
Ahmad, M., Mazzara, M., Raza, R. A., Distefano, S., Asif, M., Sarfraz, M. S., Khan, A. M., & Sohaib, A. (2020). Multiclass non-randomized spectral-spatial active learning for hyperspectral image classification. Applied Sciences, 10(14), Article 4739. https://doi.org/10.3390/app10144739

Active Learning (AL) for Hyperspectral Image Classification (HSIC) has been extensively studied. However, the traditional AL methods do not consider randomness among the existing and new samples. Secondly, very limited AL research has been carried ou... Read More about Multiclass non-randomized spectral-spatial active learning for hyperspectral image classification.

Learning Fair Representations through Uniformly Distributed Sensitive Attributes
Presentation / Conference Contribution
Kenfack, P., Rivera, A., Khan, A., & Mazzara, M. (2023, February). Learning Fair Representations through Uniformly Distributed Sensitive Attributes. Presented at 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), Raleigh, NC, USA

Machine Learning (ML) models trained on biased data can reproduce and even amplify these biases. Since such models are deployed to make decisions that can affect people's lives, ensuring their fairness is critical. One approach to mitigate possi... Read More about Learning Fair Representations through Uniformly Distributed Sensitive Attributes.