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Outputs (315)

A novel statistical analysis and autoencoder driven intelligent intrusion detection approach (2019)
Journal Article
Ieracitano, C., Adeel, A., Morabito, F. C., & Hussain, A. (2020). A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing, 387, 51-62. https://doi.org/10.1016/j.neucom.2019.11.016

In the current digital era, one of the most critical and challenging issues is ensuring cybersecurity in information technology (IT) infrastructures. With significant improvements in technology, hackers have been developing ever more complex and dang... Read More about A novel statistical analysis and autoencoder driven intelligent intrusion detection approach.

Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data (2019)
Journal Article
Ma, F., Gao, F., Sun, J., Zhou, H., & Hussain, A. (2019). Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data. Remote Sensing, 11(21), 2586. https://doi.org/10.3390/rs11212586

The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learni... Read More about Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data.

A non-parametric softmax for improving neural attention in time-series forecasting (2019)
Journal Article
Totaro, S., Hussain, A., & Scardapane, S. (2020). A non-parametric softmax for improving neural attention in time-series forecasting. Neurocomputing, 381, 177-185. https://doi.org/10.1016/j.neucom.2019.10.084

Neural attention has become a key component in many deep learning applications, ranging from machine translation to time series forecasting. While many variations of attention have been developed over recent years, all share a common component in the... Read More about A non-parametric softmax for improving neural attention in time-series forecasting.

A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks (2019)
Journal Article
Dashtipour, K., Gogate, M., Li, J., Jiang, F., Kong, B., & Hussain, A. (2020). A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks. Neurocomputing, 380, 1-10. https://doi.org/10.1016/j.neucom.2019.10.009

Social media hold valuable, vast and unstructured information on public opinion that can be utilized to improve products and services. The automatic analysis of such data, however, requires a deep understanding of natural language. Current sentiment... Read More about A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks.

Style-Neutralized Pattern Classification Based on Adversarially Trained Upgraded U-Net (2019)
Journal Article
Jiang, H., Huang, K., Zhang, R., & Hussain, A. (2021). Style-Neutralized Pattern Classification Based on Adversarially Trained Upgraded U-Net. Cognitive Computation, 13(4), 845-858. https://doi.org/10.1007/s12559-019-09660-0

Traditional machine learning approaches usually hold the assumption that data for model training and in real applications are created following the identical and independent distribution (i.i.d.). However, several relevant research topics have demons... Read More about Style-Neutralized Pattern Classification Based on Adversarially Trained Upgraded U-Net.

Lip-reading driven deep learning approach for speech enhancement (2019)
Journal Article
Adeel, A., Gogate, M., Hussain, A., & Whitmer, W. M. (2021). Lip-reading driven deep learning approach for speech enhancement. IEEE Transactions on Emerging Topics in Computational Intelligence, 5(3), 481-490. https://doi.org/10.1109/tetci.2019.2917039

This paper proposes a novel lip-reading driven deep learning framework for speech enhancement. The approach leverages the complementary strengths of both deep learning and analytical acoustic modeling (filtering-based approach) as compared to benchma... Read More about Lip-reading driven deep learning approach for speech enhancement.

Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments (2019)
Journal Article
Adeel, A., Gogate, M., & Hussain, A. (2020). Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments. Information Fusion, 59, 163-170. https://doi.org/10.1016/j.inffus.2019.08.008

Human speech processing is inherently multi-modal, where visual cues (e.g. lip movements) can help better understand speech in noise. Our recent work [1] has shown that lip-reading driven, audio-visual (AV) speech enhancement can significantly outper... Read More about Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments.

Integrated GANs: Semi-Supervised SAR Target Recognition (2019)
Journal Article
Gao, F., Liu, Q., Sun, J., Hussain, A., & Zhou, H. (2019). Integrated GANs: Semi-Supervised SAR Target Recognition. IEEE Access, 7, 113999-114013. https://doi.org/10.1109/access.2019.2935167

With the advantage of working in all weathers and all days, synthetic aperture radar (SAR) imaging systems have a great application value. As an efficient image generation and recognition model, generative adversarial networks (GANs) have been applie... Read More about Integrated GANs: Semi-Supervised SAR Target Recognition.

A Semi-Supervised Synthetic Aperture Radar (SAR) Image Recognition Algorithm Based on an Attention Mechanism and Bias-Variance Decomposition (2019)
Journal Article
Gao, F., Shi, W., Wang, J., Hussain, A., & Zhou, H. (2019). A Semi-Supervised Synthetic Aperture Radar (SAR) Image Recognition Algorithm Based on an Attention Mechanism and Bias-Variance Decomposition. IEEE Access, 7, 108617-108632. https://doi.org/10.1109/access.2019.2933459

Synthetic Aperture Radar (SAR) target recognition is an important research direction of SAR image interpretation. In recent years, most of machine learning methods applied to SAR target recognition are supervised learning which requires a large numbe... Read More about A Semi-Supervised Synthetic Aperture Radar (SAR) Image Recognition Algorithm Based on an Attention Mechanism and Bias-Variance Decomposition.