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A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia (2019)
Journal Article
Ieracitano, C., Mammone, N., Hussain, A., & Morabito, F. C. (2020). A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Networks, 123, 176-190. https://doi.org/10.1016/j.neunet.2019.12.006

Electroencephalographic (EEG) recordings generate an electrical map of the human brain that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things (IoT) and Brain-Computer Interface (BCI) applications. From a signal... Read More about A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia.

Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs (2019)
Journal Article
Alsarhan, A., Kilani, Y., Al-Dubai, A., Zomaya, A. Y., & Hussain, A. (2020). Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs. IEEE Transactions on Vehicular Technology, 69(2), 1568-1581. https://doi.org/10.1109/TVT.2019.2956228

Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintain... Read More about Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs.

A Cognitively Inspired System Architecture for the Mengshi Cognitive Vehicle (2019)
Journal Article
Zhang, X., Zhou, M., Liu, H., & Hussain, A. (2020). A Cognitively Inspired System Architecture for the Mengshi Cognitive Vehicle. Cognitive Computation, 12(1), 140-149. https://doi.org/10.1007/s12559-019-09692-6

This paper introduces the functional system architecture of the Mengshi intelligent vehicle, winner of the 2018 World Intelligent Driving Challenge (WIDC). Different from traditional smart vehicles, a cognitive module is introduced in the system arch... Read More about A Cognitively Inspired System Architecture for the Mengshi Cognitive Vehicle.

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.

A Novel Real-Time, Lightweight Chaotic-Encryption Scheme for Next-Generation Audio-Visual Hearing Aids (2019)
Journal Article
Adeel, A., Ahmad, J., Larijani, H., & Hussain, A. (2020). A Novel Real-Time, Lightweight Chaotic-Encryption Scheme for Next-Generation Audio-Visual Hearing Aids. Cognitive Computation, 12, 589-601. https://doi.org/10.1007/s12559-019-09653-z

Next-generation audio-visual (AV) hearing aids stand as a major enabler to realize more intelligible audio. However, high data rate, low latency, low computational complexity, and privacy are some of the major bottlenecks to the successful deployment... Read More about A Novel Real-Time, Lightweight Chaotic-Encryption Scheme for Next-Generation Audio-Visual Hearing Aids.

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.

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.

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/10.110

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.

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data (2019)
Journal Article
Feng, M., Zheng, J., Ren, J., Hussain, A., Li, X., Xi, Y., & Liu, Q. (2019). Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data. IEEE Access, 7, 106111-106123. https://doi.org/10.1109/access.2019.2930410

Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for vis... Read More about Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data.

A Novel Multi-Input Bidirectional LSTM and HMM Based Approach for Target Recognition from Multi-Domain Radar Range Profiles (2019)
Journal Article
Gao, F., Huang, T., Wang, J., Sun, J., Hussain, A., & Zhou, H. (2019). A Novel Multi-Input Bidirectional LSTM and HMM Based Approach for Target Recognition from Multi-Domain Radar Range Profiles. Electronics, 8(5), https://doi.org/10.3390/electronics80505

Radars, as active detection sensors, are known to play an important role in various intelligent devices. Target recognition based on high-resolution range profile (HRRP) is an important approach for radars to monitor interesting targets. Traditional... Read More about A Novel Multi-Input Bidirectional LSTM and HMM Based Approach for Target Recognition from Multi-Domain Radar Range Profiles.

Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization (2019)
Journal Article
Taha, T. M., Wajid, S. K., & Hussain, A. (2019). Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization. Journal of Computer Science, 15(5), 691-701. https://doi.org/10.3844/jcssp.2019.691.701

Speech enhancement is used in almost all modern communication systems. This is due to the quality of speech being degraded by environmental interference factors, such as: Acoustic additive noise, acoustic reverberation or white Gaussian noise. This p... Read More about Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization.

A novel visual attention method for target detection from SAR images (2019)
Journal Article
Gao, F., Liu, A., Liu, K., Yang, E., & Hussain, A. (2019). A novel visual attention method for target detection from SAR images. Chinese Journal of Aeronautics, 32(8), 1946-1958. https://doi.org/10.1016/j.cja.2019.03.021

Synthetic Aperture Radar (SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for no... Read More about A novel visual attention method for target detection from SAR images.

Computational and natural language processing based studies of hadith literature: a survey (2019)
Journal Article
Azmi, A. M., Al-Qabbany, A. O., & Hussain, A. (2019). Computational and natural language processing based studies of hadith literature: a survey. Artificial Intelligence Review, 52(2), 1369-1414. https://doi.org/10.1007/s10462-019-09692-w

Hadith is one of the most celebrated resources of Classical Arabic text. The hadiths, or Prophetic traditions (tradition for short), are narrations originating from the sayings and conduct of Prophet Muhammad. For Muslims, hadiths are the second most... Read More about Computational and natural language processing based studies of hadith literature: a survey.

Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations (2019)
Journal Article
Chouikhi, N., Ammar, B., Hussain, A., & Alimi, A. M. (2019). Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations. Neurocomputing, 341, 195-211. https://doi.org/10.1016/j.neucom.2019.03.012

The Multi-Layered Echo-State Network (ML-ESN) is a recently developed, highly powerful type of recurrent neural network. It has succeeded in dealing with several non-linear benchmark problems. On account of its rich dynamics, ML-ESN is exploited in t... Read More about Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations.

Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF (2019)
Journal Article
Ma, F., Gao, F., Sun, J., Zhou, H., & Hussain, A. (2019). Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF. Remote Sensing, 11(5), Article 512. https://doi.org/10.3390/rs11050512

Synthetic aperture radar (SAR) image segmentation aims at generating homogeneous regions from a pixel-based image and is the basis of image interpretation. However, most of the existing segmentation methods usually neglect the appearance and spatial... Read More about Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF.

A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection (2019)
Journal Article
Khan, F. A., Gumaei, A., Derhab, A., & Hussain, A. (2019). A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection. IEEE Access, 7, 30373-30385. https://doi.org/10.1109/access.2019.2899721

The network intrusion detection system is an important tool for protecting computer networks against threats and malicious attacks. Many techniques have recently been proposed; however, these techniques face significant challenges due to the continuo... Read More about A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection.