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A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers (2020)
Journal Article
Ieracitano, C., Paviglianiti, A., Campolo, M., Hussain, A., Pasero, E., & Carlo Morabito, F. (2021). A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers. IEEE/CAA Journal of Autom

The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope ( SEM ) images of the electrospun nanofiber, to ensure that no structural defects are produced. The... Read More about A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers.

Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding (2020)
Journal Article
Chen, R., Yu, Y., Chen, J., Zhong, Y., Zhao, H., Hussain, A., & Tan, H. (2020). Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding. Sensors, 20(17), Article 4926. https://doi.org/10.339

With the development of commodity economy, the emergence of fake and shoddy products has seriously harmed the interests of consumers and enterprises. To tackle this challenge, customized 2D barcode is proposed to satisfy the requirements of the enter... Read More about Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding.

A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System (2020)
Journal Article
Farah, L., Hussain, A., Kerrouche, A., Ieracitano, C., Ahmad, J., & Mahmud, M. (2020). A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System. IEEE Access, 8, 163225-163237. h

Most conventional Fuzzy Logic Controller ( FLC ) rules are based on the knowledge and experience of expert operators: given a specific input, FLCs produce the same output. However, FLCs do not perform very well when dealing with complex problems that... Read More about A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System.

Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images (2020)
Journal Article
Gao, F., He, Y., Wang, J., Hussain, A., & Zhou, H. (2020). Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images. Remote Sensing, 12(16), Article 2619. https://doi.org/10.3390/rs12162619

In recent years, with the improvement of synthetic aperture radar (SAR) imaging resolution, it is urgent to develop methods with higher accuracy and faster speed for ship detection in high-resolution SAR images. Among all kinds of methods, deep-learn... Read More about Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images.

A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation (2020)
Journal Article
Yue, Z., Gao, F., Xiong, Q., Wang, J., Hussain, A., & Zhou, H. (2020). A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,

As an important step of synthetic aperture radar image interpretation, synthetic aperture radar image segmentation aims at segmenting an image into different regions in terms of homogeneity. Because of the deficiency of the labeled samples and the ex... Read More about A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation.

Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning (2020)
Journal Article
Xiong, F., Liu, Z., Huang, K., Yang, X., Qiao, H., & Hussain, A. (2020). Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning. Neural Networks, 129, 163-173. https://doi.org/

Continual learning, a widespread ability in people and animals, aims to learn and acquire new knowledge and skills continuously. Catastrophic forgetting usually occurs in continual learning when an agent attempts to learn different tasks sequentially... Read More about Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning.

Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes (2020)
Journal Article
Zhong, X., Cambria, E., & Hussain, A. (2020). Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes. Cognitive Computation, 12, 844-862. https://doi.org/10.1007/s12559-020-09714-8

Time expressions and named entities play important roles in data mining, information retrieval, and natural language processing. However, the conventional position-based tagging schemes (e.g., the BIO and BILOU schemes) that previous research used to... Read More about Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes.

CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement (2020)
Journal Article
Gogate, M., Dashtipour, K., Adeel, A., & Hussain, A. (2020). CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement. Information Fusion, 63, 273-285. https://doi.org/10.1016/j.inffus.2020.04.001

Noisy situations cause huge problems for the hearing-impaired, as hearing aids often make speech more audible but do not always restore intelligibility. In noisy settings, humans routinely exploit the audio-visual (AV) nature of speech to selectively... Read More about CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement.

Novel deep neural network based pattern field classification architectures (2020)
Journal Article
Huang, K., Zhang, S., Zhang, R., & Hussain, A. (2020). Novel deep neural network based pattern field classification architectures. Neural Networks, 127, 82-95. https://doi.org/10.1016/j.neunet.2020.03.011

Field classification is a new extension of traditional classification frameworks that attempts to utilize consistent information from a group of samples (termed fields). By forgoing the independent identically distributed (i.i.d.) assumption, field c... Read More about Novel deep neural network based pattern field classification architectures.

BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework (2020)
Journal Article
Howard, N., Chouikhi, N., Adeel, A., Dial, K., Howard, A., & Hussain, A. (2020). BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework. Frontiers in Computational Neuroscience, 14, https://doi.org/10.3389/fncom.2020.00016

Human intelligence is constituted by a multitude of cognitive functions activated either directly or indirectly by external stimuli of various kinds. Computational approaches to the cognitive sciences and to neuroscience are partly premised on the id... Read More about BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework.

Design and evaluation of a biologically-inspired cloud elasticity framework (2020)
Journal Article
Ullah, A., Li, J., & Hussain, A. (2020). Design and evaluation of a biologically-inspired cloud elasticity framework. Cluster Computing, 23, 3095-3117. https://doi.org/10.1007/s10586-020-03073-7

The elasticity in cloud is essential to the effective management of computational resources as it enables readjustment at runtime to meet application demands. Over the years, researchers and practitioners have proposed many auto-scaling solutions usi... Read More about Design and evaluation of a biologically-inspired cloud elasticity framework.

Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications (2020)
Journal Article
Tanoli, S. A. K., Shah, S. A., Khan, M. B., Nawaz, F., Hussain, A., Al-Dubai, A. Y., Khan, I., Shah, S. Y., & Alsarhan, A. (2020). Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications. IEEE Ac

Wireless communication using existing coding models poses several challenges for RF signals due to multipath scattering, rapid fluctuations in signal strength and path loss effect. Unlike existing works, this study presents a novel cod... Read More about Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications.

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 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.

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.