Skip to main content

Research Repository

Advanced Search

All Outputs (304)

A new two-layer mixture of factor analyzers with joint factor loading model for the classification of small dataset problems (2018)
Journal Article
Yang, X., Huang, K., Zhang, R., Goulermas, J., & Hussain, A. (2018). A new two-layer mixture of factor analyzers with joint factor loading model for the classification of small dataset problems. Neurocomputing, 312, 352-363. https://doi.org/10.1016/j.neuc

Dimensionality Reduction (DR) is a fundamental topic of pattern classification and machine learning. For classification tasks, DR is typically employed as a pre-processing step, succeeded by an independent classifier training stage. However, such ind... Read More about A new two-layer mixture of factor analyzers with joint factor loading model for the classification of small dataset problems.

A control theoretical view of cloud elasticity: taxonomy, survey and challenges (2018)
Journal Article
Ullah, A., Li, J., Shen, Y., & Hussain, A. (2018). A control theoretical view of cloud elasticity: taxonomy, survey and challenges. Cluster Computing, 21(4), 1735-1764. https://doi.org/10.1007/s10586-018-2807-6

The lucrative features of cloud computing such as pay-as-you-go pricing model and dynamic resource provisioning (elasticity) attract clients to host their applications over the cloud to save up-front capital expenditure and to reduce the operational... Read More about A control theoretical view of cloud elasticity: taxonomy, survey and challenges.

A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications (2018)
Journal Article
Mahmud, M., Kaiser, M. S., Rahman, M. M., Rahman, M. A., Shabut, A., Al-Mamun, S., & Hussain, A. (2018). A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications. Cognitive Computation, 10(5),

Rapid advancement of Internet of Things (IoT) and cloud computing enables neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable... Read More about A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications.

Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization (2018)
Journal Article
Yang, X., Huang, K., Zhang, R., & Hussain, A. (2018). Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(6), 450-463. https://doi.org/10.1109/tetci.2018.

Nonnegative matrix factorization (NMF) has been widely exploited in many computational intelligence and pattern recognition problems. In particular, it can be used to extract latent features from data. However, previous NMF models often assume a fixe... Read More about Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization.

Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis (2018)
Journal Article
Ma, Y., Peng, H., Khan, T., Cambria, E., & Hussain, A. (2018). Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis. Cognitive Computation, 10(4), 639-650. https://doi.org/10.1007/s12559-018-9549-x

Sentiment analysis has emerged as one of the most popular natural language processing (NLP) tasks in recent years. A classic setting of the task mainly involves classifying the overall sentiment polarity of the inputs. However, it is based on the ass... Read More about Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis.

Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study (2018)
Journal Article
Arafat, S., Aljohani, N., Abbasi, R., Hussain, A., & Lytras, M. (2019). Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study. Computers in Human Behavior, 92,

In this paper we explore the interrelationship between the sociotechnical-pedagogical culture of e-learning, the emerging disciplines of Web science, Social Sensing and that of Cognitive Computation–as an emerging paradigm of computation. We comment... Read More about Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study.

Guided Policy Search for Sequential Multitask Learning (2018)
Journal Article
Xiong, F., Sun, B., Yang, X., Qiao, H., Huang, K., Hussain, A., & Liu, Z. (2019). Guided Policy Search for Sequential Multitask Learning. IEEE Transactions on Systems, Man and Cybernetics: Systems, 49(1), 216-226. https://doi.org/10.1109/tsmc.2018.2800040

Policy search in reinforcement learning (RL) is a practical approach to interact directly with environments in parameter spaces, that often deal with dilemmas of local optima and real-time sample collection. A promising algorithm, known as guided pol... Read More about Guided Policy Search for Sequential Multitask Learning.

Spatial-temporal representatives selection and weighted patch descriptor for person re-identification (2018)
Journal Article
Zheng, A., Wang, F., Hussain, A., Tang, J., & Jiang, B. (2018). Spatial-temporal representatives selection and weighted patch descriptor for person re-identification. Neurocomputing, 290, 121-129. https://doi.org/10.1016/j.neucom.2018.02.039

How to represent the sequential person images is a crucial issue in multi-shot person re-identification. In this paper, we propose to select the spatial-temporal informative representatives to describe the image sequence. Specifically, we address rep... Read More about Spatial-temporal representatives selection and weighted patch descriptor for person re-identification.

Applications of Deep Learning and Reinforcement Learning to Biological Data (2018)
Journal Article
Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018). Applications of Deep Learning and Reinforcement Learning to Biological Data. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2063-2079. https://doi.org/10.1109/tnnls.2018.2

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine... Read More about Applications of Deep Learning and Reinforcement Learning to Biological Data.

A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data (2018)
Journal Article
Abdullah, A., Hussain, A., & Khan, I. H. (2018). A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data. Cognitive Computation, 10(4), 591-609. https://doi.org/10.10

Globally, there has been a dramatic increase in obesity, with prevalence in males and females expected to increase to 18 and 21%, respectively (NCD Risk Factor Collaboration, Lancet 387(10026):1377–96, 2016). However, there are hardly any data-analyt... Read More about A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data.

A Novel Method of Signal Fusion Based on Dimension Expansion (2018)
Journal Article
Zhang, T., Xu, L., Yang, E., Yan, X., Qin, E. A., Wang, Q., & Hussain, A. (2018). A Novel Method of Signal Fusion Based on Dimension Expansion. Circuits, Systems, and Signal Processing, 37(10), 4295-4318. https://doi.org/10.1007/s00034-018-0760-5

A novel method of signal fusion, namely multi-dimensional unified signal (MDUS) fusion algorithm, is proposed based on dimensionality expansion of the cognitive radio (CR). The paper focuses on the issue of under-utilized and overcrowded spectrum ban... Read More about A Novel Method of Signal Fusion Based on Dimension Expansion.

The Fundamental Code Unit of the Brain: Towards a New Model for Cognitive Geometry (2018)
Journal Article
Howard, N., & Hussain, A. (2018). The Fundamental Code Unit of the Brain: Towards a New Model for Cognitive Geometry. Cognitive Computation, 10(3), 426-436. https://doi.org/10.1007/s12559-017-9538-5

This paper discusses the problems arising from the multidisciplinary nature of cognitive research and the need to conceptually unify insights from multiple fields into the phenomena that drive cognition. Specifically, the Fundamental Code Unit (FCU)... Read More about The Fundamental Code Unit of the Brain: Towards a New Model for Cognitive Geometry.

Computational Intelligence Techniques for Mobile Network Optimization [Guest Editorial] (2018)
Journal Article
Qadir, J., Hussain, A., Yau, K., Imran, M., & Wolisz, A. (2018). Computational Intelligence Techniques for Mobile Network Optimization [Guest Editorial]. IEEE Computational Intelligence Magazine, 13(1), 28. https://doi.org/10.1109/MCI.2017.2773799

Modern society has become increasingly reliant on mobile networks for their communication needs. Such networks are characterized by their dynamic, heterogeneous, complex, and data intensive nature, which makes them more amenable to automated mobile n... Read More about Computational Intelligence Techniques for Mobile Network Optimization [Guest Editorial].

IEEE Access Special Section Editorial: Health Informatics for the Developing World (2017)
Journal Article
Qadir, J., Mujeeb-U-Rahman, M., Rehmani, M., Pathan, A., Imran, M., Hussain, A., …Luo, B. (2017). IEEE Access Special Section Editorial: Health Informatics for the Developing World. IEEE Access, 5, 27818-27823. https://doi.org/10.1109/ACCESS.2017.278311

We live in a world with growing disparity in the quality of life available to people in the developed and developing countries. Healthcare in the developing world is fraught with numerous problems such as the lack of health infrastructure, and human... Read More about IEEE Access Special Section Editorial: Health Informatics for the Developing World.

Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique (2017)
Journal Article
Wajid, S. K., Hussain, A., & Huang, K. (2018). Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique. Expert Systems with Applications, 112, 388-400. https://doi.org/10.1016/j.eswa.2017.11.057

In this paper, we present a novel feature extraction technique, termed Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH), and exploit it to detect breast cancer in volumetric medical images. The technique is incorporated as part of an in... Read More about Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique.

Affective Reasoning for Big Social Data Analysis (2017)
Journal Article
Cambria, E., Hussain, A., & Vinciarelli, A. (2017). Affective Reasoning for Big Social Data Analysis. IEEE Transactions on Affective Computing, 8(4), 426-427. https://doi.org/10.1109/TAFFC.2017.2763218

This special section focuses on the introduction, presentation, and discussion of novel techniques that further develop and apply affective reasoning tools and techniques for big social data analysis. A key motivation for this special section, in par... Read More about Affective Reasoning for Big Social Data Analysis.

Learning from Few Samples with Memory Network (2017)
Journal Article
Zhang, S., Huang, K., Zhang, R., & Hussain, A. (2018). Learning from Few Samples with Memory Network. Cognitive Computation, 10(1), 15-22. https://doi.org/10.1007/s12559-017-9507-z

Neural networks (NN) have achieved great successes in pattern recognition and machine learning. However, the success of a NN usually relies on the provision of a sufficiently large number of data samples as training data. When fed with a limited data... Read More about Learning from Few Samples with Memory Network.