All Outputs (25)

IEEE Access Special Section Editorial: Health Informatics for the Developing World (2017)
Journal Article
Qadir, J., Mujeeb-U-Rahman, M., Rehmani, M., Pathan, A.-S., Imran, M., Hussain, A., Rana, R., & 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.2017.2783118

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.

Towards Next-Generation Lip-Reading Driven Hearing-Aids: A preliminary Prototype Demo (2017)
Presentation / Conference Contribution
Adeel, A., Gogate, M., & Hussain, A. (2017, August). Towards Next-Generation Lip-Reading Driven Hearing-Aids: A preliminary Prototype Demo. Presented at 1st International Workshop on Challenges in Hearing Assistive Technology (CHAT 2017), Stockholm, Sweden

Speech enhancement aims to enhance the perceived speech quality and intelligibility in the presence of noise. Classical speech enhancement methods are mainly based on audio only processing which often perform poorly in adverse conditions, where overw... Read More about Towards Next-Generation Lip-Reading Driven Hearing-Aids: A preliminary Prototype Demo.

Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data (2017)
Presentation / Conference Contribution
Abdullah, A., Hussain, A., & Khan, I. (2017, May). Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data. Presented at ICCDA '17: International Conference on Compute and Data Analysis, Lakeland, FL, USA

Globally there has been a dramatic increase in obesity. Thus understanding, predicting and managing obesity has the potential to save lives and billions. Behavioral studies suggest that binging by obese persons is prompted by inflated brain reward ce... Read More about Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data.

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.

Persian Named Entity Recognition (2017)
Presentation / Conference Contribution
Dashtipour, K., Gogate, M., Adeel, A., Algarafi, A., Howard, N., & Hussain, A. (2017, July). Persian Named Entity Recognition. Presented at 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Oxford, UK

Named Entity Recognition (NER) is an important natural language processing (NLP) tool for information extraction and retrieval from unstructured texts such as newspapers, blogs and emails. NER involves processing unstructured text for classification... Read More about Persian Named Entity Recognition.

Machine learning based computer-aided diagnosis of liver tumours (2017)
Presentation / Conference Contribution
Ali, L., Khelil, K., Wajid, S. K., Hussain, Z. U., Shah, M. A., Howard, A., Adeel, A., Shah, A. A., Sudhakar, U., Howard, N., & Hussain, A. (2017, July). Machine learning based computer-aided diagnosis of liver tumours. Presented at 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Oxford, UK

Image processing plays a vital role in the early detection and diagnosis of Hepatocellular Carcinoma (HCC). In this paper, we present a computational intelligence based Computer-Aided Diagnosis (CAD) system that helps medical specialists detect and d... Read More about Machine learning based computer-aided diagnosis of liver tumours.

Formal Ontology Generation by deep machine learning (2017)
Presentation / Conference Contribution
Wang, Y., Valipour, M., Zatarain, O., Gavrilova, M., Hussain, A., Howard, N., & Patel, S. (2017, July). Formal Ontology Generation by deep machine learning. Presented at 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Oxford, UK

An ontology is a taxonomic hierarchy of lexical terms and their syntactic and semantic relations for representing a framework of structured knowledge. Ontology used to be problem-specific and manually built due to its extreme complexity. Based on the... Read More about Formal Ontology Generation by deep machine learning.

Complex-valued computational model of hippocampal CA3 recurrent collaterals (2017)
Presentation / Conference Contribution
Shiva, A., Gogate, M., Howard, N., Graham, B., & Hussain, A. (2017, July). Complex-valued computational model of hippocampal CA3 recurrent collaterals. Presented at 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Oxford, UK

Complex planes are known to simplify the complexity of real world problems, providing a better comprehension of their functionality and design. The need for complex numbers in both artificial and biological neural networks is equally well established... Read More about Complex-valued computational model of hippocampal CA3 recurrent collaterals.

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.

Improve deep learning with unsupervised objective (2017)
Presentation / Conference Contribution
Zhang, S., Huang, K., Zhang, R., & Hussain, A. (2017, November). Improve deep learning with unsupervised objective. Presented at International Conference on Neural Information Processing, Guangzhou, China

We propose a novel approach capable of embedding the unsupervised objective into hidden layers of the deep neural network (DNN) for preserving important unsupervised information. To this end, we exploit a very simple yet effective unsupervised method... Read More about Improve deep learning with unsupervised objective.

A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks (2017)
Journal Article
Anbar, M., Abdullah, R., Al-Tamimi, B. N., & Hussain, A. (2018). A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks. Cognitive Computation, 10(2), 201-214. https://doi.org/10.1007/s12559-017-9519-8

Router advertisement (RA) flooding attack aims to exhaust all node resources, such as CPU and memory, attached to routers on the same link. A biologically inspired machine learning-based approach is proposed in this study to detect RA flooding attack... Read More about A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks.

Semi-supervised learning for big social data analysis (2017)
Journal Article
Hussain, A., & Cambria, E. (2018). Semi-supervised learning for big social data analysis. Neurocomputing, 275, 1662-1673. https://doi.org/10.1016/j.neucom.2017.10.010

In an era of social media and connectivity, web users are becoming increasingly enthusiastic about interacting, sharing, and working together through online collaborative media. More recently, this collective intelligence has spread to many different... Read More about Semi-supervised learning for big social data analysis.

Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution (2017)
Journal Article
Ren, P., Sun, W., Luo, C., & Hussain, A. (2018). Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution. Cognitive Computation, 10(1), 165-178. https://doi.org/10.1007/s12559-017-9512-2

In contrast to the human visual system (HVS) that applies different processing schemes to visual information of different textural categories, most existing deep learning models for image super-resolution tend to exploit an indiscriminate scheme for... Read More about Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution.

A Bayesian Assessment of Real-World Behavior During Multitasking (2017)
Journal Article
Bergmann, J., Fei, J., Green, D., Hussain, A., & Howard, N. (2017). A Bayesian Assessment of Real-World Behavior During Multitasking. Cognitive Computation, 9, 749-757. https://doi.org/10.1007/s12559-017-9500-6

Multitasking is common in everyday life, but its effect on activities of daily living is not well understood. Critical appraisal of performance for both healthy individuals and patients is required. Motor activities during meal preparation were monit... Read More about A Bayesian Assessment of Real-World Behavior During Multitasking.

A novel decision support system for the interpretation of remote sensing big data (2017)
Journal Article
Boulila, W., Farah, I. R., & Hussain, A. (2018). A novel decision support system for the interpretation of remote sensing big data. Earth Science Informatics, 11(1), 31-45. https://doi.org/10.1007/s12145-017-0313-7

Applications of remote sensing (RS) data cover several fields such as: cartography, surveillance, land-use planning, archaeology, environmental studies, resources management, etc. However, the amount of RS data has grown considerably due to the incre... Read More about A novel decision support system for the interpretation of remote sensing big data.

A Review of Sentiment Analysis Research in Chinese Language (2017)
Journal Article
Peng, H., Cambria, E., & Hussain, A. (2017). A Review of Sentiment Analysis Research in Chinese Language. Cognitive Computation, 9(4), 423-435. https://doi.org/10.1007/s12559-017-9470-8

Research on sentiment analysis in English language has undergone major developments in recent years. Chinese sentiment analysis research, however, has not evolved significantly despite the exponential growth of Chinese e-business and e-markets. This... Read More about A Review of Sentiment Analysis Research in Chinese Language.

Dual-branch deep convolution neural network for polarimetric SAR image classification (2017)
Journal Article
Gao, F., Huang, T., Wang, J., Sun, J., Hussain, A., & Yang, E. (2017). Dual-branch deep convolution neural network for polarimetric SAR image classification. Applied Sciences, 7(5), https://doi.org/10.3390/app7050447

The deep convolution neural network (CNN), which has prominent advantages in feature learning, can learn and extract features from data automatically. Existing polarimetric synthetic aperture radar (PolSAR) image classification methods based on the C... Read More about Dual-branch deep convolution neural network for polarimetric SAR image classification.

Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques (2017)
Presentation / Conference Contribution
Wajid, S., Hussain, A., Huang, K., & Boulila, W. (2016, August). Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques. Presented at 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Palo Alto, CA, USA

The novel application of Local Energy-based Shape Histogram (LESH) feature extraction technique was recently proposed for breast cancer diagnosis using mammogram images [22]. This paper extends our original work to apply the LESH technique to detect... Read More about Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques.

Genetic optimization of fuzzy membership functions for cloud resource provisioning (2017)
Presentation / Conference Contribution
Ullah, A., Li, J., Hussain, A., & Shen, Y. (2016, December). Genetic optimization of fuzzy membership functions for cloud resource provisioning. Presented at 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece

The successful usage of fuzzy systems can be seen in many application domains owing to their capabilities to model complex systems by exploiting knowledge of domain experts. Their accuracy and performance are, however, primarily dependent on the desi... Read More about Genetic optimization of fuzzy membership functions for cloud resource provisioning.