Outputs (73)

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.

Visualization of Online Datasets (2017)
Journal Article
Peng, T., & Downie, C. (2017). Visualization of Online Datasets. International Journal of Networked and Distributed Computing, 6(1), 11-23. https://doi.org/10.2991/ijndc.2018.6.1.2

As computing technology advances, computers are being used to orchestrate and advance wide spectrums of commercial and personal life, information visualization becomes even more significant as we immerse ourselves into the era of big data, leading to... Read More about Visualization of Online Datasets.

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.

A novel image encryption based on Lorenz equation, Gingerbreadman chaotic map and S8 permutation (2017)
Journal Article
Khan, F. A., Ahmed, J., Khan, J. S., Ahmad, J., & Khan, M. A. (2017). A novel image encryption based on Lorenz equation, Gingerbreadman chaotic map and S8 permutation. Journal of Intelligent and Fuzzy Systems, 33(6), 3753-3765. https://doi.org/10.3233/JIF

Internet is used as the main source of communication throughout the world. However due to public nature of internet data are always exposed to different types of attacks. To address this issue many researchers are working in this area and proposing d... Read More about A novel image encryption based on Lorenz equation, Gingerbreadman chaotic map and S8 permutation.

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.

Towards graduate employment: exploring student identity through a university-wide employability project (2017)
Journal Article
Smith, S., Smith, C., Taylor-Smith, E., & Fotheringham, J. (2019). Towards graduate employment: exploring student identity through a university-wide employability project. Journal of Further and Higher Education, 43(5), 628-640. https://doi.org/10.1080/03

Students have expectations of their university education leading to graduate careers, with universities investing considerable resources in institution-wide initiatives designed to enhance opportunities for student work placements and work-related le... Read More about Towards graduate employment: exploring student identity through a university-wide employability project.

Fast Millimeter Wave Assisted Beam-Steering for Passive Indoor Optical Wireless Networks (2017)
Journal Article
Torres Vega, M., Koonen, A. M. J., Liotta, A., & Famaey, J. (2018). Fast Millimeter Wave Assisted Beam-Steering for Passive Indoor Optical Wireless Networks. IEEE Wireless Communications Letters, 7(2), 278-281. https://doi.org/10.1109/lwc.2017.2771771

In light of the extreme radio congestion, the time has come to consider the upper parts of the electromagnetic spectrum. Optical beam-steered wireless communications offer great potential for future indoor short-range connectivity, due to virtually u... Read More about Fast Millimeter Wave Assisted Beam-Steering for Passive Indoor Optical Wireless Networks.

A virtual uneven grid-based routing protocol for mobile sink-based WSNs in a smart home system (2017)
Journal Article
Liu, X., & Liu, Q. (2018). A virtual uneven grid-based routing protocol for mobile sink-based WSNs in a smart home system. Personal and Ubiquitous Computing, https://doi.org/10.1007/s00779-017-1093-2

In a non-uniformly distributed network, the dataconcentrating centre equipped with sparse nodes rapidly depletes its battery energy due to the hotspot problem. To solve this problem, a Virtual Uneven Grid-based Routing protocol (VUGR) is proposed in... Read More about A virtual uneven grid-based routing protocol for mobile sink-based WSNs in a smart home system.

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.

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.