Outputs (274)

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.

Quantum Encrypted Signals on Multiuser Optical Fiber Networks: Simulation Analysis of Next Generation Services and Technologies (2017)
Presentation / Conference Contribution
Asif, R. (2017, November). Quantum Encrypted Signals on Multiuser Optical Fiber Networks: Simulation Analysis of Next Generation Services and Technologies. Presented at IEEE Network of the Future (NoF) conference, London, UK

Data encryption is gaining much attention these days from the research community and industry for transmitting secure information over access networks, i.e. 'fiber-to-the-home (FTTH)' networks and data centers. It is important that the newly designed... Read More about Quantum Encrypted Signals on Multiuser Optical Fiber Networks: Simulation Analysis of Next Generation Services and Technologies.

Reliable multipath multi-channel route migration over multi link-failure in wireless ad hoc networks (2017)
Presentation / Conference Contribution
Mirza, N. S., King, P. J. B., Romdhani, I., Abdelshafy, M. A., & Alghamdi, A. A. (2017). Reliable multipath multi-channel route migration over multi link-failure in wireless ad hoc networks. In Wireless and Mobile Computing, Networking and Communications

The route recovery algorithm is a crucial part of an ad hoc routing protocol. Designing an efficient and fast route recovery mechanism scheme without incurring extra overheads or delays to repair the broken link is a desirable goal for any routing pr... Read More about Reliable multipath multi-channel route migration over multi link-failure in wireless ad hoc networks.

Sensing domestic energy use (2017)
Presentation / Conference Contribution
Davison, B. (2017, November). Sensing domestic energy use. Paper presented at U!REKA 2017: Towards and Education and Research Strategy, Edinburgh

Building accurate models of domestic energy use is difficult because of a lack of reliable representative data. This paper outlines a strategy for recruiting home owners to provide data from actual dwellings. Sensor networks are deployed to provide b... Read More about Sensing domestic energy use.

What can we learn from simulating commuters? (2017)
Presentation / Conference Contribution
Urquhart, N. (2017, November). What can we learn from simulating commuters?. Presented at U!REKA

Commuting affects just about every member of the workforce in the UK, those who do not commute are affected by the congestion and pollution generated by such activities. There is increasing pressure on organisations to adopt practices and measures th... Read More about What can we learn from simulating commuters?.

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.

A robust data-driven approach to the decoding of pyloric neuron activity (2017)
Presentation / Conference Contribution
dos Santos, F., Andras, P., Collins, D., & Lam, K. (2017). A robust data-driven approach to the decoding of pyloric neuron activity. In 2017 IEEE International Workshop on Signal Processing Systems (SiPS). https://doi.org/10.1109/SiPS.2017.8110017

The combination of intra and extra-cellular recording of small neuronal circuits such as stomatogastric nervous systems of the crab (Cancer borealis) is well documented and routinely practised. Voltage sensitive dye imaging (VSDi) is a promising tech... Read More about A robust data-driven approach to the decoding of pyloric neuron activity.

Persian Named Entity Recognition (2017)
Presentation / Conference Contribution
Dashtipour, K., Gogate, M., Adeel, A., Algarafi, A., Howard, N., & Hussain, A. (2017). Persian Named Entity Recognition. . https://doi.org/10.1109/ICCI-CC.2017.8109733

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., …Hussain, A. (2017). Machine learning based computer-aided diagnosis of liver tumours. . https://doi.org/10.1109/ICCI-CC.2017.8109742

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). Formal Ontology Generation by deep machine learning. . https://doi.org/10.1109/ICCI-CC.2017.8109723

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.