Skip to main content

Research Repository

Advanced Search

Outputs (79)

An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence (2019)
Journal Article
Yuan, B., Panneerselvam, J., Liu, L., Antonopoulos, N., & Lu, Y. (2019). An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence. IEEE Transactions on Industrial Informatics, 15(7), 4295-4305. https://doi.org/10.1109/tii.2019.2902877

Real-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge computing environments. Network embedding techniques can potentially aid effect... Read More about An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence.

Cloud-based video analytics using convolutional neural networks (2018)
Journal Article
Yaseen, M. U., Anjum, A., Farid, M., & Antonopoulos, N. (2019). Cloud-based video analytics using convolutional neural networks. Software: Practice and Experience, 49(4), 565-583. https://doi.org/10.1002/spe.2636

Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management. Traditional video analytics systems work on shallow networks and are unable to harness the power of... Read More about Cloud-based video analytics using convolutional neural networks.

Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds (2018)
Journal Article
Yaseen, M. U., Anjum, A., Rana, O., & Antonopoulos, N. (2019). Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds. IEEE Transactions on Systems, Man and Cybernetics: Systems, 49(1), 253-264. https://doi.org/10.1109/TSMC.2018.2840341

A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, preprocessed, and a model for supporting classification is developed on these video streams using cloud-based infr... Read More about Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds.

An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity. (2018)
Journal Article
Panneerselvam, J., Liu, L., & Antonopoulos, N. (2018). An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity. IEEE Transactions on Emerging Topics in Computing, https://doi.org/10.1109/TETC.2018.2794328

Cloud workloads are increasingly heterogeneous such that a single Cloud job may encompass one to several tasks, and tasks belonging to the same job may behave distinctively during their actual execution. This inherent task heterogeneity imposes incre... Read More about An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity..

An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres (2018)
Journal Article
Panneerselvam, J., Liu, L., Lu, Y., & Antonopoulos, N. (2018). An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres. Future Generation Computer Systems, 83, 239-249. https://doi.org/10.1016/j.future.2017.12.064

Cloud datacentre resources and the arriving jobs are addressed to be exhibiting increased level of heterogeneity. A single Cloud job may encompass one to several number of tasks, such tasks usually exhibit increased level of behavioural heterogeneity... Read More about An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres.

InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments (2017)
Journal Article
Panneerselvam, J., Liu, L., & Antonopoulos, N. (2018). InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments. Future Generation Computer Systems, 80, 322-341. https://doi.org/10.1016/j.future.2017.05.022

Cloud Computing has emerged as a low cost anywhere anytime computing paradigm. Given the energy consumption characteristics of the Cloud resources, service providers are under immense pressure to reduce the energy implications of the datacentres. For... Read More about InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments.

The Digital Library (2017)
Journal Article
Ennis, L. (2017). The Digital Library. Teaching fellows journal,

The meteoric rise of the internet has meant that we have access to more information than ever. Our informational lives, and those of our students are complex – fraught with issues of access, immediacy, discoverability, and quality. Increasingly, to b... Read More about The Digital Library.

Efficient service discovery in decentralized online social networks (2017)
Journal Article
Yuan, B., Liu, L., & Antonopoulos, N. (2018). Efficient service discovery in decentralized online social networks. Future Generation Computer Systems, 86, 775-791. https://doi.org/10.1016/j.future.2017.04.022

Online social networks (OSN) have attracted millions of users worldwide over the last decade. There are a series of urgent issues faced by existing OSN such as information overload, single-point of failure and privacy concerns. The booming Internet o... Read More about Efficient service discovery in decentralized online social networks.