Outputs (114)

Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry. (2020)
Journal Article
VILELA, M., OLUYEMI, G. and PETROVSKI, A. 2020. Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry. International journal of applied decision sciences [online], 13(3), pages 344-362. Available from: https://doi.org/10.1504/IJADS.2020.10026404

Value of information is a widely accepted methodology for evaluating the need to acquire new data in the oil and gas industry. In the conventional approach to estimating the value of information, the outcomes of a project assessment relate to the dec... Read More about Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry..

Evolved ensemble of detectors for gross error detection. (2020)
Conference Proceeding
NGUYEN, T.T., MCCALL, J., WILSON, A., OCHEI, L., CORBETT, H. and STOCKTON, P. 2020. Evolved ensemble of detectors for gross error detection. In GECCO '20: proceedings of the Genetic and evolutionary computation conference companion (GECCO 2020), 8-12 July 2020, Cancún, Mexico. New York: ACM [online], pages 281-282. Available from: https://doi.org/10.1145/3377929.3389906

In this study, we evolve an ensemble of detectors to check the presence of gross systematic errors on measurement data. We use the Fisher method to combine the output of different detectors and then test the hypothesis about the presence of gross err... Read More about Evolved ensemble of detectors for gross error detection..

Does good ESG lead to better financial performances by firms? Machine learning and logistic regression models of public enterprises in Europe. (2020)
Journal Article
DE LUCIA, C., PAZIENZA, P. and BARTLETT, M. 2020. Does good ESG lead to better financial performances by firms? Machine learning and logistics regression models of public enterprises in Europe. Sustainability [online], 12(13), article ID 5317. Available from: https://doi.org/10.3390/su12135317

The increasing awareness of climate change and human capital issues is shifting companies towards aspects other than traditional financial earnings. In particular, the changing behaviors towards sustainability issues of the global community and the a... Read More about Does good ESG lead to better financial performances by firms? Machine learning and logistic regression models of public enterprises in Europe..

Automated anomaly recognition in real time data streams for oil and gas industry. (2020)
Thesis
MAJDANI SHABESTARI, F. 2020. Automated anomaly recognition in real time data streams for oil and gas industry. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

There is a growing demand for computer-assisted real-time anomaly detection - from the identification of suspicious activities in cyber security, to the monitoring of engineering data for various applications across the oil and gas, automotive and ot... Read More about Automated anomaly recognition in real time data streams for oil and gas industry..

ETAREE: an effective trend-aware reputation evaluation engine for wireless medical sensor networks. (2020)
Conference Proceeding
HAJAR, M.S., AL-KADRI, M.O. and KALUTARAGE, H. 2020. ETAREE: an effective trend-aware reputation evaluation engine for wireless medical sensor networks. In Proceedings of 2020 Institute of Electrical and Electronics Engineers (IEEE) Communications and network security conference (CNS 2020), 29 June - 1 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9162325. Available from: https://doi.org/10.1109/CNS48642.2020.9162325

Wireless Medical Sensor Networks (WMSN) will play a significant role in the advancements of modern healthcare applications. Security concerns are still the main obstacle to the widespread adoption of this technology. Conventional security approaches,... Read More about ETAREE: an effective trend-aware reputation evaluation engine for wireless medical sensor networks..

Multi-layer heterogeneous ensemble with classifier and feature selection. (2020)
Conference Proceeding
NGUYEN, T.T., VAN PHAM, N., DANG, M.T., LUONG, A.V., MCCALL, J. and LIEW, A.W.C. 2020. Multi-layer heterogeneous ensemble with classifier and feature selection. In GECCO '20: proceedings of the Genetic and evolutionary computation conference (GECCO 2020), 8-12 July 2020, Cancun, Mexico. New York: ACM [online], pages 725-733. Available from: https://doi.org/10.1145/3377930.3389832

Deep Neural Networks have achieved many successes when applying to visual, text, and speech information in various domains. The crucial reasons behind these successes are the multi-layer architecture and the in-model feature transformation of deep le... Read More about Multi-layer heterogeneous ensemble with classifier and feature selection..

On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks. (2020)
Journal Article
LUGHOFER, E., ZAVOIANU, A.-C., POLLAK, R., PRATAMA, M., MEYER-HEYE, P., ZÖRRER, H., EITZINGER, C. and RADAUER, T. 2020. On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks. Information sciences [online], 537, 425-451. Available from: https://doi.org/10.1016/j.ins.2020.06.034

Anomaly detection in todays industrial environments is an ambitious challenge to detect possible faults/problems which may turn into severe waste during production, defects, or systems components damage, at an early stage. Data-driven anomaly detecti... Read More about On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks..

The importance of embedding meta skills in computer science graduate apprenticeship programmes. (2020)
Conference Proceeding
YOUNG, T. 2020. The importance of embedding meta skills in computer science graduate apprenticeship programmes. In Proceedings of the 25th Association for Computing Machinery (ACM) Innovation and technology in computer science education conference 2020 (ITiCSE '20), 15-19 June 2020, Trondheim, Norway. New York: ACM [online], pages 589-590. Available from: https://doi.org/10.1145/3341525.3394010

The purpose of this proposal is to investigate the need for the increased focus on developing transferable and meta skills of Graduate Apprentice Computer Science students and how the advancements of technology can impact the need for this. The Fourt... Read More about The importance of embedding meta skills in computer science graduate apprenticeship programmes..

Through the lens: enhancing assessment with video-based presentation. (2020)
Conference Proceeding
ZARB, M. and BIRTLESKELMAN, J. 2020. Through the lens: enhancing assessment with video-based presentations. In Proceedings of the 25th Association for Computing Machinery (ACM) Innovation and technology in computer science education conference 2020 (ITiCSE '20), 15-19 June 2020, Trondheim, Norway. New York: ACM [online], pages 187-192. Available from: https://doi.org/10.1145/3341525.3387376

This paper discusses a video-based approach trialled within Robert Gordon University. Students are typically asked to formally deliver presentations (either individually, or in groups) for summative assessment. Timetabling issues, large student numbe... Read More about Through the lens: enhancing assessment with video-based presentation..

Programming heterogeneous parallel machines using refactoring and Monte–Carlo tree search. (2020)
Journal Article
BROWN, C. JANJIC, V., GOLI, M. and MCCALL, J. 2020. Programming heterogeneous parallel machines using refactoring and Monte–Carlo tree search. International journal of parallel programming [online], 48(4): high level parallel programming, pages 583-602. Available from: https://doi.org/10.1007/s10766-020-00665-z

This paper presents a new technique for introducing and tuning parallelism for heterogeneous shared-memory systems (comprising a mixture of CPUs and GPUs), using a combination of algorithmic skeletons (such as farms and pipelines), Monte–Carlo tree s... Read More about Programming heterogeneous parallel machines using refactoring and Monte–Carlo tree search..