Picture of fish in sea

Open Access research that uses mathematical models to solve ecological problems...

Solving a variety of ecological and biological problems is the focus of marine population modelling research conducted within the Department of Mathematics & Statistics. Here research deploys mathematical models to better understanding issues relating to fish stock management, ecosystem dynamics, ocean currents, and the effects of multispecies interactions within diverse marine ecosystems.

Research work in marine population modelling interfaces with a number of other key research specialisms, including mathematical biology, epidemiology and statistical informatics, where investigations are improving human understanding of the behaviour of infectious diseases, particularly in relation to animal infections; but also the modelling of complex biological processes such as antibiotic prodcution in actinobacteria.

Explore some of the Open Access research from Mathematics & Statistics. Or explore all of Strathclyde's Open Access research...

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Group by: Publication Date | Item type | No Grouping
Jump to: 2020 | 2019 | 2018 | 2017 | 2016
Number of items: 9.

2020

Gkerekos, Christos and Lazakis, Iraklis (2020) A novel, data-driven heuristic framework for vessel weather routing. Ocean Engineering, 197. 106887. ISSN 0029-8018

2019

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos (2019) Machine learning models for predicting ship main engine Fuel Oil Consumption : a comparative study. Ocean Engineering, 188. 106282. ISSN 0029-8018

Cheliotis, Michail and Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos (2019) A novel data condition and performance hybrid imputation method for energy effcient operations of marine systems. Ocean Engineering. pp. 1-48. ISSN 0029-8018 (In Press)

Lazakis, Iraklis and Gkerekos, Christos and Theotokatos, Gerasimos (2019) Investigating an SVM-driven, one-class approach to estimating ship systems condition. Ships and Offshore Structures, 14 (5). pp. 432-441. ISSN 1754-212X

2018

Gkerekos, Christos and Lazakis, Iraklis and Papageorgiou, Stylianos; (2018) Leveraging big data for fuel oil consumption modelling. In: 17th Conference on Computer and IT Applications in the Maritime Industries. Technische Universita╠łt Hamburg-Harburg, Hamburg, pp. 144-152. ISBN 9783892207078

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos; (2018) Exploiting machine learning for ship systems anomaly detection and healthiness forecasting. In: Proceedings of the 2018 Smart Ship Technology Conference. Royal Institution of Naval Architects, GBR.

2017

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos; Soares, C. Guedes and Teixeira, Angelo P., eds. (2017) Implementation of a self-learning algorithm for main engine condition monitoring. In: Maritime Transportation and Harvesting of Sea Resources. CRC Press, PRT, pp. 981-989. ISBN 978-0-8153-7993-5

Gkerekos, C and Lazakis, I and Theotokatos, G; (2017) Ship machinery condition monitoring using performance data through supervised learning. In: Proceedings of the 2017 Smart Ship Technology Conference. Royal Institution of Naval Architects, GBR, pp. 105-111. ISBN 9781909024632

2016

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos; Lazakis, Iraklis and Theotokatos, Gerasimos, eds. (2016) Ship machinery condition monitoring using vibration data through supervised learning. In: Proceedings of MSO 2016, International Conference on Maritime Safety and Operations. University of Strathclyde Publishing, GBR, pp. 103-110. ISBN 9781909522169

This list was generated on Sat Jul 4 14:19:16 2020 BST.