All Outputs (4)

Deep learning for the early detection of harmful algal blooms and improving water quality monitoring (2022)
Thesis
Dagtekin, O. (2022). Deep learning for the early detection of harmful algal blooms and improving water quality monitoring. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4220252

Climate change will affect how water sources are managed and monitored. The frequency of algal blooms will increase with climate change as it presents favourable conditions for the reproduction of phytoplankton. During monitoring, possible sensory fa... Read More about Deep learning for the early detection of harmful algal blooms and improving water quality monitoring.

The blessings of explainable AI in operations & maintenance of wind turbines (2021)
Thesis
Chatterjee, J. (2021). The blessings of explainable AI in operations & maintenance of wind turbines. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4223982

Wind turbines play an integral role in generating clean energy, but regularly suffer from operational inconsistencies and failures leading to unexpected downtimes and significant Operations & Maintenance (O&M) costs. Condition-Based Monitoring (CBM)... Read More about The blessings of explainable AI in operations & maintenance of wind turbines.

Deep learning with knowledge graphs for fine-grained emotion classification in text (2021)
Thesis
Schoene, A. M. (2021). Deep learning with knowledge graphs for fine-grained emotion classification in text. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4223160

This PhD thesis investigates two key challenges in the area of fine-grained emotion detection in textual data. More specifically, this work focuses on (i) the accurate classification of emotion in tweets and (ii) improving the learning of representat... Read More about Deep learning with knowledge graphs for fine-grained emotion classification in text.

Middle-out domain-specific aspect languages and their application in agent-based modelling runtime inspection (2019)
Thesis
Maddra, C. A. (2019). Middle-out domain-specific aspect languages and their application in agent-based modelling runtime inspection. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4221550

Domain-Specific Aspect Languages (DSALs) are a valuable tool for separating cross-cutting concerns, particularly within fields with endemic cross-cutting practices. Agent-Based Modelling (ABM) runtime inspection, which cuts across the core concern of... Read More about Middle-out domain-specific aspect languages and their application in agent-based modelling runtime inspection.