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Dr GRAZZIELA FIGUEREDO's Outputs (17)

Utilising User Data from a Food-Sharing App to Evidence the "Heat-or-Eat" Dilemma (2024)
Presentation / Conference Contribution
Semple, T., Harvey, J., Rodrigues, L., Gillott, M., Figueredo, G., & Nica-Avram, G. (2024, May). Utilising User Data from a Food-Sharing App to Evidence the "Heat-or-Eat" Dilemma. Presented at 2nd Digital Footprints Conference: Linking Digital Data for Social Impact, Bristol, UK

Introduction & Background
Previous literature has found that financially vulnerable households often make involuntary spending trade-offs between necessities, particularly energy and food. This effect is especially pronounced during winter, when hom... Read More about Utilising User Data from a Food-Sharing App to Evidence the "Heat-or-Eat" Dilemma.

An Intelligent Toolkit for Benchmarking Data-Driven Aerospace Prognostics (2019)
Presentation / Conference Contribution
Rengasamy, D., Mase, J. M., Rothwell, B., & Figueredo, G. P. (2019, October). An Intelligent Toolkit for Benchmarking Data-Driven Aerospace Prognostics. Presented at 2019 IEEE Intelligent Transportation Systems Conference - ITSC, Auckland, New Zealand

© 2019 IEEE. Machine Learning (ML) has been largely employed to sensor data for predicting the Remaining Useful Life (RUL) of aircraft components with promising results. A review of the literature, however, has revealed a lack of consensus regarding... Read More about An Intelligent Toolkit for Benchmarking Data-Driven Aerospace Prognostics.

A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening (2019)
Presentation / Conference Contribution
Figueredo, G. P., Shi, P., Parkes, A. J., Evans, K., Garibaldi, J. M., Negm, O., Tighe, P. J., Sewell, H. F., & Robertson, J. (2019, June). A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening. Presented at 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand

Current methods to identify cutoff values for tumour-associated molecules (antigens) discrimination are based on statistics and brute force. These methods applied to cancer screening problems are very inefficient, especially with large data sets with... Read More about A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening.

Microarray Feature Selection and Dynamic Selection of Classifiers for Early Detection of Insect Bite Hypersensitivity in Horses (2019)
Presentation / Conference Contribution
Maciel Guerra, A., Figueredo, G. P., Von Zuben, F., Marti, E., Twycross, J., & Alcocer, M. J. (2019, June). Microarray Feature Selection and Dynamic Selection of Classifiers for Early Detection of Insect Bite Hypersensitivity in Horses. Presented at 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand

Microarrays can be employed to better characterise allergies, as interactions between antibodies and allergens in mammals can be monitored. Once the joint dynamics of these elements in both healthy and diseased animals are understood, a model to pred... Read More about Microarray Feature Selection and Dynamic Selection of Classifiers for Early Detection of Insect Bite Hypersensitivity in Horses.

Evolving Deep CNN-LSTMs for Inventory Time Series Prediction (2019)
Presentation / Conference Contribution
Xue, N., Triguero, I., Figueredo, G. P., & Landa-Silva, D. (2019, June). Evolving Deep CNN-LSTMs for Inventory Time Series Prediction. Presented at 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand

Inventory forecasting is a key component of effective inventory management. In this work, we utilise hybrid deep learning models for inventory forecasting. According to the highly nonlinear and non-stationary characteristics of inventory data, the mo... Read More about Evolving Deep CNN-LSTMs for Inventory Time Series Prediction.

Fuzzy Hot Spot Identification for Big Data: An Initial Approach (2019)
Presentation / Conference Contribution
Triguero, I., Tickle, R., Figueredo, G. P., Mesgarpour, M., Ozcan, E., & John, R. I. (2019, June). Fuzzy Hot Spot Identification for Big Data: An Initial Approach. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

Hot spot identification problems are present across a wide range of areas, such as transportation, health care and energy. Hot spots are locations where a certain type of event occurs with high frequency. A recent big data approach is capable of iden... Read More about Fuzzy Hot Spot Identification for Big Data: An Initial Approach.

A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food (2019)
Presentation / Conference Contribution
Xue, N., Landa-Silva, D., Figueredo, G. P., & Triguero, I. (2019, February). A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food. Presented at 8th International Conference on Operations Research and Enterprise Systems, Prague, Czech Republic

The taste and freshness of perishable foods decrease dramatically with time. Effective inventory management requires understanding of market demand as well as balancing customers needs and references with products’ shelf life. The objective is to av... Read More about A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food.

Position Paper: The Usefulness of Data-driven, Intelligent Agent-Based Modelling for Transport Infrastructure Management (2018)
Presentation / Conference Contribution
Faboya, O., Figueredo, G. P., Ryan, B., & Siebers, P.-O. (2018, November). Position Paper: The Usefulness of Data-driven, Intelligent Agent-Based Modelling for Transport Infrastructure Management. Presented at The 21st IEEE International Conference on Intelligent Transportation Systems, Maui, Hawaii, USA

The uneven utilisation of modes of transport has a big impact on traffic in transport pathway infrastrutures. For motor vehicles for instance, this situation explains rapid road deterioration and the large amounts of money invested in maintenance and... Read More about Position Paper: The Usefulness of Data-driven, Intelligent Agent-Based Modelling for Transport Infrastructure Management.

Deep learning approaches to aircraft maintenance, repair and overhaul: a review (2018)
Presentation / Conference Contribution
Rengasami, D., Morvan, H., & Patrocinio Figueredo, G. (2018, November). Deep learning approaches to aircraft maintenance, repair and overhaul: a review. Presented at IEEE International Conference on Intelligent Transportation Systems

The use of sensor technology constantly gathering aircrafts' status data has promoted the rapid development of data-driven solutions in aerospace engineering. These methods assist, for instance, with determining appropriate actions for aircraft maint... Read More about Deep learning approaches to aircraft maintenance, repair and overhaul: a review.

How do travellers decide: a stochastic modelling approach to determine decision factor significance (2018)
Presentation / Conference Contribution
Olusola, F. T., Siebers, O., Ryan, B., & Figueredo, G. P. (2018, September). How do travellers decide: a stochastic modelling approach to determine decision factor significance. Presented at European Modeling and Simulation Symposium 2018 (EMSS2018), Budapest, Hungary

Many factors are involved in travellers' mode choice decision processes. Such factors include individuals' physical, cognitive, and emotional abilities, which play a significant role in travellers' attitude and mode usage patterns. Understanding how... Read More about How do travellers decide: a stochastic modelling approach to determine decision factor significance.

A genetic algorithm with composite chromosome for shift assignment of part-time employees (2018)
Presentation / Conference Contribution
Xue, N., Landa-Silva, D., Triguero, I., & Figueredo, G. P. A genetic algorithm with composite chromosome for shift assignment of part-time employees. Presented at 2018 IEEE Congress in Evolutionary Computation (IEEE CEC 2018)

Personnel scheduling problems involve multiple tasks, including assigning shifts to workers. The purpose is usually to satisfy objectives and constraints arising from management, labour unions and employee preferences. The shift assignment problem is... Read More about A genetic algorithm with composite chromosome for shift assignment of part-time employees.

A Novel Modal Shift Modelling Framework for Transport Systems (2017)
Presentation / Conference Contribution
Faboya, O. T., Siebers, P.-O., Ryan, B., & Figueredo, G. P. (2017, September). A Novel Modal Shift Modelling Framework for Transport Systems. Paper presented at Social Simulation Conference 2017 (SSC2017), Dublin, Ireland

The challenges from transport modes on human environments, health and economy, have called for investigations into how behavioural changes can be achieved for better resource utilisation. Trip makers' travel demands have been identified, and they inc... Read More about A Novel Modal Shift Modelling Framework for Transport Systems.

Vehicle incident hot spots identification: An approach for big data (2017)
Presentation / Conference Contribution
Triguero, I., Figueredo, G. P., Mesgarpour, M., Garibaldi, J. M., & John, R. (2017, August). Vehicle incident hot spots identification: An approach for big data. Presented at 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, Australia

In this work we introduce a fast big data approach for road incident hot spot identification using Apache Spark. We implement an existing immuno-inspired mechanism, namely SeleSup, as a series of MapReduce-like operations. SeleSup is composed of a nu... Read More about Vehicle incident hot spots identification: An approach for big data.

Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework (2015)
Presentation / Conference Contribution
Figueredo, G. P., Wagner, C., Garibaldi, J. M., & Aickelin, U. (2015, August). Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework. Presented at Proceedings - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015, Helsinki, Finland

In this position paper, we present ideas about creating a next generation framework towards an adaptive interface for data communication and visualisation systems. Our objective is to develop a system that accepts large data sets as inputs and provid... Read More about Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework.

A data analysis framework to rank HGV drivers (2015)
Presentation / Conference Contribution
Figueredo, G. P., Quinlan, P., Mesgarpour, M., Garibaldi, J. M., & John, R. A data analysis framework to rank HGV drivers. Presented at 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC 2015)

We report on the details of the methodology applied to support shortlisting the nominees for the Microlise Driver of the Year awards. The aim was to recognise the United Kingdom’s most talented heavy goods vehicle (HGV) drivers, with the list of top... Read More about A data analysis framework to rank HGV drivers.

Comparing System Dynamics and Agent-Based Simulation for tumour growth and its interactions with effector cells (2011)
Presentation / Conference Contribution
Figueredo, G. P., & Aickelin, U. Comparing System Dynamics and Agent-Based Simulation for tumour growth and its interactions with effector cells. Presented at Summer Computer Simulation Conference (SCSC 2011)

There is little research concerning comparisons and combination of System Dynamics Simulation (SDS) and Agent Based Simulation (ABS). ABS is a paradigm used in many levels of abstraction, including those levels covered by SDS. We believe that the est... Read More about Comparing System Dynamics and Agent-Based Simulation for tumour growth and its interactions with effector cells.