Dr GRAZZIELA FIGUEREDO's Outputs (40)

Active learning-driven uncertainty reduction for in-flight particle characteristics of atmospheric plasma spraying of silicon (2023)
Journal Article
Memon, H., Gjerde, E., Lynam, A., Chowdhury, A., De Maere, G., Figueredo, G., & Hussain, T. (2024). Active learning-driven uncertainty reduction for in-flight particle characteristics of atmospheric plasma spraying of silicon. Engineering Applications of Artificial Intelligence, 128, Article 107465. https://doi.org/10.1016/j.engappai.2023.107465

The first-of-its-kind use of the active learning (AL) framework in thermal spray is adapted to enhance the prediction accuracy of the in-flight particle characteristics. The successful AL framework implementation via Bayesian Optimisation is benefici... Read More about Active learning-driven uncertainty reduction for in-flight particle characteristics of atmospheric plasma spraying of silicon.

A comprehensive description of kidney disease progression after acute kidney injury from a prospective, parallel-group cohort study (2023)
Journal Article
Horne, K. L., Viramontes-Hörner, D., Packington, R., Monaghan, J., Shaw, S., Akani, A., Reilly, T., Trimble, T., Figueredo, G., & Selby, N. M. (2023). A comprehensive description of kidney disease progression after acute kidney injury from a prospective, parallel-group cohort study. Kidney International, 104(6), 1185-1193. https://doi.org/10.1016/j.kint.2023.08.005

Acute kidney injury (AKI) is associated with adverse long-term outcomes, but many studies are retrospective, focused on specific patient groups or lack adequate comparators. The ARID (AKI Risk in Derby) Study was a five-year prospective parallel-grou... Read More about A comprehensive description of kidney disease progression after acute kidney injury from a prospective, parallel-group cohort study.

Influence of setting-dependent contacts and protective behaviours on asymptomatic SARS-CoV-2 infection amongst members of a UK university (2023)
Journal Article
Fairbanks, E. L., Bolton, K. J., Jia, R., Figueredo, G. P., Knight, H., & Vedhara, K. (2023). Influence of setting-dependent contacts and protective behaviours on asymptomatic SARS-CoV-2 infection amongst members of a UK university. Epidemics, 43, Article 100688. https://doi.org/10.1016/j.epidem.2023.100688

We survey 62 users of a university asymptomatic SARS-CoV-2 testing service on details of their activities, protective behaviours and contacts in the 7 days prior to receiving a positive or negative SARS-CoV-2 PCR test result in the period October 202... Read More about Influence of setting-dependent contacts and protective behaviours on asymptomatic SARS-CoV-2 infection amongst members of a UK university.

The association between prescription drugs and vaccines commonly prescribed to older people and bullous pemphigoid: a UK population-based study (2023)
Preprint / Working Paper
Swiderski, M., Vinogradova, Y., Knaggs, R., Harman, K., Harwood, R., Prasad, V., Persson, M. S., Figueredo, G., Layfield, C., & Gran, S. The association between prescription drugs and vaccines commonly prescribed to older people and bullous pemphigoid: a UK population-based study

Introduction
Bullous pemphigoid (BP) is a serious skin disease that results in large painful blisters developing over the body and occurs most commonly in older people (over 70 years). Despite several comorbidities such as stroke and a threefold inc... Read More about The association between prescription drugs and vaccines commonly prescribed to older people and bullous pemphigoid: a UK population-based study.

Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates (2023)
Journal Article
Contreas, L., Hook, A. L., Winkler, D. A., Figueredo, G., Williams, P., Laughton, C. A., Alexander, M. R., & Williams, P. M. (2023). Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates. ACS Applied Materials and Interfaces, 15(11), 14155-14163. https://doi.org/10.1021/acsami.2c23182

Bacterial infections are increasingly problematic due to the rise of antimicrobial resistance. Consequently, the rational design of materials naturally resistant to biofilm formation is an important strategy for preventing medical device-associated i... Read More about Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates.

Innate immune cell instruction using micron-scale 3D objects of varied architecture and polymer chemistry: The ChemoArchiChip (2023)
Journal Article
Vassey, M., Ma, L., Kämmerling, L., Mbadugha, C., Trindade, G. F., Figueredo, G. P., Pappalardo, F., Hutchinson, J., Markus, R., Rajani, S., Hu, Q., Winkler, D. A., Irvine, D. J., Hague, R., Ghaemmaghami, A. M., Wildman, R., & Alexander, M. R. (2023). Innate immune cell instruction using micron-scale 3D objects of varied architecture and polymer chemistry: The ChemoArchiChip. Matter, 6(3), 887-906. https://doi.org/10.1016/j.matt.2023.01.002

To design effective immunomodulatory implants, innate immune cell interactions at the surface of biomaterials need to be controlled and understood. The architectural design freedom of two-photon polymerization is used to produce arrays of surface-mou... Read More about Innate immune cell instruction using micron-scale 3D objects of varied architecture and polymer chemistry: The ChemoArchiChip.

Deep Learning with Attention Mechanisms for Road Weather Detection (2023)
Journal Article
Samo, M., Mafeni Mase, J. M., & Figueredo, G. (2023). Deep Learning with Attention Mechanisms for Road Weather Detection. Sensors, 23(2), Article 798. https://doi.org/10.3390/s23020798

There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assi... Read More about Deep Learning with Attention Mechanisms for Road Weather Detection.

Feature importance in machine learning models: A fuzzy information fusion approach (2022)
Journal Article
Rengasamy, D., Mase, J. M., Kumar, A., Rothwell, B., Torres, M. T., Alexander, M. R., Winkler, D. A., & Figueredo, G. P. (2022). Feature importance in machine learning models: A fuzzy information fusion approach. Neurocomputing, 511, 163-174. https://doi.org/10.1016/j.neucom.2022.09.053

With the widespread use of machine learning to support decision-making, it is increasingly important to verify and understand the reasons why a particular output is produced. Although post-training feature importance approaches assist this interpreta... Read More about Feature importance in machine learning models: A fuzzy information fusion approach.

Clinical laboratory parameters and comorbidities associated with severity of coronavirus disease 2019 (COVID-19) in Kurdistan Region of Iraq (2022)
Journal Article
Ali, H. N., Ali, K. M., Rostam, H. M., Ali, A. M., Tawfeeq, H. M., Fatah, M. H., & Figueredo, G. P. (2022). Clinical laboratory parameters and comorbidities associated with severity of coronavirus disease 2019 (COVID-19) in Kurdistan Region of Iraq. Practical Laboratory Medicine, 31, Article e00294. https://doi.org/10.1016/j.plabm.2022.e00294

Background: The pandemic coronavirus disease (COVID-19) dramatically spread worldwide. Considering several laboratory parameters and comorbidities may facilitate the assessment of disease severity. Early recognition of disease progression associated... Read More about Clinical laboratory parameters and comorbidities associated with severity of coronavirus disease 2019 (COVID-19) in Kurdistan Region of Iraq.

The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review (2021)
Journal Article
Majid, S., Reeves, S., Figueredo, G., Brown, S., Lang, A., Moore, M., & Morriss, R. (2021). The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review. JMIR Mental Health, 8(12), Article e27991. https://doi.org/10.2196/27991

Background: The number of self-monitoring apps for bipolar disorder (BD) is increasing. The involvement of users in human-computer interaction (HCI) research has a long history and is becoming a core concern for designers working in this space. The a... Read More about The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review.

Hypoalbuminemia in patients following their recovery from severe coronavirus disease 2019 (2021)
Journal Article
Ali, K. M., Ali, A. M., Tawfeeq, H. M., Figueredo, G. P., & Rostam, H. M. (2021). Hypoalbuminemia in patients following their recovery from severe coronavirus disease 2019. Journal of Medical Virology, 93(7), 4532-4536. https://doi.org/10.1002/jmv.27002

Coronavirus disease 2019 (COVID-19) is caused by a contagious virus that has spread to more than 200 countries, territories, and regions. Thousands of studies to date have examined all aspects of this disease, yet little is known about the postrecove... Read More about Hypoalbuminemia in patients following their recovery from severe coronavirus disease 2019.

An innovative approach to multi-method integrated assessment modelling of global climate change (2020)
Journal Article
Siebers, P. O., Lim, Z. E., Figueredo, G. P., & Hey, J. (2020). An innovative approach to multi-method integrated assessment modelling of global climate change. Journal of Artificial Societies and Social Simulation, 23(1), Article 10. https://doi.org/10.18564/jasss.4209

© 2020, University of Surrey. All rights reserved. Modelling and simulation play an increasingly significant role in exploratory studies for informing policy makers on climate change mitigation strategies. There is considerable research being done in... Read More about An innovative approach to multi-method integrated assessment modelling of global climate change.

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management (2020)
Journal Article
Rengasamy, D., Jafari, M., Rothwell, B., Chen, X., & Figueredo, G. P. (2021). Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management. Sensors, 20(3), Article 723. https://doi.org/10.3390/s20030723

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architectur... Read More about Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management.

Using agent-based modelling for investigating modal shift: The case of university travel (2019)
Journal Article
Olusola, F. T., Siebers, P.-O., Faboya, O., Ryan, B., & Figueredo, G. P. (2020). Using agent-based modelling for investigating modal shift: The case of university travel. Computers and Industrial Engineering, 139, Article 106077. https://doi.org/10.1016/j.cie.2019.106077

© 2019 Travel mode choices are a result of several factors and how they affect individual travellers. This paper examines those factors influencing travellers’ mode choices commuting to and from a university. Furthermore, we investigate how a shift t... Read More about Using agent-based modelling for investigating modal shift: The case of university travel.

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.

PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams (2019)
Journal Article
Tickle, R., Triguero, I., Figueredo, G. P., Mesgarpour, M., & John, R. I. (2019). PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams. Cognitive Computation, 11(3), 434–458. https://doi.org/10.1007/s12559-019-09638-y

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Hot spot identification is a very relevant problem in a wide variety of areas such as health care, energy or transportation. A hot spot is defined as a region of high likelihood o... Read More about PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams.

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.