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“Off the beaten map”: Navigating with digital maps on moorland (2019)
Journal Article
Smith, T. A., Laurier, E., Reeves, S., & Dunkley, R. A. (2019). “Off the beaten map”: Navigating with digital maps on moorland. Transactions of the Institute of British Geographers, https://doi.org/10.1111/tran.12336

The information, practices and views in this article are those of the author(s) and do not necessarily reflect the opinion of the Royal Geographical Society (with IBG). © 2019 Royal Geographical Society (with the Institute of British Geographers) Res... Read More about “Off the beaten map”: Navigating with digital maps on moorland.

A Novel Autonomous Perceptron Model for Pattern Classification Applications (2019)
Journal Article
Sagheer, A., Zidan, M., & Abdelsamea, M. M. (2019). A Novel Autonomous Perceptron Model for Pattern Classification Applications. Entropy, 21(8), Article 763. https://doi.org/10.3390/e21080763

Pattern classification represents a challenging problem in machine learning and data science research domains, especially when there is a limited availability of training samples. In recent years, artificial neural network (ANN) algorithms have demon... Read More about A Novel Autonomous Perceptron Model for Pattern Classification Applications.

Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures (2019)
Presentation / Conference Contribution
Agrawal, U., Wagner, C., Garibaldi, J. M., & Soria, D. (2019, June). Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

Aggregation operators are mathematical functions that enable the fusion of information from multiple sources. Fuzzy Integrals (FIs) are widely used aggregation operators, which combine information in respect to a Fuzzy Measure (FM) which captures the... Read More about Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures.

Measuring Inter-group Agreement on zSlice Based General Type-2 Fuzzy Sets (2019)
Presentation / Conference Contribution
Navarro, J., & Wagner, C. (2019, June). Measuring Inter-group Agreement on zSlice Based General Type-2 Fuzzy Sets. Presented at 2019 IEEE International Conference on Fuzzy Systems, New Orleans, Lousiana, USA

Recently, there has been much research into modelling of uncertainty in human perception through Fuzzy Sets (FSs). Most of this research has focused on allowing respondents to express their (intra) uncertainty using intervals. Here, depending on the... Read More about Measuring Inter-group Agreement on zSlice Based General Type-2 Fuzzy Sets.

From Cubes to Twisted Cubes via Graph Morphisms in Type Theory (2019)
Presentation / Conference Contribution
Pinyo, G., & Kraus, N. (2019, June). From Cubes to Twisted Cubes via Graph Morphisms in Type Theory. Paper presented at TYPES 2019, Oslo, Norway

Cube categories are used to encode higher-dimensional categorical structures. They have recently gained significant attention in the community of homotopy type theory and univalent foundations, where types carry the structure of such higher groupoids... Read More about From Cubes to Twisted Cubes via Graph Morphisms in Type Theory.

Modelling the home health care nurse scheduling problem for patients with long-term conditions in the UK (2019)
Presentation / Conference Contribution
He, F., Chaussalet, T., & Qu, R. (2019, June). Modelling the home health care nurse scheduling problem for patients with long-term conditions in the UK. Presented at 33rd International ECMS Conference on Modelling and Simulation (ECMS 2019), Universita degli Studi della Campania, Caserta, Area of Napoli, Italy

In this work, using a Behavioural Operational Research (BOR) perspective, we develop a model for the Home Health Care Nurse Scheduling Problem (HHCNSP) with application to renal patients taking Peritoneal Dialysis (PD) at their own homes as treatment... Read More about Modelling the home health care nurse scheduling problem for patients with long-term conditions in the UK.

Paid Crowdsourcing, Low Income Contributors, and Subjectivity (2019)
Book Chapter
Haralabopoulos, G., Wagner, C., McAuley, D., & Anagnostopoulos, I. (2019). Paid Crowdsourcing, Low Income Contributors, and Subjectivity. In I. Maglogiannis, J. MacIntyre, L. Iliadis, & E. Pimenidis (Eds.), Artificial Intelligence Applications and Innovations: AIAI 2019 IFIP WG 12.5 International Workshops: MHDW and 5G-PINE 2019, Hersonissos, Crete, Greece, May 24–26, 2019, Proceedings (225-231). Springer Verlag. https://doi.org/10.1007/978-3-030-19909-8_20

Scientific projects that require human computation often resort to crowdsourcing. Interested individuals can contribute to a crowdsourcing task, essentially contributing towards the project's goals. To motivate participation and engagement, scientist... Read More about Paid Crowdsourcing, Low Income Contributors, and Subjectivity.

Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles (2019)
Journal Article
Agrawal, U., Soria, D., Wagner, C., Garibaldi, J., Ellis, I. O., Bartlett, J. M. S., Cameron, D., Rakha, E. A., & Green, A. R. (2019). Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles. Artificial Intelligence in Medicine, 97, 27-37. https://doi.org/10.1016/j.artmed.2019.05.002

Breast Cancer is one of the most common causes of cancer death in women, representing a very complex disease with varied molecular alterations. To assist breast cancer prognosis, the classification of patients into biological groups is of great signi... Read More about Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles.