All Outputs (5)

A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening (2019)
Conference Proceeding
Figueredo, G. P., Shi, P., Parkes, A. J., Evans, K., Garibaldi, J. M., Negm, O., …Robertson, J. (2019). A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening. In 2019 IEEE Congress on Evolutionary Computation (CEC) (95-102). https://doi.org/10.1109/CEC.2019.8790316

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)
Conference Proceeding
Maciel Guerra, A., Figueredo, G. P., Von Zuben, F., Marti, E., Twycross, J., & Alcocer, M. J. (2019). Microarray Feature Selection and Dynamic Selection of Classifiers for Early Detection of Insect Bite Hypersensitivity in Horses. In 2019 IEEE Congress on Evolutionary Computation (CEC) (1157-1164). https://doi.org/10.1109/CEC.2019.8790319

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)
Conference Proceeding
Xue, N., Triguero, I., Figueredo, G. P., & Landa-Silva, D. (2019). Evolving Deep CNN-LSTMs for Inventory Time Series Prediction. . https://doi.org/10.1109/CEC.2019.8789957

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)
Conference Proceeding
Triguero, I., Tickle, R., Figueredo, G. P., Mesgarpour, M., Ozcan, E., & John, R. I. (2019). Fuzzy Hot Spot Identification for Big Data: An Initial Approach. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ-IEEE.2019.8858979

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)
Conference Proceeding
Xue, N., Landa-Silva, D., Figueredo, G. P., & Triguero, I. (2019). A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food. In Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES (406-413). https://doi.org/10.5220/0007401304060413

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.