All Outputs (17)

A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation (2021)
Journal Article
Gibbs, J. A., Mcausland, L., Robles-Zazueta, C. A., Murchie, E. H., & Burgess, A. J. (2021). A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation. Frontiers in Plant Science, 12, Article 780180. https://doi.org/10.3389/fpls.2021.780180

Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. Howev... Read More about A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation.

Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models (2021)
Journal Article
Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2021). Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models. Scientific Reports, 11(1), Article 23279. https://doi.org/10.1038/s41598-021-02466-x

Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is mu... Read More about Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models.

Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification (2021)
Journal Article
Guo, Y., Jiao, L., Qu, R., Sun, Z., Wang, S., Wang, S., & Liu, F. (2022). Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5217818. https://doi.org/10.1109/TGRS.2021.3128908

The increasing applications of polarimetric synthetic aperture radar (PolSAR) image classification demand for effective superpixels’ algorithms. Fuzzy superpixels’ algorithms reduce the misclassification rate by dividing pixels into superpixels, whic... Read More about Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification.

Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation (2021)
Journal Article
Bowler, A. L., Pound, M. P., & Watson, N. J. (2021). Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation. Fermentation, 7(4), Article 253. https://doi.org/10.3390/fermentation7040253

Beer fermentation processes are traditionally monitored through sampling and off-line wort density measurements. In-line and on-line sensors would provide real-time data on the fermentation progress whilst minimising human involvement, enabling ident... Read More about Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation.

Self-Adaptive Particle Swarm Optimization-Based Echo State Network for Time Series Prediction (2021)
Journal Article
Xue, Y., Zhang, Q., & Neri, F. (2021). Self-Adaptive Particle Swarm Optimization-Based Echo State Network for Time Series Prediction. International Journal of Neural Systems, 31(12), Article 2150057. https://doi.org/10.1142/s012906572150057x

Echo state networks (ESNs), belonging to the family of recurrent neural networks (RNNs), are suitable for addressing complex nonlinear tasks due to their rich dynamic characteristics and easy implementation. The reservoir of the ESN is composed of a... Read More about Self-Adaptive Particle Swarm Optimization-Based Echo State Network for Time Series Prediction.

SPMS-ALS: A Single-Point Memetic structure with accelerated local search for instance reduction (2021)
Journal Article
Le, H. L., Neri, F., & Triguero, I. (2022). SPMS-ALS: A Single-Point Memetic structure with accelerated local search for instance reduction. Swarm and Evolutionary Computation, 69, Article 100991. https://doi.org/10.1016/j.swevo.2021.100991

Real-world optimisation problems pose domain specific challenges that often require an ad-hoc algorithmic design to be efficiently addressed. The present paper investigates the optimisation of a key stage in data mining, known as instance reduction,... Read More about SPMS-ALS: A Single-Point Memetic structure with accelerated local search for instance reduction.

Locating Identities in Time: An Examination of the Formation and Impact of Temporality on Presentations of the Self Through Location-based Social Networks (2021)
Journal Article
Papangelis, K., Lykourentzou, I., Khan, V., Chamberlain, A., Cao, T., Saker, M., & Lalone, N. (2021). Locating Identities in Time: An Examination of the Formation and Impact of Temporality on Presentations of the Self Through Location-based Social Networks. ACM Transactions on Social Computing, 4(3), 1-23. https://doi.org/10.1145/3473043

Studies of identity and location-based social networks (LBSN) have tended to focus on the performative aspects associated with marking one’s location. Yet, these studies often present this practice as being an a priori aspect of locative media. What... Read More about Locating Identities in Time: An Examination of the Formation and Impact of Temporality on Presentations of the Self Through Location-based Social Networks.

Convolutional feature extraction for process monitoring using ultrasonic sensors (2021)
Journal Article
Bowler, A., Pound, M., & Watson, N. (2021). Convolutional feature extraction for process monitoring using ultrasonic sensors. Computers and Chemical Engineering, 155, Article 107508. https://doi.org/10.1016/j.compchemeng.2021.107508

Ultrasonic sensors are a low-cost and in-line technique and can be combined with machine learning for industrial process monitoring. However, training accurate machine learning models for process monitoring using sensor data is dependant on the featu... Read More about Convolutional feature extraction for process monitoring using ultrasonic sensors.

Beyond global and local multi-target learning (2021)
Journal Article
Basgalupp, M., Cerri, R., Schietgat, L., Triguero, I., & Vens, C. (2021). Beyond global and local multi-target learning. Information Sciences, 579, 508-524. https://doi.org/10.1016/j.ins.2021.08.022

In multi-target prediction, an instance has to be classified along multiple target variables at the same time, where each target represents a category or numerical value. There are several strategies to tackle multi-target prediction problems: the lo... Read More about Beyond global and local multi-target learning.

An empirical analysis of the information security culture key factors framework (2021)
Journal Article
Tolah, A., Furnell, S. M., & Papadaki, M. (2021). An empirical analysis of the information security culture key factors framework. Computers and Security, 108, Article 102354. https://doi.org/10.1016/j.cose.2021.102354

Information security is a challenge facing organisations, as security breaches pose a serious threat to sensitive information. Organisations face security risks in relation to their information assets, which may also stem from their own employees. Or... Read More about An empirical analysis of the information security culture key factors framework.

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion (2021)
Journal Article
Sanchez, S., Rengasamy, D., Hyde, C. J., Figueredo, G. P., & Rothwell, B. (2021). Machine learning to determine the main factors affecting creep rates in laser powder bed fusion. Journal of Intelligent Manufacturing, 32(8), 2353–2373. https://doi.org/10.1007/s10845-021-01785-0

There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, par... Read More about Machine learning to determine the main factors affecting creep rates in laser powder bed fusion.

Health Care Cybersecurity Challenges and Solutions Under the Climate of COVID-19: Scoping Review (2021)
Journal Article
He, Y., Aliyu, A., Evans, M., & Luo, C. (2021). Health Care Cybersecurity Challenges and Solutions Under the Climate of COVID-19: Scoping Review. Journal of Medical Internet Research, 23(4), Article e21747. https://doi.org/10.2196/21747

Background: COVID-19 has challenged the resilience of the health care information system, which has affected our ability to achieve the global goal of health and well-being. The pandemic has resulted in a number of recent cyberattacks on hospitals, p... Read More about Health Care Cybersecurity Challenges and Solutions Under the Climate of COVID-19: Scoping Review.

Generalised Pattern Search Based on Covariance Matrix Diagonalisation (2021)
Journal Article
Neri, F., & Rostami, S. (2021). Generalised Pattern Search Based on Covariance Matrix Diagonalisation. SN Computer Science, 2, Article 171. https://doi.org/10.1007/s42979-021-00513-y

Pattern Search is a family of gradient-free direct search methods for numerical optimisation problems. The characterising feature of pattern search methods is the use of multiple directions spanning the problem domain to sample new candidate solution... Read More about Generalised Pattern Search Based on Covariance Matrix Diagonalisation.

Text Data Augmentations: Permutation, Antonyms and Negation (2021)
Journal Article
Haralabopoulos, G., Torres, M. T., Anagnostopoulos, I., & McAuley, D. (2021). Text Data Augmentations: Permutation, Antonyms and Negation. Expert Systems with Applications, 177, Article 114769. https://doi.org/10.1016/j.eswa.2021.114769

Text has traditionally been used to train automated classifiers for a multitude of purposes, such as: classification, topic modelling and sentiment analysis. State-of-the-art LSTM classifier require a large number of training examples to avoid biases... Read More about Text Data Augmentations: Permutation, Antonyms and Negation.

Teaching Mathematics to Computer Scientists: Reflections and a Case Study (2021)
Journal Article
Neri, F. (2021). Teaching Mathematics to Computer Scientists: Reflections and a Case Study. SN Computer Science, 2(2), Article 75. https://doi.org/10.1007/s42979-021-00461-7

Mathematics, despite being the foundation of computer science, is nowadays often considered a totally separate subject. The fact that many jobs in computer science do not explicitly require any specific mathematical knowledge posed questions about th... Read More about Teaching Mathematics to Computer Scientists: Reflections and a Case Study.

Exploring touch-based behavioral authentication on smartphone email applications in IoT-enabled smart cities (2021)
Journal Article
Li, W., Meng, W., & Furnell, S. (2021). Exploring touch-based behavioral authentication on smartphone email applications in IoT-enabled smart cities. Pattern Recognition Letters, 144, 35-41. https://doi.org/10.1016/j.patrec.2021.01.019

© 2021 Elsevier B.V. The Internet of Things (IoT) allows various embedded devices and smart sensors to be connected with each other, which provides a basis for building smart cities. The IoT-enabled smart city can greatly benefit people's daily lives... Read More about Exploring touch-based behavioral authentication on smartphone email applications in IoT-enabled smart cities.

The impact of algorithmic decision-making processes on young people’s well-being (2021)
Journal Article
Perez Vallejos, E., Dowthwaite, L., Creswick, H., Portillo, V., Koene, A., Jirotka, M., …McAuley, D. (2021). The impact of algorithmic decision-making processes on young people’s well-being. Health Informatics Journal, 27(1), 1-21. https://doi.org/10.1177/1460458220972750

This study aims to capture the online experiences of young people when interacting with algorithm mediated systems and their impact on their well-being. We draw on qualitative (focus groups) and quantitative (survey) data from a total of 260 young pe... Read More about The impact of algorithmic decision-making processes on young people’s well-being.