Outputs (8)

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning (2020)
Journal Article
Khan, F. A., Voß, U., Pound, M. P., & French, A. P. (2020). Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning. Frontiers in Plant Science, 11, Article 1275. https://doi.org/10.3389/fpls.2020.01275

© Copyright © 2020 Khan, Voß, Pound and French. Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development—a process referred to as plant phenotyping—is increasing... Read More about Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning.

An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities (2020)
Journal Article
Standen, P., Brown, D. J., Taheri, M., Galvez Trigo, M. J., Boulton, H., Burton, A., …Hortal, E. (2020). An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities. British Journal of Educational Technology, 51(5), 1748-1765. https://doi.org/10.1111/bjet.13010

Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a... Read More about An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities.

LabelSens: enabling real-time sensor data labelling at the point of collection using an artificial intelligence-based approach (2020)
Journal Article
Woodward, K., Kanjo, E., Oikonomou, A., & Chamberlain, A. (2020). LabelSens: enabling real-time sensor data labelling at the point of collection using an artificial intelligence-based approach. Personal and Ubiquitous Computing, https://doi.org/10.1007/s00779-020-01427-x

Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks (2020)
Journal Article
Soltaninejad, M., Sturrock, C. J., Griffiths, M., Pridmore, T. P., & Pound, M. P. (2020). Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks. IEEE Transactions on Image Processing, 29, 6667-6679. https://doi.org/10.1109/TIP.2020.2992893

© 1992-2012 IEEE. We address the complex problem of reliably segmenting root structure from soil in X-ray Computed Tomography (CT) images. We utilise a deep learning approach, and propose a state-of-the-art multi-resolution architecture based on enco... Read More about Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks.

Immune-Instructive Polymers Control Macrophage Phenotype and Modulate the Foreign Body Response In Vivo (2020)
Journal Article
Rostam, H. M., Fisher, L. E., Hook, A. L., Burroughs, L., Luckett, J. C., Figueredo, G. P., …Ghaemmaghami, A. M. (2020). Immune-Instructive Polymers Control Macrophage Phenotype and Modulate the Foreign Body Response In Vivo. Matter, 2(6), 1564-1581. https://doi.org/10.1016/j.matt.2020.03.018

© 2020 The Author(s) Implantation of medical devices can result in inflammation. A large library of polymers is screened, and a selection found to promote macrophage differentiation towards pro- or anti-inflammatory phenotypes. The bioinstructive pro... Read More about Immune-Instructive Polymers Control Macrophage Phenotype and Modulate the Foreign Body Response In Vivo.

Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content (2020)
Journal Article
Haralabopoulos, G., Anagnostopoulos, I., & McAuley, D. (2020). Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content. Algorithms, 13(4), https://doi.org/10.3390/a13040083

Multigranulation Super-Trust Model for Attribute Reduction (2020)
Journal Article
Ding, W., Pedrycz, W., Triguero, I., Cao, Z., & Lin, C. (2020). Multigranulation Super-Trust Model for Attribute Reduction. IEEE Transactions on Fuzzy Systems, 29(6), 1395-1408. https://doi.org/10.1109/tfuzz.2020.2975152

As big data often contains a significant amount of uncertain, unstructured, and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information... Read More about Multigranulation Super-Trust Model for Attribute Reduction.

A Bibliometric Overview of the Field of Type-2 Fuzzy Sets and Systems [Discussion Forum] (2020)
Journal Article
Shukla, A. K., Kumar Bansal, S., Seth, T., Basu, A., John, R., & Muhuri, P. K. (2020). A Bibliometric Overview of the Field of Type-2 Fuzzy Sets and Systems [Discussion Forum]. IEEE Computational Intelligence Magazine, 15(1), 89-98. https://doi.org/10.1109/MCI.2019.2954669

© 2005-2012 IEEE. Fuzzy Sets and Systems is an area of computational intelligence, pioneered by Lotfi Zadeh over 50 years ago in a seminal paper in Information and Control. Fuzzy Sets (FSs) deal with uncertainty in our knowledge of a particular situa... Read More about A Bibliometric Overview of the Field of Type-2 Fuzzy Sets and Systems [Discussion Forum].