1 code implementation • 12 Apr 2021 • Jordan J. Bird, Chloe M. Barnes, Luis J. Manso, Anikó Ekárt, Diego R. Faria
Contemporary Artificial Intelligence technologies allow for the employment of Computer Vision to discern good crops from bad, providing a step in the pipeline of selecting healthy fruit from undesirable fruit, such as those which are mouldy or gangrenous.
no code implementations • 12 Oct 2020 • Jordan J. Bird, Anikó Ekárt, Diego R. Faria
We find that all models are improved when training data is augmented by the T5 model, with an average increase of classification accuracy by 4. 01%.
no code implementations • 11 Jul 2020 • Jordan J. Bird, Diego R. Faria, Cristiano Premebida, Anikó Ekárt, George Vogiatzis
The image and the audio datasets are first classified independently, using a fine-tuned VGG16 and an evolutionary optimised deep neural network, with accuracies of 89. 27% and 93. 72%, respectively.
no code implementations • 1 Jul 2020 • Jordan J. Bird, Diego R. Faria, Anikó Ekárt, Cristiano Premebida, Pedro P. S. Ayrosa
In speech recognition problems, data scarcity often poses an issue due to the willingness of humans to provide large amounts of data for learning and classification.
1 code implementation • 28 Oct 2019 • Jordan J. Bird, Anikó Ekárt, Diego R. Faria
In CIFAR-10, the QRNG outperforms PRNG by + 0. 92%.
no code implementations • 10 Oct 2019 • Jordan J. Bird, Anikó Ekárt, Diego R. Faria
In 50 Dense Neural Networks (25 PRNG/25 QRNG), QRNG increases over PRNG for accent classification at +0. 1%, and QRNG exceeded PRNG for mental state EEG classification by +2. 82%.
no code implementations • 13 Aug 2019 • Jordan J. Bird, Diego R. Faria, Luis J. Manso, Anikó Ekárt, Christopher D. Buckingham
This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks.