Search Results for author: Diego R. Faria

Found 10 papers, 3 papers with code

Reducing Overconfidence Predictions for Autonomous Driving Perception

no code implementations16 Feb 2022 Gledson Melotti, Cristiano Premebida, Jordan J. Bird, Diego R. Faria, Nuno Gonçalves

In state-of-the-art deep learning for object recognition, SoftMax and Sigmoid functions are most commonly employed as the predictor outputs.

Autonomous Driving Decision Making +1

Fruit Quality and Defect Image Classification with Conditional GAN Data Augmentation

1 code implementation12 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.

Classification Data Augmentation +4

A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation

3 code implementations17 Feb 2021 Pilar Bachiller, Daniel Rodriguez-Criado, Ronit R. Jorvekar, Pablo Bustos, Diego R. Faria, Luis J. Manso

This paper leverages Graph Neural Networks to model robot disruption considering the movement of the humans and the robot so that the model built can be used by path planning algorithms.

Autonomous Navigation Robot Navigation

Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification

no code implementations12 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%.

Chatbot Data Augmentation +3

Look and Listen: A Multi-modality Late Fusion Approach to Scene Classification for Autonomous Machines

no code implementations11 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.

Scene Classification

LSTM and GPT-2 Synthetic Speech Transfer Learning for Speaker Recognition to Overcome Data Scarcity

no code implementations1 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.

Classification General Classification +4

On the Effects of Pseudo and Quantum Random Number Generators in Soft Computing

no code implementations10 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%.

Classification EEG +2

A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction

no code implementations13 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.

Classification EEG +4

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