96 citations · 311 across the 20 of their papers we have counts for
9 papers · 1 filter
AI-enabled Efficient and Safe Food Supply Chain
Ilianna Kollia, Jack Stevenson, Stefanos Kollias
This paper provides a review of an emerging field in the food processing sector, referring to efficient and safe food supply chains, from farm to fork, as enabled by Artificial Int…
An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth
Bashar Alhnaity, Stefanos Kollias, Georgios Leontidis +3
Multi-step prediction is considered of major significance for time series analysis in many real life problems. Existing methods mainly focus on one-step-ahead forecasting, since mu…
Deep Transparent Prediction through Latent Representation Analysis
D. Kollias, N. Bouas, Y. Vlaxos +6
The paper presents a novel deep learning approach, which extracts latent information from trained Deep Neural Networks (DNNs) and derives concise representations that are analyzed…
Multi-Source Deep Domain Adaptation for Quality Control in Retail Food Packaging
Mamatha Thota, Stefanos Kollias, Mark Swainson +1
Retail food packaging contains information which informs choice and can be vital to consumer health, including product name, ingredients list, nutritional information, allergens, p…
A Unified Deep Learning Approach for Prediction of Parkinson's Disease
James Wingate, Ilianna Kollia, Luc Bidaut +1
The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging. The approach includes analysis and use of the knowledge e…
Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments
Bashar Alhnaity, Simon Pearson, Georgios Leontidis +1
Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general. Developing models which can effectively model growth and yie…
Capsule Routing via Variational Bayes
Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias
Capsule networks are a recently proposed type of neural network shown to outperform alternatives in challenging shape recognition tasks. In capsule networks, scalar neurons are rep…
Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks
Ilianna Kollia, Andreas-Georgios Stafylopatis, Stefanos Kollias
This paper presents a new method for medical diagnosis of neurodegenerative diseases, such as Parkinson's, by extracting and using latent information from trained Deep convolutiona…
Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis
Fabio De Sousa Ribeiro, Francesco Caliva, Dionysios Chionis +5
In this paper, we take the first steps towards a novel unified framework for the analysis of perturbations in both the Time and Frequency domains. The identification of type and so…