96 citations · 199 across the 9 of their papers we have counts for
9 papers
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…
A Hybrid Natural Language Generation System Integrating Rules and Deep Learning Algorithms
Wei Wei, Bei Zhou, Georgios Leontidis
This paper proposes an enhanced natural language generation system combining the merits of both rule-based approaches and modern deep learning algorithms, boosting its performance…
Imputation of missing sub-hourly precipitation data in a large sensor network: a machine learning approach
Benedict Delahaye Chivers, John Wallbank, Steven J. Cole +4
Precipitation data collected at sub-hourly resolution represents specific challenges for missing data recovery by being largely stochastic in nature and highly unbalanced in the du…
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…
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…
Nemesyst: A Hybrid Parallelism Deep Learning-Based Framework Applied for Internet of Things Enabled Food Retailing Refrigeration Systems
George Onoufriou, Ronald Bickerton, Simon Pearson +1
Deep Learning has attracted considerable attention across multiple application domains, including computer vision, signal processing and natural language processing. Although quite…
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…
Deep Bayesian Self-Training
Fabio De Sousa Ribeiro, Francesco Caliva, Mark Swainson +3
Supervised Deep Learning has been highly successful in recent years, achieving state-of-the-art results in most tasks. However, with the ongoing uptake of such methods in industria…
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…