2 citations · 4 across the 11 of their papers we have counts for
11 papers
ChaosNetBench: Benchmarking Spatio-Temporal Graph Neural Networks on Chaotic Lattice Dynamics
Henok Tenaw Moges, Charalampos Skokos, Deshendran Moodley
Spatio-temporal graph neural networks (STGNNs) are widely used for short-term forecasting in dynamic physical systems such as traffic and weather. However, the prevailing evaluatio…
A lightweight Spatial-Temporal Graph Neural Network for Long-term Time Series Forecasting
Henok Tenaw Moges, Deshendran Moodley
We propose Lite-STGNN, a lightweight spatial-temporal graph neural network for long-term multivariate forecasting that integrates decomposition-based temporal modeling with learnab…
Integrating Knowledge Graphs and Bayesian Networks: A Hybrid Approach for Explainable Disease Risk Prediction
Mbithe Nzomo, Deshendran Moodley
Multimodal electronic health record (EHR) data is useful for disease risk prediction based on medical domain knowledge. However, general medical knowledge must be adapted to specif…
Re-imagining health and well-being in low resource African settings using an augmented AI system and a 3D digital twin
Deshendran Moodley, Christopher Seebregts
This paper discusses and explores the potential and relevance of recent developments in artificial intelligence (AI) and digital twins for health and well-being in low-resource Afr…
Semantic web technologies in sensor-based personal health monitoring systems: A systematic mapping study
Mbithe Nzomo, Deshendran Moodley
In recent years, there has been an increased focus on early detection, prevention, and prediction of diseases. This, together with advances in sensor technology and the Internet of…
Brain Structural Saliency Over The Ages
Daniel Taylor, Jonathan Shock, Deshendran Moodley +2
Brain Age (BA) estimation via Deep Learning has become a strong and reliable bio-marker for brain health, but the black-box nature of Neural Networks does not easily allow insight…
An analysis of deep neural networks for predicting trends in time series data
Kouame Hermann Kouassi, Deshendran Moodley
Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was proposed for predicting trends in time series data. While TreNet was shown to have superior performance for trend…
Automatic deep learning for trend prediction in time series data
Kouame Hermann Kouassi, Deshendran Moodley
Recently, Deep Neural Network (DNN) algorithms have been explored for predicting trends in time series data. In many real world applications, time series data are captured from dyn…
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa
Wiebke Toussaint, Deshendran Moodley
Clustering is frequently used in the energy domain to identify dominant electricity consumption patterns of households, which can be used to construct customer archetypes for long…
Using competency questions to select optimal clustering structures for residential energy consumption patterns
Wiebke Toussaint, Deshendran Moodley
During cluster analysis domain experts and visual analysis are frequently relied on to identify the optimal clustering structure. This process tends to be adhoc, subjective and dif…
A Hybrid POMDP-BDI Agent Architecture with Online Stochastic Planning and Plan Caching
Gavin Rens, Deshendran Moodley
This article presents an agent architecture for controlling an autonomous agent in stochastic environments. The architecture combines the partially observable Markov decision proce…