Design of an Improved Model for Lumpy Skin Disease Prediction Using Graph Neural Networks and Multiple Modal Data Fusion

An effective Lumpy Skin Disease prediction system mitigates economic losses and ensures animal health. Conventional approaches always fall short in terms of spatial-temporal analytics, very oft coming from a single source of data samples. To this regard, this research addresses the limitations by proposing a holistically integrated approach that puts together advanced machine learning techniques with enhanced disease prediction and management. In this paper, we provide a new framework that integrates graph neural networks for the fusion of graph-structured data with multivariables, attention mechanism, and self-supervised learning into compact feature representations. First, graph convolutional neural networks are utilized for spatial-temporal analysis. GCNs use node features, for instance, epidemiological data, and edge features in the form of connectivity data to generate node embeddings that capture complex dynamics of disease spread. It improved the prediction accuracy by up to 85%, greatly improving the spatial understanding in LSD dynamics. It basically fuses satellite image data, weather data, and livestock movement records through a multiple modal data fusion approach based on multiple head attention networks according to these embeddings. This will result in an enriched feature space in which the attention weights give insights into the relative importance of each data modality. This step improved the prediction accuracy by a further 5%, making the total prediction accuracy to be at 90%. Further fine-tuning of the model is done using self-supervised learning techniques through contrastive learning: SimCLR. This pre-paid feature representations using a large data set with unlabeled historic records on animal health data and environmental data samples. It improves model performance by up to 10% when fine-tuning the representations using LSD-specific data, thereby reducing reliance on labeled data and significantly improving the robustness and scalability of the model. This resulted in an overall accuracy of 95% for the fusion model, attained from the combination of these state-of-the-art techniques. In view of such comprehensive and diversified feature space, it showed very high resilience to adversarial attacks. Veterinary applications, in particular, support interpretability through attention mechanisms and node embeddings. Therefore, this work offers an accurate, robust, and interpretable solution to predict and manage Lumpy Skin Disease, contributing seriously to the domain of veterinary epidemiology.