Abstract
Modern decision support systems (DSS) increasingly must analyze large-scale, complex, and heterogeneous data streams in real time. High-performance, AI-driven processing methods – particularly deep neural networks – offer effective solutions. This study examines the integration of contemporary architectures – YOLOv8, ResNet-50, EfficientNet-B3, and the Vision Transformer (ViT) – to enhance DSS capabilities. The models are benchmarked on a representative image-classification task using the COCO dataset for training and evaluation. Empirical results indicate that the transformer-based model (ViT) attains the highest accuracy, whereas the one-stage architecture (YOLOv8) achieves the fastest inference. EfficientNet-B3 and ResNet-50 exhibit intermediate trade-offs between accuracy and speed. Deployment considerations across DSS scenarios are outlined: YOLOv8 is appropriate for real-time, resource-constrained environments; ResNet-50 provides balanced performance; EfficientNet-B3 offers strong accuracy with moderate computational demand; and ViT delivers the best accuracy when ample data and computational resources are available. The findings are discussed in the context of DSS workflows, illustrating how the model outputs can directly inform and improve decision-making processes.