Joint Neural

Joint neural networks represent a powerful approach to tackling complex problems by simultaneously processing multiple related tasks within a single model. Current research focuses on diverse applications, including image fusion, object recognition and detection, resource allocation in wireless networks, and medical image analysis, employing architectures such as Graph Neural Networks (GNNs), convolutional neural networks (CNNs), transformers, and autoencoders. This approach improves efficiency by leveraging shared information between tasks and often outperforms sequential or parallel processing methods, leading to advancements in areas like autonomous driving, medical diagnosis, and efficient resource management.