Predicting ship trajectories is an essential task for maritime stakeholders, encompassing economic, security, and logistical considerations. Accurate trajectory prediction plays a pivotal role in optimising shipping routes, ensuring maritime safety, and managing resources efficiently. However, this endeavour has posed several challenges due to the vast amount of trajectory data generated in real-time and the intricate interplay of spatial and temporal factors. Traditionally, Long Short-Term Memory (LSTM) [1] and Gated Recurrent Units (GRU) [2] networks have been employed to model sequential and temporal data, and many researchers have tried to adapt these recurrent neural network (RNN) architectures to the spatio-temporal domain. While these RNN-based approaches have demonstrated success in various applications [3, 4, 5, 6], they typically neglect the crucial spatial component inherent in ship trajectories, such as the geographical coordinates and the intricate relationships between waypoints in a trajectory.

Knee OsteoArthritis (KOA) is a prevalent musculoskeletal condition that impairs the mobility of senior citizens. The lack of sufficient data in the medical field is always a challenge for training a learning model due to the high cost of labelling. At present, Deep neural network training strongly depends on data augmentation to improve the model's generalization capability and avoid over-fitting. However, existing data augmentation operations, such as rotation, gamma correction, etc., are designed based on the original data, which does not substantially increase the data diversity. In this paper, we propose a learning model based on the convolutional Auto-Encoder and a hybrid loss strategy to generate new data for early KOA (KL-0 vs KL-2) diagnosis. Four hidden layers are designed among the encoder and decoder, which represent the key and unrelated features of each input, respectively. Then, two key feature vectors are exchanged to obtain the generated images. To do this, a hybrid loss function is derived using different loss functions with optimized weights to supervise the reconstruction and key-exchange learning. Experimental results show that the generated data are valid as they can significantly improve the model's classification performance.