Inventory forecasting is a key component of effective inventory management. In this work, we utilise hybrid deep learning models for inventory forecasting. According to the highly nonlinear and non-stationary characteristics of inventory data, the models employ Long Short-Term Memory (LSTM) to capture long temporal dependencies and Convolutional Neural Network (CNN) to learn the local trend features. However, designing optimal CNN-LSTM network architecture and tuning parameters can be challenging and would require consistent human supervision. To automate optimal architecture searching of CNN-LSTM, we implement three meta-heuristics: a Particle Swarm Optimisation (PSO) and two Differential Evolution (DE) variants. Computational experiments on real-world inventory forecasting problems are conducted to evaluate the performance of the applied meta-heuristics in terms of evolved network architectures for obtaining prediction accuracy. Moreover, the evolved CNN-LSTM models are also compared to Seasonal Autoregressive Integrated Moving Average (SARIMA) models for inventory forecasting problems. The experimental results indicate that the evolved CNN-LSTM models are capable of dealing with complex nonlinear inventory forecasting problem.

The proposed method is an efficient and robust approach for the automated registration between morphological knee datasets and T2 MRI relaxation maps.

This study suggests that the proposed method is an accurate and robust approach for the automated landmark detection in various MR datasets. The proposed method can be used as the initialization or constraint in higher level medical image processing workflows such as in kinematic description, segmentation and registration.