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Anomaly detection in surveillance video scenes is one of the current research hotspots. Due to the small sample collection of anomalous events, the lack of negative sample labeling data training in anomaly detection research adds a lot of difficulties. Therefore, we adopt the method of unsupervised training and improve the method of anomaly detection based on the reconstruction of the potential features of the predicted frame and ground truth based on u-net. We reduce the reconstruction error between the potential features of u-net in the predicted frame and the potential features of the real frame. Then through other constraints, the reconstruction error of the entire predicted frame is minimized according to the generative adversarial training. Due to the use of normal behavior sample training, when the abnormal behavior is detected, the reconstruction error value exceeds the set threshold to judge whether abnormal behavior occurs in the surveillance video. Experiments prove that our improved method is effective and accurate.

Cancer is one of the most common causes of death in the world, while gastric cancer has the highest incidence in Asia. Predicting gastric cancer patients' survivability can inform patients care decisions and help doctors prescribe personalized medicine. Classification techniques have been widely used to predict survivability of cancer patients. However, very few attention has been paid to patients who cannot survive. In this research, we consider survival prediction to be a twostaged problem. The first is to predict the patients' five-year survivability. If the patient's predicted outcome is death, the second stage predicts the remaining lifespan of the patient. Our research proposes a custom ensemble method which integrated multiple machine learning algorithms. It exhibits a significant predictive improvement in both stages of prediction, compared with the state-of-the-art machine learning techniques. The base machine learning techniques include Decision Trees, Random Forest, Adaboost, Gradient Boost Machine (GBM), Artificial Neural Network (ANN), and the most popular GBM framework-LightGBM. The model is comprehensively evaluated on open source cancer data provided by the Surveillance, Epidemiology, and End Results Program (SEER) in terms of accuracy, area under the curve, Fscore, precision, recall rate, training and predicting time in the classification stage, and root mean squared error, mean absolute error, coefficient of determination (R 2) in the regression stage.