Accurate prediction for node CPU load is crucial for resource allocation in cluster. In this paper, we proposed a novel deep learning model named R-TPA-LSTM for the cluster node CPU load prediction. The proposed model is composed of two components, non-linear and linear component. The non-linear component contains residual LSTM-Conv module and attention module. Residual LSTM-Conv module includes two LSTM layers with residual connection and convolutional neural network for the sake of choosing the most informative timestep in the historical window while attention module captures the relationship among different features. The goal of the linear component, which is an AR module, is to catch the drastic changes in the data. The experimental results on a real-world dataset, show that the proposed model achieves better prediction performance for CPU load than conventional models.

Cohort study is one of the most commonly used study methods in medical and public health researches, which result in longitudinal data. Conventional statistical models and machine learning methods are not capable of modeling the evolution trend of the variables in longitudinal data. In this paper, we propose a Trend Analysis Neural Networks (TANN), which models the evolution trend of the variables by adaptive feature learning. TANN was tested on dataset of Kaiuan research. The task was to predict occurrence of death within 5 years, with 3 repeated medical examinations from 2008 to 2013. The AUC of the TANN is 0.7888, which is a slightly improvement than that of conventional methods, while that of GBDT is 0.7824, that of random forests is 0.7822, and that of logistic regression is 0.7789. The experimental results show that the proposed TANN achieves better prediction performance on death events prediction than conventional models. Furthermore, by analyzing the weights of TANN, we could find out important trends of the indicators. The trend discovery mechanism interprets the model well. TANN is an appropriate trade-off between high performance and interpretability.

Atrial Fibrillation (AF) is the most common chronic arrhythmia. Effective detection of the AF would avoid serious consequences like stroke. Conventional AF detection methods need heuristic or hand-craft feature extraction. In this paper, A deep neural network named multi-scale convolutional neural networks (MCNN) based AF detector is proposed. Instant heart rate sequence is extracted from ECG signal, then an end-to-end MCNN detects AF with the instant heart rate sequence as input and detection result as output. The algorithm was tested on both public and private datasets. On the public dataset, with the sensitivity achieved being 0.9822, the corresponding specificity is 0.9811, and the overall accuracy is 0.9818. The area under an ROC curve is as high as 0.9962, compared to the AUC of the best conventional method is 0.9947. Comparison shows that the MCNN based AF detector give superior accuracy than conventional methods. Test on private dataset also shows significant improvement.