Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish–Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

With the recent rapid development of cloud computing technology, how to reduce the costs of a cloud data center effectively has become an important issue. The study on virtual machine deployment mainly aims at deploying virtual machine resources required by users on a physical server rationally and effectively. This paper proposes a multi-population ant colony algorithm to solve problems of virtual machine deployment. With resource wastage and energy consumption as optimization objectives, this algorithm uses multiple ant colonies for the solution and determines strategies for information exchange among ant colonies according to the information entropy of each population to guarantee the balance of its convergence and diversity. The simulation results show that this algorithm has better performance than the single-population ant colony algorithm and can reduce resource wastage and energy consumption effectively for high-demand virtual machine deployment.

The theory of traditional Chinese medicine (TCM) is an important part of Chinese culture. In the long history, there are a large number of excellent prescriptions, whose laws have been explored by many studies, but few works directly studied the generation of prescriptions. With the rapid development of deep learning, many applications of text generation using neural networks have emerged. Prescriptions are the doctors’ clinical experience and the results of neural networks also come from the accumulated experience. So, it is very feasible to apply deep learning techniques to the recommendation on TCM prescriptions. GAN and its variants have been applied in text generation recently. It has advantages in many aspects, such as the rapid speed of computation, the update of parameters by back propagation without Markov chain and more real data generation with two-players game. We attempted to know the important attributes of prescriptions and use these contents as the training data for variants of GAN to generate prescriptions. Specifically, we attempted to apply SeqGAN (Sequence Generative Adversarial Nets) and CGAN (Conditional Generative Adversarial Nets) to prescription generations. By underlying the knowledge of TCM, the prescriptions with different characteristics can be successfully generated. In the experiments, we conducted the comparative evaluations on the original data with other models. The results showed that applications in the innovation of prescription sequence generations have certain feasibility and significance, even can provide some reference values for the innovation of TCM.

Speech information is the most important means of human communication, and it is crucial to separate the target voice from the mixed sound signals. This paper proposes a speech separation model based on convolutional neural networks and attention mechanism. The magnitude spectrum of the mixed speech signals, as the input, has its high dimensionality. By analyzing the characteristics of the convolutional neural network and attention mechanism, it can be found that the convolutional neural network can effectively extract low-dimensional features and mine the spatiotemporal structure information in the speech signals, and the attention mechanism can reduce the loss of sequence information. The accuracy of speech separation can be improved effectively by combining two mechanisms. Compared to the typical speech separation model DRNN-2 + discrim, this method achieves 0.27 dB GNSDR gain and 0.51 dB GSIR gain, which illustrates that the speech separation model proposed in this paper has achieved an ideal separation effect.

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.