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TE is a critical and difficult problem that tries to map traffic with various requirements to paths in dynamic communication networks. The emerging SDN enables centralized TE optimization with a global view. From the architectural viewpoint, ICN facilitates TE from many aspects, such as in-network caching which can reduce redundant traffic and content-awareness which can extract prior knowledge of content type directly. Thus, we leverage ICN to optimize SDN TE. However, ICN brings more complexities and dynamics to the network environment, which makes model-based TE methods inefficient. Inspired by recent advances in applying artificial intelligence techniques to solve complex online control problems, we investigate deep learning for content-awareness and DRL for TE decision. In addition, we propose a parallel online learning mechanism to safely utilize DRL which has trial-and-error nature. Results show that our proposal significantly improves network performance in terms of total network throughput, bandwidth utilization, and load balance. [ABSTRACT FROM AUTHOR]

The construction of Smart Cities is inseparable from the healthy operation of markets. Reasonable data analysis can provide a crucial foundation for the development of market behavior by considering the enormous amount of data generated by a market economy. To this end, we propose enhanced cluster generative adversarial networks (eClusterGAN) to achieve latent space clustering. However, data storage security is crucial. Moreover, we suggest a GAN-based network intrusion detection system (GAN NIDS) that uses adversarial learning to assist the generator in learning the spatial distribution of normal network flows. The simulation results showed that the proposed eClusterGAN and GAN NIDS outperformed the benchmarks in terms of clustering accuracy, running time, precision, recall, and F1, which can support researchers in studying economic data trends. The construction of Smart Cities can effectively ensure healthy market development by discovering and disseminating the potential value of market economic data. [ABSTRACT FROM AUTHOR]

• We propose a personalized course recommendation method based on BERT model, which integrates deep learning and big data technology based on MOOC system. • We design a domain feature difference learning strategy, which is able to learn the difference features between course contents to better extract information in the text and improve the recommendation performance of the model. • We experimentally demonstrate the effectiveness of the method proposed in this paper on the open MoocCube datasets. Finding the courses that users are interested in quickly in the massive data can make a very important contribution to the accurate dissemination of knowledge. In this paper, we integrate the deep learning and big data technology to investigate a personalized recommendation method based on Massive Open Online Course (MOOC) system. Based on the Bidirectional Encoder Representations from Transformers (BERT) model, we propose some corresponding strategies to improve the accuracy of the recommendation system. First, we introduce the acquisition and preprocessing of the open dataset. Second, we design a recommendation model framework by taking advantage of the BERT model and incorporating a self-attention mechanism. Finally, to obtain deep feature information between course texts, we design a domain feature difference learning strategy to improve the model's recommendation performance. The results of our experiments prove that the proposed model in this paper performs good recommendation results compared with other methods. [Display omitted] [ABSTRACT FROM AUTHOR]