JNMOpen Access

Journal of New Media

ISSN:2579-0110(print)
ISSN:2579-0129(online)
Publication Frequency:Quarterly

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About Journal

Journal of New Media (JNM) aims to provide a high quality and timely forum for researchers, engineers whose research interests focus on digital multimedia processing to share their state-of-the-art achievements, to learn the multimedia processing developments.Read More

  • ARTICLE

    Design of Hybrid Recommendation Algorithm in Online Shopping System

    Journal of New Media, Vol.3, No.4, pp. 119-128, 2021, DOI:10.32604/jnm.2021.016655
    Abstract In order to improve user satisfaction and loyalty on e-commerce websites, recommendation algorithms are used to recommend products that may be of interest to users. Therefore, the accuracy of the recommendation algorithm is a primary issue. So far, there are three mainstream recommendation algorithms, content-based recommendation algorithms, collaborative filtering algorithms and hybrid recommendation algorithms. Content-based recommendation algorithms and collaborative filtering algorithms have their own shortcomings. The contentbased recommendation algorithm has the problem of the diversity of recommended items, while the collaborative filtering algorithm has the problem of data sparsity and scalability. On the basis of these two algorithms, the hybrid… More >

  • REVIEW

    Review of Unsupervised Person Re-Identification

    Journal of New Media, Vol.3, No.4, pp. 129-136, 2021, DOI:10.32604/jnm.2021.023981
    Abstract Person re-identification (re-ID) aims to match images of the same pedestrian across different cameras. It plays an important role in the field of security and surveillance. Although it has been studied for many years, it is still considered as an unsolved problem. Since the rise of deep learning, the accuracy of supervised person re-ID on public datasets has reached the highest level. However, these methods are difficult to apply to real-life scenarios because a large number of labeled training data is required in this situation. Pedestrian identity labeling, especially cross-camera pedestrian identity labeling, is heavy and expensive. Why we cannot… More >

  • ARTICLE

    Blockchain-Based Decentralized Reputation Management System for Internet of Everything in 6G-Enabled Cybertwin Architecture

    Journal of New Media, Vol.3, No.4, pp. 137-150, 2021, DOI:10.32604/jnm.2021.024543
    Abstract Internet of Everything (IoE) has emerged as a promising paradigm for the purpose of connecting and exchanging data among physical objects and humans over the Internet, and it can be widely applied in the fields of industry, transportation, commerce, and education. Recently, the emergence of 6G-enabled cybertwin network architecture provides the technical and theoretical foundation for the realization of IoE paradigm. However, the IoE has three open issues in the 6G-enabled cybertwin architecture, i.e., data authenticity, data storage and node reliability. To address these issues, we propose a blockchain-based decentralized reputation management system (BC-DRMS) for IoE in 6G-enabled Cybertwin architecture.… More >

  • ARTICLE

    Ground Nephogram Enhancement Algorithm Based on Improved Adaptive Fractional Differentiation

    Journal of New Media, Vol.3, No.4, pp. 151-180, 2021, DOI:10.32604/jnm.2021.024665
    Abstract The texture of ground-based nephogram is abundant and multiplicity. Many cloud textures are not as clear as artificial textures. A nephogram enhancement algorithm based on Adaptive Fractional Differential is established to extract the natural texture of visible ground-based cloud image. GrunwaldLentikov (G-L) and Grunwald-Lentikov (R-L) fractional differential operators are applied to the enhancement algorithm of ground-based nephogram. An operator mask based on adaptive differential order is designed. The corresponding mask template is used to process each pixel. The results show that this method can extract image texture and edge details and simplify the process of differential order selection. More >

  • ARTICLE

    Mixed Noise Removal by Residual Learning of Deep CNN

    Journal of New Media, Vol.2, No.1, pp. 1-10, 2020, DOI:10.32604/jnm.2020.09356
    Abstract Due to the huge difference of noise distribution, the result of a mixture of multiple noises becomes very complicated. Under normal circumstances, the most common type of mixed noise is to add impulse noise (IN) and then white Gaussian noise (AWGN). From the reduction of cascaded IN and AWGN to the latest sparse representation, a great deal of methods has been proposed to reduce this form of mixed noise. However, when the mixed noise is very strong, most methods often produce a lot of artifacts. In order to solve the above problems, we propose a method based on residual learning… More >

  • ARTICLE

    Analysis and Prediction of New Media Information Dissemination of Police Microblog

    Journal of New Media, Vol.2, No.2, pp. 91-98, 2020, DOI:10.32604/jnm.2020.010125
    Abstract This paper aims to analyze the microblog data published by the official account in a certain province of China, and finds out the rule of Weibo that is easier to be forwarded in the new police media perspective. In this paper, a new topic-based model is proposed. Firstly, the LDA topic clustering algorithm is used to extract the topic categories with forwarding heat from the microblogs with high forwarding numbers, then the Naive Bayesian algorithm is used to topic categories. The sample data is processed to predict the type of microblog forwarding. In order to evaluate this method, a large… More >

  • ARTICLE

    Edge Detection Based on Generative Adversarial Networks

    Journal of New Media, Vol.2, No.2, pp. 61-77, 2020, DOI:10.32604/jnm.2020.010062
    Abstract Aiming at the problem that the detection effect of traditional edge detection algorithm is not good, and the problem that the existing edge detection algorithm based on convolution network cannot solve the thick edge problem from the model itself, this paper proposes a new edge detection method based on the generative adversarial network. The confrontation network consists of generator network and discriminator network, generator network is composed of U-net network and discriminator network is composed of five-layer convolution network. In this paper, we use BSDS500 training data set to train the model. Finally, several images are randomly selected from BSDS500… More >

  • ARTICLE

    User Behavior Path Analysis Based on Sales Data

    Journal of New Media, Vol.2, No.2, pp. 79-90, 2020, DOI:10.32604/jnm.2020.010088
    Abstract With the rapid development of science and technology and the increasing popularity of the Internet, the number of network users is gradually expanding, and the behavior of network users is becoming more and more complex. Users’ actual demand for resources on the network application platform is closely related to their historical behavior records. Therefore, it is very important to analyze the user behavior path conversion rate. Therefore, this paper analyses and studies user behavior path based on sales data. Through analyzing the user quality of the website as well as the user’s repurchase rate, repurchase rate and retention rate in… More >

  • ARTICLE

    A LoRaWAN Access Technology Based on Channel Adaptive Adjustment

    Journal of New Media, Vol.2, No.1, pp. 11-20, 2020, DOI:10.32604/jnm.2020.09715
    Abstract Low-power wide area network (LPWAN) has developed rapidly in recent years and is widely used in various Internet of Things (IoT) services. In order to reduce cost and power consumption, wide coverage, LPWAN tends to use simple channel access control protocols, such as the Aloha protocol. This protocol is simple with poor extension capability. In high-density environment, Aloha protocol will lead to low channel utilization, prolonged access and high conflict probability. Therefore, in order to solve the above problems, we propose an enhanced channel access control mechanism based on the existing LoRaWAN protocol, that is, a dynamic listening backoff mechanism.… More >

  • REVIEW

    A Review of Person Re-Identification

    Journal of New Media, Vol.2, No.2, pp. 45-60, 2020, DOI:10.32604/jnm.2020.09823
    Abstract Recently, person Re-identification (person Re-id) has attracted more and more attention, which has become a research focus of computer vision community. Person Re-id is used to ascertain whether the target pedestrians captured by cameras in different positions at different moments are the same person or not. However, due to the influence of various complex factors, person Re-id still has a lot of holes to be filled. In this paper, we first review the research process of person Re-id, and then, two kinds of mainstream methods for person Re-id are introduced respectively, according to the different types of training data they… More >

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