Home / Journals / JBD / Vol.3, No.2, 2021
  • Open Access

    ARTICLE

    Research on Key Technologies of Electronic Shelf Labels Based on LoRa

    Malak Abid Ali Khan1,2, Xiaofeng Lian1,*, Imran Khan Mirani1,3, Li Tan4
    Journal on Big Data, Vol.3, No.2, pp. 49-63, 2021, DOI:10.32604/jbd.2021.016213
    Abstract The demand for Electronic Shelf Labels (ESL), according to the Internet of Things (IoT) paradigm, is expected to grow considerably in the immediate future. Various wireless communication standards are currently contending to gain an edge over the competition and provide the massive connectivity that will be required by a world in which everyday objects are expected to communicate with each other. Low-Power Wide-Area Networks (LPWANs) are continuously gaining momentum among these standards, mainly thanks to their ability to provide long-range coverage to devices, exploiting license-free frequency bands. The main theme of this work is one of the most prominent LPWAN… More >

  • Open Access

    ARTICLE

    Grain Yield Predict Based on GRA-AdaBoost-SVR Model

    Diantao Hu, Cong Zhang*, Wenqi Cao, Xintao Lv, Songwu Xie
    Journal on Big Data, Vol.3, No.2, pp. 65-76, 2021, DOI:10.32604/jbd.2021.016317
    Abstract Grain yield security is a basic national policy of China, and changes in grain yield are influenced by a variety of factors, which often have a complex, non-linear relationship with each other. Therefore, this paper proposes a Grey Relational Analysis–Adaptive Boosting–Support Vector Regression (GRAAdaBoost-SVR) model, which can ensure the prediction accuracy of the model under small sample, improve the generalization ability, and enhance the prediction accuracy. SVR allows mapping to high-dimensional spaces using kernel functions, good for solving nonlinear problems. Grain yield datasets generally have small sample sizes and many features, making SVR a promising application for grain yield datasets.… More >

  • Open Access

    ARTICLE

    Encoder-Decoder Based Multi-Feature Fusion Model for Image Caption Generation

    Mingyang Duan, Jin Liu*, Shiqi Lv
    Journal on Big Data, Vol.3, No.2, pp. 77-83, 2021, DOI:10.32604/jbd.2021.016674
    Abstract Image caption generation is an essential task in computer vision and image understanding. Contemporary image caption generation models usually use the encoder-decoder model as the underlying network structure. However, in the traditional Encoder-Decoder architectures, only the global features of the images are extracted, while the local information of the images is not well utilized. This paper proposed an Encoder-Decoder model based on fused features and a novel mechanism for correcting the generated caption text. We use VGG16 and Faster R-CNN to extract global and local features in the encoder first. Then, we train the bidirectional LSTM network with the fused… More >

  • Open Access

    ARTICLE

    A Secure Visual Secret Sharing Scheme with Authentication Based on QR Code

    Xinwei Zhong*, Lizhi Xiong, Zhihua Xia
    Journal on Big Data, Vol.3, No.2, pp. 85-95, 2021, DOI:10.32604/jbd.2021.018618
    Abstract With the rise of the Internet of Things (IoT), various devices in life and industry are closely linked. Because of its high payload, stable error correction capability, and convenience in reading and writing, Quick Response (QR) code has been widely researched in IoT. However, the security of privacy data in IoT is also a very important issue. At the same time, because IoT is developing towards low-power devices in order to be applied to more fields, the technology protecting the security of private needs to have the characteristics of low computational complexity. Visual Secret Sharing (VSS), with its features of… More >

  • Open Access

    RETRACTION

    Retraction Notice to: Recent Approaches for Text Summarization Using Machine Learning & LSTM0

    Neeraj Kumar Sirohi, Mamta Bansal, S. N. Rajan
    Journal on Big Data, Vol.3, No.2, pp. 97-97, 2021, DOI:10.32604/jbd.2021.041299
    Abstract This article has no abstract. More >

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