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Yan Chen and Chunchun Hu
Accurate prediction of fine particulate matter (PM2.5) concentration is crucial for improving environmental conditions and effectively controlling air pollution. However, some existing studies could ignore the nonlinearity and spatial correlation of time...
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Zengyu Cai, Chunchen Tan, Jianwei Zhang, Liang Zhu and Yuan Feng
As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and ...
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Sara Rajaram and Cassie S. Mitchell
The ability to translate Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) into different modalities and data types is essential to improve Deep Learning (DL) for predictive medicine. This work presents DACMVA, a novel framework ...
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Zepeng Wang and Yongjun Zhao
The exhaust gas temperature (EGT) baseline of an aeroengine is key to accurately analyzing engine health, formulating maintenance decisions and ensuring flight safety. However, due to the complex performance characteristics of aeroengine and the constrai...
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Lexin Zhang, Ruihan Wang, Zhuoyuan Li, Jiaxun Li, Yichen Ge, Shiyun Wa, Sirui Huang and Chunli Lv
This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and time attention mechanism. Taking into account the complex characteristics of time-series data, such...
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Qingyong Zhang, Lingfeng Zhou, Yixin Su, Huiwen Xia and Bingrong Xu
Considering the spatial and temporal correlation of traffic flow data is essential to improve the accuracy of traffic flow prediction. This paper proposes a traffic flow prediction model named Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU). In p...
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Ying Liu, Peng Wang and Di Yang
Knowledge graph embedding learning aims to represent the entities and relationships of real-world knowledge as low-dimensional dense vectors. Existing knowledge representation learning methods mostly aggregate only the internal information of triplets an...
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Chunwei Hu, Xianfeng Liu, Sheng Wu, Fei Yu, Yongkun Song and Jin Zhang
Accurate crowd flow prediction is essential for traffic guidance and traffic control. However, the high nonlinearity, temporal complexity, and spatial complexity that crowd flow data have makes this problem challenging. This research proposes a dynamic g...
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Runze Zhang, Yujie Zhu, Zhongshen Liu, Guohong Feng, Pengfei Diao, Hongen Wang, Shenghong Fu, Shuo Lv and Chen Zhang
(1) Background: Traditional kinetic-based shelf-life prediction models have low fitting accuracy and inaccurate prediction results for blueberries. Therefore, this study aimed to develop a blueberry shelf-life prediction method based on a back propagatio...
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Jinlong Wang, Dong Cui and Qiang Zhang
With sentiment prediction technology, businesses can quickly look at user reviews to find ways to improve their products and services. We present the BertBilstm Multiple Emotion Judgment (BBMEJ) model for small-sample emotion prediction tasks to solve th...
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