KEYWORDS: Data modeling, Education and training, Performance modeling, Neural networks, Deep learning, Tunable filters, Data processing, Windows, Machine learning, Convolution
Unlike other stock markets participants, the participants in China mainland are composed of individual investors, which account for 82% of the trading volume of the stock market. The decision-making basis of individual investors is mainly public opinion and recent stock prices. Therefore, the public opinion on professional stock social sites has an important impact on the decision of individual investors, which in turn affects the trend of the stock market. However, the previous stock market forecasting methods mostly ignored the influence of public opinion information on the market. For this reason, this paper proposes a novel framework to predict the stock trend by using both public opinion and stock numerical data. The original contributions of this paper include stock commentary word embedding model based on the stock comment text data crawled from https://xueqiu.com through two-stage training and LSTM-CNN layered model based on the improved self-attention mechanism. Two main experiments are conducted: the first experiment extract stock commentary word embedding, and the second experiment forecasts the stock price trends of Shanghai and Shenzhen A-share market. Results show that: 1)LSTM-CNN layered model is better than previous methods; 2)The combination of public opinion information and numerical data can improve the performance of the model; 3)Stock commentary word embedding model is better than pre-training word embedding model; 4) The longer the data span, the better the stock forecasting model will perform
KEYWORDS: Image fusion, 3D image reconstruction, 3D image processing, Image quality, 3D acquisition, Image processing, Signal to noise ratio, Data fusion, Imaging systems, Reconstruction algorithms
The acquisition of sharp, full-focus images and the restoration of microscopic scenes require complex instrumentation and algorithms. To conveniently obtain the three-dimensional (3-D) structural information of full-focus images and high-quality microscopic scenes, we construct a zoomable 3-D microscope imaging system and propose new image fusion and depth reconstruction algorithms based on this imaging system. The image acquisition environment of the system is analyzed using interference factors such as light transmission variation and jitter, and the corresponding image preprocessing methods are discussed. We combined the new sum-modified-Laplacian (SML) and local band-limited contrast methods, which contain multiple image features but measure the image definition from different angles, then proposed a mixed-contrast factor, and combined it with the SML method to propose an image fusion method. We then proposed a depth reconstruction method based on the structural similarity of multifocus images. For cases where depth reconstruction results in large distortion with high noise, we proposed a depth value restoration method based on anisotropic diffusion to improve the 3-D reconstruction results. Experimental results show that the proposed image fusion and depth reconstruction methods exhibit excellent performance.
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