This article analyzes and predicts stock raw data from two aspects of linear regression and LSTM, and finally generates the corresponding result graph to predict the trend of the stock market and individual stocks. Th...
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The residual network is designed for image classification, which is very effective for image classification. But for image segmentation, it is far from enough. Previous PSPNET uses multi-scale features and achieves gr...
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In this paper, the face recognition problem is researched, and an improved algorithm based on Fisherface andmachinelearning is proposed. The proposed method mainly performs face recognition by combining Fisherface m...
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This paper is about statistical machinelearning's hyperparameter tuning, using Bayesian Optimization and its Acquisition Functions (AF). We coded on Python and carried out a simple SVM classification task. In con...
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Adversarial networks are commonly used in Image reconstruction, segmentation, detection, classification and cross-modal synthesis. In our research programmer, we studied some basic adversarial networks like Generative...
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In order to improve the performance of target detection in embedded devices, an improved YOLOV3-Tiny is proposed in this paper. The detection accuracy is improved by redesigning new backbone, introducing the Spatial P...
In this paper, in order to reduce interference between medical IoT device networks, balance network energy consumption, establish an optimal game model of joint channel distribution and power control. The model is cha...
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In this study, a 3D game "Dark Knight" is designed and developed by using unity3d game making engine as the development environment and combined with C# script. The game development chooses adventure puzzle ...
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machinelearning is applied to every aspect of people's life. Especially in the field of games, machines gradually show their own set of mechanisms and occupy a certain position in this field. In 2016, Alpha Go an...
Missing data is a growing concern in social science research. This paper introduces novel machine-learning methods to explore imputation efficiency and its effect on missing data. The authors used the Internet and pub...
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