Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.
Water is one of the main elements of the environment, which determines the existence of life on the earth such as humans, aquatic animals, and plants. In order to control the water quality environment more efficiently and intelligently, numerous water quality models have been developed for predicting and evaluating water quality accurately and intelligently. In order to control the water quality environment more effectively and intelligently, artificial neural network (ANN) and the hybrid models that contain it are applied to accurately and intelligently predict and evaluate water quality, improving the reliability and assessment capabilities of water quality prediction. Therefore, this paper is a literature review aimed at analysing and comparing the characteristics and applications of existing artificial neural network models. According to the direction of information transmission in the network, we divide them into feed-forward networks and recurrent networks. In addition, we compare the pros and cons of each model. Our analysis provides guidance for model improvement in future research. Moreover, these models can be applied to aquaculture in the future to promote their development.
The paper study the impact of margin trading on the volatility of the stock market, We selected 469 observation values among the daily Shanghai and Shenzhen 300 index from May 2014 to March 2016. the Granger causality test results are obtained for the model. Empirically study shows that one of the factors affecting stock price fluctuation does include margin trading business, and shows a negative correlation, which plays a more stable role in the stock market.
The paper study the impact of margin trading on the volatility of the stock market, We selected 469 observation values among the daily Shanghai and Shenzhen 300 index from May 2014 to March 2016. the Granger causality test results are obtained for the model. Empirically study shows that one of the factors affecting stock price fluctuation does include margin trading business, and shows a negative correlation, which plays a more stable role in the stock market.
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