3 research outputs found
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining
With the spread and development of new epidemics, it is of great reference
value to identify the changing trends of epidemics in public emotions. We
designed and implemented the COVID-19 public opinion monitoring system based on
time series thermal new word mining. A new word structure discovery scheme
based on the timing explosion of network topics and a Chinese sentiment
analysis method for the COVID-19 public opinion environment is proposed.
Establish a "Scrapy-Redis-Bloomfilter" distributed crawler framework to collect
data. The system can judge the positive and negative emotions of the reviewer
based on the comments, and can also reflect the depth of the seven emotions
such as Hopeful, Happy, and Depressed. Finally, we improved the sentiment
discriminant model of this system and compared the sentiment discriminant error
of COVID-19 related comments with the Jiagu deep learning model. The results
show that our model has better generalization ability and smaller discriminant
error. We designed a large data visualization screen, which can clearly show
the trend of public emotions, the proportion of various emotion categories,
keywords, hot topics, etc., and fully and intuitively reflect the development
of public opinion
An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM
New coronavirus disease (COVID-19) has constituted a global pandemic and has
spread to most countries and regions in the world. By understanding the
development trend of a regional epidemic, the epidemic can be controlled using
the development policy. The common traditional mathematical differential
equations and population prediction models have limitations for time series
population prediction, and even have large estimation errors. To address this
issue, we propose an improved method for predicting confirmed cases based on
LSTM (Long-Short Term Memory) neural network. This work compared the deviation
between the experimental results of the improved LSTM prediction model and the
digital prediction models (such as Logistic and Hill equations) with the real
data as reference. And this work uses the goodness of fitting to evaluate the
fitting effect of the improvement. Experiments show that the proposed approach
has a smaller prediction deviation and a better fitting effect. Compared with
the previous forecasting methods, the contributions of our proposed improvement
methods are mainly in the following aspects: 1) we have fully considered the
spatiotemporal characteristics of the data, rather than single standardized
data; 2) the improved parameter settings and evaluation indicators are more
accurate for fitting and forecasting. 3) we consider the impact of the epidemic
stage and conduct reasonable data processing for different stage
Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey
In light of growing challenges in agriculture with ever growing food demand
across the world, efficient crop management techniques are necessary to
increase crop yield. Precision agriculture techniques allow the stakeholders to
make effective and customized crop management decisions based on data gathered
from monitoring crop environments. Plant phenotyping techniques play a major
role in accurate crop monitoring. Advancements in deep learning have made
previously difficult phenotyping tasks possible. This survey aims to introduce
the reader to the state of the art research in deep plant phenotyping.Comment: Featured as an article at Journal of Advanced Computing and
Communications, April 2020. arXiv admin note: text overlap with
arXiv:1805.00881 by other author