The research status and prospect of "Song of the Wanderer": visual analysis based on CNKI journals

2021 ◽  
Author(s):  
Yijiao Wang
Keyword(s):  
Visual Analysis ◽  
Research Status ◽  
The Wanderer

scholarly journals Bibliometrics Analysis of the Research Status and Trends of the Association Between Depression and Insulin From 2010 to 2020

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaohan Zou ◽  
Yuan Sun
Keyword(s):  
Insulin Resistance ◽  
Visual Analysis ◽  
The United States ◽  
Research Area ◽  
Research Trends ◽  
Mental Illnesses ◽  
Field Analysis ◽  
Future Research ◽  
Research Status ◽  
New Research

Depression is one of the common mental illnesses. Because it is an important complication of diabetes, its association with changes in insulin levels and insulin resistance, the causative factors of diabetes, has attracted widespread attention. However, the association between insulin and depression has not been systematically studied through bibliometric and visual analysis. This study is based on 3131 publications of Web of Science to identify the current research status and research trends in this field. The results show that since 2010, the number of publications has been growing rapidly. Cooperative network analysis shows that the United States, the University of Toronto and Roger S Mcintyre are the most influential countries, research institutes and scholars, respectively. Insulin resistance, obesity, and metabolic syndrome are hot topics in this field. Analysis of keywords and references reveals that “sex hormones,” is new research area that constantly emerging. As far as we know, this study is the first one to visualize the association between depression and insulin and predict potential future research trends through bibliometric and visual analysis.

scholarly journals Analysis of domestic and foreign research status of agricultural pollution control based on VOSviewer

2020 ◽  
Vol 214 ◽  
pp. 03026
Author(s):  
Zhang Xin ◽  
Zhao Keyu ◽  
Peng Lin
Keyword(s):  
Pollution Control ◽  
Prevention And Control ◽  
Visual Analysis ◽  
Research Work ◽  
Pollution Prevention ◽  
The United States ◽  
Agricultural Pollution ◽  
Research Status ◽  
Pollution Prevention And Control ◽  
And Control

Visual analysis of the field of agricultural pollution prevention and control will help researchers to fully understand the research status at home and abroad, and better fit the current situation for further research. Mothod: Web of Science database was used to retrieve 2,214 literatures related to agricultural pollution prevention and control from 2000 to 2018 as data sources, and VOSviewer software was used for visual analysis. The results showed that the research heat of agricultural pollution control showed a good upward trend; Since 2010, China’s rapid growth in the volume of publications has ranked first, while the United States has remained stable for a long time, ranking second; Chinese institutions hold eight of the top 10 spots, with the Chinese academy of sciences at the absolute center of the field; In the research hotspot, it is divided into three clusters.1# In the field of agricultural pollution prevention and control, we have the ability to effectively control nitrogen, quality and phosphorus; China has made in-depth research on sediments, heavy metals and agricultural soils.3# Indian researchers have paid considerable attention to the prevention and control of surface water, drainage basins and groundwater pollution, and carried out in-depth research work for this purpose.

scholarly journals News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ningfeng Sun ◽  
Chengye Du
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Text Classification ◽  
Visual Analysis ◽  
Online News ◽  
Network News ◽  
Classification Model ◽  
Systematic Research ◽  
Research Status ◽  
Development Context

This paper uses the database as the data source, using bibliometrics and visual analysis methods, to statistically analyze the relevant documents published in the field of text classification in the past ten years, to clarify the development context and research status of the text classification field, and to predict the research in the field of text classification priorities and research frontiers. Based on the in-depth study of the background, research status, related theories, and developments of online news text classification, this article analyzes the annual publication trend, subject distribution, journal distribution, institution distribution, author distribution, highly cited literature analysis, and research hotspots. Forefront and other aspects clarify the development context and research status of the text classification field and provide a theoretical reference for the further development of the text classification field. Then, on the basis of systematic research on text classification, deep learning, and news text classification theories, a deep learning-based network news text classification model is constructed, and the function of each module is introduced in detail, which will help the future news text classification of application and improvement provide theoretical basis. On the basis of the predecessors, this article separately studied and improved the neural network model based on the convolutional neural network, cyclic neural network, and attention mechanism and merged the three models into one model, which can obtain local associated features and contextual features and highlight the role of keywords. Finally, experiments are used to verify the effectiveness of the model proposed in this paper and compared with traditional text classification to prove the superiority of the network news text classification based on deep learning proposed in this paper. This article aims to study the internal connection between news comments and the number of votes received by news comments, and through the proposed model, the number of votes for news comments can be predicted.

Sign in / Sign up

Export Citation Format

Share Document