scholarly journals Research on the Cultivation and Promotion of College Students’ Legal Quality and Legal Knowledge Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Cheng Cheng ◽  
Wenlin Chen ◽  
Ao Li
Keyword(s):  
Neural Network ◽  
College Students ◽  
Deep Learning ◽  
Survey Method ◽  
Legal Consciousness ◽  
Legal Knowledge ◽  
Factors Affecting ◽  
Interview Method ◽  
Knowledge Based ◽  
Explanatory Structure

Under the new situation, it is urgent to strengthen the cultivation of the legal consciousness of young college students, which makes the research on the cultivation and promotion of college students’ legal quality and legal knowledge become very important. This article is aimed at studying the cultivation and promotion of college students’ legal quality and legal knowledge based on deep learning. This article first analyzes the legal quality and legal knowledge level of college students by proposing a questionnaire survey method, interview method, and interdisciplinary research method and establishes an explanatory structure model of the factors affecting the legal quality of college students; secondly, it introduces the basic theories of deep learning and explains its practical application; finally, the training rules of neural network are constructed through experiments, and the stage method of legal quality training based on BP neural network is introduced in detail. Considering that virtual reality technology has recently penetrated into the game industry and other fields, we have reason to believe that it will find ways to enter the legal knowledge training industry. The experimental results of this article show that the cultivation and improvement of college students’ legal quality and legal knowledge based on deep learning can reduce the momentary confusion and impulsiveness of college students. Among them, more than 80% of college students who know a little about the Constitution account for more than 80%, which also shows the importance of cultivating and improving the laws and regulations of college students. The results show that the method is accurate and fast.

Deep Learning-based Mental Health Monitoring Scheme for College Students Using Convolutional Neural Network

2021 ◽  
Vol 30 (06n08) ◽  
Author(s):  
Chao Du ◽  
Chang Liu ◽  
P. Balamurugan ◽  
P. Selvaraj
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
College Students ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Health Monitoring ◽  
Simulation Analysis ◽  
Personality Development ◽  
Monitoring Scheme ◽  
Depression Level

Artificial intelligence (AI) in healthcare has recently been promising using deep neural networks. It is indeed even been in clinical trials more and more, with positive outcomes. Deep learning is the process of using algorithms to train a neural network model using huge quantities of data to learn how to execute a given task and then make an accurate classification or prediction. Apart from physical health monitoring, such deep learning models can be used for the mental health evaluation of individuals. This study thus designs a deep learning-based mental health monitoring scheme (DL-MHMS) for college students. This model uses the most efficient convolutional neural network (CNN) to classify the mental health status as positive, negative, and normal using the EEG signals collected from college students. The simulation analysis achieves the highest classification accuracy and F1 scores of 97.54% and 98.35%, less sleeping disorder rate of 21.19%, low depression level of 18.11%, reduced suicide attention level of 28.14%, increasing personality development ratio of 97.52%, enhance self-esteem ratio of 98.42%, compared to existing models.

scholarly journals Taking AI Neural Network to Analyze Labor Rights and Labor Disputes

2021 ◽  
Vol 290 ◽  
pp. 02028
Author(s):  
Jia Liu ◽  
Ying Yang ◽  
Bao-Yao Xiao ◽  
Zhi-Tao Huang ◽  
Xiao-Hui Nie ◽  
...  
Keyword(s):  
Neural Network ◽  
College Students ◽  
Negative Correlation ◽  
Survey Method ◽  
Labor Rights ◽  
Labor Disputes ◽  
Direct Role ◽  
Labor Safety ◽  
Contract Signing

Exploring labor rights on the cognition of relationship with labor dispute is not only from the direct role of workers and HR, but also from the fight for labor rights of college students. However, many labor disputes in labor process are mainly related to labor rights. Therefore, we explore the relationship between labor rights and labor disputes, use neural network analysis and questionnaire survey method, and execute AI combining with big data analysis tools to collect first-hand data from two aspects of college students, workers in the industry, HR and labor dispatch personnel. Finally, the results show that: (1) there is a negative correlation between social security and labor disputes; (2) there is a negative correlation between labor safety and labor disputes; (3) there is a negative correlation between wages and labor disputes; (4) there is a negative correlation between labor contract signing and labor disputes; (5) there is a positive correlation between rights awareness and labor disputes.

scholarly journals Modeling of Badminton Intelligent Teaching System Based on Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ping Wang
Keyword(s):  
Neural Network ◽  
Teaching Methods ◽  
Survey Method ◽  
Control Group ◽  
Traditional Teaching ◽  
Teaching System ◽  
Interview Method ◽  
Daily Exercise ◽  
Learning Interest ◽  
Experimental Group

With the popularity of neural networks and the maturity of network technology, fully functional intelligent terminals have become indispensable devices for people’s lives, research, and entertainment. However, in the badminton teaching of people’s daily exercise, the old traditional teaching mode is still used, which cannot achieve good teaching effects. In order to study the best of badminton teaching, this article is based on the previous research, by introducing neural network, using literature data method, questionnaire survey method, interview method, experimental method, and other research methods to conduct research. The intelligent learning of the network is connected, experiments are designed to be applied, and then, data analysis is conducted. The research results show that with the use of smartphone mobile learning teaching methods, the experimental group students’ technical movements, theoretical knowledge, learning interest, and learning enthusiasm are about 20% higher than those of the control group, and the badminton intelligent teaching system based on neural network is better than the control group’s traditional teaching methods. The satisfaction of the students in the experimental group was also higher than that of the students in the control group. Based on what network, the satisfaction of badminton teaching can reach more than 90%. This student recognizes and accepts the teaching methods of intelligent teaching.

scholarly journals A comprehensive investigation of the critical factors influencing knowledge management strategic alignment

2019 ◽  
pp. 215-232
Keyword(s):  
Knowledge Management ◽  
Strategic Alignment ◽  
Entropy Method ◽  
Business Strategies ◽  
Survey Method ◽  
Sign Test ◽  
Group Method ◽  
Management Support ◽  
Factors Affecting ◽  
Knowledge Based

Despite the huge investment in Knowledge Management (KM) initiatives by many organizations, KM projects are facing a high failure rate. One of the main reasons is the lack of alignment between business and KM strategies. This study aims to identify and prioritize the factors affecting strategic alignment between business and KM strategies. A comprehensive literature review integrated with the focus group method was used to identify and classify effective factors of KM strategic alignment. Next, a survey method was conducted to evaluate and prioritize the extracted factors suggested by the experts. Further, the sign test was used to analyze the priorities of these factors using Shannon’s entropy method. The results reveal that the key factors affecting strategic alignment between business strategies and KM include knowledge-based culture, KM governance, and strategic approach to KM, communication between KM and business, top management support, human resource capabilities, environmental and competitive factors and IT management capabilities. The findings provide a comprehensive KM-business strategic framework.

scholarly journals Analysis on Behavior Factors of Successful Entrepreneurship of College Students of Local University in Guangdong Province

2020 ◽  
Vol 2 (1) ◽  
pp. 35-44
Author(s):  
Li Bing-quan ◽  
Hu Rong ◽  
Du Hai-xin ◽  
Zhang Xu-dong
Keyword(s):  
College Students ◽  
Guangdong Province ◽  
Behavioral Factors ◽  
Five Dimensions ◽  
Factors Affecting ◽  
Four Dimensions ◽  
Interview Method ◽  
Daily Behavior ◽  
Psychometric Method ◽  
Behavior Scale

Research purpose: To understand the behavioral factors affecting the success of college students’ Entrepreneurship. Research tools: the College Students’ Daily Success Behavior Scale (CSDSB) and the College Students’ Entrepreneurship Scale (CSES). Research methods: Psychometric method and Interview method. Research objects or samples: 32 college students in Guangdong Province who are starting their own businesses. Results: (1) The total score of college students’ daily success behavior and its five dimensions are positively correlated with the total score of college students’ entrepreneurship and its four dimensions. (2) The total score of college students’ daily success behavior and the dimension of “Excelsior (Ex)” have significant positive predictive effects on their entrepreneurial ability (F = 32.375, P < 0.001). (3) Successful entrepreneurs possess behavioral quality consistent with the dimensions of the college students’ daily behavior scale. Conclusion: Daily behavioral factors have a significant positive predictive effect on college students’ entrepreneurship.

Differential factors affecting testing and career choices of Black college students

1972 ◽  
Author(s):  
Ronald G. Taylor ◽  
Richard D. Grosz ◽  
Robert Whetstone ◽  
Catherine Joseph ◽  
Leon Willis
Keyword(s):  
College Students ◽  
Career Choices ◽  
Black College ◽  
Black College Students ◽  
Factors Affecting

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

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