scholarly journals Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students

2020 ◽  
Vol 11 ◽  
Author(s):  
Zhen Chen ◽  
Xiaoxuan Yu
Keyword(s):  
Neural Network ◽  
College Students ◽  
Deep Neural Network ◽  
Personality Development ◽  
Entrepreneurship Education ◽  
Development Theory ◽  
Human Personality

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 Prediction Method of College Students’ Psychological Pressure Based on Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bing Wang ◽  
Sitong Liu
Keyword(s):  
Neural Network ◽  
College Students ◽  
Deep Neural Network ◽  
Prediction Method ◽  
Theory Model ◽  
Network Information ◽  
Physical Network ◽  
Gray Theory ◽  
Psychological Pressure ◽  
Current Psychological

Aiming at the problems of low prediction accuracy and efficiency and poor prediction effect in the current psychological pressure prediction methods, a psychological pressure prediction method for college students based on deep neural network is proposed. The structure and algorithm of depth neural network and gray theory model are analyzed. Using the deep neural network, this paper establishes the sample set data of college students’ psychological pressure prediction and constructs the college students’ psychological pressure prediction model combined with the deep neural network algorithm of gray theory. The physical network information model is formed through the relationship between neurons. According to the dynamic changes of college students’ psychological pressure in each neuron of the physical network, the prediction of college students’ psychological pressure is completed. The experimental results show that the proposed method is effective in predicting college students’ psychological pressure and can effectively improve the accuracy and efficiency of college students’ psychological pressure prediction.

scholarly journals An Exploratory Research on Constructing a Model of Innovation and Entrepreneurship Education for College Students Based on Fuzzy Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chen Hao
Keyword(s):  
Neural Network ◽  
College Students ◽  
Fuzzy Neural Network ◽  
Entrepreneurship Education ◽  
Network Algorithm ◽  
Fuzzy Neural ◽  
Control Objective ◽  
Neural Network Algorithm ◽  
Education Model ◽  
Innovation And Entrepreneurship

In order to improve the adaptive scheduling ability of the innovation and entrepreneurship education model for college students, a brain computing-based innovation and entrepreneurship education model for college students is proposed. Through career-planning education, team training will gradually cultivate the leadership skills of entrepreneurs and improve the overall level of innovation and entrepreneurship of college students. The paper proposes to use the fuzzy neural network algorithm to analyze the control constraint parameters of the college students’ innovation and entrepreneurship education model and optimize the design of the control objective function of the university. The control of students’ innovation and entrepreneurship education model is carried out using quantitative optimization methods. The fuzzy neural network is used to identify the control rights of college students’ innovation and entrepreneurship education model through label recognition technology, and the control objective model of college students’ innovation and entrepreneurship education model is constructed. The adaptive load-balancing control method is adopted to control the innovation and entrepreneurship education model of college students. Research shows that this method has a good balance and adaptive control ability in the construction of college students’ innovation and entrepreneurship education model.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Underwater Fish Images Classification by Deep Neural Network

2020 ◽  
Author(s):  
Ala Supriya ◽  
Chiluka Venkat ◽  
Aliketti Deepak ◽  
GV Hari Prasad
Keyword(s):  
Neural Network ◽  
Deep Neural Network
Sign in / Sign up

Export Citation Format

Share Document