The Information Grading Management System of College Students Based on Deep Learning

2020 ◽  
pp. 308-317
Author(s):  
Tao Jiang ◽  
Ye Song
Keyword(s):  
College Students ◽  
Deep Learning ◽  
Management System

scholarly journals Design and Implementation of Intelligent Sports Training System for College Students' Mental Health Education

2021 ◽  
Vol 12 ◽  
Author(s):  
Ting Wang ◽  
Jinkyung Park
Keyword(s):  
Artificial Intelligence ◽  
College Students ◽  
Deep Learning ◽  
Management System ◽  
Course Evaluation ◽  
Learning Technology ◽  
Sports Management ◽  
Sports Training ◽  
Recommendation Algorithm ◽  
Registration System

In order to solve the problems of poor physical fitness of college students and low efficiency of college sport venues' management, an intelligent sports management system based on deep learning technology is designed by using information technology and human-computer interaction under artificial intelligence. Based on the Browser/Server (B/S) structure, the intelligent sports management system is constructed. The basic framework of Spring Cloud is used to integrate the framework and components of each part, and a distributed microservice system is built. The artificial intelligence recommendation algorithm is used to analyze the user's age, body mass index (BMI), and physical health status, and recommend sports programs suitable for students, thus realizing the intelligent sports program recommendation function. At the same time, the recommendation algorithm is used to complete the course recommendation according to the students' preferences, teaching distance, opening time, course evaluation, and other indexes, and the course registration system is constructed; after the analysis of the entity and the relationship between the entities of the intelligent sports system, the database relational model of the system is designed with the entity relationship (E-R) diagram. The results of the functional test show that the system can run well. In conclusion, the sports training environment instructional system based on artificial intelligence and deep learning technology can meet the teaching needs of colleges, improve the sports' quality for college students, and promote psychological education.

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm

scholarly journals Automatic Attendance Management System under Unconstrained Video using Face Recognition

2020 ◽  
Vol 9 (10) ◽  
pp. 229-232
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Management System ◽  
Traditional Method ◽  
Surveillance Camera ◽  
Recognition Systems ◽  
End To End ◽  
Learning By Using

Attendance Management System under unconstrained video using face recognition technology has made a great variation from the traditional method of attendance marking system. This attendance management system has been developed under the domain of Deep Learning by using Face recognition. Automatic Attendance Management under unconstrained video using face recognition systems which automatically mark attendance by detecting end to end face from the frames obtained from live stream video of surveillance camera which placed in center of the classroom. From the recognized faces, it will be compared with stored images in database, then the attendance report will be generated and it also provides attendance reports to parents of the absentee’s student.

scholarly journals Smart Attendance Management System Using Face Recognition

2021 ◽  
pp. 706-711
Author(s):  
K. V. Prasad Reddy ◽  
R. Chaitanya Latha ◽  
M. Lohitha ◽  
R. Sonia ◽  
A. B. Usha
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Conventional Method ◽  
Management System ◽  
Reading And Writing ◽  
Manual Method

To Maintain the attendance record with day to day activities is a challenging task. The conventional method of calling name of each student is time consuming and there is always a chance of proxy attendance. The smart attendance management will replace the manual method, which takes a lot of time consuming and difficult to maintain. There are many biometric processes, in that face recognition is the best method. Here we are using the computer vision which is a field of deep learning that is used for the camera reading and writing and using TkInter to create a GUI application.

scholarly journals A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 260
Author(s):  
Mahendiran T. Vellingiri ◽  
Ibrahim M. Mehedi ◽  
Thangam Palaniswamy
Keyword(s):  
Renewable Energy ◽  
Deep Learning ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management System ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Soc Estimation ◽  
Hybrid Electric

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

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.

ATTENDANCE MANAGEMENT SYSTEM USING FACE RECOGNITION AND DEEP LEARNING

2021 ◽  
Author(s):  
Filip Marenčić ◽  
Zlatko Stapić ◽  
Petra Grd
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Management System
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