Application of Deep CNN in the Class Status of Students

Computer & Telecommunication

Current Issue | Archive | Adv Search

Li Senlin,Peng Xiaoning

School of Computer Science and Engineering, Huaihua University

Download:

PDF(0KB)
Export: BibTeX | EndNote (RIS)

Abstract At present, playing with mobile phones in the class is no longer an individual phenomenon for college students, and most teachers cannot be aware and stop it in time due to the focus on teaching. This paper proposes the application of convolution neural network model in class status for students. The state of students obtained by shooting is automatically recognized by network model and the listening status is identified. After class, teachers can give some guidance to the students purposely, to improve the teaching effects.

Key words： class status deep learning convolutional neural networks

TP183

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Li Senlin
	Peng Xiaoning

Cite this article:

Li Senlin, Peng Xiaoning. Application of Deep CNN in the Class Status of Students. Computer & Telecommunication, 2017, 1(10): 35-37.

URL:

https://www.computertelecom.com.cn/EN/ OR https://www.computertelecom.com.cn/EN/Y2017/V1/I10/35

[1]	LI Chun-hui WANG Xiao-ying ZHANG Qing-jie LIU Han-zhuo LIANG Jia-ye GAO Ning-kang. DDoS Attack Detection Method Based on Multi-scale Convolutional Neural Network [J]. 电脑与电信, 2024, 1(6): 35-.
[2]	WANG Jin WANG Rui. Facial Expression Recognition Algorithm Based on Multi-scale Feature Deep Learning[J]. 电脑与电信, 2024, 1(5): 75-.
[3]	NIE Cheng WANG Jie. A Review of Research and Development in Data Analysis Methods [J]. 电脑与电信, 2024, 1(4): 200-25.
[4]	XIN Bo-fu. Aerial Vehicle Detection in Low Light Environment Based on Yolov5[J]. 电脑与电信, 2024, 1(1): 78-83.
[5]	SU Cui-wen CHAI Guo-qiang. Real-time Facial Expression Recognition Based on Facial Feature Detection [J]. 电脑与电信, 2023, 1(1-2): 17-21.
[6]	HE Zong-xi JIANG Ming-zhong XIE Ming-xia PANG Jia-bao CHEN Qiu-yan HU Yi-bo. Design of Driver Fatigue Recognition Algorithm Based on YOLOv8 and Face Key Points Detection[J]. 电脑与电信, 2023, 1(11): 1-6.
[7]	ZHANG Miao-miao CHAI Guo-qiang YU Hai-le XU Hao-xuan. Facial Feature Test and Fatigue Driving Warning Based on Deep Learning [J]. 电脑与电信, 2022, 1(12): 1-.
[8]	XU Ming-yuan YANG Yan-hong. Design of Baggage Detection System Based on Deep Learning[J]. 电脑与电信, 2021, 1(4): 36-40.
[9]	LIN Long. Model-based Robust RecognitionAlgorithm for Deep Learning Communication Signals[J]. 电脑与电信, 2021, 1(1): 20-22.
[10]	YAN Lei. Study on the Deep Learning Diagnosis and Intervention of Online Courses for Higher Vocational College Enrollment Students under LearningAnalysis Technology[J]. 电脑与电信, 2020, 1(6): 30-33.
[11]	. Time Window Setting on PredictionAccuracy of Rock Pressure in Fully Mechanized Working Face[J]. 电脑与电信, 2020, 1(4): 14-18.
[12]	ZHANG Gang , CHEN Jia-lian, SONG Jian, GUO Jun-qi, ZHOU Chen-rui. Chinese Landscape Painting Automated Generation Model Based on Generative Adversarial Networks[J]. 电脑与电信, 2020, 1(3): 1-.
[13]	YANG Zhen-lin. Low Complexity LDPC Decoder Based on Deep Learning[J]. 电脑与电信, 2020, 1(3): 62-65.
[14]	GENG Yi-wen. Research on Recommendation System Based on StackedAutoencoder[J]. 电脑与电信, 2020, 1(11): 65-70.
[15]	HUANG Ling-zhen. Development in the Research on Steganalysis[J]. 电脑与电信, 2018, 1(1-2): 79-81.

Viewed

Full text

Abstract

Cited

Shared

Discussed