An Online Education Data Classification Model Based on Tr_MAdaBoost Algorithm

2019 ◽  
Vol 28 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Lasheng Yu ◽  
Xu Wu ◽  
Yu Yang
Keyword(s):  
Online Education ◽  
Data Classification ◽  
Classification Model ◽  
Model Based ◽  
Education Data

scholarly journals An IoT Time Series Data Security Model for Adversarial Attack Based on Thermometer Encoding

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhongguo Yang ◽  
Irshad Ahmed Abbasi ◽  
Fahad Algarni ◽  
Sikandar Ali ◽  
Mingzhu Zhang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Data Classification ◽  
Classification Model ◽  
Series Data ◽  
Security Model ◽  
Learning Methods ◽  
Model Based ◽  
Adversarial Attack

Nowadays, an Internet of Things (IoT) device consists of algorithms, datasets, and models. Due to good performance of deep learning methods, many devices integrated well-trained models in them. IoT empowers users to communicate and control physical devices to achieve vital information. However, these models are vulnerable to adversarial attacks, which largely bring potential risks to the normal application of deep learning methods. For instance, very little changes even one point in the IoT time-series data could lead to unreliable or wrong decisions. Moreover, these changes could be deliberately generated by following an adversarial attack strategy. We propose a robust IoT data classification model based on an encode-decode joint training model. Furthermore, thermometer encoding is taken as a nonlinear transformation to the original training examples that are used to reconstruct original time series examples through the encode-decode model. The trained ResNet model based on reconstruction examples is more robust to the adversarial attack. Experiments show that the trained model can successfully resist to fast gradient sign method attack to some extent and improve the security of the time series data classification model.

scholarly journals Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shu-tong Xie ◽  
Qiong Chen ◽  
Kun-hong Liu ◽  
Qing-zhao Kong ◽  
Xiu-juan Cao
Keyword(s):  
Online Education ◽  
Behavior Analysis ◽  
Student Behavior ◽  
Feature Selection Method ◽  
Multiclass Classification ◽  
Classification Model ◽  
Learning Behavior ◽  
Teaching Activities ◽  
Education Data ◽  
Output Code

In recent years, online and offline teaching activities have been combined by the Small Private Online Course (SPOC) teaching activities, which can achieve a better teaching result. Therefore, colleges around the world have widely carried out SPOC-based blending teaching. Particularly in this year’s epidemic, the online education platform has accumulated lots of education data. In this paper, we collected the student behavior log data during the blending teaching process of the “College Information Technology Fundamentals” course of three colleges to conduct student learning behavior analysis and learning outcome prediction. Firstly, data collection and preprocessing are carried out; cluster analysis is performed by using k-means algorithms. Four typical learning behavior patterns have been obtained from previous research, and these patterns were analyzed in terms of teaching videos, quizzes, and platform visits. Secondly, a multiclass classification framework, which combines a feature selection method based on genetic algorithm (GA) with the error correcting output code (ECOC) method, is designed for training the classification model to achieve the prediction of grade levels of students. The experimental results show that the multiclass classification method proposed in this paper can effectively predict the grade of performance, with an average accuracy rate of over 75%. The research results help to implement personalized teaching for students with different grades and learning patterns.

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