scholarly journals De-identification is Insufficient to Protect Student Privacy, or – What Can a Field Trip Reveal?

2021 ◽  
Vol 8 (2) ◽  
pp. 83-92
Author(s):  
Elad Yacobson ◽  
Orly Fuhrman ◽  
Sara Hershkovitz ◽  
Giora Alexandron
Keyword(s):  
Machine Learning ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Complete Solution ◽  
Machine Learning Techniques ◽  
Student Identity ◽  
Sensitive Information ◽  
Student Privacy ◽  
Learning Techniques ◽  
K 12

Learning analytics have the potential to improve teaching and learning in K–12 education, but as student data is increasingly being collected and transferred for the purpose of analysis, it is important to take measures that will protect student privacy. A common approach to achieve this goal is the de-identification of the data, meaning the removal of personal details that can reveal student identity. However, as we demonstrate, de-identification alone is not a complete solution. We show how we can discover sensitive information about students by linking de-identified datasets with publicly available school data, using unsupervised machine learning techniques. This underlines that de-identification alone is insufficient if we wish to further learning analytics in K–12 without compromising student privacy.

scholarly journals Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

2018 ◽  
Vol 11 (1) ◽  
pp. 105 ◽  
Author(s):  
Syed Abidi ◽  
Mushtaq Hussain ◽  
Yonglin Xu ◽  
Wu Zhang
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Teaching And Learning ◽  
Performance Metrics ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Tutoring System ◽  
Learning Techniques

Incorporating substantial, sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study was to identify the confused students who had failed to master the skill(s) given by the tutors as homework using the Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study, and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models including: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated, and tested learning algorithms, performed stratified cross-validation, and measured the performance of the models through various performance metrics, i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity, and Specificity. We found RF, GLM, XGBoost, and DL were high accuracy-achieving classifiers. However, other perceptions such as detecting unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students that were confused when attempting the homework exercise, to help foster their knowledge and talent to play a vital role in environmental development.

Formjacking attack: Are we safe?

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vijaya Geeta Dharmavaram
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Defense Mechanism ◽  
Machine Learning Techniques ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Modus Operandi ◽  
Content Type ◽  
Learning Techniques ◽  
Cyber Criminals

Purpose Today, online shopping and online business has become a new norm especially in the current pandemic scenario. With more businesses running online, cyber criminals are coming up with different tactics to steal identity and sensitive information such as credit card and banking credentials either for personal monetary gain or to sell in the dark Web. One form of such attack that is seen in the recent times is formjacking attack. This paper aims to review the current scenario of formjacking attack and its modus operandi. The paper also provides certain counter measures that can be adopted by the users and website owners. Design/methodology/approach The paper mainly focuses on the modus operandi of formjacking attack to understand the severity of the problem. Based on the way the attack is carried out, some guidelines to be followed are provided. Later, a brief review of machine learning techniques is furnished to understand how it may help as secure defense mechanism. Findings Formjacking attacks are on a rise in the past two years, especially during the holiday season. Cyber criminals have been using smart tactics to carry out these attacks which are very difficult to detect. Machine learning techniques may prove to be effective in combating these attacks. Originality/value Formjacking attack is not just a concern of the customers who may lose their sensitive data, but the onus also lies on the companies itself to ensure they protect their customer’s data from theft. Not much research is found regarding formjacking attack, as it is relatively a new form of attack. The paper reviews this attack and provides some measure that can be followed. It also provides few guidelines which can be used for further research in devising a security tool to mitigate this problem.

scholarly journals A review on prediction of diabetes type 2 by machine learning techniques

2021 ◽  
Vol 12 (3) ◽  
pp. 5638-5651
Author(s):  
Alka Singh
Keyword(s):  
Machine Learning ◽  
Diabetes Type 2 ◽  
Relevant Information ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
Sensitive Information ◽  
Information Usage ◽  
Learning Techniques ◽  
Prediction Of Diabetes

Machine learning is considered to be one of the most promising tools when it comes to working with heterogeneous data. It provides a new dimension which enables one to extract relevant data and take decision for the effective functioning of the network, making use of network generated data. Every sphere of our life is now dependent on machine learning. It has flourished in every dimension. Making it versatile and ever demanding. Department of healthcare contains very abundant and sensitive information which is needed to be carefully handled. Diabetes mellitus is increasing exponentially and is spreading like anything in the world. A reliable prediction system should be present for diagnosing diabetes. Variety of machine learning techniques find their use in the examination of data from variant perspectives and summarizing it into effective information. Usage of new patterns is done to elucidate these patterns in order to deliver relevant information for their users. By making use of techniques such as SVM, random forest, logistic regression, naïve bayes etc the prediction of diabetes can be done easily and accurately. In this study we will make use of different machine learning techniques and try to find accurate prediction regarding the same.

Digital Transformation and Cybersecurity Challenges

2021 ◽  
pp. 203-226
Author(s):  
Fatimah Al Obaidan ◽  
Saqib Saeed
Keyword(s):  
Machine Learning ◽  
Communication Systems ◽  
Human Life ◽  
Malware Detection ◽  
Digital Transformation ◽  
Machine Learning Techniques ◽  
Sensitive Information ◽  
Static And Dynamic Analysis ◽  
Learning Techniques

Digital transformation has revolutionized human life but also brought many cybersecurity challenges for users and enterprises. The major threats that affect computers and communication systems by damaging devices and stealing sensitive information are malicious attacks. Traditional anti-virus software fails to detect advanced kind of malware. Current research focuses on developing machine learning techniques for malware detection to respond in a timely manner. Many systems have been evolved and improved to distinguish the malware based on analysis behavior. The analysis behavior is considered a robust technique to detect, analyze, and classify malware, categorized into two models: a static and dynamic analysis. Both types of previous analysis have advantages and limitations. Therefore, the hybrid method combines the strength of static and dynamic analyses. This chapter conducted a systematic literature review (SLR) to summarize and analyze the quality of published studies in malware detection using machine learning techniques and hybrid analysis that range from 2016 to 2021.

scholarly journals Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

2018 ◽  
Author(s):  
Syed Muhammad Raza Abidi ◽  
Mushtaq Hussain ◽  
Yonglin Xu ◽  
Wu Zhang
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Teaching And Learning ◽  
Performance Metrics ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Tutoring System ◽  
Learning Techniques

Incorporating substantial sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study is to identify the confused students who have failed to master the skill(s) given by the tutors as a homework using Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models that include: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated and tested learning algorithms, performed stratified cross-validation and measured the performance of the models through various performance metrics i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity & Specificity. We found GLM, DT & RF are high accuracies achieving classifiers. However, other perceptions such as detection of unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students which were confused attempting the homework exercise and can help students foster their knowledge, and talent to play a vital role in environmental development.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques
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