Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques

2021 ◽  
Author(s):  
Shalli Rani ◽  
Manpreet Kaur ◽  
Munish Kumar ◽  
Vinayakumar Ravi ◽  
Uttam Ghosh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Machine Learning Techniques ◽  
Shilling Attack ◽  
Learning Techniques

A recommender system for component-based applications using machine learning techniques

2019 ◽  
Vol 164 ◽  
pp. 68-84 ◽  
Author(s):  
Antonio Jesús Fernández-García ◽  
Luis Iribarne ◽  
Antonio Corral ◽  
Javier Criado ◽  
James Z. Wang
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 932
Author(s):  
Rathimala Kannan ◽  
Ivan Zhi Wei Wang ◽  
Hway Boon Ong ◽  
Kannan Ramakrishnan ◽  
Andry Alamsyah
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Low Income ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Cash Assistance ◽  
Learning Techniques ◽  
Boosted Tree ◽  
Machine Learning Models ◽  
Stimulus Package

Background: The Malaysian government reacted to the pandemic’s economic effect with the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the novel coronavirus 2019 (COVID-19) impact on households. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund (EPF) cash withdrawals, credit guarantee scheme and wage subsidies. A survey carried out by the Department of Statistics Malaysia (DOSM) shows that households prefer different types of financial assistance. These preferences forge the need to effectively customise ESPs to manage the economic burden among low-income households. In this study, a recommender system for such ESPs was designed by leveraging data analytics and machine learning techniques. Methods: This study used a dataset from DOSM titled “Effects of COVID-19 on the Economy and Individual - Round 2,” collected from April 10 to April 24, 2020. Cross-Industry Standard Process for Data Mining was followed to develop machine learning models to classify ESP receivers according to their preferred subsidies types. Four machine learning techniques—Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes—were used to build the predictive models for each moratorium, utility discount and EPF and Private Remuneration Scheme (PRS) cash withdrawals subsidies. The best predictive model was selected based on F-score metrics. Results: Among the four machine learning techniques, Gradient Boosted Tree outperformed the rest. This technique predicted the following: moratorium preferences with 93.8% sensitivity, 82.1% precision and 87.6% F-score; utilities discount with 86% sensitivity, 82.1% precision and 84% F-score; and EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score. Households that prefer moratorium subsidies did not favour other financial aids except for cash assistance.  Conclusion: Findings present machine learning models that can predict individual household preferences from ESP. These models can be used to design customised ESPs that can effectively manage the financial burden of low-income households.

scholarly journals COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 932
Author(s):  
Rathimala Kannan ◽  
Ivan Zhi Wei Wang ◽  
Hway Boon Ong ◽  
Kannan Ramakrishnan ◽  
Andry Alamsyah
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Low Income ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Cash Assistance ◽  
Learning Techniques ◽  
Boosted Tree ◽  
Machine Learning Models ◽  
Stimulus Package

Background: The Malaysian government reacted to the pandemic’s economic effect with the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the novel coronavirus 2019 (COVID-19) impact on households. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund (EPF) cash withdrawals, credit guarantee scheme and wage subsidies. A survey carried out by the Department of Statistics Malaysia (DOSM) shows that households prefer different types of financial assistance. These preferences forge the need to effectively customise ESPs to manage the economic burden among low-income households. In this study, a recommender system for such ESPs was designed by leveraging data analytics and machine learning techniques. Methods: This study used a dataset from DOSM titled “Effects of COVID-19 on the Economy and Individual - Round 2,” collected from April 10 to April 24, 2020. Cross-Industry Standard Process for Data Mining was followed to develop machine learning models to classify ESP receivers according to their preferred subsidies types. Four machine learning techniques—Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes—were used to build the predictive models for each moratorium, utility discount and EPF and Private Remuneration Scheme (PRS) cash withdrawals subsidies. The best predictive model was selected based on F-score metrics. Results: Among the four machine learning techniques, Gradient Boosted Tree outperformed the rest. This technique predicted the following: moratorium preferences with 93.8% sensitivity, 82.1% precision and 87.6% F-score; utilities discount with 86% sensitivity, 82.1% precision and 84% F-score; and EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score. Households that prefer moratorium subsidies did not favour other financial aids except for cash assistance.  Conclusion: Findings present machine learning models that can predict individual household preferences from ESP. These models can be used to design customised ESPs that can effectively manage the financial burden of low-income households.

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.

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