scholarly journals Evaluating Factors for Predicting the Life Dissatisfaction of South Korean Elderly using Soft Margin Support Vector Machine based on Communication Frequency, Social Network Health Behavior and Depression

2019 ◽  
Vol 10 (9) ◽  
Author(s):  
Haewon Byeon ◽  
Seong-Tae Kim
Keyword(s):  
Support Vector Machine ◽  
Social Network ◽  
Health Behavior ◽  
Support Vector ◽  
South Korean ◽  
Life Dissatisfaction ◽  
Soft Margin ◽  
Communication Frequency ◽  
Korean Elderly

A Soft-Margin Support Vector Machine based on Normal Convex Hulls

2011 ◽  
Vol 3 (7) ◽  
pp. 244-254
Author(s):  
Hong Peng ◽  
Meixiang Luo ◽  
Weihang Lv ◽  
Linkai Luo
Keyword(s):  
Support Vector Machine ◽  
Convex Hulls ◽  
Support Vector ◽  
Soft Margin

Improved Model for Detecting Fake Profiles in Online Social Network: A Case Study of Twitter

2019 ◽  
pp. 1-17
Author(s):  
Adebola K. Ojo
Keyword(s):  
Support Vector Machine ◽  
Social Network ◽  
Language Processing ◽  
Naive Bayes ◽  
Online Social Network ◽  
Virtual Community ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Internet ◽  
Sensitive Information

Online Social Network (OSN) is like a virtual community where people build social networks and relations with one another. The open access to the Internet has increased the growth of OSN which has attracted intruders to exploit the weaknesses of the Internet and OSN to their own gain. The rise in the usage of OSN has posed security threats to OSN users as they share personal and sensitive information online which could be exploited by these intruders by creating profiles to carry out a series of malicious activities on the social network. In fact, it is no gain saying that the intent of creating fake accounts has adverse effect and the Internet has made it quite easy to concede one’s identity; and this makes it difficult to detect fake accounts as they try to imitate real accounts. In this study, a model that can accurately identify fake profiles in OSN which uses Natural Language Processing Technique to eliminate or reduce the size of the dataset thereby improving the overall performance of the model was proposed.  Principal Component Analysis was used for appropriate feature selection. After extraction, six attributes/features that influenced the classifier were found. Support Vector Machine (SVM), Naïve Bayes and Improved Support Vector Machine (ISVM) were used as Classifiers. ISVM introduced a penalty parameter to the standard SVM objective function to reduce the inequality constraints between the slack variables. This gave a better result of 90% than the SVM and Naïve Bayes which gave 77.4% and 77.3% respectively.

Detection of crack eggs by image processing and soft-margin support vector machine

2018 ◽  
Vol 18 (1) ◽  
pp. 21-31 ◽  
Author(s):  
Lanlan Wu ◽  
Qiaohua Wang ◽  
Dengfei Jie ◽  
Shucai Wang ◽  
Zhihui Zhu ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Soft Margin
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