A Vector Space Model based spam SMS filter

Li, Wei; Zeng, Sisheng

doi:10.1109/iccse.2016.7581640

Cited by 7 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Younge and Kuhn (2016) used VSM as a measure to detect patent similarity and concluded that VSM is a better measure to use for this purpose. Li and Zeng (2016) also used VSM as a foundational technology to develop a system that can be used to filter spams in mobile text messages.…”

Section: Previous Workmentioning

confidence: 99%

Analyzing students online learning behavior in blended courses using Moodle

Estacio

Raga

2017

AAOUJ

View full text Add to dashboard Cite

For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Purpose -The purpose of this paper is to describe a proposal for a data-driven investigation aimed at determining whether students' learning behavior can be extracted and visualized from action logs recorded by Moodle. The paper also tried to show whether there is a correlation between the activity level of students in online environments and their academic performance with respect to final grade. Design/methodology/approach -The analysis was carried out using log data obtained from various courses dispensed in a university using a Moodle platform. The study also collected demographic profiles of students and compared them with their activity level in order to analyze how these attributes affect students' level of activity in the online environment.Findings -This work has shown that data mining algorithm like vector space model can be used to aggregate the action logs of students and quantify it into a single numeric value that can be used to generate visualizations of students' level of activity. The current investigation indicates that there is a lot of variability in terms of the correlation between these two variables.Practical implications -The value presented in the study can help instructors monitor course progression and enable them to rapidly identify which students are not performing well and adjust their pedagogical strategies accordingly. Originality/value -A plan to continue the work by developing a complete dashboard style interface that instructors can use is already underway. More data need to be collected and more advanced processing tools are necessary in order to obtain a better perspective on this issue.

show abstract

Section: Previous Workmentioning

confidence: 99%

Analyzing students online learning behavior in blended courses using Moodle

Estacio

Raga

2017

AAOUJ

View full text Add to dashboard Cite

show abstract

“…Authors in 2016 [6] perform many modifications in Support Vector Machine (VSM) method in order to address the difficulties in filtering issue of spam SMS. The dataset they are working on has been collected from 'Dahan Tricom database'.…”

Section: Application Of Detecting Spam In Smsmentioning

confidence: 99%

A survey of Emerging Techniques in Detecting SMS Spam

Alqahtani

Alghazzawi

2019

TMLAI

View full text Add to dashboard Cite

In the past years, spammers have focused their attention on sending spam through short messages services (SMS) to mobile users. They have had some success because of the lack of appropriate tools to deal with this issue. This paper is dedicated to review and study the relative strengths of various emerging technologies to detect spam messages sent to mobile devices. Machine Learning methods and topic modelling techniques have been remarkably effective in classifying spam SMS. Detecting SMS spam suffers from a lack of the availability of SMS dataset and a few numbers of features in SMS. Various features extracted and dataset used by the researchers with some related issues also discussed. The most important measurements used by the researchers to evaluate the performance of these techniques were based on their recall, precision, accuracies and CAP Curve. In this review, the performance achieved by machine learning algorithms was compared, and we found that Naive Bayes and SVM produce effective performance.

show abstract