Identifying Phishing Through Web Content and Addressed Bar-Based Features

Author(s):  
Nureni A. Azeez ◽  
Joseph Ade ◽  
Sanjay Misra ◽  
Adewole Adewumi ◽  
Charles Van der Vyver ◽  
...  
Keyword(s):  
2017 ◽  
Vol 9 (2) ◽  
pp. 67-71
Author(s):  
Herru Darmadi ◽  
Yan Fi ◽  
Hady Pranoto

Learning Object (LO) is a representation of interactive content that are used to enrich e-learning activities. The goals of this case study were to evaluate accessibility and compatibility factors from learning objects that were produced by using BINUS E-learning Authoring Tool. Data were compiled by using experiment to 30 learning objects by using stratified random sampling from seven faculties in undergraduate program. Data were analyzed using accessibility and compatibility tests based on Web Content Accessibility Guidelines 2.0 Level A. Results of the analysis for accessibility and compatibility tests of Learning Objects was 90% better than average. The result shows that learning objects is fully compatible with major web browser. This paper also presents five accessibility problems found during the test and provide recommendation to overcome the related problems. It can be concluded that the learning objects that were produced using BINUS E-learning Authoring Tool have a high compatibility, with minor accessibility problems. Learning objects with a good accessibility and compatibility will be beneficial to all learner with or without disabilities during their learning process. Index Terms—accessibility, compatibility, HTML, learning object, WCAG2.0, web


2012 ◽  
Author(s):  
Kamlesh Padaliya ◽  
Amarjeet Singh ◽  
Ashutosh Kumar Bhatt ◽  
Manoj Chandra Lohani

2002 ◽  
Vol 45 (11) ◽  
pp. 33-37 ◽  
Author(s):  
Soroush Sedaghat ◽  
Josef Pieprzyk ◽  
Ehsan Vossough
Keyword(s):  

2019 ◽  
Vol 15 (4) ◽  
pp. 41-56 ◽  
Author(s):  
Ibukun Tolulope Afolabi ◽  
Opeyemi Samuel Makinde ◽  
Olufunke Oyejoke Oladipupo

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.


2009 ◽  
Vol 13 (3) ◽  
pp. 215-249 ◽  
Author(s):  
Elisa Bertino ◽  
Elena Ferrari ◽  
Andrea Perego

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