scholarly journals A Study of University Website Content Classification Using Machine Learning

2021 ◽  
Vol 2129 (1) ◽  
pp. 012043
Author(s):  
H R Mohd Sharul ◽  
I Nor Azman ◽  
M Mohd Su Elya
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Web Pages ◽  
Web Content ◽  
Web Based ◽  
Content Classification ◽  
Learning Techniques ◽  
Information Products ◽  
Website Content ◽  
New Framework

Abstract A university website is a gateway to the institution’s information, products, and services. As websites grow into millions in numbers, it is essential to ensure that the content reflects the needs of its students, staff, and other academic institution as their primary users. This research investigates the development of a new framework that uses machine learning techniques based on webometrics and web usability to classify the web pages of academic websites automatically. The framework briefly introduced how it can help classify web content and eliminate unrelated content and reduce storage space. The findings can also be used to analyse other web-based data to give additional insights that may be beneficial for webometrics studies and identify university website’ characteristics.

scholarly journals Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures

Computers ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 35 ◽  
Author(s):  
Xuan Dau Hoang ◽  
Ngoc Tuong Nguyen
Keyword(s):  
Machine Learning ◽  
Web Applications ◽  
False Positive Rate ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Web Pages ◽  
Government Organizations ◽  
Detection Model ◽  
Learning Techniques ◽  
Positive Rate

Defacement attacks have long been considered one of prime threats to websites and web applications of companies, enterprises, and government organizations. Defacement attacks can bring serious consequences to owners of websites, including immediate interruption of website operations and damage of the owner reputation, which may result in huge financial losses. Many solutions have been researched and deployed for monitoring and detection of website defacement attacks, such as those based on checksum comparison, diff comparison, DOM tree analysis, and complicated algorithms. However, some solutions only work on static websites and others demand extensive computing resources. This paper proposes a hybrid defacement detection model based on the combination of the machine learning-based detection and the signature-based detection. The machine learning-based detection first constructs a detection profile using training data of both normal and defaced web pages. Then, it uses the profile to classify monitored web pages into either normal or attacked. The machine learning-based component can effectively detect defacements for both static pages and dynamic pages. On the other hand, the signature-based detection is used to boost the model’s processing performance for common types of defacements. Extensive experiments show that our model produces an overall accuracy of more than 99.26% and a false positive rate of about 0.27%. Moreover, our model is suitable for implementation of a real-time website defacement monitoring system because it does not demand extensive computing resources.

scholarly journals The use of Machine Learning Techniques in a Web-Based Learning Diagnosis System Program

2020 ◽  
Vol 8 (6) ◽  
pp. 4861-4865
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Analysis Framework ◽  
Web Based ◽  
Web Based Learning ◽  
Learning Techniques ◽  
Insightful Analysis ◽  
System Program

This work proposes a canny learning finding framework that bolsters a Web-based topical learning model, which expects to develop students' capacity of information incorporation by giving the students the chances to choose the learning themes that they are intrigued, and gain information on the particular subjects by surfing on the Internet to look through related adapting course-product and examining what they have realized with their associates. In view of the log documents that record the students' past web-based learning conduct, an insightful analysis framework is utilized to give fitting learning direction to help the students in improving their investigation practices and grade online class interest for the teacher. The accomplishment of the students' last reports can likewise be anticipated by the conclusion framework precisely. Our trial results uncover that the proposed learning finding framework can proficiently assist students with expanding their insight while surfing in the internet Web-based "topic based learning" model.

Retrieval of Web Pages on Real-World Events related to Physical Objects

2012 ◽  
Vol 2 (1) ◽  
pp. 65-80 ◽  
Author(s):  
Takeshi Okadome ◽  
Hajime Funai ◽  
Sho Ito ◽  
Junya Nakajima ◽  
Koh Kakusho
Keyword(s):  
Machine Learning ◽  
Search Engine ◽  
Real World ◽  
Machine Learning Techniques ◽  
Web Pages ◽  
Learning Techniques ◽  
Physical Objects

The method proposed in this paper searches for web pages using an event-related query consisting of a noun, verb, and genre term. It re-ranks web pages retrieved using a standard search engine on the basis of scores calculated from an expression consisting of weighted factors such as the frequency of query words. For the genres that are characterized by their genre terms, the method optimizes the weights of the expression. Furthermore, the method attempts to improve the scores provided of relevant pages by using machine learning techniques. In addition, some evaluations are provided to show the effectiveness of the method.

Classification of Web Pages Using Machine Learning Techniques

2010 ◽  
pp. 134-150
Author(s):  
K. Selvakuberan ◽  
M. Indra Devi ◽  
R. Rajaram
Keyword(s):  
Machine Learning ◽  
Search Engines ◽  
Research Problem ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Web Pages ◽  
Current Research Problem ◽  
Learning Techniques ◽  
The Web

The explosive growth of the Web makes it a very useful information resource to all types of users. Today, everyone accesses the Internet for various purposes and retrieving the required information within the stipulated time is the major demand from users. Also, the Internet provides millions of Web pages for each and every search term. Getting interesting and required results from the Web becomes very difficult and turning the classification of Web pages into relevant categories is the current research topic. Web page classification is the current research problem that focuses on classifying the documents into different categories, which are used by search engines for producing the result. In this chapter we focus on different machine learning techniques and how Web pages can be classified using these machine learning techniques. The automatic classification of Web pages using machine learning techniques is the most efficient way used by search engines to provide accurate results to the users. Machine learning classifiers may also be trained to preserve the personal details from unauthenticated users and for privacy preserving data mining.

Classification of Web Pages Using Machine Learning Techniques

2012 ◽  
pp. 50-65 ◽  
Author(s):  
K. Selvakuberan ◽  
M. Indra Devi ◽  
R. Rajaram
Keyword(s):  
Machine Learning ◽  
Search Engines ◽  
Research Problem ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Web Pages ◽  
Current Research Problem ◽  
Learning Techniques ◽  
The Web

The explosive growth of the Web makes it a very useful information resource to all types of users. Today, everyone accesses the Internet for various purposes and retrieving the required information within the stipulated time is the major demand from users. Also, the Internet provides millions of Web pages for each and every search term. Getting interesting and required results from the Web becomes very difficult and turning the classification of Web pages into relevant categories is the current research topic. Web page classification is the current research problem that focuses on classifying the documents into different categories, which are used by search engines for producing the result. In this chapter we focus on different machine learning techniques and how Web pages can be classified using these machine learning techniques. The automatic classification of Web pages using machine learning techniques is the most efficient way used by search engines to provide accurate results to the users. Machine learning classifiers may also be trained to preserve the personal details from unauthenticated users and for privacy preserving data mining.

scholarly journals Keratoconus Diagnostic and Treatment Algorithms Based on Machine-Learning Methods

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1933
Author(s):  
Boris Malyugin ◽  
Sergej Sakhnov ◽  
Svetlana Izmailova ◽  
Ernest Boiko ◽  
Nadezhda Pozdeyeva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Treatment Strategies ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Clinical Environment ◽  
Visual Rehabilitation ◽  
Web Based ◽  
Treatment Algorithms ◽  
Management Algorithm ◽  
Learning Techniques

The accurate diagnosis of keratoconus, especially in its early stages of development, allows one to utilise timely and proper treatment strategies for slowing the progression of the disease and provide visual rehabilitation. Various keratometry indices and classifications for quantifying the severity of keratoconus have been developed. Today, many of them involve the use of the latest methods of computer processing and data analysis. The main purpose of this work was to develop a machine-learning-based algorithm to precisely determine the stage of keratoconus, allowing optimal management of patients with this disease. A multicentre retrospective study was carried out to obtain a database of patients with keratoconus and to use machine-learning techniques such as principal component analysis and clustering. The created program allows for us to distinguish between a normal state; preclinical keratoconus; and stages 1, 2, 3 and 4 of the disease, with an accuracy in terms of the AUC of 0.95 to 1.00 based on keratotopographer readings, relative to the adapted Amsler–Krumeich algorithm. The predicted stage and additional diagnostic criteria were then used to create a standardised keratoconus management algorithm. We also developed a web-based interface for the algorithm, providing us the opportunity to use the software in a clinical environment.

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