Effort estimation of web-based applications using machine learning techniques

2016 ◽  
Author(s):  
Shashank Mouli Satapathy ◽  
Santanu Kumar Rath
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Effort Estimation ◽  
Web Based ◽  
Learning Techniques

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.

Story Point-Based Effort Estimation Model with Machine Learning Techniques

2020 ◽  
Vol 30 (01) ◽  
pp. 43-66
Author(s):  
Muaz Gultekin ◽  
Oya Kalipsiz
Keyword(s):  
Machine Learning ◽  
Empirical Evaluation ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Effort Estimation ◽  
Estimation Model ◽  
Software Projects ◽  
Learning Techniques ◽  
Better Than

Until now, numerous effort estimation models for software projects have been developed, most of them producing accurate results but not providing the flexibility to decision makers during the software development process. The main objective of this study is to objectively and accurately estimate the effort when using the Scrum methodology. A dynamic effort estimation model is developed by using regression-based machine learning algorithms. Story point as a unit of measure is used for estimating the effort involved in an issue. Projects are divided into phases and the phases are respectively divided into iterations and issues. Effort estimation is performed for each issue, then the total effort is calculated with aggregate functions respectively for iteration, phase and project. This architecture of our model provides flexibility to decision makers in any case of deviation from the project plan. An empirical evaluation demonstrates that the error rate of our story point-based estimation model is better than others.

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.

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