An Experimental Comparison of Three Machine Learning Techniques for Web Cost Estimation

Many comparative studies on the performance of machine learning (ML) techniques for web cost estimation (WCE) have been reported in the literature. However, not much attention have been given to understanding the conceptual differences and similarities that exist in the application of these ML techniques for WCE, which could provide credible guide for upcoming practitioners and researchers in predicting the cost of new web projects. This paper presents a comparative analysis of three prominent machine learning techniques – Case-Based Reasoning (CBR), Support Vector Regression (SVR) and Artificial Neural Network (ANN) – in terms of performance, applicability, and their conceptual differences and similarities for WCE by using data obtained from a public dataset (www.tukutuku.com). Results from experiments show that SVR and ANN provides more accurate predictions of effort, although SVR require fewer parameters to generate good predictions than ANN. CBR was not as accurate, but its good explanation attribute gives it a higher descriptive value. The study also outlined specific characteristics of the 3 ML techniques that could foster or inhibit their adoption for WCE

Using Ontology-based Information Extraction for Subject-based Auto-grading

The procedure for the grading of students’ essays in subject-based examinations is quite challenging particularly when dealing with large number of students. Hence, several automatic essay-grading systems have been designed to alleviate the demands of manual subject grading. However, relatively few of the existing systems are able to give informative feedbacks that are based on elaborate domain knowledge to students, particularly in subject-based automatic grading where domain knowledge is a major factor. In this work, we discuss the vision of subject-based automatic essay scoring system that leverages on semiautomatic creation of subject ontology, uses ontology-based information extraction approach to enable automatic essay scoring, and gives informative feedback to students

The Impact of Knowledge-Based Trust (Kbt) on The Adoption and Acceptability of Cashless Economy in Nigeria

ABSTRACT The introduction of cashless policy in Nigeria had gained a number of reactions over the adoption of cashless economy (or cashless banking). The implication of this is that not man

Development of Classification Model for the Prediction of Churn among Customers using Decision tree Algorithm

No Abstract

A Model for Ranking the Usability Attributes of Mobile Health Applications in Nigeria (MCDM Approach)

In Nigeria, the mobile health trend is gradually improving and generally upgrading the way healthcare services are being rendered. Assessing the usability of these apps is still a major task as a result of the many attributes embedded in most usability models. In other to rank some of these attributes, a Multi-Criteria Decision Making technique (MCDM) was used. The attributes ranked were adopted from the Enhanced Usability Model (EUM) which was designed based on the People at the Center of Mobile Application Development (PACMAD) model and the Integrated Measurement Model (IMM). Attributes were ranked using their priority weights based on the Triangular Fuzzy Numbers (TFN) and Fuzzy Chang extent analysis model. Results of evaluation showed that effectiveness and efficiency had the highest priorities with 40% and 33% while satisfaction, user interface aesthetics and universality had the lowest ranks. Three mHealth apps were analyzed and results showed that Omomi had the highest ranking with a weight of 41%, Find-A-Med ranked second with a weight of 30% while Hudibia ranked lowest with a weight of 29%. In conclusion, it was established that the mathematical technique used is a powerful tool for analyzing human decision-making process. Future works would consider other MCDM models and comparisons done