The story of five MENA cities: Urban growth prediction modeling using remote sensing and video analytics

This paper presents a novel methodology for urban growth prediction using a machine learning approach. The methodology treats successive historical satellite images of an urban area as a v...

Modeling urban growth using video prediction technology: A time‐dependent convolutional encoder–decoder architecture

This paper presents an enrollment management model by applying artificial neural network (ANN). The aim of the research, which has been presented in this paper, is to show that ANNs are more succes...

Enrollment Management Model: Artificial Neural Networks versus Logistic Regression

A smart education system uses emerging technologies and generates a vast amount of heterogeneous data in the learning environment. The conventional methods presently used by the educational administrators for decision-making are minimal and take more time to generate the results. The educational administrators could not be able to predict the results quickly and advance for better decision-making. Today, artificial intelligence approaches are widely used in educational systems for automating educational processes. These approaches achieve a better, efficient, and effective modern education system. Integrating machine learning deep learning techniques with a smart education system can automatically analyze the generated data for better decision-making and provide recommendations to students and educational administrators. This chapter aims to introduce a machine learning model to predict the outcomes in a smart education system.

A Machine Learning Model for Advanced Decision Making in Smart Education Systems

Due to the of use of ICTs and ODL, Virtual University (VU) has become one of leading distance learning university in Pakistan. However, the retention rate among online learners found considerably low. The primary objective of this research was to dig out determinants of retention of MS /MPhil students at VU and modeling their retention by considering important influences. For sampling purpose, three departments with the most students were considered and complete enumeration was done. There were 4,608 students from three departments; Computer Science & Technology, Management Sciences and Education have been included in this study. To dig out the important retention factors, this research has used a Chi-Square test, optimal scaling, a decision tree using CHAID analysis, and then developed a suitable model for student retention. Binary logistic regression techniques were applied. Results have revealed that gender, scholarship, province, location, and division are significant factors and contributing in predicting students' retention at VU. Detailed outputs are shown in respective tables and figures. At the end, different recommendations and suggestions are proposed.

Development of a Model for Retention of MS/MPhil Students at Virtual University (VU) of Pakistan.

This paper presents the development of a model for forecasting and decision-making by applying logistic regression. The prediction of professional choices for graduates has been verified on the sample of 159 graduates. Predictor variables are grouped in nine input variables and the data collected from the Unique Education Information System database. Professional choices of graduates after finishing vocational school are grouped into three output variables and the data collected in the survey. The obtained results show that the application of logistic regression can predict the number and structure of students enrolled in higher education.

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Marija Bozic

Papers

Enrollment management: Development of prediction model based on logistic regression