Raghad Al-Shabandar

According to our database1, Raghad Al-Shabandar authored at least 13 papers between 2017 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2023
A Risk Model for Assessing Exposure Factors Influence Oil Price Fluctuations.
Proceedings of the Advanced Intelligent Computing Technology and Applications, 2023

Intelligent Measuring for a Customer Satisfaction Level Inspired by Transformation Language Model.
Proceedings of the 16th International Conference on Developments in eSystems Engineering, 2023

Multivariate Comparative Analysis of Statistical and Deep Learning Models for Prediction Hardware Failure.
Proceedings of the Data Science and Emerging Technologies, 2023

Telecom Customer Experience Analysis Using Sentiment Analysis and Natural Language Processing - Comparative Study.
Proceedings of the Data Science and Emerging Technologies, 2023

2020
Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics.
Remote. Sens., 2020

Students Performance Prediction in Online Courses Using Machine Learning Algorithms.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

2019
The application of machine learning for early detection of at-risk learners in Massive Open Online Courses.
PhD thesis, 2019

Detecting At-Risk Students With Early Interventions Using Machine Learning Techniques.
IEEE Access, 2019

The Application of Artificial Intelligence in Financial Compliance Management.
Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing, 2019

2018
Analyzing Learners Behavior in MOOCs: An Examination of Performance and Motivation Using a Data-Driven Approach.
IEEE Access, 2018

The Application of Gaussian Mixture Models for the Identification of At-Risk Learners in Massive Open Online Courses.
Proceedings of the 2018 IEEE Congress on Evolutionary Computation, 2018

2017
Machine learning approaches to predict learning outcomes in Massive open online courses.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Towards the Differentiation of Initial and Final Retention in Massive Open Online Courses.
Proceedings of the Intelligent Computing Theories and Application, 2017


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