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Items where Author is "Keight, R"

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Number of items: 13.

Article

Al-Shabandar, R, Hussain, A, Liatsis, P and Keight, R (2019) Detecting At-Risk Students with Early Interventions Using Machine Learning Techniques. IEEE Access, 7. pp. 149464-149478. ISSN 2169-3536

Al-Shabandar, R, Hussain, A, Liatsis, P and Keight, R (2018) Analyzing Learners Behavior in MOOCs: An Examination of Performance and Motivation Using a Data-Driven Approach. IEEE Access, 6. pp. 73669-73685. ISSN 2169-3536

Al-Shabandar, R, Hussain, A, Laws, A, Keight, R and Lunn, J (2017) Towards the Differentiation of Initial and Final Retention in Massive Open Online Courses. Lecture Notes in Computer Science, 10361. ISSN 0302-9743

Khalaf, M, Hussain, A, Keight, R, Al-Jumeily, D, Fergus, P, Keenan, R and Tso, P (2017) Machine Learning approaches to the application of Disease Modifying Therapy for Sickle Cell using Classification Models. NEUROCOMPUTING, 228. pp. 154-164. ISSN 0925-2312

Conference or Workshop Item

Alshabandar, R, Hussain, A, Keight, R and Khan, W (2020) Students Performance Prediction in Online Courses Using Machine Learning Algorithms. In: 2020 International Joint Conference on Neural Networks (IJCNN) . (2020 International Joint Conference on Neural Networks (IJCNN), 19 July 2020 - 24 July 2020, Glasgow, UK).

Keight, R, Al-Jaaf, A, Hussain, A, Al-Jumeily, D, Ozge, A and Mallucci, C (2017) An Intelligent Systems Approach to Primary Headache Diagnosis. In: Lecture Notes in Computer Science . pp. 61-72. (International Conference Intelligent Computing, 07 August 2017 - 10 August 2017, Liverpool).

Al-Shabandar, R, Hussain, A, Laws, A, Keight, R and Lunn, J (2017) Machine Learning Approaches to Predict Learning Outcomes in Massive Open Online Courses. In: Neural Networks (IJCNN) . (2017 International Joint Conference on Neural Networks (IJCNN 2017), 14 May 2017 - 19 May 2017, Anchorage, Alaska, USA).

Keight, R, Al-Jumeily, D, Hussain, A, Al-Jumaily, M and Mallucci, C (2017) Towards the Discrimination of Primary and Secondary Headache: An Intelligent Systems Approach. In: International Journal of Neural Networks . (2017 International Joint Conference on Neural Networks (IJCNN 2017), 14 May 2017 - 19 May 2017, Anchorage, Alaska, USA).

Khalaf, M, Hussain, A, Al-Jumeily, D, Keight, R, Keenan, R, Fergus, P, AlAskar, H, Shaw, A and Olatunji, I (2016) Training Neural networks for Experimental models: Classifying Biomedical Datasets for Sickle Cell Disease. In: Intelligent Computing Theories and Application: Lecture Notes in Computer Science , 9771. pp. 784-795. (2016 International Conference on Intelligent Computation, 02 August 2016 - 05 August 2016, Lanzhou,China).

Khalaf, M, Hussain, A, Keight, R, Al-Jumeily, D, Keenan, R, Fergus, P and Idowu, IO (2016) The Utilisiation of composite Machine Learning models for the Classification of Medical Datasets For Sickle Cell Disease. In: 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC) . (Sixth International Conference on Digital Information Processing and Communications (ICDIPC), 21 April 2016 - 23 April 2016, Lebanon).

Idowu, IO, Fergus, P, Hussain, A, Dobbins, C, Khalaf, M, Casana Eslava, R and Keight, R (2015) Artificial Intelligence for Detecting Preterm Uterine Activity in Gynacology and Obstertric Care. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM) . pp. 215-220. (15th IEEE International Conference on Computer and Information Technology (CIT’15), 26 October 2015 - 28 October 2015, Liverpool, UK).

Alshabandar, R, Hussain, A, Keight, R, Laws, A and Baker, T The Application of Gaussian Mixture Models for the Identification of At-Risk Learners in Massive Open Online Courses. In: IEEE Congress on Evolutionary Computation, 08 July 2018 - 13 July 2018, Brazil. (Accepted)

Khalaf, M, Hussain, A, Al-Jumeily, D, Baker, T, Keight, R, Lisboa, P, Alkafri, A and Fergus, P A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction. In: IEEE Congress on Evolutionary Computation, 08 July 2018 - 13 July 2018, Brazil. (Accepted)

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