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Outputs (8)

A machine learning driven solution to the problem of perceptual video quality metrics (2020)
Book Chapter
Katsigiannis, S., Rabah, H., & Ramzan, N. (2020). A machine learning driven solution to the problem of perceptual video quality metrics. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET

The advent of high-speed internet connections, advanced video coding algorithms, and consumer-grade computers with high computational capabilities has led videostreaming-over-the-internet to make up the majority of network traffic. This effect has le... Read More about A machine learning driven solution to the problem of perceptual video quality metrics.

Machine learning-based affect detection within the context of human-horse interaction (2020)
Book Chapter
Althobaiti, T., Katsigiannis, S., West, D., Rabah, H., & Ramzan, N. (2020). Machine learning-based affect detection within the context of human-horse interaction. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET

This chapter focuses on the use of machine learning techniques within the field of affective computing, and more specifically for the task of emotion recognition within the context of human-horse interaction. Affective computing focuses on the detect... Read More about Machine learning-based affect detection within the context of human-horse interaction.

EEG-based biometrics: Effects of template ageing (2020)
Book Chapter
Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., & Ramzan, N. (2020). EEG-based biometrics: Effects of template ageing. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET

This chapter discusses the effects of template ageing in EEG-based biometrics. The chapter also serves as an introduction to general biometrics and its main tasks: Identification and verification. To do so, we investigate different characterisations... Read More about EEG-based biometrics: Effects of template ageing.

Information Retrieval from Electronic Health Records (2020)
Book Chapter
Al-Qahtani, M., Katsigiannis, S., & Ramzan, N. (2020). Information Retrieval from Electronic Health Records. In M. A. Imran, R. Ghannam, & Q. H. Abbasi (Eds.), Engineering and technology for healthcare (117-128). Wiley-IEEE Press

Temporal Analysis in Massive Open Online Courses – Towards Identifying at-Risk Students Through Analyzing Demographical Changes (2020)
Book Chapter
Shi, L., Yang, B., & Toda, A. (2020). Temporal Analysis in Massive Open Online Courses – Towards Identifying at-Risk Students Through Analyzing Demographical Changes. In A. Siarheyeva, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Advances in information systems development (146-163). Springer Verlag. https://doi.org/10.1007/978-3-030-49644-9_9

This chapter demonstrates a temporal analysis in Massive Open Online Courses (MOOCs), towards identifying at-risk students through analyzing their demographical changes. At-risk students are those who tend to drop out from the MOOCs. Previous studies... Read More about Temporal Analysis in Massive Open Online Courses – Towards Identifying at-Risk Students Through Analyzing Demographical Changes.

Exploring Navigation Styles in a FutureLearn MOOC (2020)
Book Chapter
Shi, L., Cristea, A. I., Toda, A. M., & Oliveira, W. (2020). Exploring Navigation Styles in a FutureLearn MOOC. In V. Kumar, & C. Troussas (Eds.), Intelligent Tutoring Systems (45-55). Springer Verlag. https://doi.org/10.1007/978-3-030-49663-0_7

This paper presents for the first time a detailed analysis of fine-grained navigation style identification in MOOCs backed by a large number of active learners. The result shows 1) whilst the sequential style is clearly in evidence, the global style... Read More about Exploring Navigation Styles in a FutureLearn MOOC.