| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Al-Ibaisi, L. | Investigating Machine Learning Techniques for Student and Education Analytics | 2021- | School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] | phdthesis | |
| Comment: Funding Source: Self-funded | |||||
BibTeX:
@phdthesis{AlIbaisi2021,
author = {Lama Al-Ibaisi},
title = {Investigating Machine Learning Techniques for Student and Education Analytics},
school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)]},
year = {2021-}
}
|
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| Alshehhi, T. | Improving Alzheimer Prediction from Behavioural Data using Analytics Techniques | 2020- | School: Faculty of Computing, Engineering and Media, De Montfort University - [first supervisor (director of study)] | phdthesis | |
| Comment: Funding Source: Self-funded | |||||
BibTeX:
@phdthesis{Alshehhi2020,
author = {Talib Alshehhi},
title = {Improving Alzheimer Prediction from Behavioural Data using Analytics Techniques},
school = {Faculty of Computing, Engineering and Media, De Montfort University - [first supervisor (director of study)]},
year = {2020-}
}
|
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| Ishola, O. | Data Mining and Re-identification: Analysis of Database Query Patterns That Pose a Threat to Anonymised Information | 2018- | School: Faculty of Technology, De Montfort University - [second supervisor] | phdthesis | |
| Comment: Funding Source: DMU University Fees Only Scholarship | |||||
BibTeX:
@phdthesis{Ishola2018,
author = {Olabayo Ishola},
title = {Data Mining and Re-identification: Analysis of Database Query Patterns That Pose a Threat to Anonymised Information},
school = {Faculty of Technology, De Montfort University - [second supervisor]},
year = {2018-}
}
|
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| Mishael, Q. | Investigating Social Recommendation Systems based on User Intention Mining Models | 2016- | School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] | phdthesis | |
| Abstract: February 23, 2017 Nowadays, online social networks have become essential part of people?s life. Social networks users like to share information by posts. Share of information could be done by posting about daily activities, feelings, opinions, interests or goals. The posts could vary from text, images, video clips or even URLs. Generally, discovering user intention from the posts on social networks can be understood easily by the others. For instance, a user who wants to buy a lap- top could post information like: "I want to buy a laptop for my daughter!", a purchase intention can be noticed. However, in some cases, user?s intention may be implicit and not clear to others. For example, user?s intention for shar- ing a link of a video clip might be hard to extract. The user?s intention can, therefore, be explicit or implicit, which might become a challenge within social networks, when seeking to extract. Understanding the user?s intention is con- sidered a valuable source of information. This information could benefit online businesses by means of understanding customers?needs. Also it would support improving online social networks by recommending better user services. In our research we aim to analyse the online social data. Moreover, we seek to develop a user intention model for online social networks. We will use machine learn- ing techniques to identify and classify online social data into intent categories. Moreover, the developed model will be applied for a recommendation system that will generate effective recommendations based on users? current needs and activities. |
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| Comment: Previous title: Mobile Services Based On User Preference/Profiling Models Funding Source: Self-funded |
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BibTeX:
@phdthesis{Mishael2016,
author = {Mishael, Qadri},
title = {Investigating Social Recommendation Systems based on User Intention Mining Models},
school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)]},
year = {2016-}
}
|
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| Mohammed, K. | Machine Learning and Data Mining Approaches for Privacy Preserving Techniques | 2018- | School: Faculty of Computing, Engineering and Media, De Montfort University - [first supervisor (director of study)] | phdthesis | |
| Comment: Funding Source: Self-funded | |||||
BibTeX:
@phdthesis{Mohammed2018,
author = {Kabiru Mohammed},
title = {Machine Learning and Data Mining Approaches for Privacy Preserving Techniques},
school = {Faculty of Computing, Engineering and Media, De Montfort University - [first supervisor (director of study)]},
year = {2018-}
}
|
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