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Deep learning methods for noisy and unbalanced data (CO/CG/-Un6/2020)


Department of Computer Science

This project is no longer listed on FindAPhD.com and may not be available.

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Dr G Cosma No more applications being accepted Self-Funded PhD Students Only

About the Project

It is a challenging task to train deep learning models on unbalanced data which is commonly generated in real-time scenarios. It is also a big challenge training Deep Learning algorithms on limited data.

The complexity escalates when training deep learning algorithms on multi-modal data spaces. Projects include the development of algorithms for classifying unbalanced and/or data obtained from smart environments.

You can find out more here: https://datascienceplus.blog

Start date: January 2020, July 2020, October 2020, January 2021.
Entry requirements:
Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Computer Science with a strong interest in Artificial Intelligence and data science. Applicants must be competent in programming and applied mathematics, and should have a strong ability to write computer programs preferably in the Python programming language. An MSc degree in Artificial Intelligence and/or Data Science is highly desirable.
How to apply:
All applications are made online, please select the school/department name under the programme name section and include the quote reference number.

https://www.lboro.ac.uk/study/postgraduate/apply/research-applications/

Funding Notes

This is an open call for candidates who are sponsored or who have their own funding. If you do not have funding, you may still apply, however Institutional funding is not guaranteed. Outstanding candidates (UK/EU/International) without funding will be considered for funding opportunities which may become available in the School.

Band RB (UK/EU: TBC; international: £22,350).

References

Cosma, G and Mcginnity, TM (2019) Feature extraction and classification using leading eigenvectors: Applications to biomedical and multi-modal mHealth data, IEEE Access, 7, pp.107400-107412, DOI: 10.1109/access.2019.2932868.

Taherkhani, A, Cosma, G, Alani, AA, McGinnity, TM (2019) Activity Recognition from Multi-modal Sensor Data Using a Deep Convolutional Neural Network. In , pp.203-218, ISBN: 9783030011765. DOI: 10.1007/978-3-030-01177-2_15.
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