Computing Research Students

The current research students (MSc by Research, PhD) in the School of Computing are working in a large variety of different subjects.

If you are interested in more information about studying on a research degree at Buckingham, please contact the School of Computing (by email: computing-admissions@buckingham.ac.uk or phone: +44 (0)1280 828 322) or visit our Postgraduate Study page.

DongXu Han

Decision support systems for medical diagnostic decision making: Towards confidence-centric classification based on Gaussian models and Bayesian principle

Ammar Al Abd Alazeez

This research investigates the velocity aspects of big data, designs and evaluates more effective algorithms in coping with dynamically changing data sets.

Saif Hussein

Automatic processing and analysis of high throughput skin images.

Fatina Shukur

Biometrics systems includes several processes, each of which affects the final outcome of the system which is to accurately identify or authenticate a person. Typical processes of a biometrics system are: quality assessment; pre-processing; feature extraction and classification. Depending on the type of biometric feature used and the operational context of the system, one could adaptively select the most appropriate techniques for each of the above processes to optimise the final outcome of the system. This research will investigate the use of software agents and game theory to develop a framework for a context-aware adaptive biometrics system.

Dhurgham Al-Karawi

This project investigates and develops effective and novel computer based solutions in automatically classifying ovarian tumours based on domain specific and image specific features extracted from ultrasound images of ovarian masses.

Tahir Hassan

Comparative study of the effect of various Dimension Reduction techniques for Big Data Analysis.

Tahseen Al-Baidhani

Automatic processing and analysis of microscopic brain images.

Nasiru (Nasir) Ibrahim

Modern smartphones and tablet devices uses ‘pattern unlock’ techniques as an alternative to passwords and PINs to gain access to device functionality. In pattern unlock, users perform a gesture swipe connecting set of dots in a display on the device’s touch screen. The authentication is based on the order in which the dots are connected, which is predefined by each user. This research will evaluate the security of gesture-based pattern unlock mechanism. This will then be followed by the incorporation of dynamic features of touch gestures such as gesture speed, finger pressure, finger movement-time, and gesture accuracy to enhance the security of gesture-based pattern unlock techniques used in smartphones. In addition to these dynamic features, the project will also consider the use of data from smartphone’s accelerometer and/or the gyroscope to enhance the authentication.