As information technology touches each part of our lives, so do the research interests of the Faculty of Information Technology (FoIT). In FoIT research generally underpins learning and teaching. Research interests range from Big Data to the Internet of Things and wireless networks, and from Datacentre virtualization to information security. The outcomes of such research are readily incorporated into the learning and teaching practices of FoIT staff.
FoIT academic staff are also keenly interested in research areas such as: Enterprise Information Security, Computer Vision, Machine Learning, and Image Processing and Feature Extraction/Classification.
Strategic research areas span several departments. For example, we are engaged in research within the fields of:
- Big Data
- Edge detection system & Digital Ecosystems
- Smart-grid with Robotics and Interfacing
- Field Programmable Gate Arrays
- Cyber Security
- Student's Performance Analytics using Data Mining Techniques
- Pattern Recognition and Machine learning
Recent Research from FoIT staff
Two recent research outputs from FoIT staff are:
Ashour, M.W. (2016). Multi-class support vector machines for texture classification using grey-level histogram and edge detection features. Proceedings of 57th The IIER International Conference, 1-5. Retrieved from http://www.worldresearchlibrary.org/up_proc/pdf/162-14543298221-5.pdf
Menezes, J., & Poojary, N. (2016, March). Dimensionality reduction and classification of hyperspetral images using DWT and DCCF. In 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC) (pp. 1-6). IEEE.
Student Involvement in Research
Research projects often involve undergraduate and graduate students, strengthening student learning. Recent student projects have addressed such areas as: cloud school, VOIP codec performance analysis, and network performance testing using Riverbed modeller.