Arwa Hamid earned her Master’s degree in Computer Science from the College of Engineering and Computer Science at Oregon State University, USA, in 2013, following her Bachelor’s degree in Information Technology and Computer Science from the Arab Open University in 2010. She is presently a full-time lecturer at Arab Open University in Riyadh, Saudi Arabia. Her expertise lies in Networking and Security, and she is a certified CCNA instructor through Cisco Academy. Additionally, she has authored numerous research papers published in various international journals and conferences.
Teaching – AOU (09/2016-Present)
- TU170- Learning Online.
- M150A- Data, computing, and information I
- M150B- Data, computing, and information II
- T175A- Networked livingI
- T175B- Networked livingII ·
- T103- Computer Architecture Logic and Information Processing
- T215A- Communication & information technology I
- T215B- Communication & information technology II
- T325- Technologies for digital media
- M106- Introduction to MatLab
- T324- Keeping ahead in ICT
- TM355- Communications Technology
- TM103- Computer organization and Architecture
- TM112- Introduction to Computing and Information Technology II
- TM111- Introduction to Computing and Information Technology I
- T216A- Cisco Networking (CCNA) Part 1
-Teaching – AOU (09/2016-Present)
-Branch Course Coordination (BCC) for many courses.
-Committees' member:
- Extracurricular activities in FCS.
- Appeal and equalization process
- Academic advising, warnings and complains
- Timetable and Academic staff evaluation
- Examination, IC cases, double marking, and Cross branch marking process
- NCAAA
- Quality Assurance Committee
Hamid, A., Ehsan, S., & Hamdaoui, B. (2014). Rate-constrained data aggregation in power-limited multi-sink wireless sensor networks. In Proceedings of the IEEE Wireless Communications and Mobile Computing Conference . IEEE.
He, Z., Chen, Y., Yuan, S., Zhao, J., Yuan, Z., Polat, K., Alhudhaif, A., Alenezi, F., & Hamid, A. (2023). A novel unsupervised domain adaptation framework based on graph convolutional network and multi-level feature alignment for inter-subject ECG classification. Expert Systems with Applications.
