Associate Professor
Ph.D. Computer Science, University of Adelaide, Australia.
Master of Technology (MTech) Software Engineering, Visvesvaraya Technological University, India.
Master of Computer Application (MCA), University of Mysore, India
Bio
Dr. Sujith Samuel Mathew completed his Ph.D. in Computer Science from the University of Adelaide, South Australia. His research interests are in distributed and mobile computing, with focus on the Internet of Things (IoT) and related smart city applications. He has over twenty years of experience working both in the IT industry and academia. He has held positions as Group Leader, Technical Evangelist, and Software Engineer within the IT industry. In academia, he has been teaching IT courses and pursuing his research interests in parallel. Presently, he is an Associate Professor with the College of Interdisciplinary Studies (CIS) at Zayed University in Abu Dhabi.
Office
Abu Dhabi - Khalifa City
Email:
sujith.mathew@zu.ac.aeTeaching Areas
Internet of Things, Distributed Computing, Mobile Computing, and Smart City Applications
Research and Professional Activities
Mathew, S.S., Hayawi, K., Dawit, N.A. et al. Integration of blockchain and collaborative intrusion detection for secure data transactions in industrial IoT: a survey. Cluster Computing (2022).
El Barachi, M., Mathew, S. S., Oroumchian, F., Ajala, I., Lutfi, S., & Yasin, R. (2022). Leveraging Natural Language Processing to Analyse the Temporal Behavior of Extremists on Social Media. Journal of Communications Software and Systems, 18(2), 193-205.
Hayawi, K., Mathew, S.S., Venugopal, N. et al. DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data. Soc. Netw. Anal. Min. 12, 43 (2022).
M. E. Barachi, S. S. Mathew and M. AlKhatib, "Combining Named Entity Recognition and Emotion Analysis of Tweets for Early Warning of Violent Actions," 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech), 2022, pp. 1-6.
S. S. Mathew, M. AlKhatib, and M. E. Barachi,. "A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events." Journal of Communications Software and Systems 17.2 (2021): 106-115.
