{"id":5188,"date":"2019-07-11T14:14:43","date_gmt":"2019-07-11T18:14:43","guid":{"rendered":"https:\/\/carleton.ca\/discoverycentre\/?p=5188"},"modified":"2021-12-14T14:30:24","modified_gmt":"2021-12-14T19:30:24","slug":"mohamed-hozayen","status":"publish","type":"post","link":"https:\/\/carleton.ca\/tls\/2019\/mohamed-hozayen\/","title":{"rendered":"Mohamed Hozayen’s Profile"},"content":{"rendered":"

Mohamed Hozayen<\/h2>\n
\n\n\n\n\n\n\n\n\n
<\/td>\n<\/tr>\n
Program:<\/strong><\/td>\n<\/tr>\n
Computer Systems Engineering<\/td>\n<\/tr>\n
Field of Research:<\/strong><\/td>\n<\/tr>\n
Wireless Communications<\/td>\n<\/tr>\n
Supervisors:<\/strong><\/td>\n<\/tr>\n
Dr. Halim Yanikomeroglu, Ms. Rawan<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/div>\n

\"Mohamed<\/a><\/p><\/div><\/div>\n

Reason For Participating<\/h3>\n

I wanted to get a jump start into my master thesis while finishing 4th year. I had a good experience and enjoyed research with I-CUREUS in the past two years. I also presented and published a paper at NCUR<\/a>\u00a0\u2013 isn\u2019t that cool!<\/p>\n

Research<\/h3>\n

This research is trying to predict\/measure user satisfaction\/feedback in a non-intrusive real-time manner. One of the main enablers of cellular network personalization is the ability of the network to measure and track in real-time the user satisfaction in a non-intrusive manner. Tracking user satisfaction requires the utilization of machine learning and big data analytics to predict user satisfaction based on different inputs from various sensors such as cameras, microphones, accelerometers, gyroscopes, etc.<\/p>\n

Wireless network personalization, an area of research that Yanikomeroglu\u2019s group is currently working on, is indispensable to achieve the ultimate balance between user satisfaction and profit and hence, deliver mobile services profitably to users in a win-win setting. Rawan Alkurd, a Ph.D. student in Electrical and Computer Engineering under the supervision of Dr. Yanikomeroglu and Dr. Abualhaol, developed a big data-driven AI-based framework to enable personalization in 5G systems.<\/p>\n

Mohamed’s Role<\/strong><\/p>\n

I am going to join the research group and work on developing a mobile application and a data model that can harness all sensors in mobile phones and wearable devices and correlate these measurements in machine learning algorithms to predict user satisfaction. The mobile application is a non-intrusive real-time system that automates the process of capturing user feedback.<\/p>\n

Process<\/strong><\/p>\n