Two Morehead State University students present their research into machine learning at annual conference

Two students from Morehead State University's School of Engineering and Computer Science have been working throughout the academic year with faculty to student machine learning. Suhana Ambol worked on a project studying how smartphones can track user behaviors. Kyle Spurlock focused on using machine learning systems to establish patterns in data collected from social media to create a way to monitor COVID-19 information. Both students presented their projects at the annual conference of Institute of Electrical and Electronics Engineers (IEEE).

Ambol is a junior double majoring in computer science and mathematics who started her research through the Undergraduate Research Fellowship Program in her freshman year. Working with faculty members Dr. Sherif Rashad and Dr. Heba Elgazzar, Ambol is studying continuous authentication (the technology that allows us to securely access things like online banking) to establish a dataset of the behaviors of smartphone users. Ambol is currently working on research using neural networks (a type of simulation of the human brain) in the detection of malware on smartphones. 

In a news release from Morehead, Ambol talked about the work and about the impact of her mentors. She explained what drew her to the field of computer science research, saying, "It always amazes me how the evolution of technology has provided immense opportunities to society. As an instance, artificial intelligence is widely used in the commercial sector providing personalized recommendations to online users based on their previous product search or online behavior." She went on to say, "Dr. Rashad is an incredible mentor. He has been a great motivation and guidance. I have learned a lot working under him. In my freshman year, all these topics were very new to me, but Dr. Rashad made sure that I understood the concepts well and move ahead in the right direction." 

Dr. Elgazzar mentored both Ambol and Spurlock. Spurlock's research centered on the use of unsupervised machine learning techniques, which are algorithms that infer patterns without defined labels or outcomes, making them useful in discovering the underlying structure of a dataset. In the university's news release, Spurlock explains the challenge that his project tackled by making connections between what users of social media say and surveillance of COVID-19 outbreaks. "Current health surveillance techniques are very poor in terms of overall ability to accurately report on disease presence, and this is especially true of countries with poor health infrastructure. While the COVID-19 focused element of the research was mostly just a sign of the times, it is interesting to see what progress can be made in various subjects just by applying algorithms to a different context," Spurlock said.

Echoling Ambol's sentiments, Spurlock expressed gratitude for the mentorship from Morehead faculty, saying, "The support and attention of the professors in my program has been what has made MSU for me. I am very grateful to them for all they have done for me and been able to share with me, of which I feel has definitely given me a better understanding and appreciation for my study."

With the help of Morehouse faculty members, Ambol and Spurlock presented their work to the annual IEEE conference.