Data Intelligence Multi-Research Platform

Fateme Akbari
PhD Candidate
Information Systems,
DeGroote School of Business
McMaster University

Research Project: Behavior Pattern Recovery in Smart Elderly Homes

Summary

My area of research is the application of machine learning and particularly deep learning in the Information Systems area with a special focus on mining system repositories in order to find anomalies in massive data sets. I am currently conducting anomaly detection research on elderly behavior utilizing state-of-the-art machine learning methods for sequential data

Publications

Akbari, F., Saberi, M., & Hussain, O. K. (2020). Social network structure-based framework for innovation evaluation and propagation for new product development. Service Oriented Computing and Applications, 1-13.

Akbari, F., & Taghiyareh, F. (2014, February). E-SoRS: A personalized and social recommender service for E-learning environments. In The 8th National and the 5th International Conference on e-Learning and e-Teaching (ICeLeT 2014) (pp. 1-12). IEEE.

Software Solutions

TBA

Related Research & Technologies

Nvidia DGX and NGC

High Performance Computing Platform for Machine Learning

Python for Machine Learning

Apache Spark Platform

Biography

I joined the Ph.D. program at DeGroote School of Business, McMaster University in September 2019. I have completed my B.Sc. in Software Engineering at Amirkabir University of Technology and continued to expand my knowledge in information technology by completing M.Sc. degree at the University of Tehran in Information Technology Engineering. I have five years of experience in the banking industry as a Data Scientist, and have conducted research and implementations on financial data sets aiming at predicting fraudulent transactions as well as preventing customer churn.