Zafar Hussain

As a committed doctoral researcher in machine learning, my primary focus revolves around developing models that convert data into valuable insights. Specializing in Natural Language Processing for cybersecurity, my ongoing research centers on evaluating the quality of incoming semi-natural and semi-structured data. Furthermore, I am actively involved in constructing language models to comprehend the underlying structure of the data.

Analyzing semi-natural language data to learn the syntax and structure of the commands - Ongoing

The aim of this research is to establish a baseline for understanding the syntax of command-line commands and to validate and verify the quality of incoming data in evolving systems. Actively collaborating with WithSecure (F-Secure Corporation) on this specific use case.

Modeling weather data to predict electricity consumption - Completed

Built machine learning model to analyze and forecast electricity demand across the industrial, commercial and residential buildings of Rome, Italy.

https://zafarhussain87.github.io/Data-Science-Electricity-Project/ 

Data analysis of Telecom Italia - Completed

Analyzed the data of Milan and Trentino to study telecommunications activities, communication patterns and services usage. 

https://zafarhussain87.github.io/Telecom-Italia-Data-Analysis/

• Hussain, Z., Nurminen, J. K. & Ranta-aho, P., 11 Mar 2023, 8th International Conference on Machine Learning Technologies. Stockholm, Sweden: ACM , p. 61-67 7 p. (Proceedings of 2023 8th International Conference on Machine Learning Technologies, Icmlt 2023).
• Hussain, Z., Nurminen, J. K., Mikkonen, T. & Kowiel, M., 31 Aug 2022, Intelligent Systems and Applications. IntelliSys 2022. Arai, K. (ed.). Springer, Cham, p. 424–441 18 p. (Lecture Notes in Networks and Systems (LNNS); vol. 542).
• Hussain, Z., Nurminen, J. K., Mikkonen, T. & Kowiel, M., 10 Aug 2021, 10th Symposium on Languages, Applications and Technologies (SLATE 2021). Dagstuhl, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik, p. 13:1 14 p. (Open Access Series in Informatics ; vol. 94).
• Z. Hussain, H. Karvonen and J. Iinatti "Energy efficiency evaluation of wake-up radio based MAC protocol for wireless body area networks" IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB) Salamanca, 2017, pp. 1-5.
• Masters Thesis: Using Metadata to Analyze Trajectories of Finnish Newspapers, https://helda.helsinki.fi/handle/10138/316951
• Masters Thesis: Performance evaluation of wake-up radio based wireless body area network, http://jultika.oulu.fi/Record/nbnfioulu-201611052965