ORCID: https://orcid.org/0009-0001-2107-926X
Google Scholar: https://scholar.google.com/citations?user=aUUhXToAAAAJ&hl=en
Research Gate: https://www.researchgate.net/profile/Md-Hassan-165
Md. Mahedi Hassan is currently working as a Lecturer at CSE Department of World University of Bangladesh (WUB). He has completed his MSc in Computer Science and Engineering degree from Hajee Mohammad Danesh Science and Technology University, Dinajpur. Before that, he also completed his BSc (Engineering ) in Computer Science and Engineering degree from the same university. Beside the teaching profession he also has devoted himself in research activities. His focal interest is in Machine Learning (ML), Deep Learning (DL), Artificial Intelligence (AI), Cybersecurity and Data Science.
Hasan, M., Hassan, M.M., Faisal-E-Alam, M. and Akter, N., 2023. Empirical Analysis of Regression Techniques to Predict the Cybersecurity Salary. In Cyber Security and Business Intelligence (pp. 65-84). Routledge.
Hassan, M.M., Abrar, M.F. and Hasan, M., 2023. An Explainable AI-Driven Machine Learning Framework for Cybersecurity Anomaly Detection. In Cyber Security and Business Intelligence (pp. 197-219). Routledge.
Mahedi Hassan, M., Fazle Rabbi, M., Hasan, M. and Roy, B., 2023, September. An Ensemble Machine Learning Approach to Classify Parkinson’s Disease from Voice Signal. In International Conference on Big Data, IoT and Machine Learning (pp. 575-590). Singapore: Springer Nature Singapore.
Hasan, M., Islam, M.M., Sajid, S.W. and Hassan, M.M., 2022, December. The Impact of Data Balancing on the Classifier's Performance in Predicting Cesarean Childbirth. In 2022 4th International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) (pp. 1-4). IEEE.
