About
Md Sultanul Arefin Sourav
Business analytics research with an academic affiliation to Trine University.
I study business analytics at Trine University and explore how data science, machine learning, and automation can support better business decisions. Explore interactive systems for fraud review, operational planning, and evidence-based analytics.
The work on this site sits at the meeting point of business analytics and machine learning: how a model's output becomes a decision that a person can inspect, question and account for. Two published studies anchor the research side, a transaction fraud framework and an intrusion detection method for industrial networks. Nine public repositories carry independent implementations and demonstrations built on authored synthetic data.
Research interests
- Business Analytics
- Data Science
- Data Analytics
- Artificial Intelligence
- Machine Learning
How claims are handled here
Every figure on this site carries one of four labels. paper-reported values come from a published table and are shown with the table or section they came from. demo values were computed on synthetic fixtures by code in the linked repositories or by this site's own build script, and are never presented as benchmark results. Nothing is labelled reproduced, because no real-data run has been performed for these repositories. Records supplied by the owner but not checked against a publisher page are marked as such on the research page (11 of 13).
Demonstrations use fictional US-style businesses such as Northwind Demo Supply. No client engagement, deployment, employment or place of residence is implied by anything on this site.
Membership
- IIBA Member (a professional membership, not a certification)