Fraud Detection Systems: Dr. Buddha is interested in developing innovative fraud detection systems for financial institutions, particularly focusing on credit card and digital transaction fraud. His research explores the application of machine learning algorithms and real-time data analysis techniques to enhance fraud detection accuracy and efficiency.
Machine Learning Applications: He is keen on exploring the practical applications of machine learning in various domains, including fraud detection, customer behavior analysis, and predictive analytics. His research aims to leverage advanced machine learning models to uncover valuable insights from large-scale datasets and improve decision-making processes.
Software Engineering Practices: Dr. Buddha investigates software engineering practices and methodologies aimed at improving the quality, reliability, and maintainability of software systems. His research interests include software design patterns, code refactoring techniques, and agile development methodologies.
Data Security and Privacy: He is interested in studying data security and privacy concerns in software systems, especially in the context of financial transactions and sensitive personal information. His research focuses on developing robust security measures and privacy-preserving techniques to safeguard user data and prevent unauthorized access.
Big Data Analytics: Dr. Buddha explores the challenges and opportunities associated with big data analytics in the context of fraud detection and financial services. His research aims to develop scalable and efficient algorithms for processing and analyzing large volumes of data to extract actionable insights and detect fraudulent activities in real-time.