Category: Research Paper

Clustering large-scale data sets is a critical challenge in modern data science and machine learning, requiring innovative approaches to manage growing complexity and volume.

Scalable encryption techniques are designed to handle large datasets while providing robust protection against unauthorized access.

Clustering algorithms play a critical role in analyzing large-scale data, offering insights into patterns, structures, and relationships within datasets.

IoT solutions are transforming urban traffic management by enabling real-time monitoring, data analysis, and adaptive control of traffic flow.

Big data processing has become a cornerstone of modern analytics, enabling organizations to extract valuable insights from massive datasets.

Factors such as technological advancements, cultural shifts, and economic conditions significantly shape how consumers make decisions.

Performance Comparison with Traditional Prediction Models. Impact of Interdependencies on Prediction Accuracy.

Data-driven fraud detection has emerged as a critical strategy for banks to safeguard their assets and maintain customer trust. By utilizing big data analytics, machine learning, and artificial intelligence, financial institutions can identify, prevent, and respond to fraudulent activities in real time.

This is where data engineering plays a crucial role. Leveraging data engineering techniques, modern banking can shift from reactive fraud responses to proactive fraud management, using real-time insights, advanced analytics, and machine learning models to predict, prevent, and combat fraudulent activities before they occur.