Accurate forecasting of mortality causes is vital for healthcare planning, epidemiological research, and public health interventions. In this study, we employ a combination of regression models and moving averages to predict mortality causes. By analyzing historical data and leveraging statistical models, we aim to provide reliable insights into the likely causes of mortality over time. Our approach contributes to informed decision-making in healthcare, enabling proactive measures to address health challenges effectively.

Data de-duplication, a vital component of data management and storage optimization, has raised concerns about data security and privacy. In response to these concerns, we present an innovative approach, "Efficient Content Discovery and Preservation for De-duplication (ECDPD)," designed to secure data during the de-duplication process. ECDPD introduces efficient content discovery mechanisms and robust preservation techniques, ensuring that sensitive data remains confidential while realizing the benefits of de-duplication. Our study addresses the pressing need for data security in an era of increasing data proliferation.