Efficient Employee Timesheet Tracker Using Object-Oriented Programming and File Handling in C++.

The article focuses on the development of an efficient Employee Timesheet Management System using object-oriented programming and file handling in C++, enhanced by Long Short-Term Memory (LSTM) networks for predictive analytics. It addresses challenges in traditional timesheet tracking such as manual errors, lack of real-time monitoring, and integration difficulties by automating time logging, role-based access, and report generation, thereby improving accuracy and workforce productivity. The system leverages historical employee data and machine learning techniques to forecast work patterns, aiding resource allocation and decision-making. Additionally, the article presents a comparative stock market prediction study using LSTM models that incorporate historical prices and external factors like market sentiment and economic indicators to improve forecasting accuracy. This multidisciplinary approach demonstrates the application of advanced programming and machine learning methods in both workforce management and financial market analysis.