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The aim of the study is to ascertain the optimal utilization of financial and human resources. When it comes to financial assets (stocks), we primarily design dashboards that have the ability to give traders, investors, and financial analysts a more precise and act as reliable instrument for determining investment choices. The primary objective of this research is to determine the most efficient machine-learning algorithm for predicting stock prices by conducting an empirical study on the effectiveness of ML techniques, specifically focusing on LSTM and Random Forest. Additionally, the study primarily provides an interactive dashboard for users. With regard to human assets, the LSTM model is mostly used to forecast emotions. Absenteeism among employees utilizing the Gradient Boosting framework.

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IoT-Based Assets Tracking Using Machine-Learning Models

  • 会议论文

  • 发表日期：

    2025年

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  • V. Srinivasa Kumar,
  • P. Suganthi,
  • Vigneshwaran

摘要

The aim of the study is to ascertain the optimal utilization of financial and human resources. When it comes to financial assets (stocks), we primarily design dashboards that have the ability to give traders, investors, and financial analysts a more precise and act as reliable instrument for determining investment choices. The primary objective of this research is to determine the most efficient machine-learning algorithm for predicting stock prices by conducting an empirical study on the effectiveness of ML techniques, specifically focusing on LSTM and Random Forest. Additionally, the study primarily provides an interactive dashboard for users. With regard to human assets, the LSTM model is mostly used to forecast emotions. Absenteeism among employees utilizing the Gradient Boosting framework.