Design of an intelligent monitoring system for electricity safety for the development of a Smart Healthcare Monitoring System

Abstract

This research work delves into the dynamics of investor decision-making and the efficacy of forecasting models used to comprehend market movements. The study aims to determine suitable models that can elucidate agent behavior, enabling easier decision-making through accurate anticipation of future asset prices. Artificial intelligence models, specifically Machine Learning and Deep Learning algorithms, are employed to gain deeper insights into asset price variations and their future evolution. A comparative analysis between classical models and advanced artificial intelligence (AI) algorithms reveals the limitations of classical statistical models, as they rely on assumptions that often do not hold in the context of financial series. Consequently, their predictive capacity for new data is compromised. In contrast, the integration of AI, particularly RNN, allows us to overcome the assumption of market efficiency and successfully model the behavior of irrational agents who engage in trading based on rumors and false news. By harnessing the power of AI, this study lays the groundwork for a more accurate and robust understanding of investor behavior and market movements. The findings present significant implications for the financial sector, offering an enhanced approach to decision-making and risk management. The intelligent monitoring system developed through this research holds great potential for fostering smarter, data-driven decisions and anticipating market trends, ultimately leading to improved performance and profitability.