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The Importance of Multilayer Neural Networks (MLNN) Systems in Economic Decision-Making
Corresponding Author(s) : Mirzayev Shoxrux Normurod o‘g‘li
American Journal of Economics and Business Management,
Vol. 8 No. 3 (2025): March
Abstract
This article examines the transformative role of multi-layer neural networks (MLNN) in econometrics and financial decision-making, particularly their influence on personal finance, automation, healthcare, transportation, and human-computer interactions. MLNNs, structured similarly to the human brain, have substantial potential to revolutionize these fields by significantly improving decision-making efficiency and accuracy. In personal finance, MLNNs optimize budgeting, saving, and expenditure decisions through dynamic analysis of financial patterns and continuous data inputs. Within healthcare, MLNNs enhance diagnostic precision and predictive treatment strategies. The application of MLNN in econometrics allows deeper financial pattern analysis, fraud detection, and risk management. Additionally, the article addresses essential ethical considerations such as data privacy, security, and algorithmic bias, emphasizing the importance of responsible and cautious deployment of neural network technologies. Despite these challenges, integrating MLNN into financial and econometric systems provides invaluable advancements crucial for modern economic growth and individual financial well-being.
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- This article examines the transformative role of multi-layer neural networks (MLNN) in econometrics and financial decision-making, particularly their influence on personal finance, automation, healthcare, transportation, and human-computer interactions. MLNNs, structured similarly to the human brain, have substantial potential to revolutionize these fields by significantly improving decision-making efficiency and accuracy. In personal finance, MLNNs optimize budgeting, saving, and expenditure decisions through dynamic analysis of financial patterns and continuous data inputs. Within healthcare, MLNNs enhance diagnostic precision and predictive treatment strategies. The application of MLNN in econometrics allows deeper financial pattern analysis, fraud detection, and risk management. Additionally, the article addresses essential ethical considerations such as data privacy, security, and algorithmic bias, emphasizing the importance of responsible and cautious deployment of neural network technologies. Despite these challenges, integrating MLNN into financial and econometric systems provides invaluable advancements crucial for modern economic growth and individual financial well-being.
References
This article examines the transformative role of multi-layer neural networks (MLNN) in econometrics and financial decision-making, particularly their influence on personal finance, automation, healthcare, transportation, and human-computer interactions. MLNNs, structured similarly to the human brain, have substantial potential to revolutionize these fields by significantly improving decision-making efficiency and accuracy. In personal finance, MLNNs optimize budgeting, saving, and expenditure decisions through dynamic analysis of financial patterns and continuous data inputs. Within healthcare, MLNNs enhance diagnostic precision and predictive treatment strategies. The application of MLNN in econometrics allows deeper financial pattern analysis, fraud detection, and risk management. Additionally, the article addresses essential ethical considerations such as data privacy, security, and algorithmic bias, emphasizing the importance of responsible and cautious deployment of neural network technologies. Despite these challenges, integrating MLNN into financial and econometric systems provides invaluable advancements crucial for modern economic growth and individual financial well-being.