2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 8 , Issue 1 , PP: 75-88, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Tamarah Alaa Diame 1 * , M. Abdul Jaleel M. 2 , Sajad Ali Ettyem 3 , Raaid Alubady 4 , Mohaned Adile 5 , Mohd K. Abd Ghani 6 , Hatıra Gunerhan 7
Doi: https://doi.org/10.54216/JISIoT.080107
Currently, Machine Learning (ML) seems very attractive since it may speed up business functions in enterprises, lower costs for supplying goods and services, and manage information to promote enterprise efficiency. Essential technological domains nowadays are the explosive period of growth in enterprise solutions, which are progressively used in almost all business platforms. The ML sessions will receive a thorough summary, and the relevant organizations will be shown procedures for relevant business processes. The data management unit is already been striving to solve related issues in ML applications for more than a generation, creating numerous customized analytical techniques. The approach described in the study uses a weighted directed graph displayed in an industrial environment to identify the core part of the neural network structure and then trains them using the relevant data source. The article proposed ML-assisted Enterprise Data Management (ML-EDM) for identifying the trade-off between ML growth in the financial sector and its consequences in precision and interpretability. According to the experimental findings, the ratio of AI for decision-making is 84.25%, the Speed and Agility proportion is 92.70%, the result of Earlier Prediction Management is 93.80%, the Infrastructure Development is 85.46%, with Data Efficiency is 84.5% and Performance efficiency of the system is 90.14%.
Machine Learning , Innovation , Management , Decision , Business , and Enterprise Business Management.
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