Database Management Systems (DBMS) advancement has been crucial to Information Technology (IT). Traditional DBMS needed help managing large and varied datasets under strict time constraints due to the emergence of Big Data and the widespread use of Internet of Things (IoT) devices. The growing intricacy of data and the need for instantaneous processing presented substantial obstacles. This research suggests a Machine Learning-based Intelligent Database Management Systems (ML-IDMS) technique. This invention combines the skills of Machine Learning with DBMS, improving flexibility and decision-making capacities. The ML-IDMS is specifically developed to tackle current obstacles by providing capabilities such as instantaneous data retrieval, intelligent heat measurement, and effective neural network initialization. The simulation results showcase the effectiveness of ML-IDMS, as shown by impressive metrics such as query execution time (19.27 sec), storage efficiency (83.78%), data accuracy (90%), redundancy reduction (66.42%), network throughput (7.93 Gbps), and end-to-end delay (14.4 ms). The results highlight the efficacy of ML-IDMS in managing various data circumstances. ML-IDMS addresses current obstacles and establishes a standard for future intelligent data management and analytics progress.

The advancement of informatization has significantly affected many sectors due to e-commerce, rendering the existing Financial Model (FM) inadequate for e-commerce customers. Artificial Intelligence (AI) substantially enhances the financial accounting capabilities and resource integration of systems using mobile networks. The insufficient FM of e-commerce tools and issues such as the administration of funds have significantly obstructed the advancement of standardized financial procedures, as the FM structure impacts managers' statistical evaluation of financial information. This research examines the financial hazards, management issues, and underlying causes of these issues inside e-commerce systems, subsequently employing AI to assess the FM operations of these websites in Peru. This research uses a Machine Learning (ML) approach to examine the clustering centers of FM information as well as the privacy aspect of the FM. This study ultimately presents ways for optimizing and constructing FMs to enhance data security and capital administration in e-commerce financing, hence fostering the continued growth of e-commerce systems using mobile networks. The experimental findings indicate that the FM’s classification value and security aspect for e-commerce systems progressively enhance under the ML. The average classification value is 1.23, whereas the average risk factor is around 1.41. AI and ML improve financial accounting requirements and the long-term growth of e-commerce systems in Peru.

Assuring the safe and effective operation of a company's technological infrastructure is an essential part of business management. Monitoring, administering, and troubleshooting the many components and systems of the network are all part of the process. Additionally, it is the responsibility of business administrators to find methods to enhance the existing system, as well as to make certain that all resources are distributed in such a manner that their use may be maximized. Management of the business is a key component of the technological infrastructure of any organization, and it is of the utmost importance that it be carried out effectively in order to guarantee a safe and effective operation. It is essential for network operators to discover strategies to improve the energy efficiency of their networks without adversely affecting the quality of service (QoS) for reasons related to both cost and sustainability. In this research, an in-depth analysis of business management techniques for effective resource utilization is presented. This analysis begins with the design of the network and continues all the way through to the delivery of accurate data. An Energy Efficient Network (EEN) must be able to give programmability and flexibility to network infrastructures, as well as the ability to operate networks in a rapid way and provide operators with more control. It is impossible to ignore energy throughout the process of meeting the needs and requirements of network services, especially when taking into consideration the consequences on the long-term viability of both the environment and businesses. Energy efficiency in both current and future networks is the topic of discussion in this research. The findings indicate that energy efficient networks are successful in overcoming the present issues that stand in the way of the application of energy efficiency techniques, also the discussed model are effective in addressing some of the obstacles that are encountered by small and medium-sized businesses.