Innovation of enterprise financial management based on machine learning and artificial intelligence technology

2020 ◽  
pp. 1-12
Author(s):  
Cao Yubo
Keyword(s):  
Artificial Intelligence ◽  
Financial Management ◽  
Route Planning ◽  
Value Added ◽  
Production Quality ◽  
Operating Efficiency ◽  
Exploratory Research ◽  
Modern Enterprise ◽  
Artificial Intelligence Technology ◽  
Effective Development

With the development of economic globalization, the competition between companies is increasing and becoming a norm. As one of the main value-added tools, financial management has greatly improved its position in business management. Traditional financial management is difficult to keep up with the pace of modern company management, which to a large extent hinders the effective development of enterprises. Therefore, under the current macroeconomic background, the necessity of studying financial management innovation has become more urgent. In this context, seeking innovation is not only a problem for enterprises, but also an important strategic goal of economic development and the concept of national modern enterprise development. Many studies have been carried out in the field of technological innovation, and few have focused on innovation in financial management. Exploratory research on the factors that affect the choice of financial management mode and route planning is important both in reality and in theory. It can help enterprises to gain greater competitive advantage through innovative financial management and improve their operating efficiency and production quality. This paper is based on learning. A research on the innovation of enterprise financial management is carried out on machine and artificial intelligence technology.

scholarly journals Use of artificial intelligence in last mile delivery

2021 ◽  
Vol 92 ◽  
pp. 04011
Author(s):  
Peter Jucha
Keyword(s):  
Artificial Intelligence ◽  
Driving Forces ◽  
Value Added ◽  
Allocation Of Resources ◽  
Existing Problems ◽  
Routing Optimization ◽  
Last Mile ◽  
Artificial Intelligence Technology ◽  
Last Mile Delivery ◽  
Vehicle Routing Optimization

Research background: Artificial intelligence is a term that is now known to almost everyone and is among the trends and innovations of Industry 4.0 for 2020. It is a much-discussed topic in the field of technology. Artificial intelligence and machine training are the driving forces across different industries. In many cases, artificial intelligence helps people in their work and simplifies it or even completely replaces the human workforce. Purpose of the article: The purpose of the article is to state how artificial intelligence can affect and solve existing problems in last mile delivery. For example, inefficiency is a major problem with last mile delivery because the last section of delivery usually involves a number of short-distance stops. However, a long waiting time for the customer to deliver the goods or incorrect allocation of resources and vehicles to the required areas can also be a problem. And it is artificial intelligence that should help solve such problems. Methods: Comparison, Empirical and retrospective analysis are used within the analysis of different modes of last-mile delivery. Findings & Value added: The research results shows the ways in which artificial intelligence can help solve problems in last mile delivery. Examples include The Vehicle Routing optimization (VRO), which aims to calculate the most optimal delivery route or artificial intelligence technology, which is used to interpret various events, manage data, and apply predictive intelligence.

Insurance payment for artificial intelligence technology: Methods used by a stroke artificial intelligence system and strategies to qualify for the new technology add-on payment

2022 ◽  
pp. 197140092110674
Author(s):  
Nick M Murray ◽  
Phillip Phan ◽  
Greg Hager ◽  
Andrew Menard ◽  
David Chin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
New Technology ◽  
Rapid Change ◽  
Value Added ◽  
Stroke Care ◽  
Case Scenario ◽  
Intelligence System ◽  
Insurance Reimbursement ◽  
First Case ◽  
Artificial Intelligence Technology

The first ever insurance reimbursement for an artificial intelligence (AI) system, which expedites triage of acute stroke, occurred in 2020 when the Centers for Medicare and Medicaid Services (CMS) granted approval for a New Technology Add-on Payment (NTAP). Key aspects of the AI system that led to its approval by the CMS included its unique mechanism of action, use of robotic process automation, and clear linkage of the system’s output to clinical outcomes. The specific strategies employed encompass a first-case scenario of proving reimbursable value for improved stroke outcomes using AI. Given the rapid change in utilization of AI technology in stroke care, we describe the economic drivers of stroke AI systems in healthcare, focusing on concepts of reimbursement for value added by AI to the stroke care system. This report reviews (1) the successful approach used by the first NTAP-approved AI system, (2) economic variables in insurance reimbursement for AI, and (3) resultant strategies that may be utilized to facilitate qualification for NTAP reimbursement, which may be adopted by other AI systems used in stroke care.

scholarly journals The structure of the enterprise’s financial management

2021 ◽  
pp. 10-12
Author(s):  
Olena BROVKOVA ◽  
Hanna KOVALOVA
Keyword(s):  
Operations Management ◽  
Financial Management ◽  
Financial Stability ◽  
Enterprise Management ◽  
Modern Enterprise ◽  
Effective Development ◽  
High Level ◽  
And Control ◽  
Relationship Of ◽  
The Relationship

The paper considers in detail the structure of financial management of the enterprise. The possibility of survival and effective development of the enterprise in dynamic and often unfavorable environmental conditions is determined by how competent managers are in matters of financial management. Skillful management of three processes – current operation, continuous improvement, radical development – determines the effectiveness of enterprise management. Financial management is a very important process of any enterprise in the process of capital movement. The paper considers the main definitions and principles of using financial management sub-processes in the management of a modern enterprise, as well as highlights the relationship of these sub-processes in the appendix to the activities of the financial manager. The principle of financial stability is characterized by such activities of the enterprise through which it could at any time, to settle their debts with their own or borrowed funds. Making a profit (the most key measure of financial efficiency of the enterprise) – getting a positive difference between the total income of the enterprise and its costs (in monetary terms). The scientific prerequisite for financial management is a fairly high level of development of economics. The activity of any enterprise is not possible without the management of facilities or processes (personnel management, operations management, etc.). Thus, any enterprise can be represented in the form of two subsystems – managed and control.

scholarly journals Research on Tourism Route Planning Based on Artificial Intelligence Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Maobin Ding
Keyword(s):  
Artificial Intelligence ◽  
Continuous Optimization ◽  
Route Planning ◽  
Recognition System ◽  
Wireless Sensor ◽  
Sensor Technology ◽  
Face Recognition System ◽  
Route Design ◽  
Artificial Intelligence Technology ◽  
Transmission Energy Consumption

Study on designing reasonable travel routes with the least time cost and the highest experience index was conducted. An artificial intelligence-based wireless sensor travel route planning study is proposed. First, the improved TSP route planning model is built at the least time consumption and combines the normal distributed random number (ND) with the genetic algorithm (GA) and proposes the ND-GA algorithm, analyzes the overall structure, node structure, communication mode, and network coverage of the wireless sensor network, and gives a mathematical model of wireless transmission energy consumption. Using the proposed algorithm to solve the travel route and detailed itinerary, with time, the 10-year travel route design model based on multitarget dynamic optimization finally detailed analysis of the model results and sensitivity analysis results showing that the application of AI wireless sensor technology can also make the scenic work more efficient; for example, a face recognition system can improve the speed of ticket checking. Although the application of AI technology is widely used in tourism activities, there are some problems, which require the continuous optimization and innovation of AI wireless sensor technology by relevant practitioners, so that it can better serve tourists.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

Sign in / Sign up

Export Citation Format

Share Document