Collaborative APIs recommendation for Artificial Intelligence of Things with information fusion

At present, many companies have many problems such as high financial costs, low financial management capabilities, and redundant frameworks; at the same time, the SASAC requires that the enterprise’s financial strategy transfer from “profit-driven” to “value-driven”, finance separate from accounting to improve the operational efficiency of the company. Under this background, more and more enterprise respond to the call of the SASAC; in order to achieve the goals of corporate financial cost savings and financial management efficiency improved, we began to provide services through financial sharing. The research of information fusion theory involves many basic theories, which can be roughly divided into two large categories from the algorithmic point of view: probabilistic statistical method and artificial intelligence method. The main task of artificial intelligence is to realize the computer for some learning, thinking process, and wisdom formation of simulation, and an important goal of information integration is the human brain comprehensive processing ability simulation, so artificial intelligence method will have broad application prospects in the field of information fusion; the common methods have D-S evidence reasoning, fuzzy theory, neural network, genetic algorithm, rough set, and other information fusion methods. The purpose of this paper is to proceed from the internal financial situation of the enterprise, analyze data security issues in the operation of financial shared services, and find a breakthrough in solving problems. But, with constantly expanding of enterprise group financial sharing service scale, the urgent problem to be solved is how to ensure the financial sharing services provided by enterprises in the cloud computing environment. This paper combines financial sharing service theory and information security theory and provides reference for building financial sharing information security for similar enterprises. For some enterprise that have not established a financial shared service center yet, they can learn from the establishment of the financial sharing information security system in this paper and provide a reference for enterprise to avoid the same types of risks and problems. For enterprise that has established and has begun to practice a financial shared information security system, appropriate risk aversion measures combined with actual situation of the enterprise with four dimensions related to information security system optimization was formulated and described in this paper. In summary, in the background of cloud computing, financial sharing services have highly simplified operational applications, and data storage capabilities and computational analysis capabilities have been improved greatly. Not only can it improve the quality of accounting information but also provide technical support for the financial sharing service center of the enterprise group, perform financial functions better, and enhance decision support and strategic driving force, with dual practical significance and theoretical significance.

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Explainable data fusion

10.32469/10355/85805 ◽

2021 ◽

Author(s):

◽

Bryce J. Murray

Keyword(s):

Artificial Intelligence ◽

Remote Sensing ◽

Deep Learning ◽

Data Fusion ◽

Case Studies ◽

Information Fusion ◽

Human Life ◽

Fuzzy Integral ◽

Highly Educated ◽

Explainable Ai

The recent resurgence of Artificial Intelligence (AI), specifically in the context of applications like healthcare, security and defense, IoT, and other areas that have a big impact on human life, has led to a demand for eXplainable AI (XAI). The production of explanations is argued to be a key aspect of achieving goals like trustworthiness and transparent versus opaque AI. XAI is also of fundamental academic interest with respect to helping us identifying weaknesses in the pursuit of making better AI. Herein, I focus on one piece of the AI puzzle, information fusion. In this work, I propose XAI fusion indices, linguistic summaries (aka textual explanations) of these indices, and local explanations for the fuzzy integral. However, a limitation of these indices is its tailored to highly educated fusion experts, and it is not clear what to do with these explanations. Herein, I extend the introduced indices to actionable explanations, which are demonstrated in the context of two case studies; multi-source fusion and deep learning for remote sensing. This work ultimately shows what XAI for fusion is and how to create actionable insights.

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