Big Data Artificial Intelligence Research Articles

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Intelligent design of network multimedia using big data and virtual Artificial Intelligence technology

AbstractIn recent years, with the rapid development of the Internet of Things and communication technology, the application of network multimedia has become increasingly popular. At present, network multimedia has become a new type of mass media. Due to its interchangeability and extensive dissemination, it creates favourable conditions for information dissemination and acceptance in terms of vision, hearing, touch etc. However, at the same time, there are also some problems. The network multimedia does not have enough original information and the information retrieval is not intelligent, so this has caused great trouble to users. In order to solve these problems, the authors attempted to use the combination of Big Data (BD) and virtual Artificial Intelligence (AI) technology to realise the intelligent design of network multimedia. Therefore, based on the theoretical analysis, the authors also verified the effect of BD and virtual AI technology on the intelligent design of network multimedia. It was found that the intelligent design method proposed by the authors had good application effect in network multimedia, and could effectively improve the information retrieval speed of network multimedia. The query time of this method was 1.87 s less than that of the traditional method when the amount of information was 1000 and 18.16 s less than that of the traditional method when the amount of information was 40,000. At the same time, this method could also improve the accuracy of network multimedia information recommendation, and make network multimedia have better sharing, so as to provide more services for users. In addition, the discussion of BD and virtual AI technology in network multimedia intelligent design could also broaden the application scope of the Internet of Things and promote its better development.

The Effects of the Covid-19 Pandemic on Big Data Artificial Intelligence

The Covid-19 pandemic has changed people's behavior. Artificial Intelligence is made based on big data of people's behavior. Financial institutions use AI in making credit decisions. The AI basis of credit decisions is made based on existing customer Big Data. This customer's Big Data has changed as a result of the Covid-19 pandemic. Changes in Big Data mean that the AI of financial institutions in decision making must be adapted to the new behavior. This study aims to discuss the changing community behavior variables related to the granting of credit approvals. This study uses a qualitative method with a literature review. This study concludes that length of stay, home ownership, occupation, length of work, expense ratio, age, bank account, awareness of fintech, usage of fintech and demographic variables have changed. These variables need to be adjusted as a result of changes in the behavior of the Covid-19 pandemic.

The Big data artificial intelligence and corporate social performance: The organizational innovation as Mediator

Businesses are under increasing pressure to demonstrate their commitment to Corporate Social Performance (CSP) and fulfill the expectations of their many stakeholders in today's environment. The purpose of this research is to look into the relationship between CSP and Big Data Artificial Intelligence (BDAI), as well as the function of Organizational Innovation (OI) as a mediator in this connection. The Resource-Based View (RBV) paradigm is used in this study, which hypothesizes that BDAI has a positive influence on CSP, and that OI mediates the interaction between these two variables. The study also shows that the implementation of BDAI can allow organizations to collect and analyze huge volumes of data, which can lead to improvements in decision-making, efficiency, and social and environmental performance. The merger of big data analytics and artificial intelligence is referred to as BDAI. Furthermore, by establishing an innovative culture inside an organization and supporting the application of BDAI into the company's operations, OI has the ability to act as a mediator between BDAI and CSP. This study adds to the existing literature on the relationship between BDAI and CSP by shedding light on the significance of OI as a mediator within that framework. The study's conclusions could have far-reaching repercussions for businesses, government decision-makers, and academia. Businesses can improve their CSP by implementing BDAI and OI, and policymakers should encourage firms to utilize BDAI and OI to improve their social and environmental performance. By analyzing the impact of various factors on the link between BDAI, OI, and CSP, researchers can acquire a clearer understanding of the nature of this interaction
 

Symbolic Model of New Media Art Expression Based on Artificial Intelligence Big Data

With the rapid development of China’s economy and the continuous optimization and improvement of Internet information technology, a new thing, new media, has emerged. On this basis, a kind of artistic behavior of creating, carrying, spreading, and appreciating is also called new media art. New media art has developed for a long time in foreign countries, but its development and popularization are relatively late in China, and it is still in a stage of improvement. As far as visual communication design is concerned, the emergence and development of new media have strongly promoted the development of its work and the dissemination of various works. Developing visual communication design based on new media art has become a key issue for all visual communication designers. Nowadays, deep learning is widely used in different fields in different ways, and image art representation is a hot research field in computer vision. Therefore, this paper mainly studies and analyzes the symbolism of new media art expression forms and proposes a new media art symbolism model based on artificial intelligence big data. In order to provide a method of real-time representation of art images, the deep learning method is used to model the artistic expression of images, and spatial reorganization of content features and style features is carried out. This paper designs simulation experiments to verify the effectiveness of the model, which can provide some help for the development and improvement of new media art in China. We compared the results of our model with a number of other models to test its effectiveness. We achieved a matching accuracy of 98.4% which is higher than or equal to the results of the existing models.

The Main Influencing Factors of Artificial Intelligence Big Data Sports News Coverage

Through systematic analysis and research on the reporting methods of various sports events and the status quo of data news under the background of big data, it is found that sports news reports under the background of big data have presented new reporting forms and contents. Combining the application of open source data with sports news can make good use of relevant technical forces to improve the reading efficiency of sports news. This can consolidate old audiences, attract new audiences, and maximize the promotion of various sports events, allowing more audiences to participate. After distributing 336 questionnaires, 303 were recovered. A total of 303 answer sheets met the requirements. The successful recovery rate of the questionnaire was 90.18%. Among them, 172 were male, accounting for 56.77% of the valid questionnaires; 131 were female, accounting for 43.23% of the valid questionnaires. Full-time students accounted for a relatively high proportion of the survey respondents, with a total of 130 people, accounting for 42.90% of the valid questionnaires. It can be seen that most of the people who pay attention to sports news reports are young and middle-aged groups.

Physical Education Resource Information Management System Based on Big Data Artificial Intelligence

With the rapid development of artificial intelligence technology, the combination of physical education and modern technology has become the development trend of today’s physical education. Especially in the context of the reform of the education system, the connection between artificial intelligence and physical education is getting closer and closer and it has penetrated into all aspects of physical education. In terms of sports management, the sports management method based on big data artificial intelligence has gradually replaced the traditional manual management method and promoted the development of physical education. Strengthening the information management of physical education teaching and providing high-quality and efficient physical education information for physical education managers and teachers are an important part of the current school physical education teaching work. The establishment of sports resource information big data platform is the key to the integration and sharing of sports resources. This paper discusses the relevant theories of artificial intelligence and studies the current status of the application of sports resources. A physical education resource information management system based on artificial intelligence is constructed. The feasibility of the system is verified by the number of system visitors and the distribution of user populations, and the real-time information, effectiveness, students’ learning initiative, and teachers’ teaching initiative of this system and the original system are compared and analyzed. The results show that the average score of the real-time information of the original system is 74.28 points, the average score of effective information is 82.29 points, the average score of students’ learning initiative is 74.97 points, and the average score of teachers’ teaching initiative is 82.98 points. The average score of real-time information in the new system is 92.05 points, the average score of information effectiveness is 93.34 points, the average score of students’ learning initiative is 82.69 points, and the average score of teachers’ teaching initiative is 90.46 points. The real-time information and information effectiveness scores of the new system are higher than those of the original system, and the scores of students’ learning initiative and teachers’ teaching initiative are higher than those of the original system.

Artificial Intelligence-Based Sustainable Development of Smart Heritage Tourism

World heritage is a kind of affirmation and high honor given by the international community to the important civilization, historical relics, or natural landscape of a country and nation. This paper studies and analyzes the sustainable development of heritage tourism boosted by smart tourism based on big data artificial intelligence. This paper first analyzes big data and then introduces the concept of smart tourism. Smart tourism is a new future-oriented tourism form that serves the public, enterprises, and governments. It uses the Internet of Things, cloud computing, next-generation communication network, high-performance information processing, intelligent data mining, and other technologies in tourism experience, industrial development, administrative management, and other applications, so that tourism physical resources and information resources have been highly systematically integrated and deeply developed and activated. And then, we analyze and discuss artificial intelligence algorithms. Artificial intelligence is a new technological science that is researched and developed on the basis of computer science as a simulation and extension of human intelligence activities. Finally, a comprehensive analysis is made on the tourism ecological footprint of natural heritage sites and the carrying capacity of tourism ecology in natural heritage sites. The experimental results of this paper show that the tourism development of this natural heritage site is in a sustainable state. The reasons are as follows: First, the average tourist ecological footprint of the place is 0.009466 hm2, the average tourism ecological carrying capacity is 0.032861 hm2, and there is an average ecological surplus of 0.02339 hm2; secondly, the average tourist natural footprint is 18285.93 hm2, the average tourism environmental carrying capacity is 40421.97 hm2, and the average ecological surplus is 22136.04 hm2. To sum up, it shows that its tourism development is in a state of sustainable development.

Intelligent question and answer analysis model of power ICT based on BI-LSTM-CRF

With the deepening of the country’s digital transformation and the development of big data artificial intelligence software definition and other technologies, customers’ demand for IT services is constantly increasing. In the electric power industry, the front-end knowledge base for traditional customer service lacks self-learning ability, the accumulated customer service data in the middle cannot be analyzed and utilized effectively, the back-end quality inspection was not fully covered and lacked emotion recognition, etc. Apply artificial intelligence, information and communication technology and big data technology to power ICT customer service. It is proposed to construct a network model by combining conditional random airport with the long- and short-term memory neural network, and to realize the intelligent customer service model combined with human–computer interaction by constructing an intelligent knowledge base and the information system fault association analysis model, so as to improve the extraction performance of context information and establish a knowledge base question answering system in the field of power service. Through the comparative analysis of experimental data, the proposed model has achieved good results and can be effectively applied to the power industry to improve customer service satisfaction.

Research on Static and Dynamic Fragile Node Identification Algorithms Based on Uncertainty in New Energy

In order to identify the uncertain static and dynamic fragile nodes in new energy, the instability and randomness of new energy bring new challenges to the identification of vulnerable nodes in a power grid. Due to the characteristics of low cost and low energy consumption of new energy, people have paid much attention to the exploration and development of new energy. Due to the uncertainty of new energy, it is needed to properly analyze the uncertainty factors. To analyze the uncertainty factors in new energy using the framework of power big data artificial intelligence analysis based on cost-benefit analysis (CBA), it is required to carry out Fourier transform and extract the data characteristic matrix so that a vulnerability risk prediction index can be obtained by using a fuzzy convolution algorithm and binarization, and the safety form between the uncertainty factors in new energy and power stations can be evaluated. In this paper, a fuzzy neural network algorithm is proposed to identify the static and dynamic fragile nodes based on the uncertainty in new energy, so as to ensure the security and stability of the power generation system. The safety performance of the power station system is detected through different levels of early warning sensitivity. The simulation model of the above algorithm is constructed in MATLAB. The simulation results show that the proposed algorithm increases the sensitivity of the early warning system of the power station and the sensitivity of triggering the early warning system and improves the security of the power station system as a whole.

Innovation and Reform of Accounting Professional Training Model Based on the Artificial Intelligence

In recent years, the continuous upgrading and transformation of high-performance computing and Internet technology have brought revolutionary impact to the development of the economic, financial and financial accounting industries. High-tech represented by cloud computing and big data artificial intelligence has promoted the “first Four Industrial Revolutions”, and the application of artificial intelligence technology has had a profound impact on the traditional accounting work process, rules, and the functional positioning of practitioners. How to adapt to the demand for accounting professionals in the artificial intelligence era, and accelerate the construction of innovating the current talent training model is a problem that should be faced with and analyzed in the future.

Understanding the in-depth AI Big Data Analysis application through Diversified Integration and Development of Functional Linguistics

ABSTRACT For promoting functional linguistic system integration, the in-depth application of artificial intelligence-big data information analysis and technological processing has provided the core technical means in recent years. Perhaps, related to artificial intelligence (AI) big data analysis, the multiple integrations of functional linguistics have frequently encountered obstacles in applications. As a result, it has created major challenges for the in-depth development of functional linguistics. The related concepts of functional linguistics development and artificial intelligence are sorted out under this background. On this basis, under the background of AI-big data analysis, the functional linguistic technological integration and internal integration are analyzed. Furthermore, to promote in-depth functional linguistics development, this study provides several suggestions, such as optimizing a distributed cluster environment, building a functional language data platform, adopting models, data-parallel modes, and distributing neural network parallel training. These suggestions provide some reference for theoretical linguistic research and enriching functions. Further put forward several suggestions to promote the in-depth development of functional linguistics, such as building a functional language data platform, optimizing a distributed cluster environment, distributing neural network parallel training, adopting models, and data-parallel modes to promote the in-depth development of functional linguistics, in order to provide some reference for enriching functions and linguistic theoretical research. REFERENCES [1] D. L. Zhang. Exploration on the Development Trend of System Functional Linguistics in 60 Years. Foreign Language Teaching and Research, 50(1):39–50, 2018. [2] H. Jiang and A.G. Zhao. The Generation Background and Academic Inheritance of Functional Grammar Theory and System Functional Linguistics. Foreign Language and Foreign Language Teaching, 31(2):32–37, 2014. [3] X.N. Li and X.Y. Fan. Research on Knowledge Representation Method in System Functional Linguistics. Journal of University of Science and Technology Beijing (Social Science Edition), 16(3):26–30, 2015. [4] G.Z. Xiao. Grammatical Events and Semantic Events-Language Research for Artificial Intelligence. Yangtze River Academic, 15(2):82–98, 2020. [5] C.W. Yuan, C.Y. Liu, and S.L. Chang. Correspondence between System Functional Linguistics and Corpus. Foreign Language, 32(1):104–109, 2016. [6] Y.Y. Group Construction in Weibo: A New Perspective of System Functional Linguistics. Dongyue Review, 34(1):187–190, 2013. [7] A.M. The Study of Chinese and Foreign Texts Based on Systemic Functional Linguistics. Language Construction, 61(23):71–72, 2016.

Big data and artificial intelligence in future patient management. How is it all started? Where are we at now? Quo tendimus?

This article is focused on the understanding of the key points and their importance and impact on the future of early disease predictive models, accurate and fast diagnosis, patient management, optimise treatment, precision medicine, and allocation of resources through the applications of Big Data (BD) and Artificial Intelligence (AI) in healthcare. BD and AI processes include learning which is the acquisition of information and rules for using the information, reasoning which is using rules to reach approximate or definite conclusions and self-correction. This can help improve the detection of diseases, rare diseases, toxicity, identifying health system barriers causing under-diagnosis. BD combined with AI, Machine Learning (ML), computing and predictive-modelling, and combinatorics are used to interrogate structured and unstructured data computationally to reveal patterns, trends, potential correlations and relationships between disparate data sources and associations. Diagnosis-assisted systems and wearable devices will be part and parcel not only of patient management but also in the prevention and early detection of diseases. Also, Big Data will have an impact on payers, devise makers and pharmaceutical companies. BD and AI, which is the simulation of human intelligence processes, are more diverse and their application in monitoring and diagnosis will only grow bigger, wider and smarter. BD connectivity and AI of diagnosis-assisted systems, wearable devices and smartphones are poised to transform patient and to change the traditional methods for patient management, especially in an era where is an explosion in medical data.

A Multi-source Data Fusion Analysis Model in the Research on Public Sentiment Index

At present, China’s economy is continuously developing and making progress. The deep integration of society and big data artificial intelligence and other technologies is constantly promoting the construction of new smart cities. The major livelihood service systems have provided great convenience to the people. At the same time, they have accumulated massive livelihood data. Mining the value of data to solve people’s main problems has been widely concerned by the government and the community. However, the data structure of each system is different. The data is noisy and the systems cannot be interconnected, which leads to the one-sided and single results of data analysis, and it is impossible to use these data to obtain effective results and conclusions. This paper proposes a Public Sentiment Index that reflects the intensity of public appeals. A new multi-source data fusion analysis model is proposed by using a multi-source data fusion method. By constructing a public sentiment database and a public sentiment index model, it can truly reflect the changes in people’s demands. This research can provide a reliable and scientific basis for the government to improve decision-making capacity and service quality.

Big data based research on the management system framework of ideological and political education in colleges and universities

Artificial intelligence model combined with data mining technology can mine useful data from college ideological and political education management, and conduct process evaluation and teaching management. Therefore, based on the superiority of data mining technology and artificial intelligence system, this paper improves the traditional algorithm and constructs a university ideological and political education management model based on big data artificial intelligence. Moreover, this study uses a local sensitive hash function to generate representative point sets and uses the generated representative point sets for clustering operations. In order to verify the performance of the algorithm model, a control experiment is designed to compare the algorithm of this paper with traditional data mining methods. It can be seen from the research results that the algorithm model constructed in this paper has good performance and can be applied to practice.

Australian administrative elites and the challenges of digital-era change

Within long-lived public sector bureaucracies, the organizational cultures developed by administrative elites have strong filtering and focusing effects on the kinds of technological changes adopted, especially in the modern era. Normally seen as very slow-moving and hard to alter, senior officials’ attitudes towards digital changes have recently begun to alter in more substantial ways in Australia. We review first a considerable reappraisal of the priority given to digital changes by top public service managers. This cultural shift has followed on from tech-lead disruptive societal changes affecting most areas of government now, and from the rise of global-scaled ICT corporations to become key management exemplars for officials. Second, we look at the chequered history of political leaders’ interventions to speed up digital change, showing that in the period 2015–19 Australia witnessed both the initial power and later limits of such involvement. Finally, we consider Australia’s recent experience with big data/artificial intelligence (BDAI), a key area of technological change for public service officials, but one that in a liberal democracy can also easily spark public resistance to their plans.