Ebook: Artificial Intelligence, Medical Engineering and Education

The 7th International Conference of Artificial Intelligence, Medical Engineering, Education (AIMEE2023) was held in Guangzhou, China on November 9–10, 2023. This conference was jointly organized by Wuhan University of Technology, Hubei University of Technology, the International Center of Informatics and Computer Science, and the International Research Association of Modern Education and Computer Science.

This volume encompasses three sections: 1) Artificial Intelligence and Scientific Methodology; 2) Systems Engineering and Analysis: Concepts, Methods, and Applications; 3) Education Reform and Innovation. A total of 238 submissions were received by AIMEE2023, with 89 papers selected for presentation and publication after a rigorous international peer-review process. These papers put forth novel concepts and methodologies that may pave the way for further research across diverse populations.

We hope this volume will contribute to the rapid evolution of artificial intelligence and its applications by facilitating collaboration and knowledge sharing among the growing interdisciplinary workforce in this exciting field. We extend our gratitude to all those who helped make this conference a success. We thank our keynote speakers (Prof. IEEE Fellow, Felix Yanovsky, Delft University of Technology, Delft, Netherlands; Prof. Harrison Hao Yang, Distinguished Teaching Professor, State University of New York at Oswego, USA) for sharing their invaluable insights and setting the stage for thought-provoking discussions. We are grateful to the program committee members for their diligent efforts in reviewing submissions and assembling an outstanding technical program. We also thank all the authors for contributing their innovative research results, as well as the attendees for joining us and making the conference a vibrant forum for learning and connecting.

Finally, our sincere appreciation goes to the publisher, IOS Press, for preparing and publishing this volume.

Conference Chairs

Prof. Felix Yanovsky, Delft University of Technology, Delft, Netherlands

Prof. Z.W. Ye, Hubei University of Technology, China

November 2023

Environmental monitoring is increasingly reliant on image recognition through neural networks, automating the identification and classification of environmental threats. This automation optimizes resource allocation, enhances monitoring accuracy, and empowers researchers to address climate-related challenges. In response to this, we present a web application built using React.js and TensorFlow.js, which enables real-time image recognition. This application facilitates tasks ranging from climate analysis to disaster response and wildlife conservation, offering valuable insights into environmental trends. Moreover, it engages the public, promotes awareness, and contributes to conservation efforts, making it a versatile and cost-effective tool for environmental monitoring. The application leverages advancements in image recognition technology, availability of datasets, open-source frameworks, and cloud computing infrastructure. It supports a wide range of applications, promotes global collaboration, and ensures data security. While challenges exist, the potential of neural networks in environmental monitoring is promising. The web application combines the power of React.js and artificial neural networks, enhancing environmental monitoring efficacy and fostering sustainable practices on a global scale. Ongoing research and standardization efforts are essential for refining and expanding this approach.

The development of universities cannot be separated from the allocation of resources. Conduct empirical research on the efficiency of resource allocation in private universities to help them identify the current situation and problems of internal resource allocation, and promote high-quality development. This paper selects the undergraduate teaching status data of eight private undergraduate universities in Guangxi from 2018 to 2022, and uses DEA model to calculate the resource allocation efficiency of eight private undergraduate universities. The results show that the overall level of resource allocation efficiency of eight private undergraduate universities in Guangxi has maintained an upward trend, and the scale efficiency is significantly higher than the pure technical efficiency, which is the main factor affecting the overall efficiency improvement. Therefore, Guangxi private undergraduate universities should optimize the technical efficiency and management efficiency of resource allocation, strengthen professional construction and improve the quality of talent training.

The assessment and analysis of the operational status and safety risks associated with Aids to navigation hold paramount significance in ensuring the safety of maritime navigation. This paper constructs a risk assessment system for Aids to navigation based on the AHP (Analytic Hierarchy Process) and entropy weight method. The evaluation system employs the AHP to subjectively weight both discrete and continuous data, while the entropy weight method is applied to objectively weight data that can be continuously monitored. The subjective and objective weights are combined with nonlinear weights to construct a risk assessment model. The model has been applied to analyze and evaluate the Aids to navigation data of the waters of Guangzhou Maritime Safety Bureau in 2023. The evaluation results show that the power risk of the aids to navigation is the main risk of the existing aids to navigation.

Tax reform, as an important measure of national macroeconomic regulation, is of great significance in stimulating resident consumption. The article selects data on individual income tax and resident consumption levels in Hubei Province from 2016 to 2021 as research samples, and uses SPSS software to empirically analyze the impact of the 2019 new individual income tax reform on resident consumption. The research results indicate that individual income tax reform can effectively promote resident consumption expenditure, and reducing residents’ tax burden is beneficial for promoting resident consumption. Based on this, the article proposes suggestions from three aspects: the individual income tax collection system, the individual income tax rate structure, and the tax regulatory system, providing theoretical reference for deepening the individual income tax reform.

Government procurement is an important link in promoting the operation and development of universities, which contains a series of risks. In the era of information technology, new types of risk vulnerabilities require advanced means and prevention. This article analyzes the risks of university government procurement from key aspects such as procurement document preparation, review process, and performance acceptance, forms a risk list, and establishes a process optimization matrix. It proposes response and prevention measures for university government procurement risks in the information age, in order to provide reference and reference for university government procurement work.

Since the reform and opening up, the rough-and-ready development model has brought about economic take-off, but it has also paid a huge price in terms of resources and environment. Under the double pressure of slowing economic growth and serious damage to the ecological environment, the intensity of China’s environmental regulations has been increasing, and in 2013, eight provinces and municipalities began to implement a pilot policy of carbon emissions trading. At the same time, the scale of China’s Outward Foreign Direct Investment (OFDI) has been increasing. Regarding the relationship between environmental regulation and OFDI, most scholars currently study it from the perspective of host-country environmental regulation, and fewer study home-country environmental regulation. Using provincial panel data for a total of 11 years from 2009 to 2019, this paper constructs a double-difference model based on the carbon emissions trading pilot policy in eight provinces and cities in China as a quasi-natural experiment and explores the relationship of home-country environmental regulation on China’s OFDI.

Digital transformation and upgrading pose new requirements for talent quality. Taking human resources positions as an example, this article constructs a “3+X” talent quality evaluation index system that includes knowledge, ability, attitude, and professional skills. Based on the data characteristics of 55 employees in human resources positions, a combination weighting model, entropy method, and BP neural network model are used, and an evaluation index system and weight evaluation index system suitable for the quality characteristics of talents in China’s hydropower industry have been constructed. The research results show that employee competence is an important indicator for driving digital transformation, with knowledge and professional skills playing a relatively central role, and attitudes being less differentiated. Finally, the research significance and shortcomings of the study are summarized, and the prospects for future development are provided.

The study focused on deconstructing the image of AI anthropomorphism in the casual food industry and investigating its impact on consumers’ intention to purchase. This was done by examining impression-based cues and interaction-based cues, while also considering the role of psychological distance and information processing fluency. Additionally, the study explored the moderating role of consumers’ self-constructed type. Previous research has shown that factors like AI anthropomorphic impression-based cues and interactive cues significantly influence consumers’ purchase intention. Notably, the influence of interactive cues appears to be stronger. Furthermore, it has been found that both psychological distance and information processing fluency partially mediate the effect of AI anthropomorphism of product image on consumer purchase intention. Furthermore, the influence of anthropomorphic interactive cues on consumer purchase intention is contingent upon the type of consumer self-construction. Notably, individuals who possess interdependent self-construal are more inclined to purchase AI anthropomorphic product images that incorporate interactive cues, as opposed to those who possess independent self-construal. Conversely, there is no substantial disparity in the willingness to purchase AI anthropomorphic product images with impressionistic cues among consumers with different self-construal.

Transportation industry is a pillar industry for the development of the economy of a nation, and is indispensable in improving the operational efficiency of national economy and enhancing social and economic benefits. However, transportation industry has characteristics of high energy consumption, pollution and emissions, it requires an effective balance between development and carbon reduction in the context of the “Carbon neutrality and carbon reduction Goals”. The problem of Poor transportation structure in China’s transportation industry is relatively serious, so optimizing the transportation structure has been urgent to be solved. Based on the TAPIO decoupling elasticity analysis theory, this article takes the Yangtze River Economic Belt region for example to analyze the decoupling elasticity between transportation structure and carbon emission efficiency. The results indicate that the overall decoupling elasticity of carbon emissions, carbon emission efficiency, and transportation structure is respectively characterized by strong negative decoupling and weak negative decoupling. In other words, when the proportion of iron and water converted turnover decreases, the carbon emissions increase, and carbon emission efficiency decreases in company.

In order to optimize the core problems encountered in the operation of urban and rural logistics and improve the informatization degree of rural logistics, the authors of this paper conduct research on the application of big data, cloud computing, and other technologies in urban and rural logistics and informatization. Based on the current problems, the authors adopt the hierarchical design, C/S architecture, and cloud API technology to improve the low level of informatization in urban and rural logistics, as well as optimize the dispersion and repetitive construction of logistics resources for logistics enterprises in urban and rural areas. Overall, a feasible sharing technology solution for urban-rural logistics informatization has been provided, providing a certain reference for optimizing urban-rural logistics operations and improving rural informatization issues.

To achieve a balance between operational effectiveness and service efficiency in container terminals has become a critical issue with the rapid growth of container trade. In this paper, the scheduling problem of frontier operation resources of container terminals is proposed and studied. And a scheduling optimization method is proposed for the container terminal frontier operation resources. Considering the mutual influence between multiple resources, a joint scheduling optimization model of container terminal frontier operation resources is established. A multi-objective fusion algorithm is designed to carry out the solution.

Artificial intelligence technology is a new technological engine of a new round of science and technology and industrial revolution. After more than 60 years of development, it has gradually moved from technology to application, and will soon become a modern science that changes human economy and society. At present, the most popular branch technology of artificial intelligence is deep learning. Starting from the research content and application fields of artificial intelligence, this paper summarizes the basic model of artificial intelligence – neuron model, and then discusses the main application fields of artificial intelligence. In the field of transportation, the current research scope is wide, and the research depth is deep in the field of unmanned driving technology. Through sensing, decision-making, mapping and vehicle networking technologies, autonomous driving realizes fully autonomous driving of vehicles, providing significant technical support for improving the efficiency of transportation and ensuring safety.

While ChatGPT’s technological innovation has attracted worldwide attention, the risk of personal information leakage is particularly significant. Upon analysis, the primary cause for people’s apprehension lies in the ambiguous origin of ChatGPT and the uncertain utilization of users’ input information. These substantive issues can be distilled into challenges to the information subject’s right to know. This paper takes ChatGPT, a representative generative artificial intelligence, as a case study to elucidate its effects on the information subject’s right to know throughout various stages. To balance technological innovation and the rights of information subjects, priority is given to guarantee the rights through technical means. It is proposed to explore a path towards countermeasures through collaborative efforts of industry, administrative and judiciary authorities.

With the continuous advancement of the information age, all kinds of short videos have exploded and gained an extremely large audience. Short video news has become an important way of news reporting because of its characteristics of easy production, fragmentation and fast transmission. As a representative of China’s mainstream new media, Tiktok account of People Daily has the credibility of traditional media and the flexibility and diversity of new media. Among the short video reports on the COVID-19 outbreak in 2019, its attention and influence ranked first. Based on the theory of crisis communication and framework, this study uses content analysis method to analyze the “three-level” framework of People’s Daily’s Tik Tok news coverage of the COVID-19s outbreak, and analyzes the framework of People’s Daily Tik Tok short video news coverage of the COVID-19s outbreak, providing useful references for other mainstream media when reporting similar crisis events.

The rapid development of the Internet era has better linked the relationship between people and society, and also brought about the emergence and vigorous development of social media. The steady development of Little red book as an emerging social media platform is also worthy of in-depth study. Based on the development status of Little red book platform, this paper introduces the basic development status of Little red book platform and the characteristics of its own development, and further studies the brand communication strategy of Little red book platform in depth, focusing on visual communication, endorsement communication, accurate communication, etc. At the same time, the problems existing in the current development of Little red book and corresponding solutions are analyzed.

Driven by the “One Belt and One Road” trade, overseas engineering projects have become the embodiment of cooperation with countries along the “One Belt and One Road”, thus bringing frequent material exchange of cross-border supply chains among countries. In order to achieve smooth and rapid development of cross-border supply chain, reduce risks and strengthen control, it is necessary to screen the risk factors of cross-border supply chain, establish a system and make correct and reasonable evaluation. In this paper, PEST method is adopted to analyze the risk factors of external cross-border supply chain of energy enterprises under the background of “One Belt and One Road”, and corresponding evaluation index system is established. The risk evaluation model of external cross-border supply chain was established by combining the rough set theory and support vector mechanism, and the risk evaluation model of phoenix risk was used to comprehensively evaluate the risk of cross-border supply chain along the “Belt and Road”, and corresponding countermeasures were put forward.

Under the background of “dual carbon”, Hubei Province has always attached great importance to the low-carbon-logistics transformation and has made green and efficient the focus of logistics industry development. The article takes energy and unexpected output into account and takes unit carbon emission added value and unit carbon emission conversion turnover as output factors. It constructs an SBM-DEA model to measure the carbon emission efficiency of the logistics industry in Hubei China. Finally, based on the research results, a carbon emission reduction path for the logistics industry in Hubei Province is proposed, including strengthen policy guidance and reasonably set freight rates; transfer of high carbon emission transportation methods; extend the logistics value chain and carry out logistics technology innovation.The research conclusion can provide theoretical support for the government.

E-commerce systems have become one of the global trends in the current era when large-scale technological innovations are rapidly applied to all fields of activity. In particular, in recent times, the digital transformation of the economy and society has created conditions for the massive replacement of traditional trade with electronic trade. The rapid development of e-commerce has led to the emergence of many modern trends in the activity sectors, the formation of new requirements for e-commerce systems, and the beginning of a new era in logistics and supply chain management. An overview analysis of relevant scientific research works was conducted and the state of problem solving was studied. Providing a personalized approach to customers, optimizing supply chain operations, and responding to customer inquiries in a timely and accurate manner are the problems that await their solution. For this reason, in the presented article, directions for improving the performance of e-commerce systems and optimizing supply chain management processes by applying innovative technologies such as artificial intelligence, the Internet of Things, and big data have been studied. A conceptual model of intelligent e-commerce systems has been proposed based on the application of these technologies. Appropriate recommendations were given for modernizing e-commerce and logistics platforms applied at the national, regional, and enterprise levels based on new digital transformation technologies.

Engineering knowledge is an important step for creating innovative products. This paper discusses the morphological knowledge-based engineering (KBE). The development of science and technology is characterized by the increasing complexity of created technical means, a sharp increase in the cost of their development, production, operation, as well as rapid obsolescence. In this connection, the solution of the problem of creating machinery with a high technical level acquires special importance. A huge number of factors of structural, technological, operational, and economic nature, affecting the process of creating new equipment, predetermined the need to use system analysis and synthesis of knowledge in the synthesis of engineering systems. All this increases the number of possible implementations of both individual units and devices, and engineering systems. The need to improve the overall design methodology (and technological processes) is constantly increasing, which is a consequence of high rates of scientific and technological progress. Requirements for the maximum reduction of time spent on the entire cycle of creating a system or technological process have increased sharply. Morphological approaches correspond to these objective processes. They are considered in this paper and an example of the creation of an innovative aerostats system is given. The purpose of the article is to analyze the application of advanced morphological approaches. The approach is based on the application of set theory and agglomerative clustering of solutions. The paper describes the approach and results of using the proposed approach. As a result, the analysis and synthesis of innovative engineering solutions were carried out and recommendations for further development of the methodology were given. The use of the approach is considered as a continuous process of improving the technical level of engineering systems.

In the article, the problems of management of the information support of regional technical and economic systems based on the digital technologies of the Industry 4.0 platform are defined and their relevance is justified. With the application of artificial intelligence technologies, some problems related to the effective management of information support in technical and economic systems, including innovative enterprises, were explained. Some aspects of the essence and activity of technical-economic systems and their information support were analyzed. An overview analysis of relevant scientific research works was conducted and the state of problem solving was studied. Digital technologies of Industry 4.0, as well as the main functions of artificial intelligence technologies, and some global development trends related to their application in various fields, were studied. An architectural-technological structure model was proposed based on the application of artificial intelligence technologies of technical-economic systems, as well as information support of innovative-industrial enterprises. In the conditions of the transfer of digital innovations, directions for increasing the sustainable activity of technical-economic systems and innovative enterprises have been determined. The level of importance of industrial-digital technologies applied in the activity of technical-economic systems and innovative enterprises is indicated. Based on artificial intelligence technologies, relevant recommendations were given on the mechanisms for solving the problems of management of the information support of technical and economic systems.

Psychological health is an important issue faced by college students, therefore conducting relevant research is meaningful. The use of Adaboost algorithm for ensemble learning, combined with the application of decision tree algorithm, can fully utilize the information in mental health test data and improve the prediction accuracy of the classifier. The C4.5 decision tree algorithm is a commonly used classification algorithm that can classify and distinguish samples based on feature attributes, so it has been selected as the basic algorithm for this study. In order to verify the effectiveness of this method, we selected the mental health test data of 2780 students from a certain university in 2020 for the experiment. Through analyzing experimental results, we found that the method can accurately identify sensitive psychological problems among students. In practical applications, this method can serve as an auxiliary tool to help schools accurately understand the distribution of students’ mental health problems, and thus develop corresponding educational measures and intervention plans. In summary, the mental health prediction method based on Adaboost algorithm proposed in the article, combined with the application of decision tree algorithm, can effectively identify psychological problems among college students. In the experiment, this method demonstrated high accuracy and robustness.

Aiming at the problems of voucher preparation errors, numerous verification rules, lengthy audit cycle and low efficiency in the process of voucher preparation and audit of financial reimbursement in Colleges and universities, based on the implementation process of financial reimbursement process, this paper designs a framework of financial original voucher classification and verification system based on deep learning, and proposes a deep learning algorithm to identify the key contents of financial original vouchers, so as to support the voucher preparation process of financial reimbursement personnel Review and verification process of financial personnel. By testing the existing financial reimbursement process, the original voucher can be accurately classified according to the requirements of the national tax system for the classification of goods, for the identification of the type of daily reimbursement; based on the preprocessing of bills, FRCNN algorithms in deep learning can be applied to identify the key contents of bills.

The backlog of pressure from multiple parties has led to a variety of psychological problems and has also led to many irreparable tragedies. It is very important for college student management to pay attention to the mental health problems of college students, and to discover and guide the mental health development of college students in time. This paper uses data mining technology to set up a questionnaire with 90 questions reflecting mental health as a sample. Using the “SCL-90 Symptom Self-Rating Scale” as the evaluation standard, data were collected, a data set was established, and data analysis was carried out, so as to detect and adjust psychological problems in time and avoid the aggravation of problems and lead to suicide and other irreversible problems.