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The development of artificial intelligence technology has brought unprecedented challenges and opportunities for higher education management and student training mechanisms. The traditional education management mode and student training strategy appear to be incompetent in the face of new technology and urgently need innovation and reform. In the face of the latest trend of education informatization, higher education institutions must explore management and cultivation means that keep pace with the times, and take artificial intelligence as the driving force to create a new system of intelligent education to meet the diversified needs of education in the digital era. This paper analyzes the design and functions of the smart education platform, as well as its practical application effect in education management and student training. By introducing the intelligent education platform with artificial intelligence, the utilization efficiency of teaching resources was improved by 20%, and student satisfaction increased by 30%. Based on data analysis, this paper thoroughly discusses the effectiveness of the intelligent education platform in improving the quality of education and optimizing the allocation of educational resources. The modernization of higher education management and comprehensive improvement of students’ abilities can be effectively promoted by incorporating artificial intelligence technology in this study, which provides new ideas for building future education.

The way that artificial intelligence technology is being developed is causing a progressive evolution in college and university teaching methods and systems. This paper presents the design of the English teaching mode in colleges and universities based on artificial intelligence technology. Research on strategies for English teaching reform in colleges and universities supported by artificial intelligence technology. A weighted inference model was used to design an AI expert system, based on which an intelligent assisted learning system based on a neural network was constructed using the law of knowledge forgetting. Based on information acquisition, the random Linsen method was selected as the assessment methodology for the impact of English instruction in colleges and universities. The assessment model’s performance and errors are examined through comparison tests of the teaching evaluation model. In this article, the educational effect evaluation model has an accuracy rate of 91% and a mean square error of less than 0.002. The impact of AI-assisted English instruction on teaching is evaluated based on this. Results from studies conducted both before and following the experimental group show that the overall score increases by 12.33 points and the P-value of the four dimensions’ teaching effect is less than 0.01. The experimental group using artificial intelligence technology for English instruction received an average comprehensive score of 95 points in the actual English assessment, which is 8 points higher than the control group receiving traditional English instruction. This paper’s artificial intelligence teaching mode is believed to have a significant impact on students’ English, which is confirmed by its effectiveness and rationality. It is beneficial for teaching reform and guides enhancing and advancing English instruction in colleges and institutions.

The development of artificial intelligence technology has promoted economic development and improved people’s living standards, but it will also cause many risks and uncertainties. The purpose of this paper is to create a reciprocal fairness model for artificial intelligence collaboration and explore the causal relationship and liability allocation in artificial intelligence criminal infringement cases based on Rabin’s theory. Through the Hande formula, the criteria for determining fault in the case, as well as the marginal costs and benefits of the aggressor’s use of AI to commit crimes, are calculated. Finally, based on the theory of behavioral game evolution, this paper discusses the cooperation law of the three parties under the regulation of the Criminal Procedure Law, and discusses the illegal cost of the illegal implementation of artificial intelligence from the amount of compensation. The results show that in 68,535 cases of using AI to commit crimes in 2022, the average compensation is 74,556.87 yuan, indicating that the cost of crime is much lower than the proceeds of crime. The proposed legal regulations and the prevention and control of AI technology risks can be practiced through this study’s practical relevance.

In recent years, the rapid development of artificial intelligence technology represented by knowledge graphs and deep learning has provided an opportunity for educational innovation and learning mode change. A smart music learning model for colleges and universities is developed in this paper with the help of artificial intelligence technology. Learning data analysis is achieved through the use of a community discovery algorithm based on graph data in the model. In order to construct the learning community, the AGNES hierarchical clustering algorithm is used to cluster individual samples in the dataset. Learning big data and music professional ability are correlated through the mining of learner portrait features. Personalized learning paths are generated using the improved convolutional neural network. As experimental subjects, sophomore music majors at X institution were tested and analyzed for the teaching model in the end. The results show that the maximum learning interaction coefficient of the experimental subjects can be obtained as 5.34 and the maximum learning path coefficient as 2.84 under the smart learning mode. The correlation coefficients of the use of the smart learning mode with the usual test scores and the learning effort values are between 0.318 and 0.502. Teachers can obtain precise teaching data from this paper to quantitatively characterize subject competence goals and facilitate the smooth implementation of smart learning.

S&T management data has a wide range of sources and types, and the innovation of S&T management data service model is an important way for the efficient utilization of S&T resources in the new era, so this paper creates an innovative model of S&T management data service based on Data-Information-Knowledge-Wisdom model and artificial intelligence technology. Heterogeneous data mining technology based on association rules is used to obtain the connection between S&T management data, Lagrange interpolation is used for heterogeneous data cleaning to predict the missing values of S&T management data, and data lineage resolution technology is used to solve the challenges brought by the complex and diverse S&T management data components. Experimental analyses are conducted from both S&T management data processing and data service cases to verify the effectiveness and scientificity of the S&T management data service innovation model proposed in this paper. The results show that in S&T management data processing, this paper’s method consumes less than 2.45s for associated data rule mining, which has high mining efficiency, and the duplication rate and missing rate of data are below 0.0260 and 0.0222. Through the analysis of the service quality data, it can be seen that the degree of explanation of the service process quality problems of the model proposed in this paper tends to be close to 1, which can reflect the differences in the service process quality problems, and provide accurate, intelligent and personalized services for the main body of science and technology innovation.

With the development and improvement of artificial intelligence technology, the teaching innovation mode of combining college sports courses with artificial intelligence has gradually received widespread attention. This paper is based on artificial intelligence technology for the design of intelligent sports detection wearable devices, through which students’ sports data are collected, low-pass filters are used to reduce the noise of the collected data, normalization is carried out, and the AlphaPose algorithm is combined with the assessment and extraction of the human movement posture of college sports. The artificial intelligence sports teaching framework is built to innovate college sports teaching based on artificial intelligence. Finally, the impact of sports recognition is examined, and a comparison experiment is carried out to examine the practical implications of this teaching method. The four experimental targets had sports recognition errors that were less than 10% on average according to the results. The two classes under the experimental control have a P-value of less than 0.05 in the comparison of physical skills and physical fitness test data, and there is an improvement of 5-20 in all scores, which indicates that the experimental class has a higher teaching effect and is significantly helpful in performance improvement. Based on the above, this paper researches the practice of artificial intelligence technology in college sports teaching to provide an innovative path for the transformation of traditional sports teaching to artificial intelligence sports teaching.

In this paper, relevant cultural materials were first collected and extracted using knowledge mapping and retrieval tools, and the co-occurrence analysis method was used to construct the co-occurrence relationship matrix between keywords. Combined with the method of graphic vectorization, the traditional cultural and creative product design patterns and motifs are directly scanned to extract the design prototype’s outer contour and polygonal path. Next, the recommendation process of cultural and creative products was realized by combining collaborative filtering and the TransD-CF algorithm. Finally, the influence of cognitive attitude and purchase behavior indicators on cultural and creative products was analyzed through correlation. The results show that the difference between genders using essay on hobby collection of cultural and creative products is 0.203, and the practicality requirement of cultural and creative products decreases by 0.09 for every 100 yuan increase in price. This paper presents a fresh perspective on cultural and creative promotion and plays a guiding role in design practice with the aid of artificial intelligence.

This paper first deconstructs the core of the college English precision education model, combines relevant theoretical knowledge, and constructs a college English precision education model based on artificial intelligence. Secondly, the convolutional neural network technology is integrated into the joint probability matrix decomposition model, and the resource recommendation list is generated through the cognitive ability level of students and their individualized needs so as to realize the precise, intelligent recommendation of English education resources. Finally, the performance of the recommendation for teaching English educational resources and the practical effect of the educational model are explored through comparative experiments. The results show that the precision rate and recall rate of the recommendation with a test set of 10% are 0.871 and 0.866, respectively, and the F1 value is 0.88. After the teaching practice, students’ listening ability improved by 0.132, expression ability improved by 0.13, reading ability improved by 0.132, writing ability improved by 0.130, and translation ability improved by 0.128. Based on the paper, the educational model has a positive impact on improving the level of English education in colleges and universities.

To solve the problems of low completeness of data collection and poor accuracy of evaluation of traditional sports training movement completion, this paper proposes an artificial intelligence-based DTW pose matching algorithm. Firstly, the sports postures are matched and evaluated for movements. Then define the limb angle deviation factor and calculate the deviation degree of each limb angle, output the limbs with a deviation degrees greater than the set standard, identify the deviated limbs that affect the overall movement standard, and realize the evaluation and result in the feedback of basic movements in sports training. Under the three feature extraction methods, the accuracy of the statistical value feature extraction algorithm, LR feature extraction algorithm and integrated feature extraction algorithm of the DTW poses matching algorithm were 91.12%, 96.84% and 96.54%, respectively. The accuracy rates of LDA, KNN, NB, CART and DTW algorithms were 25%, 43%, 32%, 33%, and 72%, respectively, when the other four models were used to compare the accuracy rates with the DTW pose matching algorithm. Among them, the accuracy of the DTW algorithm is significantly higher than the other four algorithms. Therefore, the artificial intelligence-based sports training management model proposed in this paper can significantly improve the current problems, such as difficulty in assessing the accuracy of sports movement training, inefficient supervision of teachers’ guidance to students, and difficulty for students to improve their movements directly, which has strong practical significance and application value.

In the context of the development tendency of artificial intelligence, the paper describes the opportunities and challenges artificial intelligence brings to business English talent training, innovates and reforms innovative business negotiation talent training methods, and develops independent innovation according to the future development trend of artificial intelligence. The paper discusses the practical activities of training business English talents using artificial intelligence. It mainly focuses on the indicators of building the entity model of business English talents training through artificial intelligence technical RBF ne ural network, grasping the genetic algorithm from four aspects such as the number of gender chromosomes, integral function formula calculation, genetic algorithm, RBF neural network parameters to optimize the entity model and genetic algorithm, and optimizing the limitation of RBF neural network according to the genetic algorithm. The RBF neural network entity model with the genetic algorithm is used to implement detailed analysis and scientific research exploration of the business English talent training evaluation system. The database deviation index and precision index values under the two entity models were implemented for comparison operation. The experimental results show that under the deviation index, the RBF neural network entity model, according to the genetic algorithm, converges quickly and longer than the RBF entity model. Regarding the precision index, the genetic algorithm RBF neural network has a higher precision than 0.94 for every 60 teams, while the RBF model has a precision of 0.84. This research allows for a more accurate analysis of the strengths and weaknesses of school students in business English. Teachers need to improve their academic performance and quality based on their strengths and weaknesses to facilitate the shaping of more excellent business English talents for China. It is important to explore the way of training business English talents under artificial intelligence to accelerate the innovative development of English education and training in China and the trend of English development.

To solve the conflicts between the transparency of capital and current restrictive regulations and regulations regarding consumers’ right to claim information and safety, this paper studies the legal risk and prevention path of the transparency of capital due to artificial intelligence. Firstly, the legal risk ways and means of the transparency of capital are constructed by the combined algorithm of SVR, BP, and RNN under the artificial intelligence algorithm, focusing on data tracking before and after the rectification of sharing enterprises with the combined algorithm of BP neural network and RNN to test whether the preventive measures are effectively implemented in place, and then the legal risk prevention path based on the government and enterprise levels is derived. The study concludes that shared travel legal disputes are rising between 2018 and 2022. Among the 10 types of violations sampled for investigation, age information was accessed in violation of the law the most, reaching 53,607,900, and the least in comparison, precise location information was accessed in violation of the law, 1,029,000. After warnings by government departments, the overall violations by enterprises after rectification were on a downward trend, with the incidence of violations controlled between 28.1% and 43.2%. The research on the legal risks of sharing economy in this paper has realistic value and reference significance to the legislation of transparency of capital.

In recent years, the development of artificial intelligence has led to the growing influence of new media on society. The creators of digital images no longer play the role of exporters of art and culture but gradually delegate power to the audience, who are no longer passive recipients of information but are more involved in the interactive experience of video works, better engaged in the background of artworks and the emotional exchange of art and culture that occurs with artworks. Dunhuang murals, as a representative of the excellent traditional Chinese culture, have formed a wide range of cultural expressions with the help of new media technologies with different characteristics, effectively attracting audiences to participate in dissemination activities such as knowledge learning, cultural experience, and artistic exchange of Dunhuang culture. This paper provides an overview of interactive perceptual video design methods, the impact of interactive perceptual design on the narrative line of the video, and the features of the interactive perceptual invention, such as immersion, immersion, and fun. Firstly, a comparative analysis of interactive perceptual image design cases is conducted to summarize the differences between interactive perceptual image design and traditional images and outline the advantages of the former. Finally, a complete augmented reality display work is formed through the organic combination of the interactive perceptual design in three dimensions: user experience, visual guidance, and content narrative. The results of this paper show that 80% of the 120 people believe that the interactive perceptual presentation has made Dunhuang murals more vivid and exciting; 91% believe that the production of Dunhuang murals with perceptual machine technology gives visitors an immersive experience; among them, it is found through the research that visitors are 85% satisfied with this design, which exceeds the expected guesses. This paper, as a new exploration of future cases of digital communication of cultural heritage based on enhanced interactive perception technology, also argues the feasibility and effectiveness of its design in terms of results and provides new ideas for the communication of traditional culture.

With the change of the fourth generation of information technology represented by artificial intelligence, higher education is gradually stepping into the stage of intelligent development, and leisure sports majors as one of the majors that are booming, which puts forward higher requirements on the teaching power of teachers of leisure sports majors. Therefore, studying the current situation of talent cultivation of leisure sports majors under artificial intelligence plays a good role in promoting the overall improvement of the teaching quality of leisure sports majors. In this paper, the current situation of talent training and classroom teaching of leisure sports majors is studied based on a convolutional neural network in the artificial intelligence perspective, and the framework of artificial intelligence technology for leisure sports education is constructed. It can be seen that the attitude of leisure sports students towards theory class is better; 40.93% think theory class is very important, 38.13% think theory class is more important, and 19.27% think the importance of theory class is average. Students’ attitudes toward practical classes were better than those toward theoretical classes, with 63.71% considering practical classes very important and 26.85% considering them more important. From the analysis results, we can see that there are still problems of weak basic theory research, confusion of cultivation goals, lack of science in curriculum setting, and lack of teachers in cultivating talents in leisure sports majors. In summary, this paper argues that colleges and universities and college teachers should take the initiative to apply the technical framework of artificial intelligence in leisure sports education, strengthen theoretical research, make up for the shortcomings of teaching materials, as well as integrate training objectives, improve curriculum settings, enhance faculty strength, investigate the talent specification needs of the market in-depth, proactively meet the arrival of the era of artificial intelligence, and contribute to the cultivation of talents in leisure sports majors.

In this paper, starting from Civic Education under the conditions of artificial intelligence application, an intelligent platform for counselors is constructed as a path for Civic Education, in which Spark’s big data platform is used to collect and organize the data. A collaborative filtering recommendation algorithm with a hybrid self-coding network model is proposed, and a recommendation service framework is built based on this algorithm and recommendation algorithm evaluation index to realize personalized recommendations of educational resources. The empirical analysis was conducted for the platform constructed in this paper, and the results showed that the intelligent platform for counselors and the learning analytics platform were used by most teachers of college Civics courses, accounting for 55.64% and 43.25%, respectively, and compared with the traditional teaching mode, the intelligent platform teaching was more popular among teachers and students.

In today’s rapid development of the economic environment, the company’s financial crisis can significantly reflect the enterprise crisis, and it has a significant impact on the development of the enterprise. Therefore, the prediction of the company’s financial crisis and the establishment of an early warning system have important practical significance. Based on the method of pattern recognition and artificial intelligence, this paper establishes a new forecasting model of corporate financial crisis, predicts its financial index data and realises the dynamic financial crisis warning. The model developed in this paper employs pattern recognition and artificial intelligence, and is thus endowed with high classification accuracy, good anti-noise ability and the ability to be sorted according to the importance of its index features.

With the rapid development of the Internet, security issues are becoming more and more prominent, and since most information is transmitted through the Internet today, Internet security is particularly important. When the Internet was designed, only mutual compatibility and interoperability between networks were considered, and security issues were not fully considered. As a result, as the Internet continues to grow, security issues are becoming more and more serious. One of the more difficult attacks is the Distributed Denial of Service (DDoS) attack, which has many forms of attacks, is harmful, and is difficult to identify and defend. Therefore, building a global Internet security protection system to achieve effective protection against DDoS attacks is the main work of this research paper. In this paper, we propose an artificial intelligence DDoS attack protection system, which implements a controller and switch auto-detection model by extending the protocol and establishing an optimization model to realize a low-load and low-latency traffic monitoring scheme; for DDoS attacks. We propose the attack inspection algorithm SCVAE based on Variational Encoder (VAE) and Spectral Clustering. in order to mitigate DDoS attack traffic, the protection system uses the QoS traffic control method, builds the application flow hierarchy model, and filters the attack traffic endured by the system by setting the application flow bandwidth limit as well as the traffic priority dual policy. Finally, a Mininet-based simulation test environment is built to evaluate the model, and different test indexes are set for different system modules to evaluate their actual performance. The results of this paper show that in the network traffic monitoring test, the artificial intelligence DDoS attack protection algorithm can respond to the attack more quickly by reducing the average 73ms per sampling compared with other algorithms; in the attack traffic identification test, the comparison accuracy (P) is improved by 15.14%, the accuracy (AC) is improved by 13.26%, the recall (R) is reduced by 9.23%, and the F1 measurement criteria improved by 23%. The test verifies that the artificial intelligence DDoS attack protection system can achieve real-time monitoring of each performance parameter and also illustrates the feasibility and practicality of the research content of this paper, which strengthens the construction of the technical means of Internet security protection and further enhances the Internet security defense capability.

With developments in global economic integration and the increase in future economic uncertainty, it is imperative to have the ability to predict future capital in relation to financial capital inflow and outflow predictions to ensure capital optimization is within a controllable range within the current macroeconomic environment and situation. This paper proposes an automated capital prediction strategy for the capital supply chain using time series analysis artificial intelligence methods. Firstly, to analyze the fluctuation and tail risk of the financial characteristics, the paper explores the financial characteristics for measuring the dynamic VaR from the perspectives of volatility, tail, and peak with the Bayesian peaks over threshold (POT) model. Following this, in order to make the modeling more refined, the forecast targets are split before modeling with seasonal Autoregressive Integrated Moving Average (ARIMA) models and Prophet models. Finally, the time series modeling of the wavelet Long Short-Term Memory (LSTM) model is carried out using a two-part analysis method to determine the linear separated wavelet and non-linear embedded wavelet parts to predict strong volatility in financial capital. Taking the user capital flow of the Yu’e Bao platform, the results prove the feasibility and prediction accuracy of the innovative model proposed.

To quickly and accurately realize the fault diagnosis of analog circuits, this paper introduces the graph neural network method and proposes a fault diagnosis method for digital integrated circuits. The method filters the signals present in the digital integrated circuit to remove noise signals and redundant signals and analyzes the digital integrated circuit characteristics after the filtering process to obtain the digital integrated circuit leakage current variation. To the problem of the lack of a parametric model for Through-Silicon Via (TSV) defect modeling, the method of TSV defect modeling based on finite element analysis is proposed. The common TSV defects such as voids, open circuits, leakage, and unaligned micro-pads are modeled and analyzed by using industrial-grade FEA tools Q3D and HFSS, and the equivalent circuit model of resistance inductance conductance capacitance (RLGC) for each defect is obtained. Finally, the superior performance of this paper in fault diagnosis accuracy and fault diagnosis efficiency is verified by comparing and analyzing with the traditional graph neural network method and random graph neural network method for active filter circuits.

The paper focuses on establishing a risk assessment model of femoral neck osteoporotic fracture (FNOF) in the elderly population and improving the screening efficiency and accuracy of such diseases in specific populations. In literature research, the main risk factors of femoral neck osteoporosis (FNOP) in the elderly were studied and analyzed; the femur region of interest (ROI) and the hard bone edge segmentation model were selected from the X-ray digital image by using the image depth learning method. On this basis, the femoral trabecular score and femoral neck strength (FNS) in the set region were selected as the main evaluation elements, and the quantitative analysis method was established; an X-ray image processing method was applied to the feasibility study of FNOP and compared with dual-energy X-ray absorptiometry measurements of bone mineral density; Finally, the main risk factors of FNOP were selected and the prediction model of FNOP in the elderly population was established based on medical image processing, machine learning model construction and other methods. Some FNOP health records were selected as test samples for comparative analysis with traditional manual evaluation methods. The paper shows the risk assessment model of FNOF in the elderly population, which is feasible in testing. Among them, the artificial neural network model had a better accuracy (95.83%) and recall rate (100.00%), and the support vector machine prediction model had high specificity (62.50%). With the help of a machine learning method to establish the risk assessment model of FNOF for the elderly, one can provide decision support for the fracture risk assessment of the elderly and remind the clinic to give targeted interventions for the above high-risk groups in order to reduce the fracture risk.

INTRODUCTION: Artificial intelligence is a product of high-end technological development since the 21st century, which has subverted people's traditional cognition in many aspects and greatly enriched and improved people's lives. Artificial intelligence has covered every aspect of life, and the distribution network overhead line project is also one of them. The combination of the two symbolizes the combination of modern technology and infrastructure construction, which is of great significance for modern economic and social development and transformation and upgrading. OBJECTIVES: In order to solve the practical problems in the design of artificial intelligence and distribution network overhead line engineering, this paper focuses on the practical use of such artificial intelligence as robots in distribution network overhead line engineering. METHODS: The models of spatial perception, target recognition and automatic calculation are established, and some key technical problems of robots put into actual engineering are simulated and calculated. RESULTS: In the spatial perception model, the combination of robotic arm and laser device is utilized to solve the problem of direct sunlight, which affects the localization. In the target recognition model, combining the algorithms of minimum spanning tree and maximum critical path, the computational accuracy is improved to 1 mm. in the automatic computation model, the introduction of auxiliary lines and the secondary confirmation of manpower make the error of the work further reduced. CONCLUSION: This paper's simulation algorithm for the reality of the distribution network overhead line project provides a more detailed solution to improve the technical content of the distribution network overhead line project and the quality of construction management is not a simple task, the need for the relevant distribution network overhead line project enterprises as well as the corresponding distribution network overhead line project personnel to take targeted measures.

StarCraft is a popular real-time strategy game that has been widely used as a research platform for artificial intelligence. Micromanagement refers to the process of making each unit perform appropriate actions separately, depending on the current state in the the multi-agent system comprising all of the units, i.e., the fine-grained control of individual units for common benefit. Therefore, cooperation between different units is crucially important to improve the joint strategy. We have selected multi-agent deep reinforcement learning to tackle the problem of micromanagement. In this paper, we propose a method for learning cooperative strategies in StarCraft based on role-based montonic value function factorization (RoMIX). RoMIX learns roles based on the potential impact of each agent on the multi-agent task; it then represents the action value of a role in a mixed way based on monotonic value function factorization. The final value is calculated by accumulating the action value of all roles. The role-based learning improves the cooperation between agents on the team, allowing them to learn the joint strategy more quickly and efficiently. In addition, RoMIX can also reduce storage resources to a certain extent. Experiments show that RoMIX can not only solve easy tasks, but it can also learn better cooperation strategies for more complex and difficult tasks.

Photovoltaic (PV) power generation is pivotal to the energy strategies of various nations, yet it is plagued by significant security challenges. This paper proposes a large-scale neural network model that integrates time-domain and frequency-domain techniques for the detection of arc faults in PV systems. The algorithm leverages sequence decomposition to extract trend information from current signals, and then applies the Fourier transform to convert various encoded data into the frequency domain. Due to the sparsity of frequency-domain information, the computational cost of extracting and processing information in the frequency domain is minimal, resulting in high efficiency. The selectively extracted information is then input into a separate lightweight classifier for classification and recognition. The proposed intelligent framework not only effectively filters out high-frequency noise signals, but also demonstrates strong robustness against various disturbances, yielding exceptional recognition performance with an accuracy rate consistently surpassing 97$ \% $. Code and data are available at this repository: https://github.com/yixizhuimeng?tab = projects.

Recently, artificial intelligence generated content (AIGC) has been receiving increased attention and is growing exponentially. AIGC is generated based on the intentional information extracted from human-provided instructions by generative artificial intelligence (AI) models. AIGC quickly and automatically generates large amounts of high-quality content. Currently, there is a shortage of medical resources and complex medical procedures in medicine. Due to its characteristics, AIGC can help alleviate these problems. As a result, the application of AIGC in medicine has gained increased attention in recent years. Therefore, this paper provides a comprehensive review on the recent state of studies involving AIGC in medicine. First, we present an overview of AIGC. Furthermore, based on recent studies, the application of AIGC in medicine is reviewed from two aspects: medical image processing and medical text generation. The basic generative AI models, tasks, target organs, datasets and contribution of studies are considered and summarized. Finally, we also discuss the limitations and challenges faced by AIGC and propose possible solutions with relevant studies. We hope this review can help readers understand the potential of AIGC in medicine and obtain some innovative ideas in this field.

In this paper, the Levenberg-Marquardt backpropagation scheme is used to develop a neural network model for the examination of the fluid flow on a magnetized flat surface with slip boundaries. The tangent hyperbolic fluid is considered along with heat generation, velocity, and thermal slip effects at the surface. The problem is modelled in terms of a non-linear differential system and Lie symmetry is used to get the scaling group of transformation. The order reduction of differential equations is done by using Lie transformation. The reduced system is solved by the shooting method. The surface quantity, namely skin friction, is evaluated at the surface for the absence and presence of an externally applied magnetic field. A total of 88 sample values are estimated for developing an artificial neural network model to predict skin friction coefficient (SFC). Weissenberg number, magnetic field parameter, and power law index are considered three inputs in the first layer, while 10 neurons are taken in the hidden layer. 62 (70%), 13 (15%), and 13 (15%) samples are used for training, validation, and testing, respectively. The Levenberg-Marquardt backpropagation is used to train the network by entertaining the random 62 sample values. Both mean square error and regression analysis are used to check the performance of the developed neural networking model. The SFC is noticed to be high at a magnetized surface for power law index and Weissenberg number.

The study explores the role of ChatGPT in the development of a micro-level framework for Educational Technology (EdTech). This study employed an inductive research design particularly a Constructivist Grounded Theory (CGT) approach from thirty-four private secondary high school teachers in the Philippines. To gather insights from specific participants, a semi-structured interview format was utilized. The instrument went through face validation from two experts well-versed in education research, instrumentation, and three-stage prompts. In addition, using JAMOVI software, Fleiss’ Kappa was used with a value of 0.715 indicating a substantial agreement among the expert validators. The interviews were designed to be open-ended, allowing for a flexible exploration of the research topic then subjected for data analysis with the aid of QDA Miner lite software. The findings revealed three (3) distinct milieux: pedagogical support, professional development, and ethical sense. In the (a) pedagogical support milieu, ChatGPT emerges as a virtual compass, offering real-time assistance, explanations, and guidance for teachers. It demonstrates the capability to answer questions, clarify concepts, and provide step-by-step support, crafted to individual students’ needs and learning styles; within the (b) redesigning milieu, it serves as a catalyst for adapting and recalibrating assessment and teaching strategies to suit learning purpose; and (c) ethical sense milieu highlights various implications, including data privacy, plagiarism, transparency, accountability, and overly reliant to the AI tool. Thus, this paper offers a micro-level framework anchored to the ChatGPT’s role in EdTech and aims to stimulate further scholarly discourse in the field.

Image is one of the most important forms of information expression in multimedia. It is the key factor to determine the visual effect of multimedia software. As an image restoration task, image deraining can effectively restore the original information of the image, which is conducive to the downstream task. In recent years, with the development of deep learning technology, CNN and Transformer structures have shone brightly in computer vision. In this paper, we summarize the key to success of these structures in the past, and on this basis, we introduce the concept of a layer aggregation mechanism to describe how to reuse the information of the previous layer to better extract the features of the current layer. Based on this layer aggregation mechanism, we build the rain removal network called DenseformerNet. Our network strengthens feature promotion and encourages feature reuse, allowing better information and gradient flow. Through a large number of experiments, we prove that our model is efficient and effective, and expect to bring some illumination to the future rain removal network.

This paper highlights 20 significant problems in AI research, with potential solutions via the SP Theory of Intelligence (SPTI) and its realisation in the SP Computer Model. With other evidence referenced in the paper, this is strong evidence in support of the SPTI as a promising foundation for the development of human-level broad AI, aka artificial general intelligence. The 20 problems include: the tendency of deep neural networks to make major errors in recognition; the need for a coherent account of generalisation, over- and under-generalisation, and minimising the corrupting effect of ‘dirty data’; how to achieve one-trial learning; how to achieve transfer learning; the need for transparency in the representation and processing of knowledge; and how to eliminate the problem of catastrophic forgetting. In addition to its promise as a foundation for the development of AGI, the SPTI has potential as a foundation for the study of human learning, perception, and cognition. And it has potential as a foundation for mathematics, logic, and computing.

Fuzzy logic is becoming one of the most-influential fields of modern mathematics with applications that impact not only other sciences, but society in general. This newly found interest in fuzzy logic is in part due to the crucial role it plays in the development of artificial intelligence. As a result, new tools and practices for the development of the above-mentioned field are in high demand. This is one of the issues this paper was composed to address. To be more specific, a sizable part of fuzzy logic is the study of fuzzy connectives. However, the current method used to generalize them is restricted to the use of basic automorphisms, which hinders the creation of new fuzzy connectives. For this reason, in this paper, a new method of generalization is conceived of that aims to generalize the fuzzy connectives using polynomial automorphism functions instead. The creation of these automorphisms is achieved through numerical analysis, an endeavor that is supported with programming applications that, using mathematical modeling, validate and visualize the research. Furthermore, the automorphisms satisfy all the necessary criteria that have been established for use in the generalization process and, consequently, are used to successfully generalize fuzzy connectives. The result of the new generalization method is the creation of new usable and flexible fuzzy connectives, which is very promising for the future development of the field.

The main aim of the study is to investigate the growth of oyster mushrooms in two substrates, namely straw and wheat straw. In the following, the study moves towards modeling and optimization of the production yield by considering the energy consumption, water consumption, total income and environmental impacts as the dependent variables. Accordingly, life cycle assessment (LCA) platform was developed for achieving the environmental impacts of the studied scenarios. The next step developed an ANN-based model for the prediction of dependent variables. Finally, optimization was performed using response surface methodology (RSM) by fitting quadratic equations for generating the required factors. According to the results, the optimum condition for the production of OM from waste paper can be found in the paper portion range of 20% and the wheat straw range of 80% with a production yield of about 4.5 kg and a higher net income of 16.54 $ in the presence of the lower energy and water consumption by about 361.5 kWh and 29.53 kg, respectively. The optimum condition delivers lower environmental impacts on Human Health, Ecosystem Quality, Climate change, and Resources by about 5.64 DALY, 8.18 PDF*m2*yr, 89.77 g CO2 eq and 1707.05 kJ, respectively. It can be concluded that, sustainable production of OM can be achieved in line with the policy used to produce alternative food source from waste management techniques.

This paper explores the integration of image processing, motion capture, and virtual reality technologies to digitize and visualize dance. We capture the core dynamics of dance movements by extracting key frames and movement features from dance videos. Our analysis of motion capture data, exemplified by the “Flower Drum Lantern” dance, reveals a maximum vertical foot displacement of 72 cm and hip displacement of 93 cm. Virtual display technology significantly enhances the visual representation and dissemination of dance performances. This innovative approach to documenting and showcasing dance not only aids in preserving and transmitting intangible cultural heritage but also boosts public awareness and appreciation for such heritage.

University libraries are undergoing a critical transformation and enhancement phase in their research intelligence services. The evolution of research demands and the exponential growth of information have rendered traditional service models inadequate for meeting faculty and students’ precise and personalized requirements. To address this challenge, libraries must adopt innovative service strategies that leverage technological advancements and optimize service processes. Such strategies are vital for enhancing the efficiency of access and the value of information resources, thereby bolstering high-quality academic research and teaching. This paper presents an optimized design for the existing research information service system, identifying key challenges within university library research services and introducing an improvement plan rooted in artificial intelligence (AI) technology. Empirical research indicates that the proposed system boosts information retrieval speed by 30%, user satisfaction by 25%, and research support efficiency by 40%. The enhanced research intelligence service system significantly elevates university libraries’ service capabilities and research support functions, offering more effective and precise information services to the academic community.

With the rapid development of artificial intelligence, university English translation teaching faces many challenges and opportunities. This article studies the problems in teaching in this context and proposes effective countermeasures. Diagnostic analysis and factor analysis are used in the article to conduct an in-depth study of the three levels of the teaching process: knowledge skills, autonomy, and emotional development. The students achieved a 70% mastery rate in knowledge and skills, while their independence and emotional development performance was 60% and 50%, respectively. This paper proposes an English speech translation system based on artificial intelligence to enhance teaching effectiveness. After a 14-week teaching experiment, the average grade of the students in the experimental class improved by about 6.82 points compared to the traditional teaching class, proving the system’s effectiveness. The study diagnosed the current problems in university English translation teaching, verified the potential of AI technology in improving teaching quality through empirical research, and combined with AI technology can effectively improve the quality and efficiency of university English translation teaching.

With the deepening of Sino-Japanese economic and trade cooperation, the demand for enterprises for Japanese intercultural communication talents is increasing. In this paper, with the assistance of artificial intelligence, we constructed an innovative teaching mode of automatic association of Japanese expressions based on natural language processing technology and semantic analysis model of Japanese Literature, which deepens students’ understanding, memorization, and application of Japanese vocabulary by dispersing and associating the cultural context of Japanese Literature. The semantic similarity calculation parses participles and morphemes in Literature to find the vocabulary that can be substituted for the specified objects and form new example sentences. Finally, through the specific application of the innovative teaching mode by the Japanese language program of University X, the impact of the mode on the cultivation of students’ intercultural communicative competence was explored. The results show that the before-and-after difference in ICC competence in the class taught with the innovative mode is -10.134, while the before-and-after difference in the class prepared with the traditional mode is only -1.012, indicating that the proposed creative reform teaching mode has a pronounced effect on the cultivation of students’ intercultural communicative competence. This study realizes the digital reform of teaching the selected Japanese literature course and provides a reference path for cultivating students’ intercultural communicative competence.

The application of artificial intelligence technology in language learning, especially English teaching, is subverting the traditional learning mode. This paper discusses in depth the innovative strategies of English practical teaching in higher vocational colleges. It introduces an AI-assisted teaching system that synthesizes the reasoning mechanism, the student independent learning system and the mathematical model of knowledge forgetting. Through practical application, this study verifies the effectiveness of the system. The results showed that the grades of all students who used the AI system and had an average score of more than 60 did not decrease. There was a significant linear relationship between students’ engagement and grade improvement, indicating that the more serious the students were, the more influential the grade improvement was. In addition, there is a positive correlation between exam difficulty, passing rate and the number of high scores, and a negative correlation with the number of scores below 60, which verifies the reasonableness of the correlation coefficient. Students’ foreign language learning pleasure, burnout and self-efficacy have significant mean differences between the two classes, with p-values less than 0.05, indicating substantial differences. These findings confirm the effectiveness of the AI-assisted teaching system and provide valuable references for future teaching models.

In this paper, the digital management of scientific and technological innovation services in universities based on the background of artificial intelligence is studied in depth, aiming to create a comprehensive and efficient management platform. By constructing a platform with a multi-level architecture, it realizes the informatization management of the whole process of university science and technology projects. Specifically, this study designed a functional structure of 10 subsystems, covering all stages from project declaration to results management. To improve the management efficiency, principal component analysis and cluster analysis techniques were introduced, and the effectiveness and feasibility of the platform were verified by analyzing 270 questionnaires. The average score is 3.8293, showing that users highly recognize the platform. In addition, by qualitatively and quantitatively analyzing the factors affecting management, the key factors promoting and inhibiting the management efficiency were clarified, providing a new perspective for the management of scientific and technological innovation services in universities.

This paper discusses the critical role of art therapy in solving various psychological problems, and introduces an innovative immersive and interactive art therapy mechanism based on artificial intelligence (AI), which combines machine learning and AI algorithms to realize embodied interactions between the learner, the machine, and the environment.The study analyzes the changes of students’ status before and after receiving art therapy in terms of various dimensions, including students’ social avoidance, general well-being, and self-emotions. Using regression modeling, the study validated the positive effects of art therapy on students’ mental health, cognitive abilities, and depression levels. It showed that art therapy had a statistically significant impact on improving students’ mental health, cognition, and reducing depressive symptoms at the 1% level of significance. Students who participated in art therapy had a 0.329 unit increase in mental health scores compared to non-participants, and this study provides a valuable reference and idea for the application of expressive art therapy in mental health education.

As a carrier of human cultural heritage, it is of great practical significance to apply VR technology to the construction of modern digital museums. This paper combines artificial intelligence and SNN algorithms for image segmentation and proposes the overall framework of ‘embedded image acquisition and SNN segmentation processing’. This system is utilized to improve the picture clarity and delay efficiency of VR technology and reduce the vertigo feeling of the user experience. The VR environment of the digital museum is designed according to the user’s intentions. Finally, the effectiveness of the designed museum VR environment is verified by evaluating the comfort, interaction, and emotional experience of the VR museum. The experimental results show that in terms of comfort experience, the SSQ scale score of the VR museum experience based on the embedded image processing is 7.1, which is nearly double the score of the original VR museum experience. In terms of interaction and emotional experience, the overall satisfaction score of the VR environment design of the M museum was 9.4. The two aspects of emotional experience and cultural communication effects were evaluated as 4.27 and 4.05, respectively. It indicates that the user satisfaction of the VR environment design of the AI and digital museum based on the embedded image processing is high, and that this VR environment design can effectively improve the communication of the museum.

Only by grasping the pulse of the times and continuously injecting the spirit of the times into Marxism can the construction of its discourse system have a source of living water. Based on artificial intelligence, this paper constructs the discourse system of contemporary politics and Marxism education from three aspects: synchronous resonance, comprehensive coordination, and human-computer interaction. The research data from seven universities from April to September 2022 were selected as research examples, and the degree of influence of artificial intelligence on the discourse capacity of contemporary politics and Marxism education was explored using multiple linear regression models and principal component factor analysis. The reliability test and exploratory factor analysis were conducted for the research questionnaire data, and the validation analysis was conducted for the influencing factors and influencing factors of Marxist educational discourse competence. The results show that the degree of influence of the homophily resonance ability factor on Marxist educational discourse ability is 42.78%, and the R2 test results of artificial intelligence technology homophily resonance, comprehensive coordination, and human-computer interaction are all greater than 0.5. The discourse ability of contemporary political and Marxist education is enhanced by 0.726 percentage points when university technology increases by 1 percentage point. The construction of the discourse system of contemporary political and Marxist education empowered by artificial intelligence requires deep excavation of the discourse content and the creation of high-quality discourse contexts in order to realize the full, whole and all-around nurturing of Marxist education in colleges and universities.

This paper firstly organizes and summarizes the random forest model algorithm in artificial intelligence and secondly uses the XGBoost algorithm with the classification regression tree as the base classifier to explore the relationship between digital financial inclusion, innovative factor allocation and high-quality economic development. Major databases and relevant websites are used to establish the evaluation index system, and the index homogenization is done. The economic high-quality development score is measured using the principal component analysis next. Finally, the principal component scores are weighted and summed according to the proportion of the variance contribution ratio of each principal component to the cumulative variance contribution ratio of the extracted principal components. After calculating the score, it was found that the innovation indicator, which had risen year after year between 2014 and 2018, decreased significantly from 2019 onwards, with the score falling from 2.21 to 1.48, a decrease of 33%. The growth rate of digital financial inclusion declined from 0.93 and finally maintained at 0.45 to 0.47. The economic development index first grew from -11.88 to 2.41, increasing by 3 to 5 each year, and then the growth rate was maintained at about 66%, growing to 6.58 in 2022.

This paper analyzes the elements of music curriculum based on artificial intelligence, reveals the nature of weighted linear regression through the nature of the weight function, and establishes a rural music education model using the weighted least squares method. According to the numerical characteristics of multidimensional random vectors, given multidimensional random vectors, the data set of the music teaching platform is set as the country music education indicator using the coordinate coefficient number vectors of each dimension of teaching. It was found that the passing rate of learning ability in the rural music education interactive teaching platform was as high as 97%, and schools should make full use of artificial intelligence technology to promote the better application of the interactive teaching platform in music online education and provide an effective way for rural primary and secondary school music education.

This paper applies traditional cultural elements to modern interior design and first analyzes the expression types of traditional cultural elements. Secondly, the color and pattern elements in traditional cultural elements are extracted by color matrix and SIFT algorithm, and the pattern and color elements are applied to modern interior design using an intelligent recommendation algorithm. Next, the layout elements of traditional cultural elements are modeled, and the automatic layout algorithm is created to be utilized in modern interior design. Finally, the performance of extracting traditional cultural elements and their application are analyzed. The results show that the recognition rate of various colors is above 0.88, the extraction accuracy of the overall traditional cultural pattern elements is around 0.9, the overall degree of the graphic color element recommendation is greater than 0.88, the hierarchical difference between the traditional cultural layout and the intelligent layout is between [0.001, 0.01], and the functional difference is between (0, 0.02], and all of the automatic layout algorithms can be completed within 0.2s.

With the goal of improving the teaching effect of English in colleges and universities, this paper combines artificial intelligence technology with teaching and constructs an English-intelligent tutoring teaching model. By analyzing the main forms of artificial intelligence in the field of teaching, the advantages of intelligent teaching mode in college English teaching are explored from personalized teaching as well as teaching effect. Combining the RBF neural network to analyze the students’ learning data, the function approximation principle and interpolation method are used to improve the accuracy of the analysis of the students’ data. Using the form of network topology, the information transfer process in English intelligent teaching is explored. To improve the classification of students’ abilities and the prediction of their grades, judgment trees are added to the network. The English Intelligent Assisted Teaching model was applied to teaching to explore its feasibility and effectiveness. The results show that students’ satisfaction with personalized teaching is 0.85, and their satisfaction with personalized evaluation is 0.8. The students’ translation ability under the English Intelligent Assisted Teaching Model has improved more, from 75 to 90.

In order to improve the status quo of English teaching in colleges and universities, this paper combines the basic concepts of teaching resource base construction with artificial intelligence technology to build a teaching resource base for English in colleges and universities. The overall framework is designed first, followed by the intelligent identity recognition function that is based on artificial intelligence technology. Secondly, on the basis of the text classification algorithm, it designs an intelligent classification retrieval model for English teaching by improving the resource text feature selection algorithm and the resource text representation algorithm. Finally, the performance of the designed college English teaching resource library was tested and explored practically. The results show that the overall teaching resource text feature classification performance has an accuracy, recall, and F1 score of around 0.9. After the teaching practice, the teacher’s lesson preparation and professional competence have been improved by 0.22 and 0.23, respectively, and the student’s English communication and reading competence have been improved by 0.22 and 0.21, respectively. The construction of the English teaching resource base based on this research can improve English teaching.

This paper aims to promote the safe development of artificial intelligence and improve legal policies. Combined with the cluster analysis algorithm, it analyzes the safety risks as well as legal defects of artificial intelligence. The Laplace matrix is derived using the similarity matrix, and the feature vector space is constructed by analyzing the associated features of artificial intelligence safety. Combining the spectral clustering algorithm, legal assessment indexes for artificial intelligence safety were constructed. The modular metric value method is utilized to assess the clustering effect of laws on the safety of artificial intelligence. Analyzing the security risks of artificial intelligence, improved legal policies are proposed from the perspective of technology and privacy. The results show that the effect of improving privacy protection policy on privacy protection is 0.85, and the effect of clarifying subject rights is 0.9. The introduction of laws should consider social ethics, and the effect degree of ethical principles is 0.75. Clarifying subject rights can help avoid technological risks to a certain extent, and improving privacy protection policies can help protect users’ privacy.

In this paper, we first studied the action recognition method based on image texture features, established a human body model, and realized the extraction of skeletal data and joint calibration. Based on the skeleton information, the aerobic movements are recognized, the difference between the aerobics movement sequence and the standard movement sequence is calculated, and the movements are scored. Then, the database for aerobic movements was established, and an AI-assistant aerobics teaching model was developed. Finally, the effect of the teaching method was analyzed through indicators of recognition effect, skill impact, and students’ learning interests. The results show that the accuracy of the system’s action recognition reaches 95%, and the overall response time is between 5.2-6.4s, with high real-time performance. And it has a significant effect on students’ movement participation, active interest, and independent learning behavior, with p-values of 0.013, 0.041, and 0.036, respectively, p

This paper takes the integration development path of artificial intelligence technology and clothing design as the entry point, analyzes the influence of arts and crafts culture on clothing design, and gives the correlation between artificial intelligence, clothing design and arts and crafts culture. In order to make the style and color of clothing design more diversified, Fuzzy C-means is used to extract the color of the elements of arts and crafts culture, and adaptive optimization is used to compensate for the chromaticity of the color through artificial intelligence technology. A texture generation network is constructed based on the generative adversarial network to obtain the shape and texture style of the images of arts and crafts cultural elements in apparel design. To verify the effectiveness of the above method in spreading apparel design and arts and crafts culture, experimental validation analysis was conducted. The results show that the Fuzzy C-means algorithm improves PSNR by an average of 1.094 dB and SSIM by between 1.1% and 2.5% compared with the SVD-Means feature color extraction algorithm. The error before and after the adaptive optimization color compensation of artificial intelligence technology is only about 0.2%, and the MSE value of the texture generation network is between [1.5*10-3,3*10-3], and the error of its image generation quality is not more than 1‰. Based on artificial intelligence technology, it can fully obtain the elemental patterns and colors of arts and crafts culture and then innovate the style and shape of clothing design, which can also promote the dissemination and development of arts and crafts culture.

With the development of artificial intelligence and robotics technology, the combination of artificial intelligence and medical device research and development has been promoted, which is an important product of the development of artificial intelligence. In this paper, the general structure of the intelligent medical robot is designed by combining artificial intelligence technology and robotics-related technology. Then, the binocular vision function of the robot was realized by visually acquiring the image of the target object, 3D reconstruction of the target object, and combining the SIFT image recognition algorithm and target tracking algorithm. Then, a new speech recognition algorithm was constructed to realize the human-robot interaction function with the medical robot based on the deep learning Transforme network after the construction of the human acoustic model. Finally, the designed intelligent medical robot was tested, and its overall performance was evaluated. The results show that the recognition errors of the intelligent medical robot on the features of the items are all within 0.05, the recognition errors on the features of the human body are within 0.2, and the speed of the target tracking is between 6km/h and 16km/h. The average recognition accuracy of the medical robot for voice commands is about 0.9, the recognition time is about 0.7s, the normal working rate of each function is more than 0.99, and the test speed is within 2s.

Based on subject knowledge mapping, this paper dynamically collects learning data, portrays learners’ learning situations, and accurately regulates the learning process. Personalized learning path recommendations and learning communities are constructed through learner profiling and learning services. Secondly, structural equation modeling was used to hypothesize the three-level elements of the E-GPPE-C model. Finally, 103 college students in smart teaching classes were taken as research subjects, and the utility of the smart teaching model was analyzed separately through the steps of precondition validation and cross-lag model with random intercepts. The results show that the smart teaching model has β =0.286 for deep learning strategy, β =0.211 for the smart classroom, and β =0.20 for classroom participation, and they accurately indicate that smart teaching has a positive facilitating mechanism on the learning ability of college students. This study also provides a useful reference for the practice of smart teaching in various disciplines in colleges and universities.

In recent years, the development of artificial intelligence technology and theory has been rapid, and the application in language science has been gradually comprehensive and diversified, especially the accuracy rate of artificial intelligence for Chinese language is up to 90%. In the era of artificial intelligence, the effect of different structures and parameters of arithmetic models on Chinese language recognition varies greatly. Language science is an important research area for realizing machine-human communication, and accurate comprehension of the meaning of linguistic expressions is the key to realize communication. In this paper, we construct a speech system that is different from the traditional stable time series for the irreplaceable characteristics of artificial intelligence technology to improve Chinese language ability. A dynamic Bayesian network (DBN) is used for modeling and analysis, and a DBN construction method is investigated to import a hidden Markov model in a speech recognition system to reveal the interactions between nodes within multiple time slices. The accuracy of dynamic Bayesian networks in Chinese dialect inference algorithms is demonstrated using Matlab simulations to characterize the reliability of speech features using a speech spectrogram. It proves that artificial intelligence technology and Chinese language science are complementary and mutually reinforcing, showing a good and rapid development trend.

In the environment of increasing pace of globalization and informatization, the distributed internet has effectively mobilized people from all countries to participate in the discussion of Marxist theory, which makes Marxist theory enriched in a decentralized, multi-node manner for the first time under the wave of globalization. Although the modern emerging distributed internet is faster and more rapid than the traditional information media methods, its accuracy is subject to a greater impact. In this paper, we collect the information of text-video web pages through a crawler program, and after filtering through artificial intelligence retrieval technology to obtain the Marxist globalization theory and extract key information points, and delete irrelevant information. Then K-means clustering algorithm is used to cluster the information collected in the distributed internet to obtain the multidimensional interpretation of Marxist’s globalization theory. This method has been proved to be effective through a large number of experiments. The existing distributed internet has a large amount of data that can be used to train artificial intelligence and has good practical conditions. This method helps Marxists to make better use of information from multiple nodes. The theoretical perspectives of working people from various countries are constantly gathered and pushed forward while saving a lot of manpower, so that Marxism can be better adapted to the rapidly developing globalized society.