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The application of artificial intelligence (AI) technology in the field of clothes can provide a good development mode and system under the social context of AI technology development. AI provides help for the development of intelligent clothing. Intelligent clothing is a high-tech product that integrates intelligent technology and clothing. It combines cutting-edge technologies in electronic information technology, sensor technology, textile science, and material science. In the extraction and analysis of environmental factors in clothing handicraft, AI technology has a considerable application prospect and a certain development potential. In order to improve the accuracy of environmental factors extraction in clothing handicraft, this paper uses convolutional neural network (CNN) to extract and analyze environmental factors in traditional clothing handicraft. We carried out experiments on the extraction of environmental factors in clothing handicrafts with pure color, few patterns, patterns, and complex background. The experimental results show that the CNN has a good effect on the extraction of environmental factors in clothing handicraft under different backgrounds. In addition, the model in this paper has good stability, accuracy, and feature extraction speed, which has high practical value and research significance. [ABSTRACT FROM AUTHOR]

There are a lot of environmental protection musicals and dances in public entertainment life to support the concept of environmental protection and to entice people to support the cause of environmental protection. As we all know, in a musical performance, the distance between the audience and the actors, the design of the stage environment, the training of the actors, the costumes of the actors, the makeup of the actors, and the musical accompaniment will all have more or less influence on the stage performance effects. In order to make environmental protection music and dance dramas profoundly meaningful to social production and human activities, it is especially important to analyze the stage performance of such music and dance dramas. The analysis of stage performance effects is not only beneficial to making the ideas spread by the music and dance drama penetrate into people's hearts but can also provide guiding suggestions for the production of music and dance drama. Therefore, this paper proposes a linear regression algorithm based on artificial intelligence to deeply explore and analyze the influence of the above six factors on the stage performance effects of environmental protection music and dance drama. In our method, first we preprocess the data collected by classification to remove the odd values from the data so that the various types of data conform to a normal distribution. Secondly, we obtained linear fit plots of the six factors with the stage performance effect scores by using a linear regression algorithm to deeply analyze the correlation between various types of data and the stage performance effect scores. Finally, through numerical calculations, we found that the distance between the audience and the actors, the training of the actors, and the musical accompaniment have a greater influence on the performance effect of the musical cabaret in the environmental protection category. Meanwhile, the costumes of the actors, the makeup of the actors, and the design of the stage environment have less influence on the performance effect of the musical cabaret. Therefore, in the production and performance of environmental protection music and dance dramas, producers and performers should pay more attention to the distance between the audience and the actors, the design of musical accompaniment, and the training of actors. To sum up, this paper has made a scientific, detailed, and reasonable analysis of the performance effect of environmental protection music and dance dramas on stage, contributing to the dissemination of environmental protection ideas. [ABSTRACT FROM AUTHOR]

In recent years, college students' psychological problems have occurred frequently, and the early warning of college students' psychological crisis has received social attention. Artificial intelligence and big data, as emerging technologies that have attracted much attention in recent years, have broad application and development space in improving the development of intelligent and refined education in colleges and universities. Applying artificial intelligence and big data to the practice of college students' mental health education plays a very positive role in accurately finding and scientifically solving college students' mental health problems. This paper combs the current application and research of artificial intelligence and big data in college students' mental health education and then clarifies the problems existing in the practical application. Finally, on the basis of in-depth analysis of the characteristics of college students' psychological crisis, the paper designs college students' psychological crisis early warning data collection system from six aspects, including the educational administration system and the access control system. And from the aspects of establishing a multilevel linkage feedback early warning system, building a team of big data technical personnel and mental health education personnel, it puts forward countermeasures for college students' psychological crisis, so as to provide theoretical and methodological support for college mental health management. [ABSTRACT FROM AUTHOR]

With the rise of Erhu teaching in recent years, a large number of people have joined the team to learn Erhu playing. However, due to the high cost of teaching and the unique one-to-one teaching mode between teachers and students, Erhu education resources are very scarce. Learning Erhu performance has become a luxury activity. Nowadays, with the rise of artificial intelligence, computer music is developing rapidly. Music has two important aspects: composition and performance. Different kinds of instruments convey different styles, and players inject different rhythms and dynamics into their performance, thus producing rich expressive force. The development of image style conversion, which opens people's evaluation of music performance, is an important issue in many fields of artificial intelligence (it is also known as intelligence, machine intelligence, referring to the intelligence shown by the machine made by people. Usually, artificial intelligence refers to the technique of presenting human intelligence through ordinary computer programs). For an Erhu song, there are various factors that affect its effectiveness, and there are many indexes to evaluate it, such as sense of rhythm, expressive force, musical sense, style, and so on. Using a computer to simulate the evaluation process is essential to find out the mathematical relationship between the factors that affect the performance of music and the evaluation indexes. Neural network is a kind of mathematical model proposed by simulating the way of thinking of human brain in artificial intelligence. It has the advantages of not having strict requirements on data distribution, nonlinear data processing method, strong robustness, and dynamics and is very suitable for the mathematical model of evaluation system. In addition, the neural network also has a strong theoretical basis, and their application in various industries has developed basically mature. This paper tries to introduce a deep neural network mathematical model into the evaluation system of Erhu performance, and the experimental results prove the reliability and practicality of the method in this paper. It can provide a method basis and theoretical reference for evaluation of Erhu performance effect. [ABSTRACT FROM AUTHOR]

With the development of modern networks and their fast and convenient characteristics, the construction of instructional resources has become the focus of information construction in universities. Based on AI (Artificial intelligence) technology, this paper discusses the software development technology used in the comprehensive management system of IPE instructional resources. According to the application environment and application objects of the system, a comprehensive information management system of IPE instructional resources based on AI technology is constructed. In this paper, a concept similarity algorithm based on semantic similarity and semantic correlation is proposed to calculate the distance between words and establish concept mapping pairs. The semantic analysis of the information input by students is realized, and the proposed concept similarity algorithm is used in the domain ontology knowledge base to search for similar courses and recommend them to students for their choice. The test results show that the stability of this system can reach about 96%, which is about 10% higher than other systems. The comprehensive management system of instructional resources information proposed in this paper will promote educators and learners to find high-quality IPE instructional resources. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology has become an important part of the development of Internet technology. Artificial intelligence technology can help colleges and universities optimize the network ideological and political teaching system. Artificial intelligence technology provides more accurate data resources and rich and reliable educational technology means for online public mental health education in colleges and universities. This paper comprehensively uses a variety of methods such as qualitative and quantitative analysis, case and empirical analysis, literature analysis, and artificial intelligence technology. Artificial intelligence technology has been closely integrated with online public mental health education in colleges and universities. The model systematically analyzes the optimization methods of artificial intelligence technology methods for the online public mental health education system in colleges and universities, and constructs an innovation system for online public mental health education in colleges and universities. Based on the comprehensive analysis of artificial intelligence and public health in colleges and universities, this paper further proposes the application of artificial intelligence technology in online public mental health education in colleges and universities. On this basis, the model conducts an in-depth analysis of artificial intelligence technology and the online public mental health innovation system. The model supports the development of ideological and political teaching in colleges and universities through various forms such as idea innovation, path innovation, carrier innovation, and mechanism innovation. [ABSTRACT FROM AUTHOR]

With the rise of self-driving technology research, the establishment of a scientific and perfect legal restraint and supervision system for self-driving vehicles has been gradually paid attention to. The determination of tort liability subject of traffic accidents of self-driving cars is different from that of ordinary motor vehicle traffic accident tort, which challenges the application of traditional fault liability and product liability. The tort issue of self-driving cars should be discussed by distinguishing two kinds of situations: assisted driving cars and highly automated driving, and typological analysis of each situation is needed. When the car is in the assisted driving mode, the accident occurs due to the quality defect or product damage of the self-driving car, and there is no other fault cause; then, the producer and seller of the car should bear the product liability according to the no-fault principle; if the driver has a subjective fault and fails to exercise a high degree of care; the owner and user of the car should bear the fault liability. This paper analyzes the study of the impact of autonomous driving public on public psychological health, summarizes the key factors affecting the public acceptance of autonomous driving, and dissects its impact on public psychological acceptance. In order to fully study the responsibility determination of autonomous driving system accidents and their impact on public psychological health, this paper proposes an autonomous driving risk prediction model based on artificial intelligence technology, combined with a complex intelligent traffic environment vehicle autonomous driving risk prediction method, to complete the risk target detection. The experimental results in the relevant dataset demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As a body movement art, dance has its special form of expression. In terms of dance vocabulary, it can be roughly divided into two parts: external body movement and internal modality. In the process of body movement, it conveys information through silent language and the audience directly feels the information given by the dance image through vision. This is the special way of expressing emotion and meaning in dance art. This paper combines artificial intelligence technology and BP neural network (BPNN) algorithm to intelligently control dance teaching and solve complex nonlinear control problems. This paper studies dance teaching based on artificial intelligence technology. In this paper, BPNN algorithm and PCA-BPNN algorithm are used to test the dance teaching training of dance language, dance music, and stage art. The average accuracy of the BPNN evaluation model is 85.35% when the time reaches 80, while the average accuracy of the PCA-BPNN evaluation model is 65.64%. This shows that the accuracy of the BPNN evaluation model is higher than that of the PCA-BPNN evaluation model. Under the artificial intelligence technology, the dance using BPNN algorithm brings more intense sensory stimulation to the viewer because of the accompaniment of music, so as to achieve the infection and enjoyment of beauty and achieve the harmonious unity of sports and art. [ABSTRACT FROM AUTHOR]

Based on the perspective of digital finance and technological innovation, this paper analyzes its application in economic development, green economy, and sustainable development. With the continuous development of technological economy, methods such as artificial intelligence, Internet of Things, big data, and cloud computing become increasingly mature. Economic development is inseparable from the empowerment of technology. In this paper, firstly, we introduce the basic concepts and main forms of digital finance and technological economy and list the cutting-edge technologies including blockchain, VR, sharing economy, and other modes. Then, we analyze the application trend of technology economy. Finally, we analyze the examples of digital finance and technological innovation in detail, including tourism economy, digital marketing, sharing economy, smart city, digital healthcare, and personalized education, three hot topics of technology intersection and integration. In the end, we put forward prospects for the development of a digital economy, digital finance, and technological innovation. [ABSTRACT FROM AUTHOR]

Today, with the rapid development of the Internet, society has entered the era of "information explosion." Financial data are a particularly important part of network information, and it has also reached a new level of public demand. The frequent appearance of words such as "carbon peak" and "green" indicates the transition of national policies to the field of sustainable development. Sustainable development would become an inevitable choice, and a green supply chain has become a new trend under this policy background. Supply chain finance uses the ideas and methods of key supply management to provide financial services to related enterprises. If an enterprise cannot acquire, organize, and use the information and data in the supply chain, it is likely to be outdated or even abandoned in the short term. This paper takes the artificial intelligence green financial system as the background and uses the cooperation theory model to analyze and predict the big data information of the enterprise supply chain. It realizes the transformation of information into sustainable resources for enterprises and releases the huge potential of big data. In this model, this model not only helped the company's overall profit increase by about 8.79% but also provided scientific support for corporate decision-making and promoted the development of the company. [ABSTRACT FROM AUTHOR]

With the progress of science and technology and the arrival of the big data era, people increasingly rely on computers to deal with daily life and related affairs. In recent years, machine learning has become more and more popular and has achieved good results in some fields, which also makes machine learning widely used. Among them, visual neural network technology can more intelligently analyze the emotional expression of oil painting, which is one of the current research hotspots, involving machine vision, pattern recognition, image processing, artificial intelligence, and other fields. However, in the art field, oil painting is still very different from other images. At present, there is no deep learning algorithm to identify the application of emotional expression analysis in oil painting theme creation. This paper will start with the neural network algorithm and combine the big data recognition technology to analyze the emotional expression of the oil painting subject in the public environment and establish the emotional expression analysis model of oil painting creation based on big data and neural network. The experiment shows that the graphics synthesized by this model have high resolution and good definition, but the speed is slow in the process of experimental operation. It takes about one hour to complete a round of image optimization. [ABSTRACT FROM AUTHOR]

In order to solve the problem of integrating intelligent technology into music teaching, this paper puts forward the methods of using intelligent technology to optimize the music teaching system; to enhance the effectiveness of music psychological guidance, music intelligent creation, the development of Yi Guzheng platform, the integration of online sparring technology, and the rational use of Mu class platform, and to ensure the construction of loop curriculum system with "intelligent piano." RBF algorithm has strong data processing ability, which can ensure the operation quality of music score, music score and performance learning modules, and effectively strengthen the training effect of music psychological function. The artificial intelligence platform is more progressive in evaluation. It can use emotion to evaluate courses, grasp the course direction in advance, ensure the construction quality of psychological function, and improve the effect of music teaching; take 100 people as the basis of students, and measure the time proportion of students' music learning in the intelligent system. cjσj refers to the learning situation of music courses within the learning time X of students with excellent psychological quality of music. This result is parallel. Y represents the best score of music output by each algorithm. 2024 music intelligent platforms have collected various course resources with difficulty coefficients of 1 to 5, and the course resources of pictures and videos are sufficient. It can provide students with comprehensive music psychological education and give full play to the teaching advantages of intelligent technology. It can be seen from the data: the operation of music score, music score and performance module viewed in March 2021, with high transportation times and resource download times, indicating that the operation is in good condition; The error correction accuracy of the system is greater than 99%, indicating that the system has strong error correction ability. [ABSTRACT FROM AUTHOR]

Under the current theme of environmental protection, the effect of English literature classroom teaching reform cannot be analyzed qualitatively. Based on BP neural network, we present an analytical model of the impact of College English Literature Classroom Teaching Reform on environmental protection based on artificial intelligence technology. We analyze the teaching reform of English literature classrooms with the theme of environmental protection under artificial intelligence technology and explore the concept of "intelligent education" and the construction path of university English ecological teaching mode. Based on the in-depth excavation of teaching information, this paper analyzes the impact of college English literature classroom reform on the theme of environmental protection. Combined with BP neural network, a scientific analytic hierarchy process index system model is established, and the weight relationship of various reform influencing factors is given relatively objectively. The scores of the model based on the BP neural network under the six effect analysis indicators of teaching attitude, teaching tools, reform plan, reform teaching content, classroom organization, and reform rationality are 90.97, 86.3, 80.4, 95.7, 84.8, and 87.4, respectively. The results show that this model has a good ability for analysis and evaluation, which also provides some new ideas and entry points for the reform of the current English classroom teaching model. This study has a certain contribution to the breakthrough of college English teaching goals. [ABSTRACT FROM AUTHOR]

Online teaching is carried out nationwide in the context of the new crown epidemic prevention and control, and physical education, as one of the compulsory courses in schools and universities, is included within the content of online line lessons. Online physical education teaching is a new approach and attempt to switch from an auxiliary teaching tool to the main teaching tool. The subject matter, target audience, teaching methods, and content of physical education classes have changed dramatically. The development of online physical education has played an important role in popularizing the concept of "sports for life" and establishing the concept of lifelong exercise. Through the analysis and research on the characteristics of online physical education, we propose the measures to promote the overall online teaching ability and level of physical education teachers in the postepidemic period, gradually promote the reform of online and offline physical education, and promote the integration of school physical education and social sports, as well as to build a physical education teacher training system that integrates online and offline development and improve and strengthen the management of Internet physical education resources. Since the teaching effect of online teaching is limited by the teaching means and content, which leads to the low accuracy of the evaluation of online sports teaching effect, for this reason, this paper designs a model based on big data technology and artificial intelligence algorithm for enhancing closed home sports teaching, obtains the overall situation of online physical education by using convolutional neural network to process physical education video sequences, and improves the system performance by embedding the algorithm into the big data framework, to avoid the interference of teaching environment and complete the evaluation of high precision online sports teaching effect. The experimental results show that the proposed method evaluation can improve the computing rate based on ensuring the high accuracy evaluation of physical education online teaching effect. [ABSTRACT FROM AUTHOR]

Environmental problem is an international problem that transcends national boundaries and develops into regional and global environmental pollution and ecological problems. Facing the increasing environmental pollution, the international community has successively formulated many relevant environmental pollution prevention laws, but the world situation is complicated after all, environmental problems still emerge endlessly, and the protection of environmental rights has become the consensus of the international community. Environmental right is an integral part of human rights, and protecting environmental right is the concrete expression and proper meaning of protecting human rights. Using international criminal law to protect environmental rights will play a positive role in global environmental protection. As with the development of computer technology, the research of machine learning has gradually transferred to the field of social science, especially the judicial field. While sentencing is a crucial part of environmental crime, this paper studies the sentencing of environmental rights cases from the perspective of international criminal law and uses Convolutional Neural Networks (CNN) to determine the sentencing of environmental rights cases. Through the experiment on the Integrated Database (IDB) dataset, the results show that the introduction of CNN improves the effect of the sentencing term prediction model and the fine prediction model significantly. The CNN-based model scored 91.6542 in the sentencing term prediction model and 90.8890 in the fine prediction model. [ABSTRACT FROM AUTHOR]