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On the network service platform for vocational education, there are currently over 10,000 online courses. Learners face a challenge in selecting interesting courses from the vast resources available. Learners' urgent need for personalized learning is becoming more apparent as educational informatization progresses. Personalized recommendation (PR) technology can aid personalized learning and increase learners' learning efficiency significantly. This paper constructs a smart classroom model based on AI (artificial intelligence) by studying the connotation and characteristics of smart classroom in light of the current research status and trend of smart classroom at home and abroad. The merits of the recommendation system are determined by the recommendation algorithm used by PR system. This paper primarily focuses on developing a personalized learning platform based on the CF (collaborative filtering) algorithm, as well as conducting system requirements analysis, database design, functional module design, implementation, and testing on this foundation. Experiments are carried out to see if the optimized PR algorithm in the network learning platform is effective. [ABSTRACT FROM AUTHOR]

Navigation and positioning is a new growth point of wireless services. And seamless positioning is the current development trend of navigation and positioning services. In view of the problems that dynamic handover cannot be performed during site movement, STA only selects the optimal AP according to the indication of received signal strength, and the handover delay is too long. This paper proposes a research on indoor and outdoor seamless positioning technology based on artificial intelligence and intelligent switching algorithm. The purpose is to study the influence of reducing the positioning data with too large error on the final positioning result. The method of this paper is to propose an intelligent switching algorithm and then discuss the research status and development direction of indoor positioning technology and outdoor positioning technology, respectively, and point out the switching methods between them. The role of these methods is to analyze the current situation of indoor and outdoor navigation technology, especially the development route of seamless positioning technology. It determines the strategy of integrating heterogeneous positioning systems to build a seamless positioning system to maximize the use of existing positioning system resources. This paper studies the design and implementation of the seamless positioning system through the simulation experiment of the intelligent switching algorithm and finally tests the system through the experimental vehicle. The online experiment results show that the system can achieve high positioning accuracy in indoor and outdoor environments, especially at its junction, and the positioning errors in all directions are within 0.2 m. [ABSTRACT FROM AUTHOR]

In the field of computer vision, it is a very challenging task to use artificial intelligence deep learning method to realize the programming and creation of NFT artwork. With the continuous development and improvement of deep learning technology, this task has become a reality. The generative adversarial network model used in deep learning can generate new images based on the extraction and analysis of image data features and has become an important tool for NFT artwork image generation. In order to better realize the NFT artwork programming, this paper analyzes the working principle of the traditional adversarial generation method and then uses the StyleGAN model to edit the higher-level attributes of the image, which can effectively control the generated style and style of the NFT artwork image. Finally, in order to improve the quality of the generated images, this paper introduces a channel attention mechanism and a spatial attention mechanism to ensure that the generated images are more reasonable and realistic. Finally, through a large number of experiments, it is proved that the NFT artwork transmission programming algorithm based on artificial intelligence deep learning proposed in this paper can control the overall style of image generation according to the needs of the transmission, and the generated image features have good details and high visual quality. [ABSTRACT FROM AUTHOR]

The incidence of oral cancer is still increasing. It has become very common in patients with malignant tumors, which has forced medical personnel to continuously explore its treatment methods. What kind of method can effectively and correctly diagnose the disease in the early stage and improve the survival rate has become one of the research topics that have attracted much attention. Aiming at this problem, it has great research significance for the field of oral precancerous lesions diagnosis. With the in-depth research on oral precancerous diagnosis, the research on artificial neural network (ANN) in medical diagnosis is gradually carried out. Its performance advantage is of great significance to solve the problem of early and correct disease diagnosis. This paper aimed to investigate the application of ANN-assisted cancer risk prediction method in risk prediction of oral precancerous lesions. Through the analysis and research of ANN and oral cancer, the construction of oral cancer risk prediction model was applied to solve the problem of improving the survival rate of oral cancer patients. In this paper, ANN and oral precancerous lesions were analyzed, the performance of the algorithm was experimentally analyzed, and the relevant theoretical formulas were used to explain. The results showed that the method had higher accuracy than traditional forecasting methods. When N = 2 , the output accuracy was above 90%. It can be seen that the algorithm can meet the needs of the diagnosis of high-risk groups of oral cancer lesions, and the diagnosis efficiency and patient survival rate has been greatly improved. [ABSTRACT FROM AUTHOR]

As socialism with Chinese characteristics enters a new age, thought-politics education has also entered a new phase of development. Facing new opportunities and challenges, attaching importance to quality, pursuing quality, and improving quality have become the key directions for the development of thought-politics education. In the paper, a CNN-BiLSTM-based recommendation algorithm is proposed and combined with AI-related algorithms to be used in the task of improving thought-politics education. First, deep feature extraction in temporal and spatial dimensions by using LSTM and convolutional networks, respectively; then, by using multiscale attention fusion mechanisms to enhance the expressiveness of the features and make recommendations with the help of multilayer perceptron. Through extensive experiments, it is verified that the model has higher recommendation performance in this paper while maintaining higher real-time performance. It is tested on real data sets to verify that the model in the paper has better robustness. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) scholars and mediciners have reported AI systems that accurately detect medical imaging and COVID-19 in chest images. However, the robustness of these models remains unclear for the segmentation of images with nonuniform density distribution or the multiphase target. The most representative one is the Chan-Vese (CV) image segmentation model. In this paper, we demonstrate that the recent level set (LV) model has excellent performance on the detection of target characteristics from medical imaging relying on the filtering variational method based on the global medical pathology facture. We observe that the capability of the filtering variational method to obtain image feature quality is better than other LV models. This research reveals a far-reaching problem in medical-imaging AI knowledge detection. In addition, from the analysis of experimental results, the algorithm proposed in this paper has a good effect on detecting the lung region feature information of COVID-19 images and also proves that the algorithm has good adaptability in processing different images. These findings demonstrate that the proposed LV method should be seen as an effective clinically adjunctive method using machine-learning healthcare models. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology and information technology, the posture of volleyball training is becoming increasingly strict. By analyzing the dynamic training posture monitoring algorithm, the posture information of the human body can be directly obtained, which enables more efficient management of volleyball sports training. This paper aims to study how to monitor volleyball training posture and give suggestions based on machine learning in the context of artificial intelligence. The traditional method of manually detecting volleyball training postures is too subjective and cannot be used to judge the movements. Therefore, this paper proposes an algorithm for human posture monitoring and studies human posture recognition. Human gesture recognition has been widely used in many fields. The experimental results in this paper show that the corrected serve deviation rate of five volleyball players is 13.1% at the highest and 11.3% at the lowest after the traditional manual visual monitoring. The highest error is 0.70 m and the lowest is 0.63 m. The overall error is high. The corrected service deviation rate of the machine learning-based attitude monitoring algorithm is 3.5% at the highest and 2.7% at the lowest. The highest error is 0.24 m and the lowest is 0.19 m. The overall error is much lower than the former. This also shows that the posture monitoring algorithm based on machine learning can effectively detect the movement of volleyball players. This enables athletes to correct their mistakes in a timely manner, improve training efficiency, and improve their own strength. [ABSTRACT FROM AUTHOR]

At present, economic business activities occur to the accounting element confirmation link of the accounting information system (AIS), which still occupies a large amount of manpower and material resources of the enterprise to generate information. This has been restricting the development of modern AIS. Based on this, first, this paper studies the influence of the knowledge economy on accounting innovation and in the innovation economy. The process of confirmation for accounting elements in economic business, under the traditional AIS, is analyzed. Then, the model of the accounting element confirmation, i.e., BPNN, is constructed by combining the backpropagation (BP) neural network (NN) theory with the artificial intelligence (AI) algorithm. Finally, we simulate the confirmation process of accounting elements based on the economic business data of specific online stores. The experimental outcomes illustrate that under the proposed BPNN model, the output value of the accounting entry is also increasing and has been in the interval [0, 0.14] with the continuous increase in the input value of economic business activities. Moreover, the overall simulation error of economic business activity data does not exceed 0.3% in the simulation test of the proposed accounting business data confirmation model based on the BPNN algorithm. The empirical outcomes indicate that the model has high accuracy and reliability. The purpose is to realize the identification of business events by machines and complete the automatic confirmation of accounting elements in the back-end economic business of online stores. This paper provides new ideas for realizing the overall intelligence of the AIS. [ABSTRACT FROM AUTHOR]

In recent years, as the research objects of phonetics have expanded to accent and colloquial natural speech, the construction of the dialect accent Mandarin voice database has become another important research direction in the field of computer technology. Among them, voice segmentation is a time-consuming and laborious link in the construction of the voice database. The application of artificial intelligence technology helps to improve the construction efficiency of the Mandarin dialect voice database. Based on this, this article mainly researches the application of the artificial intelligence algorithm in the automatic segmentation of dialect accent Mandarin. This paper constructs a voice corpus of dialect accents and Mandarin Chinese and specifically describes the construction process of the voice corpus. This paper uses artificial intelligence algorithms, combined with the HMM (hidden Markov model), and Viterbi algorithm to propose a new method of automatic speech segmentation. This paper studies the automatic speech segmentation model, extracts the general parameters of the training data in the Mandarin corpus, and conducts HMM training. This paper conducts tests based on the voice of the test set to verify the accuracy of the method proposed in this paper. The experimental results show that, in the speech data of 60 people, the error range of each sentence time period is less than 5 ms accounting for 79.16%, less than 10 ms accounting for 82.96%, less than 20 ms accounting for 83.14%, and less than 50 ms accounting for 86.92%. It can be seen that the algorithm proposed in this paper can meet practical applications in automatic speech segmentation. [ABSTRACT FROM AUTHOR]

This research aims to improve the rationality and intelligence of AUTOMATICALLY HIGHER MATHEMATICALLY EXAM SYSTEM (AHMES) through some AI algorithms. AHMES is an intelligent and high-quality higher math examination solution for the Department of Computer Engineering at Pai Chai University. This research redesigned the difficulty system of AHMES and used some AI algorithms for initialization and continuous adjustment. This paper describes the multiple linear regression algorithm involved in this research and the AHMES learning (AL) algorithm improved by the Q-learning algorithm. The simulation test results of the upgraded AHMES show the effectiveness of these algorithms. [ABSTRACT FROM AUTHOR]

In recent years, people's living standards are getting higher and higher, and life pressure is also increasing, and there are also many problems in eating habits. This is also the direct cause of colon cancer. The aim of this paper was to investigate whether anesthetic drugs could positively affect the proliferation and apoptosis of colon cancer cells. In this paper, the significance of anesthetic drugs is proposed, and an artificial neural network algorithm based on artificial intelligence is proposed. It is well known that artificial neural networks play an important role in medicine. The experimental results of this paper show that the incidence of colon cancer in 2020 will be in the range of 5%-35%, and the incidence of colon cancer in 2021 will be in the range of 7%-30%. While colon cancer rates in 2021 do not appear to be as high as colon cancer rates in 2020, they are generally much higher than colon cancer rates in 2020. It can be seen that as the population ages, the number of colon cancer patients is increasing due to the lack of emphasis on health. This also means that the incidence of colon cancer is getting higher and higher, and traditional drug chemotherapy has been unable to play a good role in inhibiting the proliferation of colon cancer cells. Therefore, this paper investigated the effects of anesthetic drugs on the proliferation and apoptosis of human colon cancer cells. [ABSTRACT FROM AUTHOR]

Game data collection system is a tool used to collect the behavior data of users about the game. It can be used for data analysis of user behavior so that game manufacturers can keep abreast of market dynamics and popular trends, and they also can have a deeper understanding of the behavioral habits and psychology of player user groups. The defects of the current data acquisition system include that the data are not encrypted. The network transmission efficiency is relatively low. The acquisition speed is slow, and the settings cannot be dynamically changed. This paper proposes to study how to enhance the acquisition ability and improve the analysis efficiency in the design of data acquisition system for solving these problems. Therefore, on the basis of artificial intelligence algorithm, this paper designs a game data collection system by using artificial neural network algorithm, support vector algorithm, and cluster analysis algorithm, which solves the basic problem of slow data collection in current data collection and plays a role in improving the efficiency of network transmission. The experimental results in this paper show that when the number of data is more than 300, the time-consuming time reaches more than 68 ms. When the number of written data is more than 300, it takes more than 181 ms. When the number of deleted data is more than 300, it takes more than 236 ms. From the above data, it shows that the designed game data collection system is rapid and efficient. [ABSTRACT FROM AUTHOR]

As computer and multimedia technology advances and as the variety of knowledge display forms expands, there are more ways for people to obtain knowledge and information. The best course in musicology is voice performance. A crucial issue is how to select and categorize valuable curriculum materials of varying quality levels. This paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning technology and tests it on the corresponding dataset, beginning with multidimensional association rule mining technology and the premise that multimedia data contain numerous useful characteristics. Experiments demonstrate the classification precision and data integration capacity of the proposed algorithm. Vocal performance is the most engaging course in musicology. To cultivate corresponding talents, we can rely on college and university offline courses and the rapidly developing multimedia technology to train talents online. An important topic is how to efficiently select and classify curriculum materials of varying quality in order to provide them to students with different learning needs. We can achieve the automatic classification of the course content by leveraging the superior learning capabilities of artificial intelligence and machine learning technology. Consequently, this paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning-related technology and conducts an experimental test on the corresponding dataset, beginning with multidimensional association rule mining technology and the perspective that multimedia information itself contains a large number of useful characteristics. Experiments demonstrate that the proposed algorithm has a high level of classification accuracy and data integration capability. [ABSTRACT FROM AUTHOR]

With the continuous updating and advancement of artificial intelligence technology, it gradually begins to shine in various industries, especially playing an increasingly important role in incremental music teaching and assisted therapy systems. This study designs artificial intelligence models from the perspectives of attention mechanism, contextual information guidance, and distant dependencies combined with incremental music teaching for the segmentation of MS (multiple sclerosis) lesions and achieves the automatic and accurate segmentation of MS lesions through the multidimensional analysis of multimodal magnetic resonance imaging data, which provides a basis for physicians to quantitatively analyze MS lesions, thus assisting them in the diagnosis and treatment of MS. To address the highly variable characteristics of MS lesion location, size, number, and shape, this paper firstly designs a 3D context-guided module based on Kronecker convolution to integrate lesion information from different fields of view, starting from lesion contextual information capture. Then, a 3D spatial attention module is introduced to enhance the representation of lesion features in MRI images. The experiments in this paper confirm that the context-guided module, cross-dimensional cross-attention module, and multidimensional feature similarity module designed for the characteristics of MS lesions are effective, and the proposed attentional context U-Net and multidimensional cross-attention U-Net have greater advantages in the objective evaluation index of lesion segmentation, while being combined with the incremental music teaching approach to assist treatment, which provides a new idea for the intelligent assisted treatment approach. In this paper, from algorithm design to experimental validation, both in terms of accuracy, the operational difficulty of the experiment, consumption of arithmetic power, and time cost, the unique superiority of the artificial intelligence attention-based combined with incremental music teaching adjunctive therapy system proposed in this paper can be seen in the MS lesion segmentation task. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology and information technology, the posture of volleyball training is becoming increasingly strict. By analyzing the dynamic training posture monitoring algorithm, the posture information of the human body can be directly obtained, which enables more efficient management of volleyball sports training. This paper aims to study how to monitor volleyball training posture and give suggestions based on machine learning in the context of artificial intelligence. The traditional method of manually detecting volleyball training postures is too subjective and cannot be used to judge the movements. Therefore, this paper proposes an algorithm for human posture monitoring and studies human posture recognition. Human gesture recognition has been widely used in many fields. The experimental results in this paper show that the corrected serve deviation rate of five volleyball players is 13.1% at the highest and 11.3% at the lowest after the traditional manual visual monitoring. The highest error is 0.70 m and the lowest is 0.63 m. The overall error is high. The corrected service deviation rate of the machine learning-based attitude monitoring algorithm is 3.5% at the highest and 2.7% at the lowest. The highest error is 0.24 m and the lowest is 0.19 m. The overall error is much lower than the former. This also shows that the posture monitoring algorithm based on machine learning can effectively detect the movement of volleyball players. This enables athletes to correct their mistakes in a timely manner, improve training efficiency, and improve their own strength. [ABSTRACT FROM AUTHOR]

At present, aerobics is becoming a popular fashion with the continuous development of cultural needs. Because aerobics has the characteristics of many movements, rapid changes, strong complexity, and difficult performance of difficult movements, the current aerobics teaching still presents shortcomings such as low teaching level, limited teachers' resources, and energy. Therefore, it is difficult to effectively meet the actual learning needs of students. Based on this point, artificial intelligence can be used to simulate and guide the technical movements of aerobics to effectively teach students. In this paper, an artificial intelligence aerobics image simulation system is researched and developed and the GrabCut image segmentation algorithm is mainly used. After analyzing some shortcomings of the algorithm, the GrabCut algorithm cascade and graph-based are selected to complete the optimization, so as to lay a good system foundation and then build the aerobics artificial intelligence image simulation system according to the algorithm foundation. Finally, it analyzes and researches the actual problems of aerobics teaching activities in colleges and universities and focuses on the problems, achievements, and personal satisfaction of students who use the system in actual learning, which proves that the system can effectively assist aerobics teaching activities. By studying the image segmentation algorithm and artificial intelligence technology, this paper applies it to the field of aerobics action image simulation, so as to promote its technological development. [ABSTRACT FROM AUTHOR]

With the development of The Times, social events are increasing, and emergency management has gradually become the main helper to solve the crisis in the public domain. By observing the current situation of many countries and regions, we can find that various types of public crises often occur in many countries and regions in the world, which have severely affected people's daily life, lives, and property. Through long-term research and analysis, it can be known that the emergency management mechanism currently established in China has certain shortcomings. The communication problem of emergency information is likely to cause the emergency work to not proceed smoothly. In addition, problems in the communication channels of emergency information are likely to cause problems in the cooperation of various departments when people carry out emergency management work, and the efficiency of the government in dealing with problems will also be reduced in real scenarios. In order to improve the efficiency of emergency information management, this paper aims at the various problems existing and facing in the construction of emergency management system. On this basis, the integration of various relevant emergency information management plan models is analyzed and sorted out, and based on the research and integration of the development of artificial intelligence algorithms. The main research results of emergency information management at home and abroad are comprehensively studied and evaluated. Finally, a QG algorithm based on more model fusion is developed. In the process of analysis, this article uses artificial intelligence algorithms to build a prediction model of multiple modes and collects the data needed to build the model by random extraction. Through the analysis of different data sets, it is used as the basic training data for prediction. Through comprehensive analysis, the model constructed in this paper can promote the sharing of emergency information among departments to a certain extent. [ABSTRACT FROM AUTHOR]

Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies. [ABSTRACT FROM AUTHOR]

In this paper, in-depth research and analysis of juvenile delinquency prevention and occupational therapy education guidance using artificial intelligence are conducted, and its response mechanism is designed in this way. Two crime type prediction algorithms based on time-crime type count vectorization and dense neural network and crime type prediction based on the fusion of dense neural network and long- and short-term memory neural network are proposed. The outputs of both are fed into a new neural network for training to achieve the fusion of the two neural networks. Among them, the use of the dense neural network can effectively fit the relationship between the constructed features and crime types. The behavioral manifestations and causes of the formation of deviant behavior in adolescents are discussed. They can only read numerical data, but there is a lot of information in the textual data that is closely related to the training effect. When experimenting, it is necessary to extract knowledge and build applications. The practical work with adolescents with deviant behaviors is again carried out from group work and casework, respectively, with problem diagnosis, needs assessment, and service plan development for specific clients, to carry out relevant practical service work. The causes of juvenile delinquency in the Internet culture are discussed in terms of the Internet environment, juvenile use of the Internet, Internet supervision, and crime prevention education, respectively. The fourth chapter focuses on the analysis of the prevention and control measures for juvenile delinquency in cyberculture. In response to the above-mentioned causes of juvenile delinquency in cyberculture, the prevention and control measures are discussed in four aspects, namely, strengthening the construction of cyberculture and building a healthy cyber environment, strengthening the capacity building of guiding juveniles to use cyber correctly, building a prevention and supervision system to promote the improvement of the legal system, and improving and innovating the crime prevention education in the cyber era. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, Internet of things, machine learning, and many other technologies, animation design task based on algorithm theory has become a research hotspot in the field. In recent years, perception technology has gradually become the key technology of animation design, and it is also the key research content in the current field. Whether the perception system can design animation quickly and accurately is the key of research. Compared with other algorithms, using AVOD (Aggregate view object detection) algorithm for animation design has obvious advantages. The original AVOD algorithm has some problems, such as low clustering efficiency, insufficient depth of feature extraction network, and occupying a large amount of memory. Based on this, this paper proposes to use the googleNet network and initial model of k-means++ to extract features and establish an optimized AVOD algorithm. At the same time, in order to illustrate the effectiveness of the optimization method, two typical cases are introduced to provide scientific guidance and reference for the research in this field. [ABSTRACT FROM AUTHOR]

In order to improve the effect of continuous speech recognition, this paper combines the DTW algorithm to construct a continuous Russian speech recognition system and proposes a continuous Russian speech detection method based on VGDTW-MPCA with an unequal interval process. Moreover, considering the influence of the correlation between variables on the synchronization of the DTW algorithm, this paper constructs a DTW algorithm on a local data set to synchronize in different variable groups. Then, this paper integrates the obtained data into complete 3D data for modeling. It can be seen from the simulation research that the Russian continuous speech recognition system based on DTW proposed in this paper has a high continuous Russian speech recognition accuracy. [ABSTRACT FROM AUTHOR]

In order to solve the problems of machine translation efficiency and translation quality, this paper proposes an English translation evaluation system based on the BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of the BP neural network algorithm, taking English online translation as the research object, Google's translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301, respectively, which is much higher than Google translation. A model of machine translation evaluation based on the neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on the neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation. [ABSTRACT FROM AUTHOR]

With the three industrial revolutions sweeping the world, especially since the third industrial revolution, the complexity of human work has greatly increased, and in the new era of technology and information, workers have new standards and new requirements for their work environment, and new form of work environment design has come into being. In this paper, a work environment system is designed by artificial intelligence algorithm to improve the workers' work environment by assessing the good and bad degree of their natural and social work environment, and an intelligent service system is designed by using artificial intelligence algorithm, which can not only analyze and process the work environment assessment results but also execute the subjective and reasonable requirements made by the workers to help the workers maintain a good mood at work and improve their efficiency. [ABSTRACT FROM AUTHOR]

With a large number of images provided by TV and other media flowing into the Internet and the reduction of technical barriers, images have not only become a daily practice for people to record their lives and communicate their behaviors but also become an important means for the public to express their discourse in the cyberspace. Therefore, it is of great significance to analyze the image propagation algorithm using artificial intelligence. This paper mainly studies the algorithm analysis and governance of local media image propagation in the era of artificial intelligence. In this paper, the media is the research object, with its daily dissemination of video works as the research text, in order to discover the ethical problems in its dissemination activities as the purpose, integrating disciplinary knowledge to analyze the ethical problems in this art form, and trying to find out the fundamental measures to solve the problem. The advantages and disadvantages of the video recommendation intelligent algorithm based on the BP neural network are analyzed. By comparing different algorithms, it can be seen that the video recommendation accuracy of the BP neural network algorithm based on swarm optimization (FEBP) is 15.8% higher than that of the traditional BP neural network algorithm. These intelligent algorithms are added into the image transmission system, in order to achieve the goal of improving the image transmission and recommendation effect. [ABSTRACT FROM AUTHOR]

The traditional teaching mode is to use a point-to-point mode or a computer-aided system for teaching, but this limits students' enthusiasm and interest in learning. The Internet of Things (IoT) technology is a technology that integrates sensors, the Internet, and terminals to transmit information in real time. The smart education based on the Internet of Things can realize remote teaching and actual scene teaching, and students can freely choose the learning location and time, which can greatly improve students learning interest and learning efficiency, which is a development trend of a new teaching method. Smart IoT teaching is a teaching method that combines IoT technology and artificial intelligence technology. This paper mainly studies the research and analysis of the smart education model based on the IoT in remote teaching. In this paper, sensor technologies such as cameras will be used to collect students' expressions, speech, and other actions in class from different regions. These data features will be processed by the terminal's intelligent algorithm, and the desired knowledge will be obtained according to the students' behavior information. The information processed by the intelligent algorithm will be transmitted to the terminal system where the teacher is located, such as computer and mobile phone. This paper focuses on analyzing the reliability and accuracy of the intelligent algorithm of the IoT smart education terminal. The results show that the prediction error of the student behavior information is within 3% and the correlation coefficient reaches 0.99. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence has brought rapid changes to human life and brought great convenience to human activities. The development of various modes of transportation has also brought convenience to people's travel and commodity transactions, but it has also added more issues that need to be carefully considered. Because of the diversification of transportation methods, transportation problems also arise many fields, such as air transportation, water transportation, and land transportation. The development of mathematical models and algorithms for transportation problems is also in full swing, and it is a major trend to introduce mathematical models and algorithms into the solution of transportation problems. This paper deals with the multi-index transportation problem by establishing a multi-index mathematical model and algorithm to find a scientific transportation method for the goods to be transported, so as to save the cost and time of transportation. Experiments show that the mathematical model established in this paper has high efficiency for solving the multi-index transportation problem. At the same time, the most suitable transportation method can also be selected for the transportation of goods, and the route planned by the mathematical model and algorithm can reduce the risk to 12.34%. [ABSTRACT FROM AUTHOR]

Natural language processing is an important direction in the field of computer science and artificial intelligence. It can realize various theories and methods of effective communication between humans and computers using natural language. Machine learning is a branch of natural language processing research, which is based on a large-scale English-Chinese database. Due to the relatively poor alignment corpus of English and Chinese bilingual sentences containing unknown words, machine translation is unprofessional and unbalanced, which is the problem studied in this paper. The purpose of this paper is to design and implement a length-based system for sentence alignment between English and Chinese bilingual texts. The research content of this paper is mainly divided into the following parts. First, the evaluation function of bilingual sentence alignment is designed, and on this basis, the bilingual sentence alignment algorithm based on the length and the optimal sentence pair sequence search algorithm is designed. In this paper, China National Knowledge Infrastructure (CNKI) is selected as an English-Chinese bilingual candidate website and English-Chinese bilingual web pages are downloaded. After analyzing the downloaded pages, nontext content such as page tags is removed, and bilingual text information is stored so as to establish an English-Chinese bilingual corpus based on segment alignment and retain English-Chinese bilingual keywords in the web pages. Second, extract the dictionary from the software StarDict, analyze the original dictionary format, and turn it into a custom dictionary format, which is convenient and better to use the double-sentence sentence alignment system, which is conducive to expanding the number of dictionaries and increasing the professionalism of vocabulary. Finally, we extract the stems of English words from the established corpus to simplify the complexity of English word processing, reduce the noise caused by the conversion of word parts of speech, and improve the operation efficiency. A bilingual sentence alignment system based on length is implemented. Finally, the system parameters are adjusted for comparative experiments to test the system performance. [ABSTRACT FROM AUTHOR]

Objective. The purpose is to understand the depression status of the elderly in the community, explore its influencing factors, formulate a comprehensive psychological intervention plan according to the influencing factors, implement demonstration psychological intervention, and evaluate and feedback the effect, so as to provide a reference for improving the mental health of the elderly. Method. In order to make the output of different emotional data in LSTM more discriminative, a method to dynamically filter the output of LSTM is proposed. Combining the methods of Attention-LSTM, time-dimensional AI attention, and feature-dimensional AI attention, the best model in this paper is obtained. The multistage stratified cluster sampling method was used to conduct a questionnaire survey on the elderly aged 60 and above in a certain area, including the general demographic characteristics questionnaire of the elderly, the self-rating scale of mental health symptoms, and the health self-management ability of adults. All data were entered into a database using Excel software, and SPSS 19.0 statistical software was used for statistical analysis. Results/Discussion. The detection rate of depression (GDS ≥ 11 points) among the elderly in a community in a certain area was 39.38%. Multivariate logistic regression analysis showed that family history of mental illness, more negative life events, decreased ability of daily living, living alone, and suffering from physical diseases in the past six months were the risk factors for depression in the elderly. Community health education can partially alleviate depression in the elderly. The detection rate and degree of depression of the elderly in the comprehensive psychological intervention group were significantly lower than those in the control group, and the difference was statistically significant (P < 0.05). [ABSTRACT FROM AUTHOR]

Musical choreography is usually completed by professional choreographers, which is very professional and time-consuming. In order to realize the intelligent choreography of musical, based on the mixed density network (MDN), this paper generates the dance matching with the target music through three steps: motion generation, motion screening, and feature matching. The choreography results in this paper have a high degree of matching with music, which makes it possible for the development of motion capture technology and artificial intelligence and computer automatic choreography based on music. In the process of motion generation, the average value of Gaussian model output by MDN is used as the bone position and the consistency of motion is measured according to the change rate of joint velocity in adjacent frames in the process of motion selection. Compared with the existing studies, the dance generated in this paper has improved in motion coherence and realism. In this paper, a multilevel music and action feature matching algorithm combining global feature matching and local feature matching is proposed. The algorithm improves the unity and coherence of music and action. The algorithm proposed in this paper improves the consistency and novelty of movement, the compatibility with music, and the controllability of dance characteristics. Therefore, the algorithm in this paper technically changes the way of artistic creation and provides the possibility for the development of motion capture technology and artificial intelligence. [ABSTRACT FROM AUTHOR]

To study the classification and evolution of key technologies in the transportation field, the data of 36 authoritative SCI journals in the transportation field were collected from the Web of Science core collection database from 2001 to 2020. Based on the bibliometric method, this study used Python to process and visualize data, combined with bibliometric software VOSviewer to assist data visualization. Firstly, a preprocessing data algorithm was designed to deduplicate the collected data, merge synonyms, and extract key technologies. Then the paper records that contained the key technology lexicon were filtered out. Next, the annual number of publications and the distribution of key technologies over time were counted. The least squares method was used to fit the distribution of the annual proportion of the publications, and the slope k1 of the fitted linear regression equation was used to determine the research interest trend of key technologies. The key technologies were divided into "hot technology," "cold technology," and "other technologies," according to the research heat trend. In order to further explore the research hotspots, the least squares method was also used to fit the citations of all technologies to obtain the slope k2. We use the Gaussian mixture model (GMM) algorithm to cluster k1 and k2 of each technology. As a result, the 144 technologies were divided into 13 super-key technologies, 60 key technologies, 59 relative key technologies, and 12 lower-key technologies. Then, the evolution of key technologies was analyzed from two perspectives of weighted evolution and cumulative evolution. And the technology evolution trend in the transportation field in the past 20 years was explored. Finally, the cooccurrence clustering method was adopted to divide key transportation technologies into five categories: vehicle technology and control, optimization algorithms and simulation techniques, artificial intelligence and big data, Internet of Things and computing, and communication technology. The research results can provide references for different people in the transportation field, including but not limited to researchers, journal editors, and funding agencies. [ABSTRACT FROM AUTHOR]

This paper presents an indepth analysis and research on the quantitative design of fine art images through artificial intelligence algorithms. A CycleGAN-based network model for automatic generation of sketches of fine art images is constructed to extract the edge and contour features of fine art images. The network uses 512 × 1024 high-resolution art images as input and Pitchman as a discriminator. To further enhance the sketch generation effect, a bilateral filtering algorithm is added to the generator model for noise reduction, and then a K -means algorithm is used for color quantization to solve the problem of cluttered lines in the generated sketches. The experimental results show that the network model can effectively realize the automatic generation of art image sketches and can retain the detailed part of the costume information well. A rendering platform is built to realize the application of art image generation algorithms and coloring algorithms. The platform integrates the functions of image preprocessing, sketch generation, and sketch coloring, demonstrates the results of the main research content of this paper, and finally increases the interest of the system through the rendering function of the art image grid, which further improves the practicality of the platform. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning, computer vision has also become a rapidly developing field in the field of artificial intelligence. Combining the physical training of deep learning will bring good practical value. Physical training has different effects on people's body shape, physical function, and physical quality. It is mainly reflected in the changes of relevant physical indicators after physical training. Therefore, the purpose of this article is to study the method of evaluating the impact of sports training on physical indicators based on deep learning. This paper mainly uses the convolutional neural network in deep learning to design sports training, then constructs the evaluation system of physical index impact, and finally uses the deep learning algorithm to evaluate the impact of physical index. The experimental results show that the accuracy of the algorithm proposed in this paper is significantly higher than that of the other three algorithms. Firstly, in the angular motion, the accuracy of the mean algorithm is 0.4, the accuracy of the variance algorithm is 0.2, the accuracy of the RFE algorithm is 0.4, and the accuracy of the DLA algorithm is 0.6. Similarly, in foot racing and skill sports, the accuracy of the algorithm proposed in this paper is significantly higher than that of other algorithms. Therefore, the method proposed in this paper is more effective in the evaluation of the impact of physical training on physical indicators. [ABSTRACT FROM AUTHOR]

This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Surgery is one of the larger wounds in conventional surgery, and patients often experience different pain and postural discomfort after surgery. With the ever-changing standards of medical care and patient care requirements, providing high-quality care to postoperative patients is an important measure to reduce complications and promote rapid recovery. However, in the traditional postsurgical nursing methods, there are often the phenomenon that wrong patients are connected, patient data is messy, and medicines are counted incorrectly, which directly leads to a rapid decline in nursing efficiency. In the context of the rapid development of artificial intelligence and big data, intelligent medical data analysis technology has gradually been integrated into the medical field. This paper analyzes and studies the application effect of intelligent medical data analysis technology in postoperative nursing. It is aimed at changing the traditional postoperative nursing methods and improving nursing efficiency, and it provides important suggestions for the development of postoperative nursing in the new era. Combining big data and Internet of Things technology, this paper builds a smart medical Internet of Things framework and an intelligent postoperative care system and uses machine learning algorithms to preprocess relevant medical data. The final experimental results show that the intelligent medical data analysis technology has a good effect in improving the nursing efficiency after surgery, and the nursing efficiency has increased by 6.9%. [ABSTRACT FROM AUTHOR]

Background and Aims. Diagnosing pediatric intussusception from ultrasound images can be a difficult task in many primary care hospitals that lack experienced radiologists. To address this challenge, this study developed an artificial intelligence- (AI-) based system for automatic detection of "concentric circles" signs on ultrasound images, thereby improving the efficiency and accuracy of pediatric intussusception diagnosis. Methods. A total of 440 cases (373 pediatric intussusception and 67 normal cases) were retrospectively collected from Children's Hospital affiliated to Zhejiang University School of Medicine from January 2020 to December 2020. An improved Faster RCNN deep learning framework was used to detect "concentric circle" signs. Finally, independent validation set was used to evaluate the performance of the developed AI tool. Results. The data of pediatric intussusception were divided into a training set and validation set according to the ratio of 8 : 2, with training set (298 pediatric intussusception) and validation set (75 pediatric intussusception and 67 normal cases). In the "concentric circle" detection model, the detection rate, recall, specificity, and F 1 score assessed by the validation set were 92.8%, 95.0%, 92.2%, and 86.4%, respectively. Pediatric intussusception was classified by "concentric circle" signs, and the accuracy, recall, specificity, and F 1 score were 93.0%, 92.0%, 94.1%, and 93.2% on the validation set, respectively. Conclusion. The model established in this paper can realize the automatic detection of "concentric circle" signs in the ultrasound images of abdominal intussusception in children; the AI tool can improve the diagnosis speed of pediatric intussusception. It is necessary to further develop an artificial intelligence system for real-time detection of "concentric circles" in ultrasound images for the judgment of children with intussusception. [ABSTRACT FROM AUTHOR]

In the process of choosing the best scheme in the artificial intelligence algorithm, it is impossible to accurately judge the nonlinear relationship between the innovation strategy and the film and television postproduction scheme. An improved artificial intelligence algorithm based on the integration of dynamic factors and the artificial intelligence algorithm is proposed to reduce the disturbance ability of the artificial intelligence algorithm and improve the analysis level of film and television postproduction and innovation strategy. Firstly, the initial innovation strategy set of the production set is established by using dynamic factors, which makes it discrete and reduces the influence of the scheme selection error on the results. Then, the production set is divided into dynamic subproduction sets by using the film and television production theory, and each subproduction set seeks its own parallel innovation strategy. Finally, under the guidance of film and television production theory, each subproduction set shares the matching of optimal solutions. Through MATLAB simulation analysis and verification, the improved dynamic artificial intelligence algorithm can improve the accuracy of judging the innovation strategy of film and television works in an uncertain environment and shorten the convergence time of global feature solution and is superior to the original selection method of film and television production strategy. In addition, under that condition the initial weight scheme and the threshold scheme are set. The artificial intelligence algorithm is used to analyze the innovation strategy selection of youth idol works. The results show that under different film and television production requirements, the innovation strategy selection judgment of the artificial intelligence algorithm is accurate and superior to the original film and television production strategy selection method, which further verifies the effectiveness of the artificial intelligence algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

In the recent years, the developmental speed of intelligent technology continues to accelerate, and the research on the actual needs of users is also in depth. From the current situation of the clothing industry, how to combine artificial intelligence (AI) technology with clothing fashion has become the focus of customer's attention. The application of intelligent clothing matching recommendation system (online) can effectively meet the needs of customers in dressing matching, so as to save a lot of time and energy (offline). With the maturity of artificial intelligence, machine learning, and other emerging computational technologies, the intelligent clothing matching system has laid a solid foundation. In this paper, several intelligent clothing matching recommendation systems that have been applied at present are deeply analyzed. Moreover, the basic algorithms and key technologies are elaborated in detail. In addition, the future research direction is found, so that the clothing matching recommendation system can be more personalized, and the comprehensive function is greatly improved in order to bring more ideal benefits. [ABSTRACT FROM AUTHOR]

Aluminium alloys are gaining popularity in a diversity of engineering applications because of their extraordinary features such as strength, resistance to oxidation, and so on. AA5052 (Al-Mg series) is generally used in antirust uses, particularly in desalination related activities, due to its better resistance to corrosion in marine applications at temperature ranges up to 125°C, lower cost, better heat-carrying capacity, and nontoxicity of its corrosion components. Drilling is one of the most commonly adopted material removal processes that is adopted in numerous engineering uses. Taguchi's technique is engaged to arrange and examine the tests, by treating the drilling diameter and speed and as independent process factors. The studies were carried out using an L27 orthogonal array. Material removal rate (MRR), surface roughness (SR), and form/orientation error are deemed as output characteristics. Taguchi's analysis was engaged to discover the best process factors. ANOVA is used to examine the influence of process variables. Suitable application of artificial intelligence tools for making effective decision assists the manufacturer in accomplishing the benefits in numerous engineering domains. To obtain the maximum material removal and minimum roughness, circularity (circ), and perpendicularity errors (perp), the process variables have been optimized with the help of grey-ANFIS-amalgamated with Jaya algorithm. The multiperformance index was developed using grey theory. Statistical error analysis is used to estimate the performance of the established optimization model. Based on the investigative outcomes, the best-suited process variable combinations will be used to provide improved and enhanced multiperformance characteristics. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence and big data, the concept of "Internet plus education" has gradually become popular, including automatic scoring system based on machine learning. Countries all over the world vigorously promote the deep integration of information technology and discipline teaching in various fields. English is a medium of communication in the current era of education information development trend. English composition automatic scoring mode is gradually accepted by the majority of educators and applied in the actual classroom teaching. However, the research of English composition automatic grading in teaching space is not perfect. Most systems have used traditional algorithms. Therefore, this paper constructs the automatic scoring algorithm and sentence elegance feature scoring algorithm of English composition based on machine learning, explores the influence of the algorithm on English writing teaching, and proves the correctness of the design idea and algorithm of this paper through a lot of experiments. [ABSTRACT FROM AUTHOR]

At present, big data related technologies are developing rapidly, and major companies provide big data analysis services. However, the big data analysis system formed by the combination method cannot sense each other and lacks cooperation, resulting in a certain amount of waste of resources in the big data analysis system. In order to find the key technology of the data analysis system and conduct in-depth analysis of the media data, this paper proposes a scheduling algorithm based on artificial intelligence (AI) to implement task scheduling and logical data block migration. By analyzing the experimental results, we know that the performance of LAS (Logistic-Block Affinity Scheduler) is improved by 23.97%, 16.11%, and 10.56%, respectively, compared with the other three algorithms. Based on real new media data, this article analyzes the content of media data and user behavior in depth through big data analysis methods. Compared with other methods, the algorithm model in this paper optimizes the accuracy of hot topic extraction, which has important implications for media data mining. In addition, the analysis results of the emotional characteristics, audience characteristics, and hot topic communication characteristics obtained by the research also have practical value. This method improves the recall rate and F value by 5% and 4.7%, respectively, and the overall F value of emotional judgment is about 88.9%. [ABSTRACT FROM AUTHOR]

This paper constructs an algorithm for youth school violence recognition and an occupational therapy education model for victims through the extraction of action speech features. For the characteristics of violent actions and daily actions, action features in time and frequency domains are extracted and action categories are recognized by BP neural network; for complex actions, it is proposed to decompose complex actions into basic actions to improve the recognition rate; then, LDA dimensionality reduction algorithm is introduced for the problem of the high complexity of algorithm due to high dimensionality of features, and the feature dimensionality is reduced to 8 dimensions by LDA dimensionality reduction algorithm, which reduces the system running time by about 51% and improves the accuracy of violent action recognition by 3.3% while ensuring the overall performance of the system. The LDA dimensionality reduction algorithm reduces the number of features to 8 dimensions, which reduces the running time of the system by 51%, increases the accuracy rate of violent action recognition by 3.3%, and increases the recall rate of violent action recognition by 8.86% while ensuring the overall performance of the system. Based on the classical D-S theory, we proposed an improved D-S evidence fusion algorithm by modifying the original evidence model with a new probability distribution function and constructing new fusion rules, which can solve the fusion conflict problem well. The recall rate for violent actions is increased to 90.0%, thus reducing the missed alarm rate of the system. [ABSTRACT FROM AUTHOR]

Smart tourism recommendation refers to supplementing suggestions for tourists' self-service sightseeing plans based on Internet technology, and recommending more effective and practical tourism information. This paper uses big data and artificial intelligence algorithms to study the smart tourism recommendation method in Southeast Asia. First of all, this paper gives a brief overview of the definition and classification of big data and artificial intelligence algorithms and then introduces tourism resources and smart tourism in Southeast Asia. Finally, this paper conducts a comparative experiment between the smart tourism development model based on big data and artificial intelligence algorithms and the traditional tourism development model and analyzes the utility of the development model from three aspects, namely, tourists' sense of experience and satisfaction, the scale of tourism transactions and the growth rate of tourism revenue, and the adequacy and harmony of tourism resource allocation. The final experimental results show that the overall average value of tourists' experience and satisfaction under the smart tourism recommendation mode based on big data and artificial intelligence algorithms is 88.84 points, which is 7.30 points higher than the traditional tourism mode, which verifies its effectiveness. [ABSTRACT FROM AUTHOR]

The application of mobile robots and artificial intelligence technology has shown great application prospects in many fields. The ability of intelligent obstacle avoidance is the basis for the deep application of mobile robots. However, there are often more or less uncertain factors in the actual operating environment of the robot, such as people or objects that are not updated in time or temporarily appear. Therefore, it is an important step to complete the automatic learning of obstacle avoidance for mobile robots. In a nondeterministic environment, a mobile robot intelligent obstacle avoidance algorithm based on an improved fuzzy neural network with self-learning is firstly proposed. The mobile robot intelligent obstacle avoidance system is constructed through the reaction layer, the deliberation layer, and the supervision layer. Through the analysis of sensor performance, model accuracy, path obstacle avoidance optimization, and obstacle avoidance simulation, the following conclusions are drawn. First, through network training, the accuracy rate of the test set is stable at 98%, and the loss of the function value has also been reduced from the original 0.79 to 0.08, which is 10 times smaller. Second, the traditional single sensor cannot meet the obstacle avoidance requirements of robots, and mobile robots must combine multipurpose technology. Third, the algorithm in this paper encounters the following. When there are obstacles, the path is dominated by straight lines, obstacle avoidance planning is optimal, and the distance is shorter. Fourth, the larger N : M, the larger the solution space, indicating that this algorithm gradually improves the search efficiency to the greatest extent and can handle any form of medium and large scale task allocation problem. [ABSTRACT FROM AUTHOR]

In today's information society, network security is a crucial issue. Network security technology is changing as a result of the development of emerging technologies such as big data, cloud computing, and artificial intelligence. Data-driven (DD) security has emerged as a new network security technology development direction. The key technologies for DD network security are discussed in depth in this paper. A data security protection system is designed from the perspective of ZT, based on advanced security concepts and technologies developed by ZT, as well as a foreign data security governance framework. The number of alarms generated per hour is counted, removal rules are defined, and real-time rule matching is performed to eliminate false alarms based on different combinations of attributes. By analyzing the security data generation rules and internal relations, a security aggregation method can reduce redundant data and improve alarm quality. [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]

With the popularization of driverless technology, more and more vehicles have begun to apply this technology. Under these circumstances, user satisfaction with driverless function begins to have an increasing impact on car sales. This paper adopts the KANO model, a two-dimensional model, to analyze users' attitudes toward driverless functions. It is found that users have a high dependence on functions such as forward collision warning, autonomous emergency braking, and stated-speed sign recognition. Therefore, relevant enterprises should continue to develop these functions. Besides, users have high expectations for functions such as blind spot detection systems and rear cross-traffic alerts. Enterprises can achieve more support from users by optimizing these functions. Functions like lane-keeping assist, lane departure warning, parking distance control, and door open warning systems belong to indifferent attributes of driverless function, which are not cared about by users. There is no need for enterprises to optimize these functions. However, the lane change assist system has been criticized by users and should be improved by corresponding manufacturers. [ABSTRACT FROM AUTHOR]

The sudden arrival of the new crown epidemic has had a significant and long-lasting impact on the division's economic environment as well as the production and operation activities of businesses. As far as the financial management is concerned, opportunities and difficulties are faced by enterprises of all types. With reference to the available research data, enterprises have an important contribution to GDP and jobs, but they still face a series of difficulties and challenges in their development in the context of the normalization of the epidemic. By analyzing the impact of the new crown pneumonia epidemic on the financial management work of enterprises, this paper proposes an artificial neural network-based enterprise financial forecasting and early warning method to provide an effective method for enterprise financial management. For the time-series characteristics of enterprise finances, a prediction model based on long- and short-term memory networks is developed which acknowledges the necessity of combining the temporal dimension with the spatial dimension for forecasting. This model incorporates time qualities into the data to the existing forecasting model. It also considers both working and nonworking day data and thoroughly considers the factors influencing corporate finance. Then, using BP neural network for financial risk prediction, nonfinancial index factors should be added to the financial early warning model thus eliminating the limitations of the financial early warning model. At the same time, the accuracy of the prediction can be improved which is more suitable for enterprises to apply in practice. The experimental results demonstrate that the financial prediction model built by multilayer feed forward neural networks and recurrent neural networks based on error back propagation training is inferior to the prediction model built by long- and short-term memory network. Regardless of the degree of fitting or prediction accuracy, the BP neural network model outperforms the conventional model for enterprise financial warning. Under the normalization of the pandemic, the combined use of both can offer an efficient technique for enterprise management. [ABSTRACT FROM AUTHOR]

Studies have shown that most patients after PCI cannot adhere to the cardiac rehabilitation program. The survey found that due to the lack of secondary prevention of cardiac rehabilitation, the phenomenon of drug reduction and withdrawal after PCI is very common after discharge, leading to recurrence of the patient's disease or worsen and repeated hospitalizations, so continuity of care is very important. In this paper, in addition to proposing a network care continuum with artificial intelligence handler in order to improve the healthcare system and provide new ideas for improving the postoperative recovery of CHD patients, we analyze the impact of PCI on the coronary heart disease patients' PCI postoperative quality of life. In the method part, this article introduces the concepts of continuation care and PCI after surgery, introduces the marker delivery algorithm in the field of artificial intelligence, and introduces the SF-36 scale for patient quality of life analysis. This article designs an experiment combining artificial intelligence processors to carry out network continuity care for patients and divides 100 eligible patients into an experimental group and a control group. In the analysis part, the two groups of patients were analyzed in terms of general data comparison, physical function, biochemical indicators, quality of life, and dependence. It can be seen from the experimental analysis that the anxiety and depression of the two groups of subjects have different degrees of decline. The HAMA value of the experimental group is 9.06 ± 0.77 , and the HAMD value is 9.18 ± 1.20 , which is significantly lower than that of the control group, P < 0.05. It can be seen that the use of network continuation care can reduce the negative emotions of patients more than general care. Through psychological counseling and postoperative follow-up, it can improve the optimism and positive emotions of the patients, reduce the negative emotions of the patients, and improve it to a certain extent. [ABSTRACT FROM AUTHOR]

This paper analyzes the simulation of special effects animation through fluid equations and data-driven methods. This paper also considers the needs of computer fluid animation simulation in terms of computational accuracy and simulation efficiency, takes high real-time, high interactivity, and high physical accuracy of simulation algorithm as the research focus and target, and proposes a solution algorithm and acceleration scheme based on deep neural network framework for the key problems of simulation of natural phenomena including smoke and liquid. With the deep development of artificial intelligence technology, deep neural network models are widely used in research fields such as computer image classification, speech recognition, and fluid detail synthesis with their powerful data learning capability. Its stable and efficient computational model provides a new problem-solving approach for computerized fluid animation simulation. In terms of time series reconstruction, this paper adopts a tracking-based reconstruction method, including target tracking, 2D trajectory fitting and repair, and 3D trajectory reconstruction. For continuous image sequences, a linear dynamic model algorithm based on pyramidal optical flow is used to track the feature centers of the objects, and the spatial coordinates and motion parameters of the feature points are obtained by reconstructing the motion trajectories. The experimental results show that in terms of spatial reconstruction, the matching method proposed in this paper is more accurate compared with the traditional stereo matching algorithm; in terms of time series reconstruction, the error of target tracking reduced. Finally, the 3D motion trajectory of the point feature object and the motion pattern at a certain moment are shown, and the method in this paper obtains more ideal results, which proves the effectiveness of the method. [ABSTRACT FROM AUTHOR]

With the rapid development of information society, a large amount of vague or uncertain English interpretation information appears in daily life. Uncertain information processing is an important research content in the field of artificial intelligence. In this paper, we combine the three-branch concept lattice and linguistic values with the digital elevation model and propose the three-branch fuzzy linguistic concept lattice as well as the attribute approximation method. In this paper, an improved serial algorithm for sink accumulation is proposed. The improved algorithm changes the order of cell calculation; after the cumulative amount of a "sub-basin" is calculated, all cells of the next "sub-basin" are calculated until all cells are calculated. The improved algorithm reduces the overhead space in the calculation process, reduces the pressure of cells entering and leaving the queue, and improves the calculation efficiency. The improved cumulant algorithm is compared with the commonly used recursive cumulant algorithm and the nonrecursive cumulant algorithm, and the improved algorithm improves by about 17% compared with the nonrecursive algorithm at 106 cell level, and the computation time of the recursive algorithm is about 3 times of the improved algorithm. Because the sink accumulation serial algorithm is an important part of the parallel calculation of sink accumulation, and the execution time is shorter by using the improved algorithm, this paper applies the proposed improved accumulation serial algorithm to the process of parallel calculation of accumulation. [ABSTRACT FROM AUTHOR]