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Sustainable concepts for on-demand transportation, such as ridesharing or ridehailing, require advanced technologies and novel dynamic planning and prediction methods. In this paper, we consider the prediction of taxi trip durations, focusing on the problem of the estimated time of arrival (ETA). ETA can be used to compute and compare alternative taxi schedules and to provide information to drivers and passengers. To solve the underlying hard computational problem with high precision, machine learning (ML) models for ETA are the state of the art. However, these models are mostly black box neural networks. Hence, the resulting predictions are difficult to explain to users. To address this problem, the contributions of this paper are threefold. First, we propose a novel stacked two-level ensemble model combining multiple ETA models; we show that the stacked model outperforms state-of-the-art ML models. However, the complex ensemble architecture makes the resulting predictions less transparent. To alleviate this, we investigate explainable artificial intelligence (XAI) methods for explaining the first- and second-level models of the ensemble. Third, we consider and compare different ways of combining first-level and second-level explanations. This novel concept enables us to explain stacked ensembles for regression tasks. The experimental evaluation indicates that the considered ETA models correctly learn the importance of those input features driving the prediction. [ABSTRACT FROM AUTHOR]

AI technologies transform language instruction by offering feedback, support, and guidance to students, ultimately leading to a more effective and efficient learning experience. The present study investigated the impacts of integrating Writerly and Google Docs to enhance EFL writing instruction. It also assessed students' perceptions towards using these AI technologies. The study employed a quasiexperimental pretest-posttest two-group design. It used a mixed-methods approach, utilizing tests, questionnaires, focus group discussions, and teacher diaries to gather data from a sample of 92 randomly selected participants. In the experimental group, students enhanced their writing skills through the integration of Writerly and Google Docs, while the control group students received instruction using the traditional paper and pencil feedback system. When the quantitative data were analyzed through independent samples T -test and descriptive statistics, the qualitative data were analyzed thematically. The results confirmed that the integration of Writerly and Google Docs AI technologies, significantly improved EFL writing instruction, as evidenced by a statistically significant difference in writing performance between the experimental and control groups. Hence, students who learned through the integration of Writerly and Google Docs showed improved writing performance as they were able to produce essays that effectively addressed task achievement, coherence and cohesion, lexical resource, and grammatical range and accuracy, whereas those who learned through the conventional method were less effective in producing quality essays. The findings also revealed that the experimental group students had positive perceptions towards integrating Writerly and Google Docs because they found these AI writing technologies interesting, effective, goal-oriented, and supportive. Consequently, this study recommends researchers, curriculum designers, material designers, teachers, and students pay due attention to Writerly and Google Docs. [ABSTRACT FROM AUTHOR]

Handmade is an important part of preschool education, which was aimed at improving children's ability to work with their hands. Preschool education is the most basic and important aspect of a country's educational system. As a result, individuals pursuing a degree in preschool education take on a lot of responsibility. The preschool education handmade course has become an important component of preschool education due to its practicality and creativity. Preschool education major offers classes in traditional crafts such as paper cutting, paper dyeing, origami, paper three-dimensional modeling, and ornamental painting. The teaching methods for custom-made preschool education courses are always evolving with the progress of society. The question of how to assess the efficacy of unique teaching methodologies for handmade courses has become crucial. This study employs artificial intelligence to create a neural network for assessing the creativity of teaching approaches for handmade courses in preschool education. The following is the specific work: Firstly, the idea, as well as the benefits and drawbacks of genetic algorithms, is investigated. To build an improved genetic algorithm (IGA), the chromosome encoding, fitness function, and three operation options are enhanced. Secondly, by improving the genetic algorithm, the selection of weights and thresholds in the BP neural network model is improved, and a combined model (IGA-BP) is created by integrating the improved genetic algorithm with the BP network. Finally, rigorous and systematic tests confirm the work's efficacy and viability. [ABSTRACT FROM AUTHOR]

Flight delays are a major concern for both travelers and airlines, with significant financial and reputational consequences. Accurately predicting flight delays is crucial for enhancing customer satisfaction and airline revenues. In this paper, we leverage the power of artificial intelligence and machine learning techniques to build a framework for accurately predicting flight delays. To achieve this, we collected flight information from September 2017 to April 2023, along with weather data, and performed extensive feature engineering to extract informative features to train our model. We conduct a comparative analysis of various popular machine learning architectures with distinctive characteristics, aiming to determine their efficacy in achieving optimal accuracy on our newly proposed dataset. Based on our evaluation of various architectures, our findings demonstrate that CatBoost outperformed the others by achieving the highest test accuracy and the lowest error rate in the challenging use case of Saudi Arabia. Moreover, to simulate real-world scenarios, our framework evaluates the best-performing model that has been selected for deployment in a web application, which provides users with the ability to accurately forecast flight delays and offers a user-friendly dashboard with valuable insights and analysis capabilities. [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]

Deoxyribonucleic acid (DNA) can be considered as one of the most useful biometrics. It has effectively been used for recognizing persons. However, it seems that there is still a need to propose a new approach for verifying humans, especially after the recent big wars, where too many people lost and die. This approach should have the capability to provide high personal verification performance. In this paper, a personal recognition approach based on artificial intelligence is proposed. This approach is called the artificial DNA algorithm for recognition (ADAR). It utilizes a unique identity for each person acquired from DNA nucleotides, and it can verify individuals efficiently with high performance. The ADAR has been designed and applied to multiple datasets, namely, the DNA classification (DC), sample DNA sequence (SDS), human DNA sequences (HDS), and DNA sequences (DS). For all datasets, a low value of 0% is achieved for each of the false acceptance rate (FAR) and false rejection rate (FRR). [ABSTRACT FROM AUTHOR]

Artificial intelligence agents (chatbots), which are programs to communicate with users primarily in customer service contexts, are an alternative interaction channel supporting businesses in the digital environment and vital components in customer service. The present empirical paper, which is aimed at identifying and discussing the factors motivating nonusers to adopt the specific technology in mobile contexts, proposes a comprehensive conceptual model, which combines the UTAUT 2 behavioral theory with variables of mobile service quality contexts, such as information quality, privacy concerns, interface, and equipment, as well as trust and mobility factors. Data analysis, based on the partial least squares structural equation modeling (PLS-SEM) statistical method, revealed that performance expectancy, facilitating factors, hedonic motivation, mobility, trust, and service quality positively affect nonusers' behavioral intention to adopt chatbots. In addition, equipment, interface, and trust have a significant impact on users' trust in the context of mobile chatbots. Personal data privacy issues also have a negative effect on trust, in contrast to effort expectancy, which positively affects performance expectancy. As mobile service quality factors have not been investigated before in the context of chatbots, the findings of the present research are expected to provide useful insights both to academia and the business industry. [ABSTRACT FROM AUTHOR]

Digital tools and apps are revolutionizing healthcare and provide creative answers to urgent problems. Through teamwork and the incorporation of digital technologies, dentistry has experienced a remarkable revolution. A large body of scholarly research backs up this trend. The context, trends, and content of digital health technology in oral and dental health are examined in our bibliometric analysis. Using targeted keywords and synonyms, an organized searching technique was used in the Scopus database, yielding 1942 articles that were extracted into a CSV file. To acquire insights into the content, trends, and context, visualization using VOSviewer 1.6.18 and a variety of analyses—including coauthorship, citation, cooccurrence of author keywords, bibliographic coupling, and cocitation—were executed. The analysis revealed that the USA and the UK contributed to a significant quantity of the literature, with newer contributions coming from nations like India. Cone Beam Computed Tomography, Dental Caries, and Artificial Intelligence were prominent keywords. It is important to note that BMC Oral Health was associated with a sizable number of the papers. This bibliometric analysis provides insightful information about the context, content, and trends of digital health in the field of oral and dental health. By implementing the right technology, policymakers can use this information to increase oral health, encourage dental literacy, and improve access to dental treatment. It is vital to take into account the wide variety of technologies and their classifications based on dental services and contextual variables. [ABSTRACT FROM AUTHOR]

This paper seeks to improve mobile communications performance by integrating artificial intelligence (AI) techniques. The study is centered on device-to-device (D2D) communication, which has emerged as a significant aspect in the 5th generation of mobile networks (5G) and is expected to be extended to the 6th generation (6G). Based on the fuzzy system, the proposed solution tackles the critical issue of energy harvesting in D2D communication. The solution presents a support system that selects the best relay for the transmitter-receiver communication to minimize energy consumption and maximize the network's lifetime. Minimizing energy consumption optimizes the network's lifetime, thus providing a reliable and efficient communication system. The approach taken in this study offers a novel perspective in addressing the energy harvesting challenge in D2D communication. It is expected to have a significant impact on the performance of mobile communications. [ABSTRACT FROM AUTHOR]

Energy consumption from biofuels against fossil fuels over the past few years has increased. This is due to the availability of these resources for production of different forms of energy, and the environmental benefit in the utilization of these resources. Ethanol fuel production from biomass is a complex process of known challenges in the area of handling, optimizing, and future forecasting. The existence of modelling techniques like artificial intelligence (AI) is, therefore, necessary in the design, handling, and optimization of bioethanol production. The flexibility and high accuracy of artificial neural network (ANN), a machine learning technique, to solve intricate processes is beneficial in modelling pretreatment, fermentation, and conversion stages of a bioethanol production system. This paper reviews various AI techniques in bioethanol production giving emphasis on published articles in the past decade. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) proves decisive in today's rapidly developing society and is a motive force for the evolution of financial technology. As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively used in all aspects of the daily operation and development of the supply chain. Using data mining, deductive reasoning, and other characteristics of machine learning algorithms can effectively help decision-makers of enterprises to make more scientific and reasonable decisions by using the existing financial index data. At present, globalization uncertainties such as COVID-19 are intensifying, and supply chain enterprises are facing bankruptcy risk. In the operation process, practical tools are needed to identify and opportunely respond to the threat in the supply chain operation promptly, predict the probability of business failure of enterprises, and take scientific and feasible measures to prevent a financial crisis in good season. Artificial intelligence decision-making technology can help traditional supply chains to transform into intelligent supply chains, realize smart management, and promote supply chain transformation and upgrading. By applying machine learning algorithms, the supply chain can not only identify potential risks in time and adopt scientific and feasible measures to deal with the crisis but also strengthen the connection and cooperation between different enterprises with the advantage of advanced technology to provide overall operation efficiency. On account of this, the paper puts forward an artificial intelligence-based corporate financial-risk-prevention (FRP) model, which includes four stages: data preprocessing, feature selection, feature classification, and parameter adjustment. Firstly, relevant financial index data are collected, and the quality of the selected data is raised through preprocessing; secondly, the chaotic grasshopper optimization algorithm (CGOA) is used to simulate the behavior of grasshoppers in nature to build a mathematical model, and the selected data sets are selected and optimized for features. Then, the support vector machine (SVM) performs classification processing on the quantitative data with reduced features. Empirical risk is calculated using the hinge loss function, and a regular operation is added to optimize the risk structure. Finally, slime mould algorithm (SMA) can optimize the process to improve the efficiency of SVM, making the algorithm more accurate and effective. In this study, Python is used to simulate the function of the corporate business finance risk prevention model. The experimental results show that the CGOA-SVM-SMA algorithm proposed in this paper achieves good results. After calculation, it is found that the prediction and decision-making capabilities are good and better than other comparative models, which can effectively help supply chain enterprises to prevent financial risks. [ABSTRACT FROM AUTHOR]

Artificial intelligence and deep learning have attracted much attention from researchers in industry and academia. The volleyball movement standardization and recognition model involve the application of artificial intelligence and deep learning. In order to solve the problem that human action in volleyball video is continuous and effective spatial and temporal features need to be extracted from the video stream, the Inception module is decoupled and heterogeneous, replacing the original 5 × 5 convolutional structures with two 3 × 3 convolutional structures, as well as replacing the 3 × 3 convolutional structures with 1 × 3 and a 3 × 1 convolutional structure with internal parameter optimization to ensure the accuracy of recognition. The model uses the input motion video RGB map as the spatial input and the optical flow map as the temporal input, and the two are weighted 1 : 1 for feature fusion. Experiments are conducted on the volleyball action video and homemade dataset in UCF101, and the experimental data show that the accuracy of the DNet volleyball action standardization recognition model proposed in this paper is 94.12%, which proves that the method improves the recognition ability of the model while speeding up the training speed. The research presented in this paper provides important theoretical guidance for artificial intelligence and deep learning. [ABSTRACT FROM AUTHOR]

Congenital heart defect (CHD) refers to the overall structural abnormality of the heart or large blood vessels in the chest cavity. It is the most common type of fetal congenital defects. Prenatal diagnosis of congenital heart disease can improve the prognosis of the fetus to a certain extent. At present, prenatal diagnosis of CHD mainly uses 2D ultrasound to directly evaluate the development and function of fetal heart and main structures in the second trimester of pregnancy. Artificial recognition of fetal heart 2D ultrasound is a highly complex and tedious task, which requires a long period of prenatal training and practical experience. Compared with manual scanning, computer automatic identification and classification can significantly save time, ensure efficiency, and improve the accuracy of diagnosis. In this paper, an effective artificial intelligence recognition model is established by combining ultrasound images with artificial intelligence technology to assist ultrasound doctors in prenatal ultrasound fetal heart standard section recognition. The method data in this paper were obtained from the Second Affiliated Hospital of Fujian Medical University. The fetal apical four-chamber heart section, three vessel catheter section, three vessel trachea section, right ventricular outflow tract section, and left ventricular outflow tract section were collected at 20-24 weeks of gestation. 2687 image data were used for model establishment, and 673 image data were used for model validation. The experiment shows that the map value of this method in identifying different anatomical structures reaches 94.30%, the average accuracy rate reaches 94.60%, the average recall rate reaches 91.0%, and the average F1 coefficient reaches 93.40%. The experimental results show that this method can effectively identify the anatomical structures of different fetal heart sections and judge the standard sections according to these anatomical structures, which can provide an auxiliary diagnostic basis for ultrasound doctors to scan and lay a solid foundation for the diagnosis of congenital heart disease. [ABSTRACT FROM AUTHOR]

Recent research has been focussed on renewable energy due to the rising need for electrical energy. Renewable energy has a low environmental impact compared to other energy sources. As a result, renewable energy sources (RESs) are the best option for generating electricity. Solar photovoltaic is one of the largest renewable power generators. Solar photovoltaic (PV) is connected to the load via power electronic converters. Most PV installations need a two-stage conversion process consisting of a boost converter to increase the load voltage and an AC-to-DC voltage source inverter to power the load. The Z-source inverter (ZSI) can confront the shortcomings of VSI and two-stage conversions. ZSI connects the PV system to the load and is used to increase the system's performance. This paper discusses the performance of various topologies of ZSI, such as traditional Z-source inverters (XZSIs); for integrating a PV source into a load, switched inductor Z-source inverters (SIZSIs) and transient Z-source inverters (TZSIs) are used. Also, artificial neural networks (ANNs), fuzzy logic controller (FLC), and adaptive neuro-fuzzy inference system (ANFIS)-based MPPT techniques are discussed for obtaining maximum power from PV panels. Based on the maximum power, the shoot-through duty ratio has been adjusted. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has received much attention in the domain of railway traffic planning and management (TPM) from academia and industries. While many promising applications have been reported, there remains a lack of detailed review of the many AI models/algorithms and their uses and adaptations in rail TPM. To fill this gap, this systematic literature review conducts, reports, and synthesizes the state-of-the-art of AI applied in railway TPM from four perspectives, i.e., the intersection between AI research fields (e.g., expert systems, data mining, and adversarial search) and rail TPM, the intersection between AI techniques (e.g., evolutionary computing and machine learning) and rail TPM, the intersection between AI applications (e.g., operations research, scheduling, and planning) and rail TPM, and the intersection between AI related disciplines (e.g., big data analytics and digital twins) and rail TPM. The study evaluates 95 research papers published during 1970–2022. Accordingly, a comprehensive synthesis of each intersection between AI and rail TPM is presented, and the practical roadmap for application of AI in rail TPM is proposed. Furthermore, the study identifies the research gaps and areas that need more investigation. The contribution helps researchers and practitioners to get a better understanding of the status quo of research stream, research development trends, and challenges for further related study. [ABSTRACT FROM AUTHOR]

Recently, artificial intelligence (AI) technology has great attention in transportation systems, which led to the emergence of a new concept known as Internet of Vehicles (IoV). The IoV has been associated with the IoT revolution and has become an active field of research due to the great need, in addition to the increase in the various applications of vehicle communication. AI provides unique solutions to enhance the quality of services (QoS) and performance of IoV systems as well. In this paper, some concepts related to deep learning networks will be discussed as one of the uses of machine learning in IoV systems, in addition to studying the effect of neural networks (NNs) and their types, as well as deep learning mechanisms that help in processing large amounts of unclassified data. Moreover, this paper briefly discusses the classification and clustering approaches in predicative analysis and reviews their abilities to enhance the performance of IoV application systems. [ABSTRACT FROM AUTHOR]

Summary: Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort toward the provision of uninterrupted power to the end‐users is strongly dependent on an effective and fault‐resilient Operation & Maintenance (O&M) system. Ingenious algorithms and techniques are hence been introduced aiming to minimize equipment and plant downtime. Efforts are being made to develop robust prognostic maintenance systems that can identify the faults before they occur. To this aim, complex Data Analytics and Artificial Intelligence (AI) algorithms are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. We pay a particular focus to the approaches, challenges, including data‐related issues, such as the availability of quality data and data auditing, feature engineering, interpretability, and security issues. Being a key aspect of ML‐based solutions, we also discuss some of the commonly used publicly available datasets in the domain. The paper also identifies the key future research directions to further enhance the prognostics maintenance procedures. [ABSTRACT FROM AUTHOR]

The world economy is developing rapidly. Behind the rapid development, the management of economic regions is very important. In the process of economic management, the government plays the role of macrocontrol and is responsible for the management of various economic affairs and social and economic services. While undertaking infrastructure construction, it creates a good environment for economic development. However, with the deepening of economic development and the more and more complex economic data, the current economic management has gradually exposed a range of issues that arise during the process of economic development, and these problems need to be solved urgently. At this time, the application scope of artificial intelligence in the economic field is getting wider and wider, and it has a great positive effect on economic development. Therefore, in order to solve the problem of economic management in the process of economic development, this paper proposes a development path that integrates AI and economic management and provides intelligent technology support for the development of economic management to help the smooth operation of economic development. In addition, this paper shows through experiments that the path of integration of AI and economic management can promote the development and smooth operation of the economy, and AI has a positive impact on economic management. [ABSTRACT FROM AUTHOR]

In order to explore the correlation between ICT development and consumer spending, this paper uses artificial intelligence and time series econometric models to study the correlation between ICT development and consumer spending. Moreover, this paper organically combines the advantages of wavelet analysis and hidden Markov model to construct a wavelet domain hidden Markov chain model. It is used to examine the flow of information on different scales related to the development of communication technology and consumer spending, so as to infer the potential mechanism of the interaction of different traders' behaviors from the other side. Through cluster analysis, it can be seen that the correlation analysis method of information and communication technology development and consumption expenditure based on artificial intelligence and time series econometric model proposed in this paper has certain reliability. At the same time, there is a strong correlation between the development of communication technology and consumer spending. [ABSTRACT FROM AUTHOR]

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]

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]

Through the research and development of the regression prediction function of support vector machine, this paper applies it to the prediction of drilling fluid performance parameters and the formulation design of drilling fluid. The research in this paper can reduce the experimental workload and improve the efficiency of drilling fluid formulation design. The apparent viscosity (AV), plastic viscosity (PV), API filter loss (FLAPI), and roll recovery (R) of the drilling fluid were selected as the inspection objects of the drilling fluid performance parameters, and the support vector machine was used to establish a model for predicting the drilling fluid performance parameters. This predictive model was used as part of the overall drilling fluid formulation optimization design model. For a given drilling fluid performance parameter requirement, this model can be applied to reverse the addition of various treatment agents, and finally, the prediction accuracy of the model is verified by experiments. [ABSTRACT FROM AUTHOR]

The rapid development of Internet of things mobile application technology and artificial intelligence technology has given birth to a lot of services that can meet the needs of modern life, such as augmented reality technology, face recognition services, and language recognition and translation, which are often applied to various fields, and some other aspects of information communication and processing services. It has been used on various mobile phone, computer, or tablet user clients. Terminal equipment is subject to the ultralow latency and low energy consumption requirements of the above-mentioned applications. Therefore, the gap between resource-demanding application services and resource-limited mobile devices will bring great problems to the current and future development of IoT mobile applications. Based on the local image features of depth images, this paper designs an image detection method for athletes' motion posture. First, according to the characteristics of the local image, the depth image of the athlete obtained through Kinect is converted into bone point data. Next, a 3-stage exploration algorithm is used to perform block matching calculations on the athlete's bone point image to predict the athlete's movement posture. At the same time, using the characteristics of the Euclidean distance of the bone point image, the movement behavior is recognized. According to the experimental results, for some external environmental factors, such as sun illumination and other factors, the image detection method designed in this paper can effectively avoid their interference and influence and show the movement posture of athletes, showing excellent accuracy and robustness in predicting the movement posture of athletes and action recognition. This method can simplify a series of calibration tasks in the initial stage of 3D video surveillance and infer the posture of the observation target and recognize it in real time. The one that has good application values has specific reference values for the same job. [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]

A logical and appropriate job advising system for college students is the only way to address the current dire work scenario for recent graduates in a society that is becoming more and more competitive. The process of building new career advice systems for college students is still fraught with many major issues. Existing problems are manifested in the lack of a perfect employment guidance system for college students and a lack of effective ideological and political education, which leads to the inability of college students to establish a correct employment concept. At the same time, the recommendation method used in the current college student employment guidance system is not reasonable enough and only considers students' one-way preference for employment units. Then, the history of one user's employment is likely to lead to unreliable negative samples, which affects the recommendation performance. In addition, the current employment recommendation system cannot fully integrate the service function, management function, education function, and research function of the employment guidance system. In this paper, we address the aforementioned issues and propose an unique career counseling system for college students by combining algorithmic recommendation with ideological and political education. The system adaptively aggregates the preferences and needs of students and employers and fully integrates several functions of the student career competence and career guidance system. The experimental results show that the establishment of a college students' employment guidance system integrating artificial intelligence (AI) and civic education not only has better interpretability compared with the traditional employment guidance system but also has a higher area under curve (AUC). Furthermore, we also discuss the importance of ideological and political education and referral systems in the work of the career guidance system through ablation experiments. The results show that the AUC values of integrating ideological and political education and recommender systems were higher than those of integrating only ideological and political education or recommender systems. Therefore, establishing an employment guidance system for college students that integrates AI and civic education can effectively guide students' employment. At the same time, the college student employment guidance system proposed in this paper can offer employment guidance courses in colleges and universities, help graduates to solve their doubts, and make graduates adjust their psychological state in the process of job hunting. [ABSTRACT FROM AUTHOR]

With the continuous increase in building types, people's demand for buildings is increasing. In addition to focusing on the work of the building itself, high requirements are also placed on the safety, stability, and durability of the building. Especially in other earthquake-prone areas, it is even more necessary to strengthen seismic isolation research to reduce vibration. Rapid construction of environmental protection walls pays attention to environmental protection and efficiency during construction, which can effectively solve problems in the construction process, but the seismic resistance of rapid construction environmental protection walls is unknown. Therefore, this paper used artificial intelligence technology to study the seismic performance of rapid construction environmental protection walls and analyzed its vibration control. This paper firstly established the seismic performance analysis model of the environmental protection wall, used the finite element method to construct the environmental protection wall model, and then used the artificial intelligence algorithm to analyze the seismic performance of the rapid construction environmental protection wall. The experimental results showed that the peak load of the rapid construction environmental protection wall was about 250 KN, and the seismic performance was good. [ABSTRACT FROM AUTHOR]

Energy Internet (EI) can provide consumers with flexible energy-sharing services. Recently, artificial intelligence (AI) technology has been widely used in the field of EI. Generative adversarial network (GAN) is one of the hottest research directions in the field of AI in recent years, and its excellent data generation ability has attracted wide attention. First, this paper introduces the framework, advantages, disadvantages, and improvement of classic GAN. Then, the application of GAN in the field of EI is reviewed. Finally, the paper is summarized, and the possible application of GAN in EI is prospected. [ABSTRACT FROM AUTHOR]

With the development of digital media technology, its application in teaching and learning is becoming more widespread. Digital media technology helps present information in transmitting knowledge or skills, reduces cognitive load, and promotes understanding of knowledge. Evaluation of the effectiveness of digital media teaching has also become important. A scientific and reasonable evaluation of digital media teaching effectiveness can help teachers select digital media technology and grasp the amount, degree, and timing of digital media use to change teaching effectiveness. This paper proposed using a combination of big data and artificial intelligence methods to evaluate the effectiveness of digital media teaching methods using the RBF neural network model. The digital media teaching effectiveness evaluation was used as the input variable of RBF, the degree of digital media effectiveness was the output variable and the neural network was trained through the collected sample data. The research results showed that the RBF neural network model proposed in this paper has a strong generalization and extension ability in evaluating digital media teaching effectiveness, providing a new way to evaluate digital media teaching effectiveness. [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]

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]

Vision plays an important role in the aesthetic cognition of human beings. When creating dance choreography, human dancers, who always observe their own dance poses in a mirror, understand the aesthetics of those poses and aim to improve their dancing performance. In order to develop artificial intelligence, a robot should establish a similar mechanism to imitate the above human dance behaviour. Inspired by this, this paper designs a way for a robot to visually perceive its own dance poses and constructs a novel dataset of dance poses based on real NAO robots. On this basis, this paper proposes a hierarchical processing network-based approach to automatic aesthetics evaluation of robotic dance poses. The hierarchical processing network first extracts the primary visual features by using three parallel CNNs, then uses a synthesis CNN to achieve high-level association and comprehensive processing on the basis of multi-modal feature fusion, and finally makes an automatic aesthetics decision. Notably, the design of this hierarchical processing network is inspired by the research findings in neuroaesthetics. Experimental results show that our approach can achieve a high correct ratio of aesthetic evaluation at 82.3%, which is superior to the existing methods. [ABSTRACT FROM AUTHOR]

Green smart building is the development direction of future architecture. It is of great significance to carry out risk assessment. Fire risk is the key content of building risk, so this paper takes fire risk as the research object, with the help of artificial intelligence technology, to carry out the risk assessment research of green smart buildings. With the rapid development of the economy, urban fire risk factors are increasing, and the fire situation is becoming more and more serious. Building fire risk assessment is an important measure to effectively prevent and control urban building fires. This paper uses Internet of Things data to carry out fire risk assessment and realize Internet of Things data mining. Collect a large number of expert samples to build training samples, train the green intelligent building monomer fire risk assessment and prediction model based on deep neural network, constantly adjust the model parameters to optimize the model, and finally verify and modify the model. [ABSTRACT FROM AUTHOR]

Higher education question and answer system is a current research hotspot in the field of natural language processing and artificial intelligence, which can return accurate answers directly and also allow users to input in natural language, avoiding the need to return large collections of valid and invalid data links for users to filter twice. In recent years, deep learning techniques have made great progress in the field of natural language processing, thus making it possible to apply it in the field of teaching and learning in junior high school. Most of the traditional quiz systems based on traditional retrieval techniques have problems such as insufficient semantic portrayal of text, inability to extract semantic features in context, and poor processing of complex utterances. In order to fill the gap and deficiencies of question and answer systems in junior high school teaching, this paper applies multimodal and adversarial network-related technologies to build a multimodal adversarial network-based question and answer system for higher education. The experimental results show that the principles of the traditional application system and the multimodal adversarial system designed in this paper are similar, but the current application system is relatively more effective. [ABSTRACT FROM AUTHOR]

This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively. [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]

Martial arts education has a relatively comprehensive educational function. Compared with other educational methods, it has some unique features. When martial arts education carries out moral education, it not only attaches importance to the teaching of moral norms but also requires martial arts practitioners to practice moral norms, so martial arts education is more practical in improving moral literacy. In fact, the role of martial arts education is far from just playing its role in strengthening the body. This kind of prejudice of mindset conceals the diversity characteristics of martial arts education. This paper proposes to apply artificial intelligence technology in martial arts education governance, which uses the target tracking algorithm based on deep learning to track and analyze the movement of martial arts practitioners. At the same time, this paper uses the pose estimation algorithm of coordinate regression to predict the key points of the human body from the global perspective of the human body and then locates the key points of the human body from the features. It greatly simplifies the prediction of key points and solves the problem of nonstandard movements of students in martial arts education. The experimental analysis part includes the results and analysis of the impact of AI-based flipped classroom teaching on students' martial arts learning and the comparison and analysis of students' martial arts learning in the two classes after the experiment. The analysis results show that the P values of the four aspects of learning interest, active participation attitude, independent exploration ability, and analysis and problem-solving ability of the two classes are all less than 0.01, indicating that there is a significant difference. [ABSTRACT FROM AUTHOR]

At present, artificial intelligence algorithms based on deep learning have achieved good results in image classification, biometric recognition, medical diagnosis, and other fields. However, in practice, many times researchers are unable to obtain a large number of samples due to many limitations or high sampling costs. Therefore, image sorting zero-sampling order research algorithms have become the central engine of intelligent processing and a hot spot for current research. Because of the need for the development of deep learning prediction capability, coupled with the emergence of time and technical-level drawbacks, the advantages of zero-sample and small-sample are gradually emerging, so this paper chooses to fuse the learning methods of both for image recognition research. This paper mainly introduces the current situation of zero-sample and small-sample learning and summarizes the learning of zero-sample and small-sample. And the meaning of zero-sample learning and small-sample learning and the classification of the main learning methods are introduced and compared and outlined, respectively. Finally, the methods of zero-sample and small-sample learning are fused, the design is introduced and analyzed, and the future research directions are prospected according to the current research problems. [ABSTRACT FROM AUTHOR]

This paper analyses the design of a healthy interior environment using big data intelligence. The application of big data intelligence in the design of healthy interior environments is necessary because the traditional interior design approaches consume a lot of energy and other problems. Benefited by its strong ability of computation and analytics, artificial intelligence can well improve a series of problems in the field of interior design. The proposal summarizes the sources, classifications, and expressions of behavioral data in interior spaces, carries out analysis and research on behavioral data from two aspects: display space and supermarket space, summarizes the interior methods based on behavioral data, and analyses the visualization application of behavioral data in different interior scenes, to explore the application value of behavioral data in interior design. In contrast to it is the unconscious behavioral response, the biggest characteristic of which is that it is regulated by the behavioral subject's physiological factors or habits of the behavior issuer. In this paper, we convert the layout recommendation problem of a space into a functional classification problem of segmented segments and household segments on a plane. The scene layout features are extracted by binary coding, the abstraction of the cross features between the vector segments is achieved by using a word embedding algorithm, the feature matrix is reduced in dimensionality, and finally, the segmentation network model and the layout network model are constructed, respectively, by using a bidirectional LSTM. The experiments show that the accuracy of the layout recommendation model in this paper is 98%, which can meet the demand for real-time online layouts. [ABSTRACT FROM AUTHOR]

As a special commodity, medicine plays a vital role in people's healthy life. The basic effects of drugs are excitation and inhibition. Under the action of drugs, any function that can enhance or increase the function of the body's original tissues and organs is called excitement. On the contrary, any function that can weaken or reduce the function of the original tissues and organs of the body is called inhibition. The nature of action of drugs has three aspects: regulatory function; antipathogen and antitumor; and complementary therapy. If there is a problem with the quality and safety of medicines, it is tantamount to making money and killing people. Based on artificial intelligence, this paper analyzes the current situation and improvement strategies of quality management in pharmaceutical production management enterprises and proposes how to reduce the risk of drug safety with the assistance of artificial intelligence technology. The experimental results in this paper show that the sales of heparin sodium APIs were 2.099 billion yuan, accounting for 91.6% of the operating income in 2015, when company A had not conducted a drug risk assessment in 2015. After the outbreak of drug risks, the sales in 2016 were 1.743 billion yuan, accounting for 77.1% of the operating income in 2016. After the final implementation of the measures, the sales in 2019 were 4.743 billion yuan, accounting for 329.1% of the operating income in 2016. The research method in this paper can improve the hidden safety problems of drugs more efficiently, and it can improve the profit while ensuring the safety. [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]

When studying intelligent community and property management system, network service has an important impact on intelligent community construction and property management service. How to use machine learning and other technologies to improve the network service of intelligent community and integrate it into real estate property management is worthy of further research. This paper introduces the basic model of machine learning and proposes a network data prediction model based on time series. For the time dimension, an improved prediction algorithm model of machine learning is proposed. For mobile data allocation, from the perspective of ensuring the current and future continuity of the spectrum after spectrum allocation, this paper proposes a spectrum allocation algorithm based on the joint measurement of time domain and frequency domain. In addition, the VP-tree algorithm is used to construct the spatial vector relationship of the intelligent community. At the same time, in the time trend and periodicity of the mobile data in the intelligent community network, the attention mechanism is introduced to realize the distribution of mobile data and traffic prediction in the intelligent community by machine learning. This paper analyzes the requirements of the property management system, designs the property management information system including the field subsystem layer, data acquisition layer, and cloud service layer, introduces the property management module and customer service module in detail, and carries out the system test. The test results show that the system runs well. Finally, aiming at the problems existing in the property management industry, this paper puts forward the development strategy of property management. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of music teaching, this paper constructs a multimedia music teaching system based on artificial intelligence. Moreover, this paper focuses on the technical research of intraframe prediction, intraframe filtering, and transformation technology, respectively proposes the intraframe prediction method based on brightness change and the intraframe filtering technique based on the iterative update, and respectively proposes corresponding optimization algorithms. In addition, this paper proposes intraframe prediction technology based on brightness changes and intraframe filtering technology based on an iterative update, which brings improvements in coding performance, analyzes and verifies the experimental results, and proposes corresponding optimization algorithms, respectively, which saves coding and decoding time. Finally, this paper constructs a corresponding model structure based on actual needs, and analyzes the performance of this model through experimental research. The research results show that the teaching system constructed in this paper has better performance. [ABSTRACT FROM AUTHOR]

With the rapid development of modern science and technology, the speed of digital media to disseminate information is also accelerating, and the forms of communication become more and more diversified. In order to make digital media art better innovate and develop, designers should actively explore, so that art under digital media has the characteristics of intelligence, networking, and content diversification, so that it can be better applied in modern digital media communication and let visual art reach new development heights in the context of digital media. The current rapid development of artificial intelligence technology is being gradually applied to all fields of society. From the current point of view, China's digital media art will inevitably remain in the early development stage for a long time to face many difficulties and problems. In the context of the era of artificial intelligence, there will also be a large number of art and design talents for continuous research and exploration, and the emergence of these talents will certainly prompt the further development and enhancement of digital media art and artificial intelligence technology. In this process, it is especially worth noting that China's excellent cultural connotations should not be abandoned. Committed to digging more beneficial digital media art elements from traditional culture to make digital media art always develop in a more meaningful way. This paper takes the artificial intelligence era as the background, discusses the core of the development of the mutual integration of artificial intelligence technology and digital media art, analyzes the current development status of digital media art and technology, as well as the innovative development direction and future trends, proposes a digital media art design algorithm based on the convolutional neural network, and finally proves the effectiveness of the method in the relevant data set. [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]

Machine learning is an interdisciplinary study of how to make computer programs perform similar to human learning, and its techniques are widely used in the medical industry. The purpose of this paper is to study how to use machine learning-based intelligent medicine to analyze and study the assessment of athletes' physique and health status and describe the machine learning algorithm. This paper puts forward the problem of intelligent medical diagnosis, which is based on machine learning, and then elaborates on the concept of machine learning and related algorithms, and designs and analyzes a case of an athlete's physique monitoring and health status assessment system. The experimental results show that the athlete's physical fitness monitoring and health status evaluation system can meet the needs of users. The text classification effect based on the LSTM method is slightly inferior to the SVM effect, in which the recall rate of diabetes is not more than 40%, and the recall rate of cerebral infarction is improved by 26.5% after using fuzzy matching. [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]