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After hundreds of years of changes, due to the development of chemistry, the development of human life has undergone tremendous changes. People have used chemical science to manufacture many supplies, such as various medicines and various types of cosmetics. In recent decades, electronic components and computer software technology have developed rapidly, and the fourth round of technological revolution is underway, which has a favorable impact on the development of various industries. Traditional paper-cut and cosmetic bag design methods should also take into account the advantages of wireless communication and artificial intelligence technology and combine other types of traditional industries to carry out technological reforms to help traditional craftsmanship pass down. In modern life, due to the vigorous promotion of cosmetics, there are more and more cosmetics on the market, and the traditional paper-cut art itself is an artistic design method similar to cosmetic design. Both industries will grow rapidly after the use of artificial intelligence and wireless communication technology for the update of both industries. Therefore, the purpose of this paper is to combine wireless communication technology with artificial intelligence technology to transform the traditional paper-cut art and cosmetic packaging design. After consulting the reasons for the decline of the traditional handicraft industry and the reasons for the turmoil caused by modern technology, this paper conducts a combined design of artificial intelligence technology, wireless communication technology, and cosmetic packaging and then performs wireless communication and artificial intelligence on the cutting of traditional paper design elements. The design of the technical matching system is also designed for traditional paper-cut art and cosmetic packaging. And find professional practitioners for research and discussion and multiple transformations and obtain experimental analysis results data. After many experiments, it can be seen that the combination of wireless communication and artificial intelligence technology can transform the traditional paper-cut art and cosmetic packaging design, improve their relevance, continue the inheritance of paper-cut art, and possibly improve the efficacy of cosmetics. [ABSTRACT FROM AUTHOR]

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]

Current technology trends have been gradually integrated into the production of all walks of life, which play an indispensable part in promoting the intelligent development of enterprises, and have brought a greater impact on production and reformation. With the rapid development of the economy and technology, the manufacturing industry has played a very important role. For this reason, the introduction of artificial intelligence into machinery manufacturing can not only improve production efficiency but also save labor and reduce labor costs. The application of artificial intelligence in machinery manufacturing has a critical good role in promoting industrial upgrading and transformation. This time, through the application of smart algorithms in machinery manufacturing and its automation, we expect that such a technological revolution can provide a new development prospect for the development of manufacturing intelligence and automation. Taking the malfunction identification of string striking machinery as an example, this paper studies the smart identification method of mechanical malfunction based on multisensor. In the process of malfunction identification of keyboard stroke machinery, the accuracy of malfunction identification results is low due to the influence of the identification model. Moreover, a malfunction identification and analysis method for keyboard stroke machinery based on BP optimized by GA is proposed. The mechanical data of keyboard chords are acquired by sound-sensitive sensors, and the data features are extracted by wavelet packet decomposition. Based on the optimized BP, a mechanical malfunction judgment model is constructed, and various parameters in the model are calculated. The results show that the intelligent identification method proposed has exhibited strong adaptability and superiority compared with the traditional method. [ABSTRACT FROM AUTHOR]

Background. A vast number of research papers are published every day on PubMed, making it difficult for scientists to retrieve relevant articles in a timely manner. Keyword-based searches are currently the most popular method, but determining a suitable set of keywords can be challenging. Moreover, searches based on keywords typically retrieve many irrelevant papers. We developed a natural language processing (NLP)-based keyword augmentation and screening (NKAS) method to help scientists easily refine their keywords in topic searches. This method can extract meaningful candidate keywords from the titles and abstracts of an initial search using prior knowledge, knowledge graphs, and machine learning. The method was tested on three atrial fibrillation topics. When the NKAS was applied, the number of remaining papers was less than those in the original search but showed much higher precision (73.83% vs. 34.6%) and recall (98.4% vs. 59.93%) compared with those of the original search results. In conclusion, the NKAS method showed that NLP and other artificial intelligence techniques can help enhance both the search comprehensiveness and accuracy. These results suggest a great potential for the application of artificial intelligence methods in medical publication searches and other text-based applications. [ABSTRACT FROM AUTHOR]

The integration of IoT systems and AI technology is very suitable for interconnected things to enhance intelligence, thus the artificial intelligence of things (AIoT), which enables the establishment of innovative IoT systems and applications for the modern smart city and smart home. The Internet of Things (IoT) consists of interconnected things with built-in and function-oriented sensors, essentially constituting a network of physical devices. The paper titled "Utilizing Artificial Intelligence and Lotus Effect in an Emerging Intelligent Drone for Persevering Solar Panel Efficiency" by F. A. Almalki et al. proposes a drone system with AI framework to help clean solar panels mounted on the top of buildings and enhance their efficiency. [Extracted from the article]

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]

With the rapid development of the Internet of Things (IoT), the logistics and transportation industry is booming. At the same time, with the advancement of AI technology, intelligent logistics is also gradually emerging, and the purpose of intelligent logistics is to use different types of automatic guided transport machines to replace people to handle and move products. At the same time, with the help of big data, cloud computing, artificial intelligence, sensor technology, and other technologies, we can achieve logistics automation. However, the current logistics robot creation platforms are diverse, which makes intelligent logistics robots diverse in variety and wide in application and also makes the creation and use of robots more challenging. Robot Operating System (ROS) is an open-source software platform that supports programming in multiple languages and has excellent adaptability. In addition, most of the currently used path planning focuses on a single target point, which is insufficient to support the current needs of multitasking in intelligent logistics. Therefore, this paper aimed to design an intelligent logistics management system based on ROS robot and proposed to use the A-star algorithm to calculate the shortest path of the robot so as to achieve the optimal path. In the simulation experiment, 20 ROS robots were selected and divided into two groups. In the logistics warehouse of different transportation nodes, 20 ROS robots were set up to transport goods of different weights in the experiment, and the transportation data were collected at last. The final simulation results have shown that the power consumption and response delay performance of the ROS robot are good, and the logistics transportation speed is significantly improved. In addition, compared with the traditional transportation method, the daily transportation weight of each robot is up to 310.1% and the monthly profit is up to 171%, which shows that the intelligent logistics management system designed in this paper is more efficient in logistics and transportation and can bring more profits. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) technology is one of the keys to the Industrial Revolution known as Industry 4.0 as it provides faster connectivity and allows a greater number of devices to be connected simultaneously. In the transport sector, newly produced vehicles are equipped with various sensors and applications to help drivers perform safe maneuvers. However, moving from semiautonomous to fully autonomous vehicles to cooperating systems remains a major challenge. Many researchers have focused on artificial intelligence (AI) techniques and the ability to share information to achieve this cooperative behavior. This information can be made up of different data, which can be obtained from different sensors such as laser imaging detection and ranging (LiDAR), radar, camera, global positioning system (GPS), or data related to the current speed, acceleration, or position. The combination of the different shared data is performed depending on the approach of each navigation algorithm. This data fusion will allow a better understanding of the environment but will overload the network, as the traffic generated will be massive. Therefore, this paper addresses the challenge of achieving this cooperation between vehicles from the point of view of network requirements and computational capacity. In addition, this study contributes to advancing theory into real-world practice by examining the performance of cooperative navigation algorithms in the midst of the migration of computational resources from onboard vehicle equipment to the cloud. In particular, it investigates the transition from a cooperative navigation algorithm based on a decentralized architecture to a semidecentralized one as computationally demanding processes previously performed onboard are performed in the cloud. Additionally, the paper discusses the indispensable role of 5G in fulfilling the escalating demands for high throughput and low latency in these services, particularly as the number of vehicles increases. The results of the tests show that the AI acting alone cannot achieve optimal performance, even using 100% of the computational capacity of the onboard equipment in the vehicle. However, a system that integrates 5G and AI-based joint decisions can achieve better performance, reduce the computational resources consumed in the vehicle, and increase the efficiency of collaborative choices by up to 83.3%. [ABSTRACT FROM AUTHOR]

In today's aquaculture industry, the period of fish embryos needs to be detected in real time because different periods of fish embryos require different environments for their cultivation. The paper proposes an FPGA-based real-time embedded fish embryo detection system to solve the problems of today's fish embryo period detection, which consumes a lot of human resources and has low accuracy. Based on the selection of YOLOv3 as the basic model, this paper combines model optimization and hardware acceleration to make the whole system achieve the effect of real-time detection. With only a 4.23% reduction in the mAP value of the model, the inference speed of the model was improved by nearly 150%. Finally, we achieved 34.1 fps of real-time fish embryo detection on a 100 MHz FPGA chip, while having 82.49% mAP, and the whole embedded system only consumed 2.524w while running. The results show that the FPGA-based real-time fish embryo detection system proposed in this paper is fully capable of meeting the needs of today's aquaculture embedded devices. It is beneficial to promote the transformation of fish embryo analysis from traditional manual methods to AI-based embedded devices and facilitate the rapid analysis of fish embryos. [ABSTRACT FROM AUTHOR]

Facing the dual background of risk society and the fourth scientific and technological revolution, digital emergency management (DEM) research has attracted wide attention worldwide. Based on the comparative analysis of data from Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases, the research draws the knowledge map by CiteSpace, in order to analyse the spatiotemporal distribution, hotspots, and trends of DEM research. The following are the results: (1) DEM research shows a spiral growth, with a significant increase in the number of papers. Existing highly cited references are mainly carried out from the perspective of management or technology. (2) The complex countries' cooperation network has been formed from the USA, China, and other countries; the complexity of the institutions' cooperation network must be strengthened, and the density of the authors' cooperation network is low; DEM research has significant interdisciplinary attributes. (3) The hotspots of DEM research can be divided into three categories: means, processes, and objects, but the research emphases between international and China are different. (4) The evolution of DEM research can be divided into three stages between international and China: embryonic, development, and deepening stages, and research contents in each stage show some differences. (5) The trends of DEM research between international and China are different. The former are information technology, artificial intelligence, and machine learning, whereas the latter are emergency decision making, scenario analysis, and public health. [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]

Since industry 4.0 was put forward in 2013, industrial process around the world has been moving rapidly towards the age of intelligent manufacturing. Industry 4.0 is known as the fourth industrial revolution dominated by intelligent manufacturing, which has changed the production mode of global manufacturing and triggered far-reaching industrial changes. However, when intelligent machines communicate with each other under industrial 4.0, a large amount of data adopting distributed control will be generated. The infographic in the data is mainly a visual design of industry 4.0 data. Therefore, this paper mainly studies the distributed data optimization processing for industry 4.0. Considering that data leakage is one of the biggest challenges faced by the data storage systems, this paper proposes a data storage method that considers the efficiency and security of data access. The concept of security distance not only guarantees data security but also takes into account the emphasis of different user groups on data security. To minimize data access time, this paper proposes a data access node selection algorithm to minimize data access time while ensuring data security. The simulation proves that compared with baselines, the data access time of the proposed algorithm in random topology and Internet2 topology is less than that of the current data storage algorithm while ensuring data security. The experimental results are simulated on Internet2 topology and random topology with Matlab and Omnet + + simulation platform, showing that the proposed algorithm can select the optimal data storage node under the condition of satisfying the security distance constraint, thus reducing the data access time. [ABSTRACT FROM AUTHOR]

This paper investigates AI-based interactive music design and proposes a new music learning mode. It aids in the development of students' inquiry skills and allows teachers to take the lead. At the same time, this paper systematically introduces the status of DL theory's application in music teaching evaluation and uses DL theory to develop a mathematical model for an AI music teaching evaluation system. The construction method of an AI music teaching evaluation model based on DL is detailed in this paper. The model can assess the quality of AI music teaching after the network has been trained. The designed instructional quality evaluation NN is trained and measured in this paper to verify the model's performance. The experimental results show that this model has a prediction accuracy of 94.79 percent, which is approximately 8.52 percent higher than the traditional methods. It has some practicality and feasibility, and it can serve as a useful benchmark for the development of various instructional quality evaluation systems. [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]

With the development of science and technology, more and more fields have begun to use AI to provide convenient services for humans. Artificial intelligence (AI) refers to a new technology that uses human thinking to respond accordingly through computers and robots to assist human beings. Action recognition is an important research project that needs to be broken through in many industries, such as security system, martial arts instruction, and dance training. This paper aims to study a method for action recognition using AI technology and to build a sports training management information system. In this paper, a recognition model and related algorithms using a convolutional neural network (CNN) are proposed, and an intelligent sports training management information system is constructed. The system and the model are tested, the action recognition effect of 60 athletes in a university is tested, and the comparison with the traditional recognition algorithm is carried out. The results show that the CNN recognition accuracy test results used in this paper are generally more than 90%, while the traditional recognition accuracy rate is only about 75%, and the highest is not more than 86%; the training management information system of this paper takes about 15.7 s, and the maximum time is not more than 10 s, while the traditional recognition system takes about 15.7 s, which is about twice the time of the system in this paper. Therefore, it shows that the CNN recognition method in this paper has a significantly better effect on the recognition of athletes' movements, and the sports training management information system constructed in this paper is less time-consuming and faster and has certain feasibility. [ABSTRACT FROM AUTHOR]

The Chinese nation has accumulated a lot of precious, rich, and wonderful material and intangible culture in its historical evolution, but these cultures are facing the problem of inheritance difficulties in the long term, and it is becoming more and more difficult to innovate. In order to solve these problems, this paper puts forward the innovative development of the intangible culture of arts and crafts based on artificial intelligence decision support system, with the purpose of studying cultural inheritance, regional brand hematopoiesis promotion, and organizational goal's support for intangible culture. The method of this paper is to study the basic principles of artificial bee colony algorithms and decision support systems, and then evaluate the value of intangible culture. The purpose of this paper is to analyze the development of local intangible culture and innovation models based on local knowledge systems and confirm the influence of technological innovation on intangible culture so as to promote the sustainable development of intangible culture. This paper describes the present situation of intangible cultural innovation and then explores the innovative ways of intangible culture based on the experiment of cultural development of artificial intelligence. The experimental results show that after using the decision support method mentioned in this paper, the technical progress index of Jingdezhen porcelain culture has increased by 2.4%. [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]

In recent years, as a new subject in the computer field, artificial intelligence has developed rapidly, especially in reinforcement learning (RL) and deep reinforcement learning. Combined with the characteristics of Software Defined Network (SDN) for centralized control and scheduling, resource scheduling based on artificial intelligence becomes possible. However, the current SDN routing algorithm has the problem of low link utilization and is unable to update and adjust according to the real-time network status. This paper aims to address these problems by proposing a reinforcement learning-based multipath routing for SDN (RLMR) scheme. RLMR uses Markov Decision Process (MDP) and Q-Learning for training. Based on the real-time information of network state and flow characteristics, RLMR performs routing for different flows. When there is no link that meets the bandwidth requirements, the remaining flows are redistributed according to the Quality of Service (QoS) priority to complete the multipath routing. In addition, this paper defines the forward efficiency (FE) to measure the link bandwidth utilization (LBU) under multipath routing. Simulation results show that compared with the current mainstream shortest path algorithm and ECMP algorithm, the routing algorithm in RLMR has advantages in FE, jitter, and packet loss rate. It can effectively improve the efficiency and quality of routing. [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]

Mobile Internet-based intelligent media has become a popular academic topic. This study uses the CiteSpace visualisation tool and Scientific Citation Index Expanded database to comb the existing research in the field of intelligent media from a quantitative perspective. A total of 7248 English papers were published on the topic of "intelligent media" from 2012 to 2021, and 145 highly cited papers refined were analysed. Scientific knowledge graphs were analysed from six dimensions: annual publication quantity, country of publication, institution of publication, author, keywords, and cited references. In the last 10 years, the research literature on intelligent media has been found to increase annually. Presently, the People's Republic of China and the United States of America have a high proportion of documents in this field. Chinese universities and institutions have achieved significantly in terms of the quantity and quality of documents. From the perspective of the whole intelligent media discipline, the high-yield author group has not been formed, and there is minimal cooperation amongst authors. Popular intelligent media topics include film, social media, machine learning, swarming motility, data mining, and artificial intelligence. Subject words of the main research directions are event recognition, fake news, Cable News Network model, reconfigurable intelligent surface, comprehensive survey, microblog message, strain sensor, and traffic event. Combined with popular topics and time zone maps, the future research frontier in the field of smart media is identified. [ABSTRACT FROM AUTHOR]

In the paper entitled "Optimization Strategy of Multiarea Interconnected Integrated Energy System Based on Consistency Theory" by Yu et al., they study a collaborative optimization strategy of an integrated energy system aiming at improving the energy efficiency for the cluster optimization of an integrated energy system (IES). In the paper entitled "Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network" by Shen et al., they propose an LSTM recurrent neural network-based HDD failure prediction model, which leverages the long temporal dependence feature of the drive health data to improve the prediction efficiency. Furthermore, to overcome the problems of traditional IP multicast and software-defined multicasting approaches, they propose an implementable multicast mechanism that delivers data along multicast trees but uses unicast sessions. [Extracted from the article]

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]

Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent. [ABSTRACT FROM AUTHOR]

With the application of big data, artificial intelligence, and other related technologies in the field of education, using machine learning to carry out early warning for course learning has become an effective means to improve teaching quality. However, in the scene of early warning, the samples are significantly less than the ordinary samples, and the general clustering or classification methods are difficult to achieve good results. Therefore, this paper proposes an early warning for course learning method based on SMOTE and OCSVM. First, collect and preprocess students' college entrance examination information and online course learning information data. Second, use SMOTE algorithm to expanding the samples. Then, the OCSVM model is designed, the Gaussian kernel function is used, and the Lagrange multiplier is used to solve the optimization problem for the optimization objective. The qualified student samples are selected for learning, and the classifier is trained, so as to classify the student data and realize the early warning of course learning. Select recall and F1_Score to evaluate the model, and comparative experiments are carried out. From the experiment, it is clear that in most cases, the method proposed in this paper is superior to the original sample and traditional methods in recall rate and F1_Score. [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]

Enterprise management has always been a hot issue in society. In today's society, enterprises are no longer individuals cut off from society and no longer have profit as their sole purpose, but need to exist and develop in combination with social information. With the increasing competition among enterprises, the original enterprise management methods can no longer meet the needs of sustainable development of enterprises, nor can they effectively utilize social data. To better understand the daily emotions of corporate workers, we use a multimodal emotion recognition method in this paper. Multimodal emotion recognition refers to the recognition of human emotional states through different modal information such as speech, visual, and text related to human emotional expressions, which has important research significance in the fields of human-computer interaction, artificial intelligence, and emotional computing and has received much attention from researchers. Given the great success of deep learning methods developed in recent years for various tasks, various deep neural networks are now used to learn high-level representations of emotional features for multimodal emotion recognition. The analysis of employee sentiment is complemented by traditional management methods that make full use of social data. In this paper, based on the study of a single enterprise management model, the proposed model contains five substructure modules, starting with feature inputs, extracting features at different levels through three convolutional modules and outputting recognition results through a softmax classifier. The focus is on how to utilize social data, while combining deep learning with traditional enterprise management methods to fill the research gap in this area in academia. [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]

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]

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]

The theory of composition technology is to study the basic knowledge and skills of musicology, instrumental music, composition, and composition technology, including harmony, polyphony, musical form, orchestration, and so on, and then to analyze, create, and edit music. The current theory of composition technology has resulted in the phenomenon of relatively single form. The current method is the traditional way of composing music through the creation of composers. The defect is that various elements cannot be integrated together, and the meaning of music cannot be perfectly presented. In order to solve these problems, this paper proposes the use of recurrent neural network algorithm and backpropagation algorithm in artificial intelligence algorithm. It aims to study how to innovatively integrate the composition technology theory course with the current network technology. And it utilizes recurrent neural networks in artificial intelligence to help design part of the analysis of musical characteristics, through the evaluation of the music effect generated by automatic composition. The results show that the accuracy of note prediction obtained by the automatic composition method on the basis of objective evaluation is 81.93%, 90.15%, and 92.62%, respectively, on Top1, Top2, and Top3, which basically meet the current basic requirements for composition technology theory. [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]

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 order to realize the value of packaging waste through recycling, detection, classification, maintenance, and reprocessing, the model construction and simulation study of logistics packaging recycling and utilization were put forward. This paper takes logistics companies as the research object, aiming at the business model of logistics companies, uses the Internet of things recycling machine for packaging recycling, and establishes a dual channel packaging recycling model. This model is intended to compare and analyze the difference between traditional packaging waste recycling channel and intelligent recycling machine of the internet of things in terms of recycling cost, profit, recovery rate, waste incineration rate, and other aspects. It is assumed that the recycling proportion of the two channels accounts for half of the packaging waste. The results of simulation analysis on recycling and utilization of dual-channel packaging waste show that in the simulated time period, the recycling rate of the intelligent recycling machine using the internet of things is higher than that of the traditional dispersed recycling vendors. Assume that when the recycling frequency = 2, when the recycling frequency is increased to 2 times a day, the recycling time of the internet of things recycling machine is further shortened, and the recycling efficiency is improved. At the same time, due to the improvement of the recycling rate of the internet of things recycling machine, the amount of waste packaging decreases, so the recycling rate of the dispersed recycling vendors decreases accordingly. The recycling revenue and net profit generated by channel 2 (internet of things recycling machine) are higher than those generated by channel 1 (decentralized recycling vendor responsible for recycling). The average profit of channel 2 is about 28,163.69 yuan, while the average profit of channel 1 is only 9,395.89 yuan. The waste recovered through channel 2 is lower than that in channel 1; the average for channel 1 is 15.58 tons. However, the average value of channel 2 is 14.58 tons. Comprehensive analysis shows that channel 2 has obvious advantages in reducing waste discharge. However, it is necessary to strengthen the research and development of recycling technology, improve the reuse efficiency, and reduce the incineration of nonreusable waste as much as possible to the minimum, which proves the robustness of this paper to the logistics packaging recycling model construction. It is proved that the robustness of this paper for logistics packaging recycling model construction provides strong support for the analysis of waste pallets and packaging recycling-related issues. [ABSTRACT FROM AUTHOR]

Since the beginning of the 21st century, with the development of information technology, researchers in various fields have gradually increased their research on human emotion and behavior. The current research mechanism used in emotion and behavior research is artificial intelligence technology. Through the literature survey and data analysis in related fields, it is found that the acquisition of human emotions and behaviors will be carried out through facial feature algorithm for point capture and combined with machine learning for output detection and analysis. Among them, the detection process requires machine learning of artificial intelligence first. This paper firstly analyzes and summarizes the advantages of Python programs at this stage and completes the preliminary work of system construction by setting and installing platform parameters. In the research process, this paper uses the existing algorithm to apply the σ E value algorithm to the samples and conducts preliminary tests. The overall detection values in the test data are relatively average, and there are still differences in the samples. At the same time, we compare the U E and T E detection algorithms according to the output Y value of the algorithm in the machine learning. The detection rate of some emoticons in the U E algorithm is high, but the detection rate of other emoticons is low. Finally, according to the limitation of the output method in the mathematical formula, a new algorithm σ x of taking the weighted sum and taking the logarithm and then taking the square root is proposed again. According to the statistical analysis, the overall average value of the final algorithm has been improved, and the overall detection rate is about 80%; compared with the T E and U E algorithms, the overall detection frequency fluctuates less. The σ x algorithm in the frequency fluctuation data table in the paper is also superior to the existing algorithms in machine learning, sample testing, and data in the frequency fluctuation. Our next direction will be to use the Python main program to perform AI automatic facial emotion detection work by combining the new algorithm σ x with the V value, DWT, and CNN algorithm in the facial recognition feature through machine learning. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases remain the main cause of death worldwide which makes it essential to better understand, diagnose, and treat atherosclerosis. Artificial intelligence (AI) and novel technological solutions offer us new possibilities and enable the practice of individually tailored medicine. The study was performed using the PRISMA protocol. As of January 10, 2023, the analysis has been based on a review of 457 identified articles in PubMed and MEDLINE databases. The search covered reviews, original articles, meta-analyses, comments, and editorials published in the years 2009–2023. In total, 123 articles met inclusion criteria. The results were divided into the subsections presented in the review (genome-wide association studies, radiomics, and other studies). This paper presents actual knowledge concerning atherosclerosis, in silico, and big data analyses in cardiology that affect the way medicine is practiced in order to create an individual approach and adjust the therapy of atherosclerosis. [ABSTRACT FROM AUTHOR]

Education is crucial for a productive life and providing necessary resources. With the advent of technology like artificial intelligence, higher education institutions are incorporating technology into traditional teaching methods. Predicting academic success has gained interest in education as a strong academic record improves a university's ranking and increases student employment opportunities. Modern learning institutions face challenges in analyzing performance, providing high-quality education, formulating strategies for evaluating students' performance, and identifying future needs. E-learning is a rapidly growing and advanced form of education, where students enroll in online courses. Platforms like Intelligent Tutoring Systems (ITS), learning management systems (LMS), and massive open online courses (MOOC) use educational data mining (EDM) to develop automatic grading systems, recommenders, and adaptative systems. However, e-learning is still considered a challenging learning environment due to the lack of direct interaction between students and course instructors. Machine learning (ML) is used in developing adaptive intelligent systems that can perform complex tasks beyond human abilities. Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. In this research work, K-means, a clustering data mining technique using Davies' Bouldin method, obtains clusters to find important features affecting students' performance. The study found that the SVM algorithm had the best prediction results after parameter adjustment, with a 96% accuracy rate. In this paper, the researchers have examined the functions of the Support Vector Machine, Decision Tree, naive Bayes, and KNN classifiers. The outcomes of parameter adjustment greatly increased the accuracy of the four prediction models. Naïve Bayes model's prediction accuracy is the lowest when compared to other prediction methods, as it assumes a strong independent relationship between features. [ABSTRACT FROM AUTHOR]

Intrusion detection (ID) is critical in securing computer networks against various malicious attacks. Recent advancements in machine learning (ML), deep learning (DL), federated learning (FL), and explainable artificial intelligence (XAI) have drawn significant attention as potential approaches for ID. DL-based approaches have shown impressive performance in ID by automatically learning relevant features from data but require significant labelled data and computational resources to train complex models. ML-based approaches require fewer computational resources and labelled data, but their ability to generalize to unseen data is limited. FL is a relatively new approach that enables multiple entities to train a model collectively without exchanging their data, providing privacy and security benefits, making it an attractive option for ID. However, FL-based approaches require more communication resources and additional computation to aggregate models from different entities. XAI is critical for understanding how AI models make decisions, improving interpretability and transparency. While existing literature has explored the strengths and weaknesses of DL, ML, FL, and XAI-based approaches for ID, a significant gap exists in providing a comprehensive analysis of the specific use cases and scenarios where each approach is most suitable. This paper seeks to fill this void by delivering an in-depth review that not only highlights strengths and weaknesses but also offers guidance for selecting the appropriate approach based on the unique ID context and available resources. The selection of an appropriate approach depends on the specific use case, and this work provides insights into which method is best suited for various network sizes, data availability, privacy, and security concerns, thus aiding practitioners in making informed decisions for their ID needs. [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]

With the rapid development of science and technology in recent years, more and more researchers began to explore the basic disciplines of sports, namely, biomechanics and physiology, and further update and improve the traditional sports training theory. However, the developed countries, i.e., Europe and the United States, have dedicatedly worked on the effective role of physical training particularly in middle-class farmers in sports powers. They have realized that the continuous improvement of the physical training evaluation system is directly related to the reforming in the evaluation and monitoring methods of physical training. At the same time, there is a demand for athletes' physical fitness evaluation, which requires a large amount of data support during the evaluation. These data collection and analysis are difficult and consume a lot of manpower. Therefore, this paper uses artificial intelligence technology to design the sports athletes' physical fitness evaluation information management system to improve the comprehensiveness of system data collection and the accuracy of data analysis. Through the physical attenuation calculation and physical analysis of the system developed in this paper, data accuracy is high. The coach can make a training plan according to the physical consumption of athletes in sports training or competition, which can greatly improve the ability of athletes. [ABSTRACT FROM AUTHOR]

The cultural tourism industry combines the similarities of the cultural industry and tourism industry, which can be the most effective way to meet people's spiritual and cultural needs as well as their leisure needs simultaneously, and has a vast development potential. However, there are numerous and dispersed areas where the market value of cultural tourism resources is clustered, and frequently, each city has the clustering area with the highest market value concentration of cultural tourism resources. This feature of the spatial distribution of the market value of cultural tourism resources is significant for promoting the development of cultural tourism as a whole and constructing the industry's overall structure. It has broad application potential for extracting and differentiating cultural tourism industry characteristics. Texture feature extraction is typically performed using dual-tree complex wavelet transform (DT-CWT) and Gabor wavelet. In this paper, we propose a multiscale DT-CWT and Gabor-based method for identifying the cultural tourism industry. The method first decomposes the images of cultural tourism into multiscale space using a Gaussian pyramid, then extracts the multiscale features of the images using DT-CWT and Gabor, and lastly achieves feature fusion. Using a support vector machine (SVM) classifier to achieve classification, the effectiveness of the feature extraction method is determined. The experimental findings demonstrate that the method proposed in this paper can achieve a high rate of recognition. [ABSTRACT FROM AUTHOR]

The current robotics field, led by a new generation of information technology, is moving into a new stage of human-machine collaborative operation. Unlike traditional robots that need to use isolation rails to maintain a certain safety distance from people, the new generation of human-machine collaboration systems can work side by side with humans without spatial obstruction, giving full play to the expertise of people and machines through an intelligent assignment of operational tasks and improving work patterns to achieve increased efficiency. The robot's efficient and accurate recognition of human movements has become a key factor in measuring robot performance. Usually, the data for action recognition is video data, and video data is time-series data. Time series describe the response results of a certain system at different times. Therefore, the study of time series can be used to recognize the structural characteristics of the system and reveal its operation law. As a result, this paper proposes a time series-based action recognition model with multimodal information fusion and applies it to a robot to realize friendly human-robot interaction. Multifeatures can characterize data information comprehensively, and in this study, the spatial flow and motion flow features of the dataset are extracted separately, and each feature is input into a bidirectional long and short-term memory network (BiLSTM). A confidence fusion method was used to obtain the final action recognition results. Experiment results on the publicly available datasets NTU-RGB + D and MSR Action 3D show that the method proposed in this paper can improve action recognition accuracy. [ABSTRACT FROM AUTHOR]

With the advancement of technology and the arrival of the era of integrated media, big data, artificial intelligence, cloud computing, and virtual reality are gradually changing the ecology of public opinion. This will bring new challenges to the technical discovery, data analysis, and response of network disease dissemination to public opinion. Internet data barriers are still widespread. The specific requirements are to explore the application mode of blockchain technology to bring a better service experience to the people. Therefore, this paper focuses on the dissemination of disease public opinion information and risk management research in the blockchain environment. Furthermore, the application of advanced blockchain technology is used to seek breakthroughs in the field of risk management of network disease transmission and public opinion in terms of technology and data. This paper systematically sorts out the research results of domestic and foreign disease transmission network public opinion, public opinion risk management, and blockchain through comprehensive use of literature analysis, grounded research, empirical research, simulation research, and other research methods. Based on theories of the disease transmission network, public opinion, risk management, blockchain, and system dynamics, we conduct research on network public opinion and risk management of disease transmission in the block chain environment. At the theoretical level, the research in this paper can promote the risk management of disease transmission network public opinion in the blockchain environment and carry out the construction of the theoretical system to deepen the application of blockchain technology in the field of disease public opinion risk management. At the practical level, the research in this paper can guide the improvement of the effect of public opinion risk management. Then, blockchain technology is applied to manage the risk of disease public opinion. In future research, the theory of public opinion risk management will be further enriched. We will apply blockchain technology to develop the evolution of the risk of disease public opinion transmission, carry out research on public opinion management and other aspects, and at the same time, try the design and development of a blockchain public opinion system. In this way, the innovation of blockchain in the development and application of disease network public opinion research will be promoted. [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]

Driven by capital and Internet information (IT) technology, the operating scale and capital scale of modern industrial and commercial enterprises and various organizations have increased exponentially. At present, the manual-based financial work model has been unable to adapt to the changing speed of the modern business environment and the business rhythm of enterprises. All kinds of enterprises and organizations, especially large enterprises, urgently need to improve the operational efficiency of financial systems. By enhancing the integrity, timeliness, and synergy of financial information, it improves the comprehensiveness and ability of analyzing complex problems in financial analysis. It can cope with such rapid changes and help improve the financial management capabilities of enterprises. It provides more valuable decision-making guidance for business operations and reduces business risks. In recent years, the vigorous development of artificial intelligence technology has provided a feasible solution to meet the urgent needs of enterprises. Combining data mining, deep learning, image recognition, natural language processing, knowledge graph, human-computer interaction, intelligent decision-making, and other artificial intelligence technologies with IT technology to transform financial processes, it can significantly reduce the processing time of repetitive basic financial processes, reduce the dependence on manual accounting processing, and improve the work efficiency of the financial department. Through the autonomous analysis and decision-making of artificial intelligence, the intelligentization of financial management is realized, and more accurate and effective financial decision-making support is provided for enterprises. This paper studies the company's intelligent financial reengineering process, so as to provide reference and reference for other enterprises to upgrade similar financial systems. The results of the analysis showed that at the level of α = 0.05 , there was a significant difference in the mean between the two populations. When the r value is in the range of -1 and 1, the linear relationship between the x and y variables is more obvious. This paper proposes decision-making suggestions and risk control early warning to the group decision-making body, or evaluates the financial impact of the group's decision-making, and opens the road to financial intelligence. [ABSTRACT FROM AUTHOR]

The rise of artificial intelligence technology has promoted the development of human-computer interaction and other fields. In human-computer interaction, in order to enable the machine to accurately perceive and understand the user's emotion in real time, thereby improving the service quality of the machine, user emotion recognition has been widely studied. In real life, because voice output not only is convenient, but also contains rich emotional information, human-computer interaction is mainly carried out in the form of voice. Speech carries a wealth of linguistic, paralinguistic, and nonlinguistic information that is essential for human-computer interaction. Understanding language information alone will not allow a computer to fully comprehend the speaker's purpose. For computers to behave like humans, speech recognition systems must be able to process nonverbal information, such as the emotional state of the speaker. As a result, developing machine understanding of human emotions requires speech-based emotion recognition. This paper proposes an improved long short-term memory network (ILSTM) for emotion recognition. Because the initial LSTM only analyzes the preceding moment's input, it will miss out on a lot of information for the full context scene. In this way, all the features in the speech segment can be extracted. In order to be able to select the feature that can express emotion the most among the many features, this paper also introduces the attention mechanism. Experiments are carried out on public datasets, and the experimental results show that the ILSTM used in this paper is very effective in classifying speech emotion data and the classification accuracy can reach more than 0.6. This fully shows that this research can be applied to actual products and has certain feasibility and reference value. [ABSTRACT FROM AUTHOR]

There are some problems in modern English education, such as difficulties in classroom teaching quality evaluation, lack of objective evaluation basis in teaching process management, and quality monitoring. The development of artificial intelligence technology provides a new idea for classroom teaching evaluation, but the existing classroom evaluation scheme based on artificial intelligence technology has a series of problems such as high system cost, low evaluation accuracy, and incomplete evaluation. In view of the above problems, this paper proposes a solution of English classroom concentration evaluation system based on deep learning. The program studies the evaluation methods of students' class concentration, class activity, and enrichment degree of teaching links, and constructs an information evaluation system of students' learning process and class teaching quality. Based on the edge computing system architecture, a hardware platform with cloud platform AI+ embedded visual edge computing devices managed by an FPGA deep learning accelerated server was built. The design, debugging, and testing of classroom evaluation and student behavior statistics-related functions were completed. This scheme uses edge computing hardware architecture to solve the problem of high system cost. Deep learning technology is used to solve the problem of low accuracy of classroom evaluation. It mainly evaluates the classroom objectively by extracting indicators such as the students' attention in the classroom, and solves the problems of the students' inattentiveness in the classroom. After the test, the classroom evaluation system designed by the paper runs stably and all functions run normally. The test results show that the system can basically meet the requirements of classroom teaching evaluation application. [ABSTRACT FROM AUTHOR]

With the advancement of science and technology and the further improvement of people's living standards, tourism has become a hot topic in today's society. This paper is aimed at studying the development of a smart tourism service platform using artificial intelligence recognition technology and automatic program design. This paper proposes functions such as online ticket booking through the design of intelligent access control in scenic spots (combined with intelligent voice, face recognition, and other technologies), scenic miniprograms, and WeChat public accounts. Tourists can collect tickets automatically through the intelligent assistant designed by the intelligent service system. This allows tourists to consult and interact with intelligent machine assistants, reducing human-to-human contact during travel, thereby reducing the probability of epidemic infection (mainly spread by coughing or sneezing, close contact with sick people, touching contaminated surfaces, etc.). Finally, the smart tourism service platform is promoted, and an online questionnaire survey is conducted on 2,000 tourists who use the service platform to understand the actual use of the system. The results of the questionnaire survey showed that among the 2,000 tourists' evaluations, 55.15% were satisfied with the system, 41.5% were very satisfied, 3.1% felt average, 0.25% felt dissatisfied, and 0% were very dissatisfied. At the same time, a large number of positive evaluation words were received in the feedback results of tourists, which shows that the smart service system designed in this paper is very popular with tourists. Meanwhile, a great deal of positive evaluation words were received in the feedback results of tourists (such as full of technology, special convenience, and special safety). This shows that the intelligent service system designed in this paper is very popular with tourists. [ABSTRACT FROM AUTHOR]

With the advancement of scientific collaboration in the 20th century, researchers started collaborating in many research areas. Researchers and scientists no longer remain solitary individuals; instead, they collaborate to advance fundamental understandings of research topics. Various bibliometric methods are used to quantify the scientific collaboration among researchers and scientific communities. Among these different bibliometric methods, the co-authorship method is one of the most verifiable methods to quantify or analyze scientific collaboration. In this research, the initial study has been conducted to analyze interdisciplinary research (IDR) activities in the computer science domain. The ACM has classified the computer science fields. We selected the Journal of Universal Computer Science (J.UCS) for experimentation purposes. The J.UCS is the first Journal of Computer Science that addresses a complete ACM topic. Using J.UCS data, the co-authorship network of the researcher up to the 2nd level was developed. Then the co-authorship network was analyzed to find interdisciplinary among scientific communities. Additionally, the results are also visualized to comprehend the interdisciplinary among the ACM categories. A whole working web-based system has been developed, and a forced directed graph technique has been implemented to understand IDR trends in ACM categories. Finally, the IDR values between the categories are computed to quantify the collaboration trends among the ACM categories. It was found that "Artificial Intelligence" and "Information Storage and Retrieval", "Natural Language Processing and Information Storage and Retrieval", and "Human-Computer Interface" and "Database Applications" were found the most overlapping areas by acquiring an IDR score of 0.879, 0.711, and 0.663, respectively. [ABSTRACT FROM AUTHOR]