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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]

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]

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]

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]

Chronic rhinosinusitis (CRS) is a global disease characterized by poor treatment outcomes and high recurrence rates, significantly affecting patients' quality of life. Due to its complex pathophysiology and diverse clinical presentations, CRS is categorized into various subtypes to facilitate more precise diagnosis, treatment, and prognosis prediction. Among these, CRS with nasal polyps (CRSwNP) is further divided into eosinophilic CRSwNP (eCRSwNP) and noneosinophilic CRSwNP (non‐eCRSwNP). However, there is a lack of precise predictive diagnostic and treatment methods, making research into accurate diagnostic techniques for CRSwNP endotypes crucial for achieving precision medicine in CRSwNP. This paper proposes a method using multiangle sinus computed tomography (CT) images combined with artificial intelligence (AI) to predict CRSwNP endotypes, distinguishing between patients with eCRSwNP and non‐eCRSwNP. The considered dataset comprises 22,265 CT images from 192 CRSwNP patients, including 13,203 images from non‐eCRSwNP patients and 9,062 images from eCRSwNP patients. Test results from the network model demonstrate that multiangle images provide more useful information for the network, achieving an accuracy of 98.43%, precision of 98.1%, recall of 98.1%, specificity of 98.7%, and an AUC value of 0.984. Compared to the limited learning capacity of single‐channel neural networks, our proposed multichannel feature adaptive fusion model captures multiscale spatial features, enhancing the model's focus on crucial sinus information within the CT images to maximize detection accuracy. This deep learning‐based diagnostic model for CRSwNP endotypes offers excellent classification performance, providing a noninvasive method for accurately predicting CRSwNP endotypes before treatment and paving the way for precision medicine in the new era of CRSwNP. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence and deep learning in recent years, many universities have put forward the goal of achieving digitalization, intelligent, and education informatization on campus. Throughout the lecture and learning process, the classroom status is an important reference factor to assess students' acceptance of the course and the quality of lectures. However, at present, classroom status analysis is mainly conducted manually, which can distract teachers' attention, so it is of great research significance to find a method that can improve the efficiency of classroom status analysis. In this paper, we choose an offline method to analyze the status of a classroom video recording in terms of students' behavior and attendance in terms of frames, in which student behavior is identified by an improved target detection algorithm and attendance is analyzed by face recognition. By analyzing the structure of the neural network model, an improved neural network model is proposed for its characteristics of a large number of parameters and poor detection of small targets in the basic network. The backbone network is replaced by the improved neural network, and the depth-separable convolutional network is used to reduce the network parameters and increase the computation speed. The information in the deeper feature map is fused upward into the shallow layer to improve the accuracy of small target recognition. Finally, the optimization algorithm is incorporated into the network to optimize the network model and accelerate the model convergence speed. In addition, this paper incorporates the improved behavior recognition method and face recognition method into the system to realize the analysis of the offline classroom status. The system is divided into a teacher side and a management side, where the teacher side is responsible for uploading course recordings and the management side is responsible for randomly analyzing students' status and attendance at any time, and the combination of the two forms a convenient and comprehensive classroom status analysis system platform. Users can upload classroom videos through the instructor interface and can view the classroom status analysis results of a course at any time by searching randomly in the administration. In this paper, the classroom status is mainly judged by the recognition of students' behaviors. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of sports combination training action recognition, a sports combination training action recognition model based on SMO algorithm optimization model and artificial intelligence is proposed. In this paper, by expanding the standard action data, the standard database of score comparison is established, and the system architecture and the key acquisition module design based on 3D data are given. In this paper, the background subtraction method is used to process the sports video image to obtain the sports action contour and realize the sports action segmentation and feature extraction, and the artificial intelligence neural network is used to train the feature vector to establish the sports action recognition classifier. This paper mainly uses a three-stream CNN artificial intelligence deep learning framework based on convolutional neural network and uses a soft Vlad representation algorithm based on data decoding to learn the action features. Through the data enhancement of the existing action database, it uses support vector machine to achieve high-precision action classification. The test results show that the model improves the recognition rate of sports action and reduces the error recognition rate, which can meet the online recognition requirements of sports action. [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]

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]

Basketball sport is a comprehensive nonperiodic collective sport, in which sports injuries of various parts are also prone to occur. Its higher exercise intensity not only effectively enhances the physical fitness of the athlete but also causes physical damage due to a series of actions such as frequent take-offs and landings, resulting in certain sports injuries. Therefore, the early warning of injury in basketball is very necessary, and it is very useful to protect the safety of players. In this paper, we investigate how to use the sequence video and artificial intelligence method for the basketball injury risk early warning in order to protect the player and better assist the player to improve their efficiency of training. First, we preprocess the video sequence. We convert the video sequence into image data and perform noise reduction and transformation operations on the image. Second, for the processed image data, we designed a convolutional neural network model to determine the damaged area. Third, we use the neural network model to take the image data with the detection area as the input, perform feature extraction on the data, and finally obtain the early warning value of basketball sports injury. The experimental results prove that the method proposed in this paper has good evaluation performance. [ABSTRACT FROM AUTHOR]

In today's era, the English level of college students is very important. Different English classes can cultivate different English abilities. Smarter classroom is a concept put forward in the new century. This paper proposes the construction of a smarter classroom for college English with artificial intelligence and big data and proposes a deep neural network text semantic matching original model (OM), which uses different lexical information to match different English digital information. Combined with the K-means clustering method, different lexical semantic information is matched. After the comparison of experiments, the combination of the research theory and the algorithm in this paper is effective and has good use value. [ABSTRACT FROM AUTHOR]

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

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

As 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]

In order to enhance the competitiveness of enterprises, how to evaluate and enhance the competitiveness of B2B e-commerce enterprises and promote the orderly and healthy development of B2B e-commerce industry are discussed. This paper puts forward the research on the innovation of platform economic business model driven by BP neural network and artificial intelligence technology. BP neural network is used to study and evaluate the competitiveness of B2B e-commerce companies. According to the B2B e-commerce company competitiveness theory and BP neural network algorithm, combined with BP neural network and B2B e-commerce company competitiveness evaluation index system, a BP neural network model is designed to analyze the competitiveness of B2B e-commerce enterprises. Determine the expected value of network samples, select G1 method to determine the subjective weight, and select entropy weight method to determine the objective weight. With the help of the function in the MATLAB neural network toolbox, the neural network is trained. The results show that when the training times reach 3297 times, the sample mean square error is 9.9869e − 06, and the training network reaches convergence. The samples of three enterprises test the trained neural network and input the data of three test samples into the trained BP neural network, and the output results are 0.1531, 0.1371, and 0.1557, respectively. The network model constructed in this paper is effectively close to the training samples. The established BP neural network has good performance and can be used to evaluate the competitiveness of B2B e-commerce companies. Accelerate technological change and realize innovation. Technological capability is the inexhaustible driving force for the development of enterprises. Only with the innovation of keeping pace with the times can application-oriented e-commerce enterprises meet the needs of customers and the market, form the difference between goods or services, and then enable enterprises to win more customers and market share. [ABSTRACT FROM AUTHOR]

As China's economic level and industrial volume continue to develop and expand, the traditional methods of business management and resource scheduling demonstrate significant limitations and misalignments with China's daily economic activities. Traditional industrial and commercial management resource scheduling is highly reliant on manual labor. When confronted with the requirements of large-scale project management, the pure manual mode cannot track the real-time progress of the project flexibly, effectively, and in a timely manner, resulting in the inability to complete the corresponding resource-scheduling work efficiently, which will affect the overall operation progress of the project. Deep neural networks have a high-application potential for solving extremely complex and highly nonlinear optimization problems. Artificial intelligence and deep learning research have advanced at a rapid pace over the past few decades, thanks to the efforts of numerous researchers. Combined with GPU technology, the deep learning framework can provide an extremely complex optimization problem with a practical and feasible optimization scheme and corresponding solution path in a very short amount of time. Therefore, this paper explores the potential application of deep learning technology to industrial and commercial resource-scheduling management. By analyzing the benefits of deep learning technology and the bottleneck issues of existing industrial and commercial resource scheduling, a real-time optimized industrial and commercial resource-scheduling model based on deep learning technology is developed. The model is evaluated using the respective data set. The test results demonstrate that the resource-scheduling model proposed in this paper has strong real-time and high-operation efficiency and can assist engineers in completing the corresponding resource-scheduling tasks. [ABSTRACT FROM AUTHOR]

With the rapid evolution of the Internet, the application of artificial intelligence fields is more and more extensive, and the era of AI has come. At the same time, adversarial attacks in the AI field are also frequent. Therefore, the research into adversarial attack security is extremely urgent. An increasing number of researchers are working in this field. We provide a comprehensive review of the theories and methods that enable researchers to enter the field of adversarial attack. This article is according to the "Why? → What? → How?" research line for elaboration. Firstly, we explain the significance of adversarial attack. Then, we introduce the concepts, types, and hazards of adversarial attack. Finally, we review the typical attack algorithms and defense techniques in each application area. Facing the increasingly complex neural network model, this paper focuses on the fields of image, text, and malicious code and focuses on the adversarial attack classifications and methods of these three data types, so that researchers can quickly find their own type of study. At the end of this review, we also raised some discussions and open issues and compared them with other similar reviews. [ABSTRACT FROM AUTHOR]

Facial expression recognition technology has been more and more in demand in security, entertainment, education, medical, and other domains as artificial intelligence has advanced, and face expression recognition technology based on deep learning has become one of the research hotspots. However, there are still some issues with the existing deep learning convolutional neural network; the feature extraction technique has to be improved, and the design of the detailed network structure needs to be optimized. It is critical to do more research on the deep learning convolutional neural network model in order to increase the accuracy of face facial expression detection. In this paper, a deep learning convolutional neural network structure combining VGG16 convolutional neural network and long and short-term memory networks is designed to address the shortcomings of existing deep learning methods in face expression recognition, which are prone to overfitting and gradient disappearance, resulting in low test accuracy. This structure easily and effectively collects facial expression information and then classifies the retrieved features using a support vector machine to detect face expressions. Finally, the fer2013 dataset is used to train face expression recognition, and the results demonstrate that the built deep convolutional neural network model can effectively increase face expression identification accuracy. [ABSTRACT FROM AUTHOR]

In recent years, with the rise of artificial intelligence, deep neural network models have been used in various image recognition researches. Land desertification is a major environmental problem facing the world at present, and how to do a good job in dynamic monitoring is particularly important. For remote sensing images, this paper constructs a GA-PSO-BP analysis model based on BP neural network, genetic algorithm, and particle swarm algorithm and compares the classification training accuracies of the four models of BP, GA-BP, PSO-BP, and GA-PSO-BP; GA-PSO-BP was selected for dynamic analysis of desertification images, and the results showed the following: (1) By comparing the regional classification training accuracies of the four models of BP, GA-BP, PSO-BP, and GA-PSO-BP, the GA-PSO-BP neural network remote sensing image classification method proposed in this paper is simple and easy to operate. Compared with traditional remote sensing image classification methods and traditional neural network classification methods, the classification accuracy of remote sensing effects is improved. (2) Carrying out desertification analysis on remote sensing images of Horqin area, from 2010 to 2015, the desertified land area in the test area increased by 1.56 km2; from 2015 to 2020, the desertified land area in the test area decreased by 1.131 km2, and the desertified land in the test area from 2010 to 2020 showed a trend of increasing first and then decreasing, which is consistent with the actual situation. The GA-PSO-BP remote sensing image classification model has a good performance portability. [ABSTRACT FROM AUTHOR]

Power transformer is pivotal equipment in a power system, which is responsible for energy transmission and transformation, and its operating condition is related to the safe operation of the power system. In the 21st century, computer science has entered a stage of rapid development, advanced network structures and algorithms have been applied to the field of artificial intelligence, and pattern recognition theory and technology have also made great progress. In the past, the identification of partial discharge type mainly relied on the experience of operation and maintenance personnel, and manual analysis and judgment were made based on partial discharge mapping, which was not very accurate. The application of the computer pattern recognition method in the field of partial discharge type identification has changed the status quo of manual identification, and this method has substantially improved the accuracy and efficiency of identification. Pattern recognition using computer technology has been applied to the field of partial discharge analysis. Compared with manual recognition, its recognition results are accurate, recognition speed is fast, and it has great potential for development. This paper proposes an artificial neural network-based model for transformer partial discharge pattern recognition, which combines the advantages of artificial neural networks with accurate extraction of local spatial higher-order features and provides a new solution for transformer partial discharge pattern recognition. Extended experiments show that the method proposed in this paper achieves leading performance and has practical application value. [ABSTRACT FROM AUTHOR]

The service efficiency of the university information services platform directly affects the efficiency of university management. However, the university has rich data resources and complex management, and how to construct a comprehensive, standardized, efficient, and shared university information services platform has become a research hotspot. In recent years, the progress of artificial intelligence (AI) in many aspects has created opportunities for its large-scale application in smart campuses. With the wide penetration of AI technology into all walks of life, especially in the fields of industry, commerce, finance, security, and so on, some technologies have experienced practical tests. Many scholars have discussed the significance and possibility of the application of AI in the field of education from a theoretical level. The university's secure information services platform can penetrate into all details of university management. Therefore, this paper studies the construction of a university secure information services platform based on AI, taking the dormitory allocation of freshmen and the face recognition of each building as the entry points. First, the beetle antennae search algorithm was introduced to improve the clustering efficiency and accuracy of the K-means algorithm for intelligent dormitory allocation. Then, the improved variational auto encoder-generative adversarial networks (VAE-GAN) model and convolutional neural networks (CNN)-based face recognition algorithm are proposed to enhance the security of building entry in universities. Finally, the simulation results reveal that the proposed two algorithms improve the clustering efficiency in dormitory allocation and the security of the university in the basic construction of the university information services platform, respectively. [ABSTRACT FROM AUTHOR]

The oral English test in domestic universities requires teachers to modify a large number of candidates' oral recordings. This is the work of using time repeatedly. Using the CALL system to realize the automation of conversation recording can reduce the burden of teachers' work. Therefore, it is of great practical significance to develop an automatic and accurate scoring system for oral English. With the development of artificial intelligence, deep learning technology has been gradually applied in various fields. Similarly, in the application of oral scoring, deep learning technology makes the implementation of such a system possible. Based on the deep learning technology, this paper proposes an automatic scoring algorithm for spoken language and implements a detailed design and evaluation system. The system consists of two modules. The pronunciation standard of spoken pronunciation and the content of spoken pronunciation are scored, and the sum of these two scores is the final score. Finally, this paper uses 650 oral English recordings from a college English test to train the artificial neural network. Experimental results show that if the training data set is small, the BP network model can obtain better comprehensive evaluation performance. [ABSTRACT FROM AUTHOR]

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

With the acceleration of global integration, the demand for English instruction is increasingly rising. On the other hand, Chinese English learners struggle to learn spoken English due to the limited English learning environment and teaching conditions in China. The advancement of artificial intelligence technology and the advancement of language teaching and learning techniques have ushered in a new era of language learning and teaching. Deep learning technology makes it possible to solve this problem. Speech recognition and assessment technology are at the heart of language learning, and speech recognition technology is the foundation. Because of the complex changes in speech pronunciation, a large amount of speech signal data, the high dimension of speech characteristic parameters, and a large amount of speech recognition and evaluation computation, the large volume of speech signal processing requires higher requirements of hardware and software resources and algorithms. However, traditional speech recognition algorithms, such as dynamic time-warped algorithms, hidden Markov models, and artificial neural networks, have their advantages and disadvantages. They have encountered unprecedented bottlenecks, so it is difficult to improve their accuracy and speed. To solve these problems, this paper focuses on evaluating the multimedia teaching effect of college English. A multilevel residual convolutional neural network algorithm for oral English pronunciation recognition is proposed based on a deep convolutional neural network. The experiments show that our algorithm can assist learners in identifying inconsistencies between their pronunciation and standard pronunciation and correcting pronunciation errors, resulting in improved oral English learning performance. [ABSTRACT FROM AUTHOR]

Artificial intelligence can bring convenience to human life. In the field of sports rehabilitation, the application of artificial intelligence is becoming more and more in-depth. This paper is aimed at studying the prevention and detection of sports rehabilitation in the context of artificial intelligence and proposing a compliance control method for lower limb rehabilitation robots based on artificial neural networks. In this paper, a double closed-loop control system is designed: the outer loop is an adaptive impedance control model based on sEMG feedback, and the purpose is to adjust the predicted desired joint trajectories. In the inner loop, a sliding mode iterative learning controller is designed to suppress periodic disturbance and abnormal jitter and achieve stable tracking of the target trajectory. Finally, the control method is simulated and verified by matlab/simulink, and a statistical experiment is done on the patient's recovery. The experimental results show that the use of artificial intelligence technology can effectively increase the sensitivity of the control system and improve the recovery rate of patients. Compared with the traditional sports rehabilitation control system, the sensitivity is increased by 22.7%, and the patient recovery rate is increased by 10.4%, which is of great significance in the field of sports rehabilitation. [ABSTRACT FROM AUTHOR]

Potential topics include but are not limited to the following: (1) Security machine learning modelling and architecture (2) Secure multiparty computation techniques for machine learning (3) Attacks against machine learning (4) Machine learning threat intelligence (5) Machine learning for cybersecurity (6) Machine learning for intrusion detection and response (7) Machine learning for multimedia data security (8) Machine learning for public safety After a thorough review process, this special issue has selected a set of eight papers to provide new insights on the abovementioned research areas. Machine learning (ML) has recently gained a renewed interest as the technology powering it has become more widely available and accessible to organizations of all sizes. [Extracted from the article]

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

Human logical thinking exists in the form of language, and most of the knowledge is also recorded and transmitted in the form of language. It is also an important and even core part of artificial intelligence. Communicating with computers in natural language is a long-standing pursuit of people. People can use the computer in the language they are most accustomed to and can also use it to learn more about human language abilities and intelligent mechanisms. The realization of natural language communication between humans and computers means that computers can not only understand the meaning of natural language texts but also express the intentions and thoughts given in natural language texts. This paper designs and studies a computational model for natural language processing (NLP) models for natural language processing. This paper aims to study the design of computing network security model based on natural language processing. This paper proposes three calculation models, which are based on the long-term and short-term memory neural network model (LSTM), FastText model, and text processing model (GCN) based on graph convolution neural network. Several natural language processing models are evaluated and analyzed using four indexes: accuracy, recall, exactness, and F1 vaule. Results show that the performance level of the GCN model is the best. The accuracy of the NLP recognition of this model reaches 86.66%, which is 2.93% and 1.55% higher than the accuracy of the LSTM model and the FastText model, respectively. [ABSTRACT FROM AUTHOR]

Digital predistortion technology is widely used in wireless communication. As a vital part of wireless communication system, the predistortion technology of radio frequency power amplifier has always been a hot and difficult topic. This paper will research and explore the key technologies of radio frequency channel construction and predistortion of navigation information through artificial intelligence technology and neural network. The article first conducts a simple research on a new generation of artificial intelligence systems--artificial intelligence is an engineering technology scientific research that develops theories, methods, skills, and application systems for modeling, scaling, and amplifying collective human knowledge. Then, the article combines neural network algorithms. BP network is not only a part of synthetic neural network but also a multiple layer fed-forward network. Finally, a neural network method is proposed to model and predistort the load channel model. Then, the RF channel distortion model can be constructed, and the RF channel modeling simulation experiment and the RF channel predistortion simulation experiment are carried out. The experimental results of this paper showed that compared with the situation without predistortion, the two indicators of the zero-crossing slope distortion and the zero-crossing offset of the discriminant function of the output signal of the channel with predistortion had been greatly improved. Overall, the neural network-based model outperformed the RVTDNN model by 30% on these two metrics. It also indicated that the neural network model could model and predistort the cascade model well, and the new model had better modeling accuracy and predistortion effect than the RVTDNN model. [ABSTRACT FROM AUTHOR]

An intelligent health detection model is a new technology developed under an artificial intelligence environment, which is of great significance to the care of the elderly and other people who cannot take care of themselves. This paper comprehensively reviews the structural health monitoring method based on an intelligent algorithm, introduces the application model of neural networks in structural health monitoring in detail, and points out the shortcomings of using neural network technology alone. On the basis of previous work, the genetic algorithm and fuzzy theory were introduced as optimization tools, and a new neural network training algorithm was constructed by combining genetic algorithm, fuzzy theory, and neural network technology for structural health monitoring research. Aimed at the shortcoming of insufficient samples for training neural networks based on experimental data, this paper proposes to use the finite element method to construct a genetic fuzzy RBF neural network after corresponding processing of the first six-order bending modal frequencies of the structure, so as to realize the localization and detection of delamination damage of composite beams. Injury Assessment. The experimental results of this paper show that the finite element method proposed in this paper can effectively carry out damage localization and damage assessment; compared with the traditional algorithm, the localization accuracy of this algorithm is improved by 20%, and the damage assessment performance is improved by 10%. [ABSTRACT FROM AUTHOR]

Based on the development background of the interweaving and integration of computer technology and Internet technology, China's artificial intelligence industry is quietly rising. In the social life of the information age, the artificial intelligence industry represented by machine deep learning is playing a very important role. This study in combination with the background of the new health reform, in view of the reform of the medical industry, analyzes the connotation of financial wisdom based on the important role of the hospital financial development problems, puts forward the development direction of artificial intelligence hospital financial wisdom development measures, designed to meet the changing external environment demand, reduces human costs, and improves the overall efficiency of hospital financial fund management. Based on the evaluation results, this paper proposes the correct direction for the development of hospital intelligence finance by using the BP neural network model. After the analysis, it is found that the development of artificial intelligence is an important measure to promote the development of hospital intelligent finance. In other words, hospital intelligent financial management is the product of the continuous progress of artificial intelligence technology. At the present stage, the intelligent financial management problems of hospitals are mainly as follows: (1) lack of financial informatization, (2) lack of perfect financial risk early warning system, and (3) the phenomenon of "information island" in the financial system. After analyzing the above problems, the research believes that the development of hospital intelligent finance based on artificial intelligence needs to solve the above thorny problems, so as to improve the outcome of hospital intelligent finance development. The following work should be done: (1) strengthen the design of information sharing module, (2) intelligent control of the cost of hospitals, and (3) intelligent treatment of hospital accounting. Combining the development of artificial intelligence and hospital intelligent finance theory, combined with the actual trend of financial intelligence development under the background of artificial intelligence development in the new era, it provides scientific basis for the development of hospital intelligent finance. [ABSTRACT FROM AUTHOR]

With the promotion and development of artificial intelligence, the computer major has become a hot major in innovation and entrepreneurship education. It is increasingly essential to carry out a refined evaluation of creativity and venture education to achieve professional differentiation. The purpose of this paper is to study and explore the innovative use of AI in the field of innovation and entrepreneurship of college students in Internet universities. In the article, AI is first introduced. AI is inherently a novel instrument of human praxis, a tool of productivity that embodies human ability and volition. Then we analyze the innovation and venture education on the ground of the BP neural network, and finally, we conduct an empirical study on the innovation and venture on the ground of the BP neural network. Innovation and entrepreneurship refer to entrepreneurial activities based on one or several innovations in technological innovation, product innovation, brand innovation, service innovation, business model innovation, management innovation, organizational innovation, market innovation, and channel innovation. The report analyzes the influencing factors of creative venture education and designs an evaluation model of creative venture education. The test shows that the output of the BP neural network is the actual value, the real worth is reasonable, and the desired worth is also reasonable. The maximum error between the two is 1.64%. The results tend to be generally satisfactory. The BP neural network model can highly generalize students' creative venture abilities, increasing the practical application value and research scope of BP neural networks. [ABSTRACT FROM AUTHOR]

As a member of the oldest art family of human beings, drama is also a very important part of human civilization. With the development of technology, more and more types of drama film and television appear. In today's society, bad films and commercial films are emerging one after another. The intervention of capital has brought a very poor experience to moviegoers and is not conducive to the development of the entire field of drama and film. The further development and scientific promotion of the field of drama film and television has become a relatively important issue. In this context, this paper takes this as the starting point, integrates big data and artificial intelligence algorithms, and aims to analyze the promotion and influence in the field of drama and film. It has existed for a long time in the fields of physics, biology, environmental ecology, and other industries such as military, finance, and communication, but it has attracted people's attention due to the development of the Internet and information industry in recent years. The article introduces the transition of the mainland film system and the manifestations of conflict in plot art and film and television plots. It uses the convolutional neural network algorithm in deep learning to build the system. This paper adopts an online survey, distributes it to moviegoers nationwide, and collects 4,000 valid samples for analysis of moviegoers' occupation and monthly income. In terms of the occupational distribution of the audience, the proportion of white-collar workers in the company is the highest at 40%, the student group only accounts for 17.4%, and the retired worker group accounts for the smallest proportion, at 0.6%. To sum up, the structure of Chinese films has shifted from a younger, student-dominated, middle-income group to a mature, workplace-oriented, middle- and high-income group. It has successfully completed the analysis of the promotion and impact of big data and artificial intelligence in the field of drama, film, and television. [ABSTRACT FROM AUTHOR]

The particularity and quantity of English translation terms have a great impact on the quality and effect of machine translation and can not meet the requirements of English translation of terms. At the same time, technical exchange and communication in different fields need the expression of professional terms. In addition, although the neural machine translation model has good translation performance, it is not ideal for target languages with small translation needs and limited corpus resources. In order to solve the problems existing in the English translation model, this paper constructs the transformer model by replacing the cyclic neural network variables and introducing the attention mechanism. The term information is integrated into two pretraining models to improve the learning ability of model language sentence relationship. Maintain the integrity of terminology information by fully completing the training. The experimental results show that, compared with the other three term translation models, the translation model in this paper has the advantage of term information. At the same time, the deep neural network English term translation model can obtain more fine-grained word relevance. In different corpora, the Bleu score of the model is good, showing obvious translation performance advantages. This study provides a good professional reference value for the translation of English terms. [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]

The annotated dataset is an essential requirement to develop an artificial intelligence (AI) system effectively and expect the generalization of the predictive models and to avoid overfitting. Lack of the training data is a big barrier so that AI systems can broaden in several domains which have no or missing training data. Building these datasets is a tedious and expensive task and depends on the domains and languages. This is especially a big challenge for low-resource languages. In this paper, we experiment and evaluate many various approaches on sentiment analysis problems so that they can still obtain high performances under limited training data. This paper uses the preprocessing techniques to clean and normalize the data and generate the new samples from the limited training dataset based on many text augmentation techniques such as lexicon substitution, sentence shuffling, back translation, syntax-tree transformation, and embedding mixup. Several experiments have been performed for both well-known machine learning-based classifiers and deep learning models. We compare, analyze, and evaluate the results to indicate the advantage and disadvantage points of the techniques for each approach. The experimental results show that the data augmentation techniques enhance the accuracy of the predictive models; this promises that smart systems can be applied widely in several domains under limited training data. [ABSTRACT FROM AUTHOR]

Sponge city is currently a new urban rain flood management concept. It refers to the natural disaster that adapts to environmental changes and the natural disasters that is brought by rainwater. It is also known as the sponge, and the alias can also be called as "water elastic city." The purpose of this article is, based on artificial intelligence, to study the analysis of wisdom management models in sponges. This paper first introduces the artificial intelligence algorithm and sponge city, then compares the traditional sponge city and the wisdom sponge city, then creates a LSTM neural network model, introducing artificial intelligence into sponge city intelligent dynamics in the analysis, and finally compares the rainfall data analysis to the ground. The experimental results show that, at different time points, the training results of rainfall data have shown significant regularity. The entire rainfall process exhibits a rise-decline-rise-increase trend of repetition. The maximum rainfall appeared at the 18th hour, and 7 obvious peaks occurred in 7, 11, 14, 16, 18, 21, and 23 hours. [ABSTRACT FROM AUTHOR]

Artificial intelligence has become one of the most rapidly developing disciplines in the application field of pattern recognition. In target recognition, sometimes, there are multiple identical or similar copies of the target to be recognized in the image, and it is difficult to classify and estimate by traditional methods. In this case, it is necessary to use the SOM network to separate multiple targets and use the multiple order parameters in the improved SNN to pair the target. The change of its thickness can intuitively reflect the abnormality of its tissue. Therefore, the choroidal thickness of the central fovea can be measured to study the relationship between the choroidal structure and BRVO and arteriosclerosis. The purpose of this study is to further study the correlation between branch retinal vein occlusion and arteriosclerosis by quantitatively measuring retinal vessel diameter and choroidal thickness, to analyze the correlation between different TCM syndrome types of nonischemic BRVO and retinal arteriosclerosis, and to provide theoretical basis for clinical nonischemic BRVO TCM syndrome types and traditional Chinese medicine treatment, so as to reflect its clinical application value. In order to solve the single fixed structure of traditional SNN and poor scalability, combined with the Kohonen layer structure in the self-organizing mapping network, an improved collaborative neural network model is proposed. This paper studies the network training method and operation convergence and analyzes the converged network and the pattern classification results obtained by the network. In order to solve the single fixed structure of traditional SNN and poor scalability, combined with the Kohonen layer structure in the self-organizing mapping network, an improved collaborative neural network model is proposed. The results of our proposed improved model on the MNIST dataset can achieve the same level of current state-of-the-art machine learning classifiers in recognition accuracy with a smaller network size and network complexity. [ABSTRACT FROM AUTHOR]

In the field of intelligent transportation systems (ITS), video surveillance is a hot research topic; this surveillance is used in a variety of applications, such as detecting the cause of an accident, tracking down a specific vehicle, and discovering routes between major locations. Object detection and shadow elimination are the main tasks in this area. Object detection in computer vision is a critical and vital part of object and scene recognition, and its applications are vast in the fields of surveillance and artificial intelligence. Additionally, other challenges arise in regard to video surveillance, including the recognition of text. Based on shadow elevation, we present in this work an inner-outer outline profile (IOOPL) algorithm for detecting the three levels of object boundaries. A system of video surveillance monitoring of traffic can be incorporated into this method. It is essential to identify the type of detected objects in intelligent transportation systems (ITS) to track safely and estimate traffic parameters correctly. This work addresses the problem of not recognizing object shadows as part of the object itself in-vehicle image segmentation. This paper proposes an approach for detecting and segmenting vehicles by eliminating their shadow counterparts using the delta learning algorithm (Widrow-Hoff learning rule), where the system is trained with various types of vehicles according to their appearance, colors, and build types. An essential aspect of the intelligent transportation system is recognizing the type of the detected object so that it can be tracked reliably and the traffic parameters can be estimated correctly. Furthermore, we propose to classify vehicles using a machine learning algorithm consisting of artificial neural networks trained using the delta learning algorithm, a high-performance machine learning algorithm, to obtain information regarding their travels. The paper also presents a method for recognizing the number plate using text correlation and edge dilation techniques. In regard to video text recognition, number plate recognition is a challenging task. [ABSTRACT FROM AUTHOR]

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

This paper presents an in-depth study and analysis of the model of English writing using artificial intelligence algorithms of neural networks. Based on word vectors, the unsupervised disambiguation, and clustering of multimedia contexts extracted from massive online videos, the disambiguation accuracy reaches over 0.7, and the resulting small-scale multimedia context set can cover up to 90% of vocabulary learning tasks; user experiments show that the multimedia context learning system based on this method can improve the effectiveness and experience of ESL vocabulary learning, as well as the long-term word sense memory of learners. The results are 30% better. Based on the dependency grammatical relations and semantic metrics of collocations on a large-scale professional corpus, we established a collocation intention description and retrieval method in line with users' linguistic cognition and doubled the usage rate of collocation retrieval on the actual deployment system after half a year, becoming a user "sticky" ESL writing aid, and further defined style. Dictionaries only provide basic lexical definitions, and, even if supported by example sentences, they still cannot meet the needs of ESL authors in terms of expressive accuracy and richness. However, the current machine translation is based on the black box deep neural network construction, and its translation process is not understandable and interactive. Among the three algorithmic models constructed in this paper, the multitask learning model outperforms the conditional random field model and the LSTM-CRF model because the multitask learning model with auxiliary tasks solves the problem of sparse data to a certain extent, allowing the model to be trained more adequately in the case of uneven label distribution, and thus performs better than other models in the task of grammatical error detection. [ABSTRACT FROM AUTHOR]

As one of the three pillar industries of tourism, the hotel industry has developed rapidly in recent years, especially the construction and development of urban star hotels. The major star hotel brands have poured in one after another, and a large amount of funds have been invested in the star hotel market. The continuous construction of various types of star hotels has formed the geographical agglomeration phenomenon of star hotels in Chinese cities. This phenomenon has not only brought the advantages of regional economies of scale and brand building, but also brought the competition between star hotels. Under the guidance of market demand, tourism can be created through industrial integration and functional combination to develop the tourism industry, which makes the spatial distribution of tourism hotels closely related to transportation, consumer demand, urban environment, and even industrial policy. In recent years, artificial intelligence methods are more and more applied to tourism, mainly including rough set method, genetic algorithm, fuzzy time series, grey theory, artificial neural network, and support vector machine. Based on artificial intelligence technology, this paper studies the spatial distribution characteristics and configuration of tourism hotels. The distance of the peak value of tourism hotel spatial distribution characteristics is 4.8 km and 6.6 km, respectively, and the distance of the peak value of natural tourism resources is 0.98 km. Because the spatial distribution characteristics of cultural, special, and tourism hotels are restricted by their own quantity, resource endowment, and inherent attributes, the spatial scope of location layout is relatively small. The spatial distribution characteristics and configuration of tourist hotels can also timely monitor and prevent various emergencies, prevent tourism safety accidents, and improve the ability of tourism emergency management by using artificial intelligence and other technologies. With the continuous optimization of artificial intelligence technology and the deepening of cooperation between tourism disciplines and interdisciplinary, the research on tourism big data driven by research problems will be more mature. [ABSTRACT FROM AUTHOR]

Scarcity of water resources is becoming a threatening issue in arid regions like Gulf. Accurate prediction of quantities and quality of groundwater is the first step towards better management of water resources where groundwater is the major source of water supply. Groundwater modelling with respect to its quantity and quality has been performed in this paper using Artificial Neural Networks (ANNs), Adaptive Neurofuzzy Inference System (ANFIS), and hydraulic model MODFLOW. Five types of ANN models with various training functions have been investigated to find the most efficient training function for the prediction of quantity and quality of groundwater, which is an original contribution useful for engineering sector. The results of the hydraulic model, ANFIS, and ANN have been compared. Nash-Sutcliffe Model Efficiency and Mean Square Error have been used for assessing the performance of models. Taylor's Diagram has also been used to compare various models. The part of Saq Aquifer lying in the Qassim Region has been investigated as the study area. Modern tools, including Geographical Information System (GIS) and Digital Elevation Model (DEM) are applied to process the required data for modelling. Climatic, geographical, and quality of groundwater (contaminants) data are obtained from the Ministry of Environment, Water, and Agriculture, Jeddah/Riyadh. ANFIS model is found to be the most efficient for modelling both the quality and quantity of the aquifer. Sensitivity analysis was performed, and then various future scenarios were investigated for sustainable groundwater pumping. The results of the research will be useful for the community and experts working in the field of water resources engineering, planning, and management in arid regions. [ABSTRACT FROM AUTHOR]

In today's society, China's social market continues to develop, which has caused the whole society to attach great importance to the professional ethics of Chinese citizens and put forward new requirements and expectations for it. This paper aims to research and discuss the diversified integration and integrated application of ideological and political teaching resources based on artificial intelligence model algorithms. This paper first analyzes and discusses the feasibility of the integration and application of moral education resources in ideological and political textbooks. Political classrooms provide favorable support for the integration and application of moral education resources, and political classrooms have become a practical carrier for the integration and application of moral education resources. And then it introduces the artificial intelligence model algorithm-artificial neural network algorithm. Artificial intelligence is a new intelligent machine that can make intelligent responses similar to human actions according to human actions. Finally, it studies the use of social teaching resources for ideological and political education in city A and conducts a comprehensive analysis on the integration and utilization of moral education teaching resources for ideological and political courses in colleges and universities. In the survey of "Whether the introduction of local social resources in the political class of city A is helpful to students' learning," 84 teachers thought it was very helpful, 12 teachers thought it was helpful, and only 3 teachers thought it was not helpful. It can be found that most teachers think that it is helpful for students to introduce social resources in political lessons. At the same time, in order to better improve the reliability and validity of moral resources and to internalize social moral norms into students' hearts, it is only by combining moral resources with students' moral growth. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology, deep learning has been applied to automatic modulation classification (AMC) and achieved very good results. In this paper, we introduced an improved deep neural architecture for implementing radio signal identification tasks, which is an important facet of constructing the spectrum-sensing capability required by software-defined radio. The architecture of the proposed network is based on the Inception-ResNet network by changing the several kernel sizes and the repeated times of modules to adapt to modulation classification. The modules in the proposed architecture are repeated more times to increase the depth of neural network and the model's ability to learn features. The modules in the proposed network combine the advantages of Inception network and ResNet, which have faster convergence rate and larger receptive field. The proposed network is proved to have excellent performance for modulation classification through the experiment in this paper. The experiment shows that the classification accuracy of the proposed method is highest with the varying SNR among the six methods and it peaks at 93.76% when the SNR is 14 dB, which is 6 percent higher than that of LSTM and 13 percent higher than that of MentorNet, Inception, and ResNet purely. Besides, the average accuracy from 0 to 18 dB of the proposed method is 3 percent higher than that of GAN network. It will provide a new idea for modulation classification aiming at distraction time signal. [ABSTRACT FROM AUTHOR]

Moving target detection and tracking technology is the core technology in the field of computer vision. It integrates image processing, pattern recognition and intelligence, and artificial intelligence and automatic control are the keys to an intelligent video surveillance system. The method acquires video image signals through visible light or infrared sensors, performs digital image processing on the video images, detects moving targets, and then extracts moving targets for target recognition. Then, the moving target is predicted and tracked according to the image features and spatiotemporal features, and the contour shape, position, and motion trajectory of the target are obtained, which provides data support for subsequent tasks. This paper uses the convolutional neural network model HyperNet as the technical support to study the deep learning (DL) tennis video target extraction. The final experimental results show that the loss value of the loss function in the training process of this method is stably maintained between 1.5% and 2.5%, and the speed performance is also greatly improved. The number of boxes for extracting candidate regions is significantly reduced, the calculation time for each frame will not exceed 1.8 s, the orientation accuracy of target extraction is 96.32%, and the size accuracy is 91.05%. [ABSTRACT FROM AUTHOR]

As the core of artificial intelligence, machine learning has strong application advantages in multi-criteria intelligent evaluation and decision-making. The level of sustainable development is of great significance to the safety evaluation of coal mining enterprises. BP neural network is a classical algorithm model in machine learning. In this paper, the BP neural network is applied to the sustainable development level decision-making and safety evaluation of coal mining enterprises. Based on the analysis of the evaluation method for sustainable development of coal enterprises, the evaluation index system of sustainable development of coal enterprises is established, and a multi-layer forward neural network model based on error backpropagation algorithm is constructed. Based on the system theory of man, machine, environment, and management, and taking the four single elements and the whole system in a coal mine as the research object, this paper systematically analyzes and studies the evaluation and continuous improvement of coal mine intrinsic safety. The BP neural network evaluation model is used to analyze and study the intrinsic safety of coal mines, the shortcomings of the intrinsic safety construction of coal mines are found, and then improvement measures are put forward to effectively promote the safe production of coal mines and finally realize the intrinsic safety goal of the coal mine. [ABSTRACT FROM AUTHOR]

This article is aimed at studying the legal regulation of artificial intelligence and edge computing automated decision-making risks in wireless network communications. The data under artificial intelligence is full of flexibility and vitality, which has changed the way of data existence in the whole society. Its core is various algorithm programs, which determine the existence of artificial intelligence. In this environment, society develops rapidly with unstoppable momentum. However, from a legal perspective, artificial intelligence has algorithmic discrimination, such as gender discrimination, clothing discrimination, and racial discrimination. It does not possess openness, objectivity, and accountability. The consequences are sometimes serious enough to endanger the public interest of the entire society, leading to market disorder, etc. Therefore, the problem of artificial intelligence algorithm discrimination remains to be solved. This article uses algorithms to adjust algorithm discrimination to reduce the harm caused by artificial intelligence algorithm discrimination to a certain extent. First of all, this article introduces a regulatory-based edge cloud computing architecture model. It is mentioned that distributed cloud computing can use subsystems to calculate various resources and storage resources and can make automated decisions when calculating certain data. In order to reduce the impact of algorithm discrimination and trigger data diversification to reduce the probability of discrimination, an edge computing network data capture system is designed. And this article mentions the BP neural network model. The BP neural network model is divided into input layer, output layer, and hidden layer. The training samples are passed from the input layer to the output layer through the hidden layer. If the output information does not meet expectations, the error will be back-propagated, and the connection weight will be adjusted continuously. This paper proposes a deep learning system model in real-time artificial intelligence driven by edge computing. When this model is applied to legal regulations, it can cooperate with edge computing and artificial intelligence algorithms to provide high-precision automated decision-making. Finally, this paper designs an artificial intelligence-assisted automated decision-making experiment based on the theory of legal computing. This paper proposes a Bayesian algorithm that uses edge algorithms to merge into artificial intelligence and verifies the feasibility of this hypothesis through experiments. The experimental results show that it has a certain ability to regulate algorithmic discrimination caused by artificial intelligence in legal regulations. It can improve the regulatory effects of laws and regulations to a certain extent, and the improved artificial intelligence Bayesian algorithm clustering effect of edge computing is increased by about 7.2%. [ABSTRACT FROM AUTHOR]