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With the rapid development of China's economy, people are eager for an effective way to relieve work pressure and strengthen their health at the same time. Outdoor sport is one of the best choices for people. However, the amount of recommended data on the network is very large. As a result, when people understand outdoor sports through the network, they cannot effectively obtain the information they want. This is the problem of "information overload," and personalized recommendation system can effectively alleviate this problem. In order to effectively recommend outdoor sports to users, a useful attempt was made in the personalized recommendation system for outdoor sports in this paper. The specific work of this paper is as follows: firstly, the current situation of outdoor sports in China was summarized, and the related technologies of the recommendation system were studied, including user modeling technology, recommendation target modeling technology, and recommendation algorithm. In order to obtain better recommendation effect, this paper proposes to mix user-based collaborative filtering recommendation algorithm, project-based collaborative filtering recommendation algorithm, and content-based recommendation algorithm. The hybrid algorithm adopts the way of feature expansion and weighted combination. Firstly, the hybrid model (model 1) of user-based collaborative filtering recommendation and content-based recommendation is obtained. Secondly, the hybrid model (model 2) based on project collaborative filtering recommendation and content-based recommendation was obtained. Finally, model 1 and model 2 were combined together to get a hybrid model with better final recommendation effect. For the common cold start problem in the recommendation system, the system adopts content-based recommendation algorithm to solve it. [ABSTRACT FROM AUTHOR]

Based on a machine learning algorithm, this paper deeply explores the privacy protection of personal information. In this paper, the definition of the machine learning algorithm is put forward, the design idea of privacy protection in joint machine learning platform is studied, and the architecture model and model parameter updating strategy of joint machine learning under privacy protection are designed. To protect the privacy of personal information, this paper also proposes a data homomorphic verification mechanism to prevent the global parameters from being tampered with by malicious cloud servers. In order to verify the performance of the models constructed in this paper, the comparative experiments of different models are carried out. The experimental results show that this algorithm has a fast convergence speed, and the average error rate decreases by 4.17% compared with the traditional algorithm. Moreover, the accuracy of this algorithm reaches 95.37%, which is about 8.76% higher than the previous algorithm. This model is applied to the field of personal information privacy protection, which can provide a safe and reliable environment for personal information privacy and effectively protect the privacy of data owners. And, means and reference value is provided for the development direction of privacy protection. [ABSTRACT FROM AUTHOR]

In order to effectively solve the problem of relatively large errors in individual positioning strategies in indoor environments, this paper applies the genetic optimization neural network algorithm to indoor location based on multi-source information fusion. The range of the geomagnetic fitness is constrained based on the results obtained by using the wireless WiFi positioning for combination and matching, which can reduce the value of the matching error effectively. Subsequently, the global optimal value of the indoor network is calculated based on the genetic algorithm, which can optimize the initial value and threshold of the neural network after genetic optimization so as to improve the accuracy of the network to the greatest extent possible while accelerating the convergence speed at the same time. After the optimization processing is completed, fusion training can be performed on the coordinates of the actual positions based on the obtained combination positioning situation and the predicted positioning result in the indoor network. Finally, the optimal positioning result can be obtained accordingly. Through the analysis of practical cases, it can be known that the mean square error predicted based on the genetic optimization neural network calculated by using the genetic algorithm can be effectively reduced by 76%, and the accuracy of the fusion positioning can be increased by 48% on average compared with the accuracy of a single positioning strategy. Hence, the method put forward in this paper has effectively improved the positioning accuracy, which suggests that its positioning performance is superior. [ABSTRACT FROM AUTHOR]

With the rapid development of image video and tourism economy, tourism economic data are gradually becoming big data. Therefore, how to schedule between data has become a hot topic. This paper first summarizes the research results on image video, cloud computing, tourism economy, and data scheduling algorithms. Secondly, the origin, structure, development, and service types of cloud computing are expounded in detail. And in order to solve the problem of tourism economic data scheduling, this paper regards the completion time and cross-node transmission delay as the constraints of tourism economic data scheduling. The constraint model of data scheduling is established, the fitness function is improved on the basis of an artificial immune algorithm combined with the constraint model, and the directional recombination of excellent antibodies is carried out by using the advantages of gene recombination so as to obtain the optimal solution to the problem more appropriately. When the resource node scale is 100, the response time of EDSA is 107.92 seconds. [ABSTRACT FROM AUTHOR]

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

The vigorous development of tourism has made rural tourism a highlight of the new era. In order to better realize the classification of rural tourism features, this paper proposes a knowledge recognition algorithm based on hierarchical clustering analysis. Firstly, the rationality of the optimization of the rural tourism feature algorithm is analyzed in this paper; secondly, the rural tourism feature classification index system is constructed based on the hierarchical clustering analysis; finally, the index weights are clearly divided according to the characteristics through the hierarchical clustering analysis of the knowledge identification algorithm. According to the specialties of hierarchical clustering analysis, the criteria of the algorithm are determined, and the characteristics of rural tourism are carefully classified. Rural tourists can visit different scenic spots in high density and improve the mobility of rural tourism. The experimental results show that: through the analysis of the characteristic classification data of the Rural Tourism College in this paper, it can be seen that the average daily income of the scenic spot is higher than that of the traditional rural scenic spot. The average daily income of rural tourism has increased by more than 261,900 yuan, which has largely promoted the development trend of rural tourism in my country. It is proved that the hierarchical clustering analysis method is helpful for rational zoning and serious thinking about the characteristics of rural tourism. This paper provides a reference for promoting the classification of rural tourism. [ABSTRACT FROM AUTHOR]

With the advancement of science and technology as well as the continual improvement of big data analysis technology, the accuracy of traditional data information classification has declined, making it impossible to assess English ability effectively. A competency evaluation model for college English teaching vacancies is built using this information and the big data architecture. The ability of the big data information model is evaluated and feature information of ability constraints is extracted using the predefined constraint parameter index analysis model. Simultaneously, the K-means clustering algorithm is used to cluster and integrate a series of index parameters of English ability using big data, and the English teaching resource allocation plan is completed in accordance with this, allowing for the scientific evaluation of English teaching ability. The results of the studies show that the clustering method utilized in the context of big data can aid in the evaluation of English competence. In the experiment, four test cycles of English teaching skills were set up, and the effectiveness of the English evaluation techniques described in this paper and two classical cluster evaluation methods were compared and tested. The research shows that using the method described in this paper to evaluate English teaching skills can significantly improve the full utilization of data. [ABSTRACT FROM AUTHOR]

In order to improve the development effect of private education, this paper analyzes the current situation of private education combined with the data mining algorithm and explores the problems existing in the development of private education. Moreover, this paper combines the semi-parametric product estimation method with parameter estimation and applies the estimation method to model-assisted sampling estimation. This work enhances the estimate accuracy of the sample estimation and increases the field of application of the model while enhancing the classic generalized regression estimation. It also modifies the estimation accuracy on the basis of the linear assumption. The experimental study reveals that the data mining algorithm-based analysis approach for private education development provided in this work has a certain impact, and the development strategy of private education is assessed on this premise. [ABSTRACT FROM AUTHOR]

In the field of computer vision, action recognition is a very difficult topic to study. This paper suggests a dance movement recognition method based on DL network in accordance with the characteristics of dance movements. The backbone network in this study is a thin network called Mobile Net. The two-dimensional convolution network, which can only extract spatial features, can extract and fuse time domain features and use them for dance movement recognition by combining the time domain modelling strategy of time domain feature transfer between convolution layers. It uses fewer network parameters and less computation than the original multitarget detection model. Using the clustering method to preset the prior frames of human detection with various sizes and numbers also enhances the model's performance. Finally, the experimental findings demonstrate that the algorithm suggested in this paper outperforms the Incision v3 algorithm in F1 by 9.87 percent and outperforms the traditional CNN algorithm in identification accuracy by 6.51 percent and 10.76 percent, respectively. It is evident that the algorithm used in this paper reduces running time and, to a certain extent, improves the accuracy of dance movement recognition. For related research, it offers some references. [ABSTRACT FROM AUTHOR]

In the current era of increasingly complex social life, as people's demand quality is getting higher and higher, the solution of the problem often has multiple indicators to achieve the optimum. This forces the graph coloring problem (GCP) to become complicated, and it is difficult to directly obtain the optimal solution, which brings new challenges to the solution of the problem. In response to this problem, the GCP field is of great research significance. With the in-depth study of GCP, the research on multiobjective optimization (MOO) in graph coloring algorithm (GCA) is gradually carried out. Its performance advantage is of great significance for solving multicondition constraint problems. This paper aims to study the application of Genetic Algorithm (GA) in GCA. Through the analysis and research of GA, and the fusion of numerical analysis and scientific computing, it can be applied to the construction of the neighbor distinguishable uniform V-full coloring algorithm (AVDEVTCA) to solve the AVDEVTC problem. This paper describes the basic theory of MOO and graph coloring. It conducts an experimental analysis of the algorithm performance and uses the relevant theoretical formulas to explain it. The results show that the algorithm takes 5754.142 S seconds to test 21325415 images and can color a large number of results that cannot be done manually in less time. It has greatly improved in terms of time and manpower saving and greatly improved practicability and work efficiency. [ABSTRACT FROM AUTHOR]

Within the context of the Internet's rapid development, the rise of cross-border e-commerce has undoubtedly created a once-in-a-generation opportunity for Chinese-made brands to expand internationally. Cross-border e-commerce is reshaping the way foreign trade is conducted. This paper begins by examining the development status of the cross-border e-commerce industry in Shaoxing, then investigates, and analyzes the factors influencing the development of cross-border e-commerce. Finally, it makes supporting policy recommendations. Additionally, this paper analyzes the logistics links in cross-border e-commerce and discusses its hot topics using the ART algorithm and personalized clustering method. [ABSTRACT FROM AUTHOR]

Each Chinese region has its own ancient opera, which is a treasure of folk culture and a living fossil for studying the historical origins of a local culture. It has significant academic value and historical significance at the national and local levels, whether from the perspective of promoting and disseminating national culture or from the perspective of protecting the world's intangible cultural heritage. Based on this practical significance, this paper conducts a study using the particle swarm algorithm to manage the marketing archives of drama intangible cultural heritage. The article employs the PSO algorithm to test the particle swarm optimization algorithm's convergence and conditions. The effectiveness of the algorithm is analysed in the Sphere function, Rosenbrock function, Griewanks function, and Rastrigin noncont function, and then the algorithm is compared, including the calculation speed comparison between the algorithm in this paper and the three optimal fitness functions. The experimental results show that the PSO algorithm has the highest four items in the statistics of the Schwefel function experimental results. About 45.0379 is the best value and 70.5878 is the maximum precision. The optimal average value is 6.1524, while the average value is 56.15245. In comparison to the QPSO and PSO algorithms, the algorithm in this paper has a faster convergence speed and better search accuracy. The topic of the intersection of the disciplines of drama, intangible cultural heritage marketing, and archive management using the particle swarm algorithm is well-developed. [ABSTRACT FROM AUTHOR]

In this paper, we construct a model of convolutional neural network speech emotion algorithm, analyze the classroom identified by the neural network with a certain degree of confidence together with the school used in the dataset, find the characteristics and rules of teachers' control of classroom emotion nowadays using big data, find the parts of classroom emotion, and design a classroom emotion recognition model based on convolutional neural network speech emotion algorithm according to these characteristics. This paper will investigate the factors and patterns of teachers' emotional control in the classroom. In this paper, the existing neural network is adapted and improved, and some preprocessing is performed on the current dataset to train the network. The network used in this paper is a combination of convolutional neural network (CNN) and recurrent neural network (RNN), which takes advantage of both CNN for feature extraction and RNN for memory capability in the sequence model. This network has a good effect on both object labeling and speech recognition. For the problem of extracting emotion features of whole-sentence speech, we propose an attention mechanism-based emotion recognition algorithm for variable-length speech and design a spatiotemporal attention module for the speech emotion algorithm and a convolutional channel attention module for the CNN network to reduce the contribution of the spatiotemporal data of the speech emotion algorithm and the unimportant parts of the CNN convolutional channel feature data in the subsequent recognition by the attention mechanism. In turn, the weight of core key data and features is increased to improve the model recognition accuracy. [ABSTRACT FROM AUTHOR]

The forecast of capital demand has the characteristics of uncertainty. There are known and unknown information about the capital demand for regional economic development. In fact, there are also some in between, that is, uncertain. Consumption is the ultimate goal of production and a key link in realizing a virtuous circle of economic development. This paper uses the GM (1, 1) model to compare the predicted value of the test area with the actual value in 5 years, and the loudness is as high as 90%. Under the guidance of the profit model, the regional economic capital demand has a decisive influence on the regional economic development. The predictive analysis model of capital needs is conducive to fully mobilizing the impact of infrastructure construction of all parties and is an important factor affecting economic development. The mathematical model proposed in this paper is helpful for deepening the research on the management of regional economic development and enriching the theoretical system of regional economic development. [ABSTRACT FROM AUTHOR]

The transmitting signal of wireless communication system is impaired by the nonlinearity of RF power amplifier (PA) which leads to signal distortion and spectrum spillover, by which the signal transmission quality is affected. Digital predistortion (DPD) is an efficient and economical way to correct the nonlinear effects of power amplifiers. The recursive least square (RLS) recognition algorithm is commonly used to extract the correction coefficients of the DPD model, and the accuracy of the extraction directly affects the system performance. In this paper, a new variable forgetting factor identification algorithm (new variable forgetting factor recursive least square, NVFFRLS) is proposed for recursive least square (RLS) identification algorithm. The 64-QAM signal is combined with a memory polynomial (MP) predistortion model for predistortion system simulation. The experimental results show that, compared with the RLS identification algorithm and two kinds of variable forgetting factor RLS identification algorithms, the algorithm has smaller estimation error, faster convergence, and better tracking capability, stability, and adaptability; the predistortion system based on NVFFRLS identification algorithm can compensate the nonlinear memory effects of power amplifier more effectively. [ABSTRACT FROM AUTHOR]

Recently, conformable calculus has appeared in many abstract uses in mathematics and several practical applications in engineering and science. In addition, many methods and numerical algorithms have been adapted to it. In this paper, we will demonstrate, use, and construct the cubic B-spline algorithm to deal with conformable systems of differential boundary value problems concerning two points and two fractional parameters in both regular and singular types. Here, several linear and nonlinear examples will be presented, and a model for the Lane-Emden will be one of the applications presented. Indeed, we will show the complete construction of the used spline through the conformable derivative along with the convergence theory, and the error orders together with other results that we will present in detail in the form of tables and graphs using Mathematica software. Through the results we obtained, it became clear to us that the spline approach is effective and fast, and it requires little compulsive and mathematical burden in solving the problems presented. At the end of the article, we presented a summary that contains the most important findings, what we calculated, and some future suggestions. [ABSTRACT FROM AUTHOR]

Task scheduling in Cloud Computing paradigm poses new challenges for cloud provider as heterogeneous, diversified tasks arrived on to cloud console. To schedule these type of tasks efficiently on to virtual resources in cloud paradigm, an effective scheduler is needed, which precisely maps tasks to virtual machines by considering priorities of both tasks and VMs. Existing scheduling algorithms failed to map tasks precisely to virtual resources due to high dynamic nature in cloud environment which leads to increase of makespan and SLA violations will be increased. In this paper, authors proposed a task-scheduling mechanism, which considers task priorities and VMs. To model this scheduling paradigm we have chosen whale optimization through which our scheduler will take decisions for scheduling tasks precisely onto virtual resources in cloud environment. Entire simulation was carried out on CloudSim. Initially we have chosen random generated workload to run simulation and after that, we have considered a real-time workload named as BigDataBench and ran our simulation. Finally, we compared our proposed work with classical baseline mechanisms. From simulations we observed that proposed whale scheduler improved makespan for PSO, ACO, GA, and W-schedulers by 20.07%, 17.55%, 19.9%, and 6.35%, respectively, and 17.3%, 17.86%, 17.64%, and 5.93%, respectively, for BigDataBench workloads. SLA violations improved over PSO, ACO, GA, and W-Scheduler by 56.76%, 42.17%, 35.29%, and 24.53%, respectively, and 63.42%, 23.33%, 55.51%, and 40.1%, respectively, for BigDataBench workloads. From extensive simulation results, our proposed scheduler using whale optimization approach minimizes makespan and SLA violations to a great extent. [ABSTRACT FROM AUTHOR]

In this paper, we consider a multivariate statistical model of accident frequencies having a variable number of parameters and whose parameters are dependent and subject to box constraints and linear equality constraints. We design a minorization-maximization (MM) algorithm and an accelerated MM algorithm to compute the maximum likelihood estimates of the parameters. We illustrate, through simulations, the performance of our proposed MM algorithm and its accelerated version by comparing them to Newton-Raphson (NR) and quasi-Newton algorithms. The results suggest that the MM algorithm and its accelerated version are better in terms of convergence proportion and, as the number of parameters increases, they are also better in terms of computation time. [ABSTRACT FROM AUTHOR]

In this paper, a data-driven differential dynamic programming (DDP) algorithm is presented for the optimal tracking problems of nonlinear affine systems with unknown drift dynamics. Optimal tracking dynamics are established between the system states and expected trajectory. By using the DDP method and test data, a second-order data-driven framework of the Hamilton–Jacobi–Bellman (HJB) equation is constructed, in which system approximation and value function approximation are used. By using this framework, a data-driven iteration algorithm is proposed to achieve the optimal tracking controller. A simulation example is provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of power load forecasting, this paper proposes a neural network-based short-term monitoring method. First, the original energy load signal is decomposed by the CEEMDAN algorithm to obtain several eigenmode function components and residual components; several eigenmode function components and residual functions are fed into the NARX neural network for computational purposes. The partial hypothesis is superimposed in the following part to obtain the final short-term forecast. According to the test results, the MAPE of the CEEMDAN-NARX model is 4.753%, 3.540%, and 0.343% lower than the SVM, RNN, and NARX models, respectively, and 3.741% and 2.682% lower than CEEMDAN-SVM and CEEMDAN-RNN, respectively. The MAPE and RMSE of the CEEMDAN-NARX model are 0.765% and 101.7 MW, respectively, which are 0.468% and 45.2 MW lower than NARX models, respectively. Compared to CEEMDAN-SVM, the MAPE of CEEMDAN-NARX and CEEMDAN-RNN decreased by 0.986% and 0.692%, respectively, and the RMSE of CEEMDAN-NARX decreased by 111.5 and 65.7 MW, respectively, compared to CEEMDAN-SVM. Conclusion is that the load forecasting model based on the combination of CEEMDAN algorithm and NARX neural network can effectively connect, reduce the negative impact of noise on forecasting results, and improve forecasting accuracy. [ABSTRACT FROM AUTHOR]

This paper analyzes the modeling of a computer-aided piano music automatic notation algorithm, combines the influence of music on psychological detachment, and designs the piano music automatic notation algorithm in psychological detachment model construction. This paper investigates the multiresolution time-frequency representation constant Q-transform (CQT), which is common in music signal analysis, and finds that although CQT has higher frequency resolution at low frequencies, it also leads to lower temporal resolution. The variable Q-transform is introduced as a tool for multibasic frequency estimation of the time-frequency representation of music signals, which has better temporal resolution than CQT at the exact frequency resolution and efficient coefficient calculation. The short-time Fourier transform and constant Q-transform time-frequency analysis methods are implemented, respectively, and note onset detection and multibasic tone detection are implemented based on CNN models. The network structure, training method, and postprocessing method of CNN are optimized. This paper proposes a temporal structure model for maintaining music coherence to avoid manual input and ensure interdependence between tracks in music generation. This paper also investigates and implements a method for generating discrete music events based on multiple channels, including a multitrack correlation model and a discretization process. In this paper, the automatic piano music notation algorithm can play an influential role in significantly enhancing the actual effect of psychological detoxification. [ABSTRACT FROM AUTHOR]

In recent years, commercial banks and rural financial institutions in and below the county area have developed rapidly, and the number of outlets has increased rapidly. But for the vast rural areas of the country, the number of outlets is still limited. At present, there are still a large number of areas with zero financial coverage in rural the country. The development of the financial industry in a region not only affects the level of local economic growth but also has a good effect on improving the income level of local residents and changing the way of local economic development. To better develop rural finance and county economy, this paper constructs an artificial neural network model and FA model to predict the rural finance industry, and four indicators are selected to test the performance measurement of the model. The hit rates of the five models are 54.65%, 53.75%, 59.78%, 65.1%, and 73.08%, respectively. The model proposed in this paper has the highest hit rate and can predict rural finance more accurately as well, allowing for clearer changes in county economic and industrial growth. [ABSTRACT FROM AUTHOR]

Functions such as Internet browsing and online shopping have a great impact on people's lives. The footprints of people browsing various information and news on the web page are also increasing year by year. More and more people begin to pay attention to the visual communication in web design. Network information is widely loved by people because of its convenience, quickness, and simplicity. In order to study the visual problem in network information, this paper proposes to use a feature matching algorithm to study the visual information transmission in web design. Using stereo vision and feature matching algorithm, the target recognition function in the visual communication of web design is realized. The content of web design is defined from the perspective of visual beauty and overall harmony. By extracting the feature points in the data, the two-dimensional vision is transformed into a three-dimensional vision. It not only can accurately extract feature points from data but also can convert 2D vision to 3D vision. Finally, in order to optimize the feature matching speed of web design images, the epipolar constraint algorithm is used to optimize the feature matching function. The experimental results show that the content of this paper can truly show the virtual effect in web design and can intuitively upload and transmit simple and rich information content. This paper not only meets the aesthetic needs of the public for web design but also improves the problem of fuzzy information in the process of visual information transmission. [ABSTRACT FROM AUTHOR]

Traditional English classroom teaching is difficult to meet the oral learning needs of most learners. Thanks to the continuous advancement of speech processing technology, computer-assisted language learning systems are becoming more intelligent, not only pointing out learners' pronunciation errors but also assessing their overall pronunciation level. Method. This paper uses the method of tree kernel function to measure the similarity of two ontology trees. According to the features of nodes in ontology tree, methods to calculate the external features and internal features of nodes are proposed, respectively. External features are mainly obtained by calculating the hierarchical centrality, node density, and node coverage of nodes in the ontology tree; internal features are mainly obtained by measuring the richness of internal information. According to the similarity of ontology tree and the external features and internal features of nodes, the calculation formula of structural comprehensive similarity is improved, and the features of ontology itself can be fully considered in the calculation. According to the difference of the structure, the weights of the corresponding features during the calculation are adjusted autonomously, so that the calculation results are closer to reality. In spectral image preprocessing, endpoint detection utilizes the harmonic characteristics presented by narrowband spectrograms with high frequency resolution and eliminates useless nonspeech segments by detecting the presence of voiced segments. When building the neural network model, four convolutional layers, two fully connected layers, and one softmax output layer were conceived, and dropout was used to randomly suspend the work of some neurons to avoid overfitting. Results/Discussion. Through the data analysis of mean and variance and verified by one-way analysis of variance, it proves that the sentiment evaluation method in this paper is effective. The traditional multiple linear regression method is not suitable for the corpus and application scenarios of this paper. This paper proposes a decision tree structure, which is similar to the overall scoring process of raters, and uses the Interactive Dicremiser version 3 (ID3) algorithm to build a comprehensive evaluation decision tree for pitch, rhythm, intonation, speech rate, and emotion indicators. It is proved by experiments that the accurate consistency rate of the human-machine evaluation in this paper is 93%, the adjacent consistency rate is 96%, and the Pearson correlation coefficient value of the human-machine evaluation results is 0.89. The data results prove that the evaluation method in this paper is credible. [ABSTRACT FROM AUTHOR]

Under the background of public health events, the government needs to adopt reasonable support strategies to help cultural enterprises tide over the crisis. However, the original analytic hierarchy process cannot be comprehensive and accurate analysis, resulting in that the government support strategy cannot play a role. Fish swarm algorithm is a comprehensive intelligent algorithm, which can comprehensively analyze the factors affecting the formulation of supporting strategies for cultural enterprises, and has the advantages of simple operation and strong analysis ability. Under this background, this paper puts forward a fish swarm model; this method is a comprehensive intelligent analysis method, which can help the government find the best support strategy from the perspectives of politics, economy, and society. In order to verify the effectiveness of the fish school model, this paper uses MATLAB software for verification; the results show that, in the support strategy classification, selection accuracy, and selection time, fish school model is superior to the analytic hierarchy process. Therefore, the fish swarm model can help the government to better choose supporting policies and help cultural enterprises tide over the crisis and improve the accuracy of formulating supporting strategies for cultural enterprises. [ABSTRACT FROM AUTHOR]

Accurate recommendation of tourist attractions is conducive to improving users' travel efficiency and tourism experience. However, the choice of tourism feature factors and the difference of recommendation algorithm will affect the accuracy of scenic spot recommendation. Aiming at the problems of sparse data, insufficient tourism factors, and low recommendation accuracy in the existing tourism recommendation research, this paper puts forward a scenic spot recommendation method based on microblog data and machine learning by using the characteristics of personalized expression and strong current situation of microblog data and the intelligent prediction function of machine learning, so as to realize accurate and personalized scenic spot recommendation. This paper extracts rich tourism characteristic factors. Typical tourism recommendation algorithms choose tourism characteristic factors from scenic spots, tourists, and other aspects, without considering the travel time, tourism season, and other contextual information of tourists' destination, which can help understand users' tourism preferences from different angles. Aiming at the problem of sparse data and cold start of collaborative filtering recommendation algorithm, this paper introduces deep learning algorithm and combines the proposed multifeature tourism factors to build dynamic scenic spot prediction models (random forest preferred attraction prediction (RFPAP) and neural networks preferred attraction prediction (NNPAP)). The experimental results show that RFPAP and NNPAP methods can overcome the problem of data sparsity and achieve 89.61% and 89.51% accuracy, respectively. RFPAP method is better than NNPAP method and has stronger generalization ability. [ABSTRACT FROM AUTHOR]

"Information alienation" is a concept formed by combining the idea of "alienation" in philosophy with the characteristics of information itself. Information is a universal property of matter and an objectively existing form of material movement. The purpose of this paper is to study and analyze the alienation technology control of push information based on intelligent algorithms and describe the information alienation. This paper puts forward the problem of pushing information, which is based on intelligent algorithm, then elaborates around the concept of intelligent algorithm and related algorithms, and designs and analyzes the control of media information alienation. The experimental results show that, in the questionnaire survey, 72.34% of the people who very frequently swipe unfriendly information account for a very large proportion. People will always swipe it in various software applications, which is unavoidable. How to control the problem of information alienation needs to be controlled from the user level, the design media level, and the government level. [ABSTRACT FROM AUTHOR]

In order to improve the scientificity of sports action analysis, this paper constructs a sports action analysis model based on machine learning based on the greedy algorithm and the bat algorithm. According to the structural characteristics of the model, the structure of the model is reflected in the form of face order, that is, the face neighborhood structure. Moreover, this paper judges the degree of similarity between model faces through the pros and cons of the order and applies it to the structural similarity matrix between models. In addition, this paper establishes corresponding mathematical models for the shape and structure of the model and constructs the shape similarity matrix, the surface neighborhood structure similarity matrix, and the structure similarity matrix between the source model and the target model. Finally, this paper designs and implements CAD model retrieval methods based on greedy algorithm and bat algorithm and designs experiments to compare the performance of the algorithm proposed in this paper with traditional algorithms. The result of the experiment shows that the algorithm proposed in this paper has obvious advantages in sports action analysis compared with the traditional algorithm. [ABSTRACT FROM AUTHOR]

Existing image inpainting schemes generally have the problems of structural disorder and blurred texture details. This is mainly because, in the reconstruction process of the damaged area of the image, it is difficult for the inpainting network to make full use of the information in the nondamaged area to accurately infer the content of the damaged area. Therefore, the paper has proposed an image inpainting network driven by multilevel attention progression mechanism. The proposed network has compressed the high-level features extracted from the full-resolution image into multiscale compact features and then drives the compact features to perform multilevel order according to the scale size. Attention feature progression is to achieve the goal of the full progression of high-level features including structure and details in the network. To further realize fine-grained image inpainting and reconstruction, the paper has also proposed a composite granular discriminator to achieve image inpainting process performing global semantic constraints and nonspecific local dense constraints. The related experimental results in the paper can show that the proposed method can achieve higher quality repair results than state-of-the-art ones. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low efficiency and insufficient accuracy of threshold solution in multithreshold sculpture image segmentation, this paper proposes a threshold segmentation algorithm for sculpture images based on sparse decomposition. In this paper, sparse decomposition is introduced to optimize the model to reduce the impact of local noise on segmentation accuracy, and an energy functional based on pixel coconstraint is built to make up for the defect that pixels cannot retain local details. At the same time, the weighted sum of elite solution sets is used to determine Neighborhood centers increase communication between groups. Experiments show that compared with other algorithms, the above method has significant advantages in convergence efficiency and accuracy. [ABSTRACT FROM AUTHOR]

The emergence of portable devices provides great convenience for image acquisition, but the image resolution is too low, which affects the identification and use of the image. Multiframe super-resolution algorithm makes a great contribution to extracting image features, but there is a problem with too much computation. Based on this, this paper proposes a low-resolution image fusion algorithm based on deep learning. Based on the introduction of extracting image features from low-resolution images, this paper proposes to fuse multiple low-resolution images, reduce the time of calculation error per frame, reduce the amount of calculation, extract feature information, and improve the alexnet network model to realize image extraction. The convolutional self-coding fusion network is used to realize image fusion and complete the reconstruction and fusion of low-resolution images. In the simulation analysis of the fusion algorithm, objective indexes such as network structure and loss function are selected to evaluate the effectiveness of the algorithm. This paper reduces the computational complexity of image acquisition and ensures that the amount of calculation can be reduced in each iteration. Analyze the effectiveness of the fusion algorithm, comprehensively evaluate the objective indicators, and verify the advantages of the algorithm through indicators such as gradient change and information entropy. [ABSTRACT FROM AUTHOR]

The research on artistic psychological intervention to judge emotional fluctuations by extracting emotional features from interactive vocal signals has become a research topic with great potential for development. Based on the interactive vocal music instruction theory of emotion recognition, this paper studies the design of artistic psychological intervention system. This paper uses the vocal music emotion recognition algorithm to first train the interactive recognition network, in which the input is a row vector composed of different vocal music characteristics, and finally recognizes the vocal music of different emotional categories, which solves the problem of low data coupling in the artistic psychological intervention system. Among them, the vocal music emotion recognition experiment based on the interactive recognition network is mainly carried out from six aspects: the number of iterative training, the vocal music instruction rate, the number of emotion recognition signal nodes in the artistic psychological intervention layer, the number of sample sets, different feature combinations, and the number of emotion types. The input data of the system is a training class learning video, and actions and expressions need to be recognized before scoring. In the simulation process, before the completion of the sample indicators is unbalanced, the R language statistical analysis tool is used to balance the existing unbalanced data based on the artificial data synthesis method, and 279 uniformly classified samples are obtained. The 279 ∗ 7 dataset was used for statistical identification of the participants. The experimental results show that under the guidance of four different interactive vocal music, the vocal emotion recognition rate is between 65.85%-91.00%, which promotes the intervention of music therapy on artistic psychological intervention. [ABSTRACT FROM AUTHOR]

Chinese traditional culture is a typical "moral culture," and traditional morality is the core and essence of culture. However, in recent years, the phenomenon of following the trend of rural construction has been particularly serious, and many villages have lost their original features. To solve the above problems, the genetic algorithm can be used to further explore the traditional culture of urbanization construction. A genetic algorithm is a natural evolutionary process that imitates natural selection and genetic operation in nature to obtain optimal solution, in which genetic operation mainly includes the processes of gene replication, crossover, and mutation. This paper studies the traditional culture of urbanization construction based on the genetic algorithm under the concept of environmental protection. Among the accuracy of urban construction land expansion, in 2018, the accuracy of ant colony algorithm, data mining algorithm, and particle swarm optimization algorithm is 58%, 51.8%, and 56.7%, respectively. The accuracy of this genetic algorithm is as high as 58.8%. It can be seen that the genetic algorithm in this paper has the highest accuracy in the expansion of urban construction land. Therefore, in the process of large-scale urbanization based on the genetic algorithm, we should pay attention to not being separated from traditional culture, not letting farmers lose their regional culture, local culture, and grassroots culture, and protecting the cultural-ecological environment on which these cultures depend. [ABSTRACT FROM AUTHOR]

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

The silent location algorithm of the sensor network is a time synchronization location algorithm that can serve multiple users. Based on the detailed analysis of the development status of human motion measurement technology, this paper proposes a design of human motion capture device based on sensor network and realizes data conversion and filtering through experimental design. In this experiment, the sensor data processing is used to capture the human body positioning information in real time. The established node does not need to make a sound during the whole process and, at the same time, has strong concealment and easy expansion. In the silent positioning algorithm for sensor network nodes, combined with the sound ray tracking technology, the correction parameters are controlled according to the change of the objective function, and the effectiveness of the method is verified by simulation analysis. In terms of motion capture, the system can monitor, detect, and record human motion in real time. In order to obtain more high-resolution dance images with high-definition details and correspondingly process the dance images to improve the resolution, this paper uses the silent positioning algorithm technology to study the image resolution, and this is of great help to improve the resolution. [ABSTRACT FROM AUTHOR]

This paper adopts the method of psychological data analysis to conduct in-depth research and analysis on the correlation between teachers' classroom teaching behaviors and students' knowledge acceptance. Firstly, this paper proposes a health factor prediction model, which is specifically divided into clustering and then classification model and a clustering and classification synthesis model. The classroom learning process is coded, sampled, and quantified to obtain data on students' learning behaviors, and a visualization system based on classroom students' learning behaviors is designed and developed to record and analyze students' behaviors in the classroom learning process and grasp students' classroom learning. These two models use algorithms to fine-grained divide the dataset from the perspective of subject users and mental health factors, respectively, and then use decision tree algorithms to classify and predict the mental health factor information by the subject user base information. Second, based on the collected datasets, we designed comparison experiments to validate the clustering-then-classification model and the integrated clustering-classification model and selected the optimal model for comparison. Teachers should increase effective praise and encouragement behaviors; teachers should increase meaningful teacher-student interaction behaviors; teachers should be proficient in teaching media technology to reduce unnecessary time wastage. Strategies to enhance teachers' TPACK include enriching teachers' knowledge base of CK, TK, and PK; developing teachers' integration thinking; and enriching teachers' types of activities for integrating technology. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth analysis and study of the role of piano performance in alleviating psychological trauma in people with psychological isolation disorder. In this paper, we designed a music modulation system based on EEG signals of people with isolation disorder and designed an interface with real-time emotion recognition. MATLAB is responsible for data processing and classification, while Python is responsible for communication and real-time transmission between modules, EEG signal collection, and processing. For the EEG signals in the DEAP emotion database, a Butterworth bandpass filter is used to denoise the signals, and then, a wavelet packet decomposition reconstruction is used to remove the artifacts and complete the preprocessing of the signals. Finally, the support vector machine with optimized parameters of the genetic algorithm was used to classify the positive, neutral, and negative samples with 89.23% accuracy. In this study, all subjects were divided into experimental and control groups by experimentally measuring the changes in heart rate, skin electrical conduction, skin temperature, and EEG signals before and after the experimental group, and statistical analysis was also performed on the data tabulation of the experimental and control groups. The experimental results proved that piano playing has a significant effect on relieving the psychological trauma of people with psychological isolation disorder when the training frequency of piano playing therapy reaches a certain intensity. This study provides a certain theoretical basis for clinical, educational, and health services. [ABSTRACT FROM AUTHOR]

Objective. On the basis of inheriting the classical aesthetic orientation of Japanese literature, contemporary Japanese literature changes the creative methods of traditional Japanese writers, strives to transcend the national limitations of Japanese literature, and strives to capture literary materials with a "modern feel." Combined with modern western literary techniques to express the "modern self," and integrating popular literature with pure literature, Japanese writers created a "healing" literature that is popular all over the world. There is some value in exploring the correlation between Japanese literature and the influence of psychotherapy. Method. In this paper, an equation diagnosis algorithm based on iterative learning for a class of linear discrete systems is studied, and the existing discrete iterative learning diagnostics are improved. We exchange the order of time domain and iterative domain to achieve the purpose of point-by-point equation diagnosis. On this basis, a virtual influence initial value estimation algorithm with a sliding window is proposed. The impact that has not been diagnosed is estimated and used as the initial value of the impact, so as to reduce the number of iterations and improve the efficiency of equation diagnosis. This paper takes the public and readers as different perspectives, based on the use and satisfaction theory and prospect theory, and uses interviews and questionnaires to study the communication effect of Japanese literature from the three dimensions of cognition, emotion, and behavior. The cognitive part includes the public's understanding and readers' understanding and reading; the emotional part includes the public's and readers' impressions and emotional attitudes; the behavioral part includes the public's reading situation and readers' behavior changes. Results/Discussion. In the survey of loyal readers, although readers' understanding of Japanese literature has improved compared with the general public, it is not high, and their liking and influence on behavior are slightly lower than the preset level. This paper believes that the existing problems of Japanese literature dissemination are mainly concentrated in three aspects: the market is somewhat biased, which narrows the cognitive scope of users; as a cross-cultural literary work, Japanese literature still makes readers feel that it exists; there is some sense of estrangement, which will affect their enjoyment in reading; as a niche interest, readers lack timely communication and sharing objects after reading, so it is difficult to enhance their sense of identity. [ABSTRACT FROM AUTHOR]

At present, e-commerce drives the logistics industry to develop greatly, but at the same time, there is a huge demand in this field, such as lower cost and higher efficiency. Facing the needs of logistics management development, it needs the blessing of intelligent technology, which involves countless fields at present. Intelligent logistics management has become a hot spot at present. What needs to be solved in this respect is how to shorten the transportation distance and save costs. To solve this problem, this paper proposes to introduce the shortest path algorithm. This paper compares the Dijkstra algorithm with the A ∗ algorithm under the background of logistics management and finds that the latter is more suitable for this field with huge amount of information. In order to improve the performance of the A ∗ algorithm, this paper introduces ant colony algorithm, which can better avoid obstacles. Combining these two algorithms, a ∗ -ant colony algorithm is obtained. The algorithm absorbs the advantages of the two algorithms, while maintaining high efficiency and good stability. These characteristics are very satisfying in the field of logistics management. Through the performance test and simulation experiment, it is concluded that the algorithm has excellent optimization ability and can reduce the cost for this field. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm composition network from the perspective of machine learning, based on an in-depth study of related literature. At the same time, this paper examines the characteristics of music and develops a model for recognising musical emotions. Using the model's information entropy of pitch and intensity to extract the main melody track, note features are extracted from bar features. Finally, the cosine of the vector included angle is used to judge the similarity between feature vectors of several adjacent sections, allowing the music to be divided into several independent segments. The emotional model of music is used to analyze each segment's emotion. By quantifying music features, this paper classifies and quantifies music emotion based on the mapping relationship between music features and emotion. Music emotion can be accurately identified by the model. The model's emotion recognition accuracy is up to 93.78 percent, and the algorithm's recall rate is up to 96.3 percent, according to simulation results. The recognition method used in this paper has a higher recognition ability than other methods, and the emotion recognition result is more reliable. This paper can not only meet the composer's auxiliary creative needs, but it can also help intelligent music services. [ABSTRACT FROM AUTHOR]

With the continuous deepening of artificial intelligence (AI) in the medical field, the social risks brought by the development and application of medical AI products have become increasingly prominent, bringing hidden worries to the protection of civil rights, social stability, and healthy development. There are many new problems that need to be solved in our country's existing risk regulation theories when dealing with such risks. By introducing the theory of risk administrative law, it analyzes the social risks of medical AI, organically combines the principle of risk prevention with benefit measurement, and systematically and flexibly reconstructs the theoretical system of medical AI social risk assessment. This paper has completed the following work: (1) reviewed and sorted out the works and papers related to medical AI ethics, medical AI risk, etc., and sorted out the current situation of medical AI social risk regulation at home and abroad to provide help for follow-up research. (2) The related technologies of artificial neural network (ANN) are introduced, and the risk assessment index system of medical AI is constructed. (3) With the self-designed dataset, the trained neural network model is utilized to assess risk. The experimental results reveal that the created BPNN model's error is relatively tiny, indicating that the algorithm model developed in this research is worth popularizing and applying. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is a crucial issue to maintain the reliable operation of power systems. However, achieving this objective is not easy, especially when renewable energy sources (RESs) are penetrated into the power system due to their uncertainty nature. This paper provides an optimal solution for the power flow problem including two different types of RESs based on a marine predator algorithm (MPA). The OPF model used in this paper has three different types of energy resources (thermal, wind, and solar). The output power from wind or solar generator has two probabilities either underestimation or overestimation consequently. These two probabilities have been translated into the objective function by two extra costs, penalty cost, and reserve cost, respectively. To check the validity of the proposed algorithm, it is applied to a modified IEEE-30 and IEEE-57 bus systems. The obtained results are compared with some recent optimization methods. The results show the effectiveness of marine predator algorithm in providing the optimal solution for the power flow problem with maintaining the power system constraints inviolate. [ABSTRACT FROM AUTHOR]

With the development of computer technology, digital image processing, as an emerging field, has become an indispensable tool in the information society. The action interference effect refers to the phenomenon that an individual reacts to a dangerous object longer than a safe object in the state of preparation for action. Currently, there is no effective anti-jamming technology to recognize dance movements. In order to solve these problems, this paper proposes a dance movement interference suppression algorithm based on contour model and AdaBoost algorithm, which aims to realize the application of gesture-based behavior recognition technology in the field of dance steps and complete the recognition of some dance steps. The method of this paper is to study the contour model based on the image and then propose to use the AdaBoost algorithm to detect body movements and finally use the radio frequency interference suppression algorithm to ensure the smooth operation of the technology. The function of the method is to propose a dance action recognition and anti-interference algorithm based on contour model and AdaBoost algorithm. This article describes the contour model and then uses the AdaBoost algorithm to recognize the action and adjust the action interference effect. The results show that the algorithm proposed in this paper can accurately identify dance movements, with an average recognition accuracy rate of 99.2%. [ABSTRACT FROM AUTHOR]

This paper takes the flying defense team as the operating unit and studies the route planning and tasks in the process of plant protection operations as system problems. In the route planning, the flight defense team's operating time is used as the optimization goal of route planning. Taking the UAV battery and drug load as constraints, the flight mission is divided into subtasks that can be completed by a single UAV in a single flight. On this basis, the particle swarm algorithm is used to optimize the task allocation, so as to achieve the goal of UAV plant protection route and task division with minimum time loss. In order to verify the effectiveness of the method in this paper, 3 and 4 UAVs flying defense teams were used as the experimental objects, and experiments were carried out in large and small field groups. In terms of time consumption, the method in this paper is compared with the unit operation area method and the single size method. The energy consumption advantage is weaker than time consumption. In terms of time consumption, the method in this paper can greatly improve the efficiency of large-area operation areas. Compared with the unit operation area method, 3 drones can increase the efficiency by 58.98%, which is increased by 10.22% compared with the unit size method. The efficiency of 4 units can be increased by 58.08% compared with the unit operation area method, and the efficiency is increased by 10.22% compared with the unit size method. [ABSTRACT FROM AUTHOR]

Throughout the world, the reliability-based approach to safety design of aircraft systems is quite mature and widely used. However, there are still shortcomings in the reliability-based aircraft system safety analysis method. It cannot dynamically analyze the accident evolution process and lack consideration of the complex situation of multifactor coupling. On the basis of the original aircraft system safety analysis method, this paper innovatively proposes a functional hazard analysis (FHA) method based on the analytic hierarchy process (AHP) and multifactor fuzzy comprehensive assessment (FCA). The purpose is to improve the objectivity and quantification of the FHA method in the safety design of aircraft systems. At the same time, in the terminal airworthiness verification, this paper proposes a repeatable and controllable virtual test flight verification method, which aims to reduce the cost and cycle of the terminal airworthiness verification and expand the coverage of the envelope verification. Finally, combined with the clauses in MIL-HDBK-516B, a case calculation is carried out to verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology and electronic information technology, the sports training system no longer depends on the traditional algorithm for operation support, and various advanced posture algorithms are emerging. At the same time, it also further optimizes the intelligence and accuracy of the sports training algorithm. As an advanced algorithm combined with virtual reality technology, human posture estimation algorithm plays an obvious role in optimizing the effect of sports training. This paper will design a motion training system based on the optimized and improved human posture trajectory algorithm, use the depth image correlation theory to solve the problem of non-Gaussian noise crosstalk in the depth image of the traditional human posture algorithm in principle, improve the accurate feature extraction of the depth image by the algorithm, and solve the problem of human feature redundancy, so as to further improve the accuracy of the establishment of a single human model; on the problem of multi-person posture estimation algorithm, this paper proposes a high-resolution multi-person posture high-precision network model and adds the focus mechanism. Based on this, this paper realizes the high-precision and high-speed modeling of multi-person posture, so as to provide an accurate model for the multi-person function of sports training system and improve the efficiency of the algorithm. In the experimental part, this paper takes tennis as a typical case to design the sports training system and experiments based on the system designed in this paper. The experimental results show that the system under the proposed algorithm has obvious advantages in accuracy and training effect. [ABSTRACT FROM AUTHOR]

In the research of network abnormal traffic detection, in view of the characteristics of high dimensionality and redundancy in traffic data and the loss of original information caused by the pooling operation in the convolutional neural network, which leads to the problem of unsatisfactory detection effect, this paper proposes a network abnormal traffic detection algorithm based on RIC-SC-DeCN to improve the above problems. Firstly, a recursive information correlation (RIC) feature selection mechanism is proposed, which reduces data redundancy through the maximum information correlation feature selection algorithm and recursive feature elimination method. Secondly, a skip-connected deconvolutional neural network model (SC-DeCN) is proposed to reduce the information loss by reconstructing the input signal. Finally, the RIC mechanism and the SC-DeCN model are merged to form a network abnormal traffic detection algorithm based on RIC-SC-DeCN. The experimental results on the CIC-IDS-2017 dataset show that the RIC feature selection mechanism proposed in this paper has the highest accuracy when using MSCNN as the detection model compared to the other three, which can reach 96.22%. Compared with the other five models, the SC-DeCN model has the highest detection accuracy, while the model training time is moderate and can reach 96.55%. Compared with the SC-DeCN model, the RIC-SC-DeCN model reduces the overall training time by 45.50%, while the accuracy rate is increased to 97.68%. It shows that the algorithm proposed in this paper has a good detection effect in the detection of network abnormal traffic. [ABSTRACT FROM AUTHOR]

In order to effectively solve the problem that the radar detection system is difficult to detect the "low, small, slow" UAV, the high-performance computing early warning neural network is used to recognize the air UAV in real time and extract the target category and image space location information; the PSO algorithm is used to optimize the parameters of the anti-UAV to ensure that the anti-UAV not only relies on factors but also fully combines the dependence of the visual field factor to quickly obtain the optimal solution through analyzing the high-performance computing early warning neural network in this paper. This algorithm is used to initialize the anti-UAV resources and improve the global optimization capability of the algorithm proposed in this paper. Finally, the experimental results show that the proposed PSO algorithm has better high-performance computing early warning performance to meet the actual needs of network high-performance computing early-warning neural networks. [ABSTRACT FROM AUTHOR]

To address the phenomenon of serious psychological stress among college students, there are problems of high cost and subjectivity in assessing psychological stress by collecting physiological data, and this paper proposes a stress assessment method (Improved SMOTE + XGBoost) based on intelligent data collection, which divides stress levels into five levels. In the process of processing a large amount of data, there will be too little data. Therefore, this paper applies the improved SMOTE method to the data preprocessing, which can reduce the difficulty of collecting psychological stress test data while ensuring the amount of data. Firstly, we extracted features from cell phone data to generate samples, processed the samples by SMOTE, and then filtered features by XGBoost algorithm to filter features; meanwhile, we trained RF, SVM, BP, and KNN with the data before and after sampling and before and after feature screening, and the results showed that Improved SMOTE + XGBoost outperformed other methods. [ABSTRACT FROM AUTHOR]

With the proliferation of portable digital products, image quality degradation has received a lot of attention. As the most common phenomenon in image degradation, the issue of image deblurring is the focus of much attention. Blind motion blur removal is the main target of this paper. The heavy-tailed distribution is the most dominant statistical feature of natural images. However, most image deblurring methods use a gradient prior with fixed parameters to recover a clear image, which leads to loss of details in the recovered clear image and does not consider the higher order prior of the natural image. Therefore, this paper proposes a new regularized image recovery model based on the Gaussian-scale mixture expert field(GSM-FOE) model. First, the GSM-FoE model learns filters and corresponding parameters with higher order prior information of images by training images in a natural image library; second, these learning results are used to guide the image recovery process. The GSM-FoE model and gradient-fidelity based image recovery model is proposed, which can be used with an iterative re-weighted least squares (IRLS) method. Experiments demonstrate that the suggested recovery approach is simple to use and successful at reducing blur and noise, as well as suppressing ringing effects while preserving image information. Moreover, the image restoration method performs well for large blurring kernels. The results fully reflect the effectiveness and robustness of the proposed method for complex noise scenarios. The quality of the generated images is significantly better than that of several classical methods. [ABSTRACT FROM AUTHOR]