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Digitalization brings challenges and new opportunities to the development of landscape gardening, "smart gardening," which is a product of landscape gardening in response to the development of the digital era. Based on the multimodal intelligent computing method and deep neural network machine learning algorithm, this paper adopts "digital landscape design logic" to analyze and research smart gardens and digital design. The digital landscape design process and methods are discussed based on design logic, design basis, environment analysis, and results presentation, and the greenery maintenance scheduling system is constructed. The paper focuses on the digital implementation of the environmental analysis of the site and uses Rhino software and Grasshopper visual programming language to build parametric logic, establish parametric analysis models, and conduct a comprehensive analysis of the current environment. The main theme of the whole paper is a logical approach to digital landscape design for smart gardens, using digital technology tools from the perspective of smart garden thinking, combining quantitative analysis and qualitative design, and intervening in digital landscape garden planning and design to explore the application of digital technology and tools. [ABSTRACT FROM AUTHOR]

Network compression coding technology is a research hotspot in the field of digital steganography and image synthesis. How to improve image quality while achieving short compression time is a problem currently faced. Based on the improved wavelet neural network theory, this paper constructs a digital steganography and image synthesis model. The model first tracks the contour of the digit to be recognized, then equalizes and resamples the contour to make it translation-invariant and scaling-invariant, and then uses multi-wavelet neural network clusters to stretch the contour shell to obtain orders of magnitude multi-resolution and its average, and finally, these shell coefficients are fed into a feedforward neural network cluster to identify this handwritten digit, solving the problem of multi-resolution decomposition of contour shells while having a high sampling rate. In the simulation process, the classification model that a single pixel is a text/non-text pixel is trained on the original gray value of the gray pixel and its neighboring pixels, and the classified text pixels are connected to a text area through an adaptive MeanShift method. The experimental results show that it is feasible to use multi-wavelet features for handwritten digit recognition. The model combines the neural network and the genetic algorithm, making full use of the advantages of both, so that the new algorithm has the learning ability and robustness of the neural network. The compression ratio after compression by ordinary wavelet coding, PSNR, MSE, and compression time are 8.4, 25 dB, 210, and 7 s, respectively. The values are 11.7, 24 dB, 207, and 11 s, respectively. At the same time, the peak signal-to-noise ratio is higher and the mean square error is lower, that is, the compression quality is better, and the accuracy of digital steganography and image synthesis is effectively improved. [ABSTRACT FROM AUTHOR]

As the fastest-growing crowdfunding model, equity crowdfunding (ECF) brings high returns and uncertainty. In this context, it is crucial to understand these crowdfunding projects' actual performance. Since ECF is currently in the early stage of integration, there are still a lot of risk issues, such as the uncertainty of equity structure, capital supervision, or project management. Therefore, this paper develops a new profitability indicator, "return on registered capital," to test its impact on the ECF project's actual return. This paper studies which factors affect the financial performance of ECF projects through the traditional statistical model and a deep neural network (DNN) model. There is evidence that return on registered capital affects the actual return of the project. At the same time, the company's operating time and the number of employees had an unexpected effect on project performance. In addition, the recognition accuracy of the DNN model in this study exceeds 97%, which affirms the applicability of the DNN model in the analysis of ECF success factors. This paper also uses tenfold cross-validation to prove that deep learning has certain advantages in this topic's accuracy and generalization error. This study explores whether company representatives' gender and knowledge level affect project performance. The results will be described in detail in the paper. [ABSTRACT FROM AUTHOR]

With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]

With the rise of cultural and creative industries, cultural creativity has gradually become an important factor to promote the value of design in the future, and it is also a trend to integrate "cultural elements" into the design of products. At present, the cultural and creative industries in Western countries and Taiwan are the mainstay of their economic development. We should actively absorb their successful experiences and, with the support of national policies, carry out effective and lasting development of them, as well as continuously improve the quality of cultural and creative products. In today's steady economic development, the emotionalization of cultural and creative consumption has gradually formed a new consumption trend. When cultural and creative consumers buy stationery, they will inevitably have three situations: purchasing instinct, purchasing behavior, and reflection. Therefore, this paper adopts the method based on machine learning to conduct in-depth research on the users of cultural and creative products of "Forbidden Day and Night Set Gift Box." Through the research on the cultural and creative consumption intention of "Forbidden Day and Night Set Gift Box," it can effectively promote the development of domestic cultural and creative enterprises, and then promote the customer satisfaction of cultural and creative enterprises. This paper makes a detailed analysis of it from the perspectives of data processing, feature engineering, classification prediction models, and future development directions. By studying the cultural and creative behavior of users, a deep learning model based on neural network is established. The feature extraction, feature preprocessing, feature selection, and asymmetric data collection in the process of data processing are discussed in depth. In order to further improve the prediction accuracy and conduct more in-depth research, this paper establishes a deep learning prediction model based on depth. This model is experimentally validated and it can be observed that the model is 10% more efficient than the traditional model, so the model can learn data better from the user's behavior in several aspects, and the proposed and practice of this model has good practical significance. [ABSTRACT FROM AUTHOR]

Lung disease is one of the most harmful diseases in traditional days and is the same nowadays. Early detection is one of the most crucial ways to prevent a human from developing these types of diseases. Many researchers are involved in finding various techniques for predicting the accuracy of the diseases. On the basis of the machine learning algorithm, it was not possible to predict the better accuracy when compared to the deep learning technique; this work has proposed enhanced artificial neural network approaches for the accuracy of lung diseases. Here, the discrete Fourier transform and the Burg auto-regression techniques are used for extracting the computed tomography (CT) scan images, and feature reduction takes place by using principle component analysis (PCA). This proposed work has used the 120 subjective datasets from public landmarks with and without lung diseases. The given dataset is trained by using an enhanced artificial neural network (ANN). The preprocessing techniques are handled by using a Gaussian filter; thus, our proposed approach provides enhanced classification accuracy. Finally, our proposed method is compared with the existing machine learning approach based on its accuracy. [ABSTRACT FROM AUTHOR]

Credit evaluation is a difficult problem in the process of financing and loan for small and medium-sized enterprises. Due to the high dimension and nonlinearity of enterprise behavior data, traditional logistic regression (LR), random forest (RF), and other methods, when the feature space is very large, it is easy to show low accuracy and lack of robustness. However, recurrent neural network (RNN) will have a serious gradient disappearance problem under long sequence training. This paper proposes a compound neural network model based on the attention mechanism to meet the needs of enterprise credit evaluation. The convolutional neural network (CNN) and the long short-term memory (LSTM) network were used to establish the model, using soft attention, the gradient propagates back to other parts of the model through the attention mechanism module. In the multimodel comparison experiment and three different enterprise data experiments, the CNN-LSTM-ATT model proposed in this paper is superior to the traditional models LR, RF, CNN, LSTM, and CNN-LSTM in most cases. The experimental results under multimodel comparison reflect the higher accuracy of the model, and the group test reflects the higher robustness of the model. [ABSTRACT FROM AUTHOR]

Mining and utilizing cultural heritage resources and creating and developing creative and cultural industries have become the priority direction of economic development, setting off a wave of cultural heritage resource development, and industrial transformation. Which cultural heritage resources can have the high value of industrial transformation has become one of the research topics that have attracted much attention. In view of this problem, research is of great significance to the development of cultural heritage resources and the field of industrial transformation. With the in-depth research on resource development and industrial transformation, the research on artificial neural network (ANN) in cultural industry transformation is gradually carried out, and its performance advantages are of great significance to solve the problem of value evaluation. This paper aims to study the application of the value assessment method based on the BP neural network (BPNN) in the development of cultural heritage resources and industrial transformation. Through the analysis and research of BPNN and cultural heritage resource development and industrial transformation, it can be applied to the construction of resource value assessment methods to solve the problem of improving the value level of cultural heritage resource development and industry. This paper analyzes BPNN, cultural heritage, and value evaluation methods, conducts experimental analysis on the performance of the algorithm, and uses related theoretical formulas to explain. The results show that the evaluation method has passed the random consistency test, and the results are valid. The obtained index popularity weight value is 0.134, the economic benefit weight value is 0.093, and the resource correlation degree weight value is 0.074, which can be used as the key criteria for resource value evaluation. The classification of resources through resource value assessment can provide theoretical support for the development and industrialization of cultural heritage and can meet the needs of improving the value and quality of cultural heritage development and industrial transformation, and the development level and satisfaction have been greatly improved. [ABSTRACT FROM AUTHOR]

The entire auditing process is complicated and tedious and requires a lot of human resources. Therefore, the intelligent development of auditing is the general trend. In order to improve the audit quality, this paper establishes an intelligent financial audit model that can predict the audit opinion of the consolidated financial statements. This paper proposes an audit opinion prediction model based on the fusion of deep belief neural network (DBN) and long-short term memory (LSTM). First, an indicator system is established for audit opinions, and multiple financial parameters are used to describe possible audit opinions. On this basis, a DBN network is designed to complete deep feature extraction and used for LSTM training. According to the prediction model obtained by training, the subsequent audit opinion can be scientifically predicted. In the experiment, the method in this paper is tested based on financial audit related data sets and compared with the prediction results of traditional multilayer perceptron (MLP), convolutional neural network (CNN), and LSTM models. The results verify the validity and reliability of the model in this paper. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet loans and the demand for Internet loans, Internet-based loan default prediction is particularly important. P2P online lending is based on Internet technology. With the popularization of personal PCs and mobile terminals, the borrower's financing cost has been reduced to a large extent, and the efficiency of the borrower's capital utilization has also been improved to a considerable level. Making full use of the existing data of the online lending platform, integrating third-party data, and predicting the default behavior of users are the major directions of future development. This paper mainly studies the network loan default prediction model based on DPNN. This paper first analyzes the problems and risks of the P2P online lending platform, then introduces the principle and characteristics of BPNN in detail, and determines the credit risk rating process for online lending based on BPNN. With the help of data analysis and processing software, after cleaning and variable selection of credit customer data provided by lending clubs, a set of corresponding online lending default risk assessment models are established through BPNN. This paper simulates the network loan default assessment model of the BPNN model and compares it with the support vector machine and regression model. The experimental results show that the highest accuracy rate of the BPNN model is 98.01% and the highest recall rate is 99.82%, which is better than the other two models; the AUC value of BPNN is 0.79, which is significantly higher than that of support vector machine and regression model. The above results show that the online loan default prediction model based on DPNN has high application value in practice. Predicting the probability of customer default risk in advance will help reduce the risk of P2P companies and lenders, improve the competitiveness of P2P lending institutions, and promote the development of domestic P2P platforms to be more stable. [ABSTRACT FROM AUTHOR]

The existing deep learning models have problems such as large weight parameters and slow inference speed of equipment. In practical applications such as fire detection, they often cannot be deployed on equipment with limited resources due to the huge amount of parameters and low efficiency. In response to this problem, this paper proposes a lightweight smoke detection model based on the convolutional attention mechanism module. The model is based on the YOLOv5 lightweight framework. The backbone network draws on the GhostNet design idea, replaces the CSP structure of the FPN and head layers with the GhostBottleNeck module, adds a convolutional attention mechanism module to the backbone network layer, and uses the CIoU loss function to improve the regression accuracy. Using YOLOv5s as the benchmark model, the parameter amount of the proposed lightweight neural network model is 2.75 M, and the floating-point calculation amount is 2.56 G, which is much lower than the parameter amount and calculation amount of the benchmark model. Tested on the public fire dataset, compared with the traditional deep learning algorithm, the model proposed in the paper has better detection performance and the detection speed is significantly better than the benchmark model. Tested under the unquantized simulator, the speed of the proposed model to detect a single picture is 60 ms, which can meet the requirements of real-time engineering applications. [ABSTRACT FROM AUTHOR]

The influence of music genres on children's emotional intelligence is one of the hot topics in the field of multi-modal emotion research. How to fuse multi-modal information has an important impact on children's emotional analysis. Most of the current research is based on transformer, in which the self-attention mechanism module is improved to achieve the fusion effect of multi-modal information. However, it is difficult for these methods to effectively capture the effective information of different modalities. Therefore, for the task of the influence of music genres on children's emotions, this paper proposes a transformer-based multi-modal convolutional neural network. The first is to use the BiLSTM sub-network model to extract the video and audio features and use the BERT sub-network to extract the text features. Secondly, this paper uses the improved transformer cross-modal fusion module to effectively fuse different types of modal information. Finally, the transformer module is used to judge the information of different modalities and analyze the emotion from the multi-modal information. At the same time, a large number of experiments prove that the model based on multi-modal convolutional neural network proposed in this paper surpasses other methods in prediction accuracy and effectively improves the accuracy of sentiment classification tasks. [ABSTRACT FROM AUTHOR]

Face mask-wearing detection is of great significance for safety protection during the epidemic. Aiming at the problem of low detection accuracy due to the problems of occlusion, complex illumination, and density in mask-wearing detection, this paper proposes a neural network model based on the loss function and attention mechanism for mask-wearing detection in complex environments. Based on YOLOv5s, we first introduce an attention mechanism in the feature fusion process to improve feature utilization, study the effect of different attention mechanisms (CBAM, SE, and CA) on improving deep network models, and then explore the influence of different bounding box loss functions (GIoU, CIoU, and DIoU) on mask-wearing recognition. CIoU is used as the frame regression loss function to improve the positioning accuracy. By collecting 7,958 mask-wearing images and a large number of images of people without masks as a dataset and using YOLOv5s as the benchmark model, the mAP of the model proposed in the paper reached 90.96% on the validation set, which is significantly better than the traditional deep learning method. Mask-wearing detection is carried out in a real environment, and the experimental results of the proposed method can meet the daily detection requirements. [ABSTRACT FROM AUTHOR]

At the same time that my country has shifted from high-speed development to high-quality development, my country has also put forward new requirements for education development. Due to the limited study time during college, each student's study habits and learning process are also different, and the degree of connection between tennis lessons is high, so there will be polarization when learning tennis. With the development of science and technology, more and more technological innovations are integrated into the classroom, and traditional teaching methods can no longer keep up with the pace of the times. Tennis teaching is a subject of equal proportion between theory and practice. The traditional teaching method simplifies the theory, which makes students to have some bad phenomena when they practice. Aiming at this series of problems, this paper uses algorithms such as softmax function and threshold function to construct an application model of virtual image technology based on the artificial neural network in tennis teaching. The research results of the article show that: (1) the average accuracy rate of the method in this paper is 97.22%, and the highest accuracy rate is 99.17%. The average accuracy rate also tends to increase with the increase of sample size; the recall rate is the highest, and the highest recall rate is 99.36%. The average recall rate is 96.77%; the highest correct rate is close to 100% and is significantly higher than the other three methods; the average correct rate reaches 98.8%; the response time is the shortest; the average response time is 33 ms; and the response time increases with the increase of the sample size. (2) After using this model, tennis skills have been improved, with an average of 12 in situ flips, an average of 7 in situ rackets, an average of 5 in situ forehand draws, and an average of 3 in situ backhand draws. (3) The average forehand and backhand scores of the class after the experiment were 90 and 86; the average forehand and backhand stability were 8 and 7; and the average forehand and backhand accuracy were 31 and 29, respectively. The average depth of forehand and backhand is 36 and 32. (4) Most of the students are satisfied with this model, and they all choose to strongly agree and relatively agree, and the percentage of very agree that helps stimulate learning has reached 60.52%, and no students choose to disagree very much. [ABSTRACT FROM AUTHOR]

The establishment of enterprise target inventory is directly related to the forecast of drug sales volume. Accurate sales forecasting can help businesses not only set accurate target inventory but also avoid inventory backlogs and shortages. In this paper, NN technology is used to forecast sales and is optimized using the PSO algorithm, resulting in the creation of a drug sale forecast model. The model optimizes the weights and thresholds of NN using the improved PSO optimization algorithm and predicts the periodic components based on time correlation characteristics, effectively describing the trend growth and seasonal fluctuations of sales forecast data. Furthermore, the model in this paper has been creatively improved according to the needs of practical application, which has improved the shortcomings of traditional NN, such as long training time, slow convergence speed, and ease to fall into local minima, to improve performance and quality, and has received positive results in application. The experimental results show that this model has a prediction accuracy of 96.14 percent, which is 12.78 percent higher than the traditional BP model. The optimized model can be used to forecast drug sales in a practical and feasible way. [ABSTRACT FROM AUTHOR]

Training simulators have been gradually evolving in the direction of software, virtualization, networking, and multiplatform in recent years, with the continuous development of hardware and software technologies, particularly the maturity of VR (Virtual Reality) related technologies. The network intrusion program allows remote hackers to take control of the system, posing a serious threat to the network and computer security. As a result, this paper proposes a VR-based ID (intrusion detection) simulation training system. This paper proposes an ID model based on CNN (Convolutional Neural Networks) and LSTM to address these issues (Long Short-term Memory Networks). This model oversamples data from unbalanced data sets, reducing the difference in category data and thus improving the ID model's performance and existing detection methods to compensate for the flaw. 3DSMAX technology is used to simulate the process visualization scene, as well as some key equipment models and signal transmission simulations, during the system design and implementation process. The experimental results show that CNN LSTM outperforms BP, and the overall evaluation index F1 has significantly improved, particularly the F1 index of D4. CNN LSTM outperforms GA (genetic algorithm) by 12.75 percent and BP by 14.07 percent. The system essentially accomplishes the anticipated simulation training goal, and the simulation training effect is impressive. [ABSTRACT FROM AUTHOR]

With the continuous deepening of enterprise system reform and the rapid development of national economy, enterprises are facing the great challenge of market competition. In the new market and social environment, the role of human resource management in enterprises becomes particularly important. To further improve the level of enterprise human resources strategic management has become an urgent problem to be solved. In the process of human resource management, enterprises are faced with complex and changeable environment and other influencing factors. Therefore, in the human resource information retrieval, this paper uses the method of deep learning to screen human resource management indicators and constructs the human resource management index system of power supply enterprises. In this paper, the nonlinear characteristics of neural network are used to establish a deep neural network human resource cross-media fusion model, which provides an operational method for enterprise human resource management. The human resource allocation relationship of enterprises is predicted, and the influencing factors and trends of personnel post-matching are analyzed. The demand forecasting results show that the neural network depth has a good fit with the enterprise staff, and the actual forecasting error is less than 3.0. It can accurately predict the human resource allocation of enterprises, improve the scientificity and effectiveness of human resource strategic decision-making, and make enterprises better adapt to the requirements of market economy. This will be of practical significance to the modernization of enterprise management. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the construction of cross-media collaborative filtering neural network model to design an in-depth model for fast video click-through rate projection based on cross-media collaborative filtering neural network. In this paper, by directly extracting the image features, behavioral features, and audio features of short videos as video feature representation, more video information is considered than other models. The experimental results show that the model incorporating multimodal elements improves AUC performance metrics compared to those without multimodal features. In this paper, we take advantage of recurrent neural networks in processing sequence information and incorporate them into the deep-width model to make up for the lack of capability of the original deep-width model in learning the dependencies between user sequence data and propose a deep-width model based on attention mechanism to model users' historical behaviors and explore the influence of different historical behaviors of users on current behaviors using the attention mechanism. Data augmentation techniques are used to deal with cases where the length of user behavior sequences is too short. This paper uses the input layer and top connection when introducing historical behavior sequences. The models commonly used in recent years are selected for comparison, and the experimental results show that the proposed model improves in AUC, accuracy, and log loss metrics. [ABSTRACT FROM AUTHOR]

Based on the existing optimization neural network algorithm, this paper introduces a simple and computationally efficient adaptive mechanism (adaptive exponential decay rate). By applying the adaptive mechanism to the Adadelta algorithm, it can be seen that AEDR-Adadelta acquires the learning rate dynamically and adaptively. At the same time, by proposing an adaptive exponential decay rate, the number and method of configuring hyperparameters can be reduced, and different learning rates can be effectively obtained for different parameters. The model is based on the encoder-decoder structure and adopts a dual-encoder structure. The transformer encoder is used to extract the context information of the sentence; the Bi-GRU encoder is used to extract the information of the source sentence; and the gated structure is used at the decoder side. The input information is integrated, and each part is matched with different attention mechanisms, which improves the model's ability to extract and analyze relevant features in sentences. In order to accurately capture the coherence features in English texts, an improved subgraph matching algorithm is used to mine frequently occurring subgraph patterns in sentence semantic graphs, which are used to simulate the unique coherence patterns in English texts, and then analyze the overall coherence of English texts. According to the frequency of occurrence of different subgraph patterns in the sentence semantic graph, the subgraphs are filtered to generate frequent subgraph sets, and the subgraph frequency of each frequent subgraph is calculated separately. The overall coherence quality of English text is quantitatively analyzed by extracting the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graph. According to the experimental results, the algorithm using the adaptive mechanism can reduce the error of the training set and the test set, improve the classification accuracy to a certain extent, and has a faster convergence speed and better text generalization ability. The semantic coherence diagnosis model of English text in this paper performs well in various tasks and has a good effect on improving the automatic correction system of English composition and providing reference for English teachers' composition correction. [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 this paper, a piano-assisted automated accompaniment system is designed and applied to a practical process using a heuristic dynamic planning approach. In this paper, we aim at the generation of piano vocal weaves in accompaniment from the perspective of assisting pop song writing, build an accompaniment piano generation tool through a set of systematic algorithm design and programming, and realize the generation of recognizable and numerous weaving styles within a controlled range under the same system. The mainstream music detection neural network approaches usually convert the problem into a similar way as image classification or sequence labelling and then use models such as convolutional neural networks or recurrent neural networks to solve the problem; however, the existing neural network approaches ignore the music relative loudness estimation subtask and ignore the inherent temporality of music data when solving the music detection task. However, the existing music generation neural network methods have not yet solved the problems of discrete integrability brought by piano roll representation music data and the still-limited control domain and variety of instruments generated in the controllable music generation task. To solve these two problems, this paper proposes a controlled music generation neural network model for multi-instrument polyphonic music. The effectiveness of the proposed model is verified by conducting several sets of experiments on the collected MIDICN data set, and the experimental results show that the model achieves better performance in the aspects of negative log-likelihood value, perplexity, musicality measure, domain similarity analysis, and manual evaluation. [ABSTRACT FROM AUTHOR]

The state of charge (SOC) is one of the main indexes of the lithium-ion battery, which affects the practice range of new energy vehicles and the safety of the battery. Nevertheless, the value of SOC cannot be measured directly. At present, the algorithm for estimating the state of charge is not very satisfactory. The multilayer perceptron algorithm designed during this paper encompasses a sensible impact on state estimation. During this paper, the multilayer network is designed to estimate the charged state of lithium batteries from the three-layer artificial neural network to the eleven-layer artificial neural network. After preprocessing the dataset and comparing several activation functions, the ten-layer fully connected neural network is the most efficient to estimate the SOC. In order to prevent over-fitting of the multilayer perceptron algorithm, the two techniques of the BatchNormalization layer and Dropout layer work together to inhibit over-fitting. At the same time, the accuracy of extended Kalman filter, long and short memory network, and recurrent neural network are compared. The multilayer perceptron network designed during this paper has the highest accuracy. Finally, in the open dataset, both the training and test errors achieve good results. The algorithm developed in this paper has made some progress in SOC estimation. [ABSTRACT FROM AUTHOR]

Dealing with food safety issues in time through online public opinion incidents can reduce the impact of incidents and protect human health effectively. Therefore, by the smart technology of extracting the entity relationship of public opinion events in the food field, the knowledge graph of the food safety field is constructed to discover the relationship between food safety issues. To solve the problem of multi-entity relationships in food safety incident sentences for few-shot learning, this paper adopts the pipeline-type extraction method. Entity relationship is extracted from Bidirectional Encoder Representation from Transformers (BERTs) joined Bidirectional Long Short-Term Memory (BLSTM), namely, the BERT-BLSTM network model. Based on the entity relationship types extracted from the BERT-BLSTM model and the introduction of Chinese character features, an entity pair extraction model based on the BERT-BLSTM-conditional random field (CRF) is established. In this paper, several common deep neural network models are compared with the BERT-BLSTM-CRF model with a food public opinion events dataset. Experimental results show that the precision of the entity relationship extraction model based on BERT-BLSTM-CRF is 3.29%∼23.25% higher than that of other models in the food public opinion events dataset, which verifies the validity and rationality of the model proposed in this paper. [ABSTRACT FROM AUTHOR]

College English has received increasing focus as an important part of the education system. However, the continuous development of English instruction has not simultaneously promoted students' positive learning motivation for English courses. The generation and growth of negative motivation have become a common problem among college students. Students' enthusiasm for learning English courses is gradually fading and teachers' teaching value has also become difficult to guarantee, which seriously affects the normal and orderly progress of education and teaching activities. Therefore, it is very important for the healthy development of English teaching to understand and study the affecting elements of negative motivation in English learning of university students and to provide scientific and effective suggestions for teachers and learners to establish a good teaching and learning attitude. Relying on the interpretation of a negative motivation theory, this paper studies various influencing factors by means of the artificial neural network algorithm. The principal component analysis method is introduced to improve the traditional BP algorithm in terms of the frequency of iterations and the length of computation time, which realizes the accurate and efficient analysis of college students' English learning data. The results of the analysis revealed that the comprehensive error of this algorithm in the analysis of influencing factors was in the range of 0.004 to 0.012. Through the calculation of the eigenvalues and cumulative contribution rate of negative motivation influencing factors, it is found that factors such as the curriculum setting, teaching method, and teacher-student relationship have the greatest influence on students' negative motivation in English learning. The eigenvalues were 1.027, 1.319, and 1.422, respectively. The cumulative contribution rate reached 64.57%, 26.11%, and 23.62%, respectively. From this aspect, it is necessary to improve these aspects in order to eliminate the negative motivation of learning. [ABSTRACT FROM AUTHOR]

In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure are analyzed, and both have positive effects on knowledge sharing in the actual development process, but the degree of them also possesses certain variability. A model of the factors influencing the quality of knowledge shared by users is constructed to explore the relationship between the dimensions in social capital and how they affect the community users' perceptions of knowledge quality. Exploring the strong influencing factors of product repurchase rate has key implications for promoting product sales and sales forecasting. The scale of this paper has undergone several minor revisions, and the content validity is very good. Criterion validity is generally reflected by Person correlation coefficient, and construct validity includes exploratory factor analysis and confirmatory factor analysis. First, the values were clustered, and the optimal variables were selected using stepwise regression and fitted with Poisson regression models to explore the relationship between the repurchase rate of different products and the factors that strongly influence the repurchase rate in the case. To predict the sales of goods, the advantages of the BP neural network, LSTM neural network, and Verhulst gray model are combined: BP neural network can predict sales by combining the current data corresponding to independent variables, LSTM neural network can explore the influence of historical data on sales, and Verhulst model can predict sales based on the growth trend of variables. A BP-LSTM-Verhulst nested neural network based on the AP algorithm is constructed to predict sales volume, and the accuracy of this method is proved by example. Finally, it is found that the proposed sales prediction method has higher accuracy than exponential regression and shallow neural networks. The deep learning prediction method combining unstructured data such as images proposed in the paper not only provides a more accurate sales prediction method for short life cycle products in e-commerce but also provides an effective deep learning method for management practices. The KMO value obtained after the test is less than 0.5, which is not suitable for factor analysis. Using SPSS22.0 to expand the KMO value and Bartlett's sphericity test, the KMO value is significantly greater than the minimum requirement of 0.5, the KMO value of self-efficacy is 0.826, and the KMO value of expected return is 0.870. [ABSTRACT FROM AUTHOR]

The more frequent occurrence of network security incidents has an impact on network security. Through the research on network security situational awareness, this paper constructs a multilevel network security situation evaluation index system from various aspects and uses the Elman neural network optimized by the genetic algorithm to evaluate network security situation. Aiming at the disadvantage of subjective dependence in the traditional assignment method of basic probability assignment function, Elman neural network is used to obtain the basic probability assignment function to increase its objectivity, and it is optimized with the PSD algorithm. In addition, the neural network is further improved by the genetic algorithm. In the traditional D-S evidence theory, an evidence correction step is added to optimize the situation that the final judgment result is incorrect due to evidence conflict. Finally, the fusion rules of the D-S evidence theory are used to fuse the support degrees of the four first-level situations to different security levels to obtain the final network security situation assessment result. The results show that the prediction accuracy of the GA-Elman neural network model is as high as 80%, which is significantly higher than that of the traditional D-S model, indicating that the model proposed in this paper has improved the accuracy of the assessment and prediction results. In conclusion, this study provides feasible theoretical prediction guidance for the accurate assessment of network security posture, reveals the improvement ideas for network security development, and is of great significance for the maintenance of network environment security. [ABSTRACT FROM AUTHOR]

This paper analyzes the quantitative assessment model of the swimming training effect based on the deep neural network by constructing a deep neural network model and designing a quantitative assessment model of the swimming training effect. This paper addresses the problem of not considering the influence of the uncertainties existing in the virtual environment when evaluating swimming training and adds the power of the delays in the actual training operation environment, which is used to improve the objectivity and usability of swimming training evaluation results. To better measure the degree of influence of uncertainties, a training evaluation software module is developed to validate the usability of the simulated training evaluation method using simulated case data and compare it with the data after training evaluation using the unimproved evaluation method to verify the correctness and objectivity of the evaluation method in this paper. In the experiments, the feature extractor is a deep neural network, and the classifier is a gradient-boosting decision tree with integrated learning advantages. In the experimental comparison, we can achieve more than 60% accuracy and no more than a 1.00% decrease in recognition rate on DBPNN + GBDT, 78.5% parameter reduction, and 54.5% floating-point reduction on DPBNN. We can effectively reduce 32.1% of video memory occupation. It can be concluded from the experiments that deep neural network models are more effective and easier to obtain relatively accurate experimental results than shallow learning when facing high-dimensional sparse features. At the same time, deep neural networks can also improve the prediction results of external learning models. Therefore, the experimental results of this model are most intuitively accurate when combining deep neural networks with gradient boosting decision trees. [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]

In order to improve the effect of goodwill evaluation and intangible asset management, this paper combines the improved fuzzy comprehensive evaluation method to construct an intelligent algorithm. In order to consider the many influencing factors of intangible asset prices and their own influence, this experiment adopts the ARDL model for analysis. Based on the traditional LSTM model, this paper innovatively introduces sparse principal component analysis for feature dimensionality reduction and, on the other hand, adds a convolutional neural network to the model, which is suitable for feature extraction. In addition, this paper constructs a goodwill evaluation and intangible asset management model based on the improved fuzzy comprehensive evaluation method. The research shows that the improved fuzzy comprehensive evaluation method proposed in this paper has a very good application effect in goodwill evaluation and intangible asset management. [ABSTRACT FROM AUTHOR]

With the development of deep learning theory and the decrease of the cost of acquiring massive data, the image semantic segmentation algorithm based on Convolutional Neural Networks (CNNs) is gradually replacing the conventional segmentation algorithm by its high accuracy segmentation performance. By increasing the amount of training data and stacking more convolutional layers to form Deep Convolutional Neural Networks (DCNNs), a neural network model with higher segmentation accuracy can be obtained, but it faces the problems of serious memory consumption and long latency. For some special application scenarios, such as augmented reality and mobile interaction, real-time processing cannot be performed. To improve the speed of semantic segmentation while obtaining the most accurate segmentation results as possible, this paper proposes a semantic segmentation algorithm based on lightweight convolutional neural networks. Taking the computational complexity and segmentation accuracy into account, the algorithm starts from the perspective of extracting high-level semantic features and introduces a position-attention mechanism with richer contextual information to model the relationship between different pixels, avoiding the convolutional local perceptual field to be too small. To recover clearer target boundaries, a channel attention mechanism is introduced in the decoding part of the model to mine more useful feature channel information and effectively improve the fusion of low-level features with high-level features. By verifying the effectiveness of the above model on a publicly available dataset and comparing it with the more popular semantic segmentation methods, the model proposed in this paper has higher semantic segmentation accuracy and reflects certain advantages in objective evaluation. [ABSTRACT FROM AUTHOR]

The business model of traditional market is declining day by day, and people's consumption cognition has risen to a new level with the leap in science and technology. Enterprises need to adjust and optimize their marketing strategies in time according to the new consumption characteristics, so as to smoothly adapt to the environmental changes in the Internet age. This paper briefly analyzes the relationship between sales development and psychology and constructs a fusion model that can predict preferences with the help of neural network structure of the deep learning method. Describe user portraits and characteristics, analyze users' purchasing behavior and credit literacy, and push related products combined with a hash algorithm to achieve accurate e-commerce marketing purposes. The results show that (1) the model constructed in this paper and five different models are used for multi-modal recognition analysis: the accuracy is 79.56%, the recall rate is 77.43%, F1 is 0.785, and the error value can be reduced to about 0.18 by epoch iteration; the model is superior and has great use value. (2) Using the model to extract user attribute features and predict certain preferences, 13 topics and weight ratios are obtained for users of a certain platform, and the portrait model of each user is constructed. (3) According to the portrait optimization, 8 different marketing strategies are obtained, and the marketing effect is remarkable, fluctuating between 69% and 82%, and the income situation is also satisfactory. The final model design is reasonable and the data performance is good, which provides an intelligent and efficient dynamic strategy service for enterprises. [ABSTRACT FROM AUTHOR]

In this paper, a CNN model for color element data analysis of the urban spatial environment is constructed through an in-depth study of color element data analysis. This paper investigates a high-order structure formed by a few nodes; it proposes a motif-based graph autoencoder MODEL, combining redefined first- and second-order similarities and perfectly integrating motif structure and autoencoder. The algorithm first proposes an efficient graph transformation method to add the influence of central nodes. It then offers a primary awareness mechanism to aggregate the information of noncentral neighbors. Cen GCN_D and Cen GCN_E outperform the latest algorithms in node classification, link prediction, node clustering, and network visualization. As the number of network layers increases, the advantages of these two variants become progressively more prominent. This paper uses a support vector machine to implement classification validation based on CNN. The experimental results show that when 450 images are randomly selected as training data, the classification accuracy obtained by using the features of different CNN output layers is distributed between 91.4% and 95.2%. When the training set of the experiment reaches more than 300, the accuracy can exceed 90%, and the experimental results corresponding to different training sets a more stable trend. Finally, the trained classifier model is obtained in this thesis, which achieves the purpose of fast classification prediction based on CNN for color element data analysis of urban spatial environments. [ABSTRACT FROM AUTHOR]

Winter wheat is one of the most important food products. Increasing food demand and limited land resources have forced the development of agricultural production to be more refined and efficient. The most important part of agricultural production is sowing. With the promotion of precision agriculture, precision seeding has become the main component of modern agricultural seeding technology system, and the adoption of precision seeding technology is an important means of large-scale production and cost saving and efficiency enhancement. However, the current sowing technology and sowing equipment cannot meet the requirements of wheat sowing accuracy. In this context, a differential perturbation particle swarm optimization (DPPSO) algorithm is proposed by embedding differential perturbation into particle swarm optimization, which shows fast convergence speed and good global performance. After that the DPPSO is used to optimize the convolutional neural network (CNN) to build an optimized CNN (DPPSO-CNN) model and applied to the field of crops fine sowing. Finally, the experimental results show that the proposed method not only has a faster convergence rate but also achieves better wheat seeding performance. The research of this paper an effectively improves the accuracy and uniformity of wheat seeding and lay a foundation for improving wheat yield per unit area and promotes the intelligent development of agriculture in the future. [ABSTRACT FROM AUTHOR]

The Internet has now ingrained itself into every aspect of people's lives and has grown to be a necessity. Machine learning technology is applied to the design of music teaching and teaching assistant systems with the aim of bringing convenience to students and teachers in teaching and learning and improving teaching efficiency and quality through the design and implementation of a friendly and interactive intelligent music assistant teaching system. The B/S mode framework aids in the realisation of the hierarchy and module structure of the system design during the system implementation process. Last but not least, this paper looks at the system modules, confirms all of the system's operations, and makes sure each functional module is working properly. The algorithm is straightforward, effective, simple to use, and user-friendly, and the system is expandable, portable, and transparent. It is an excellent teaching tool for music. [ABSTRACT FROM AUTHOR]

The ideological and political collaborative education mechanism is an important course teaching method that uses all courses as a carrier to cultivate students' all-round development in morality, intelligence, physique, and beauty. The purpose of this paper is to conduct a better research on the construction of the ideological and political collaborative education mechanism by building models based on edge computing and neural network algorithm. This paper first gave a general introduction to edge computing and neural network algorithm and then analyzed the current situation of ideological and political courses in a certain school. Then, edge computing and neural network algorithm were introduced into the analysis of an important course teaching method that used all courses as a carrier to cultivate students' comprehensive development in morality, intelligence, physique, and beauty. The BP neural network model was established. Through analysis and comparison, the experimental results showed that 56.47% of the students believed that the impact of personal morality on the future development of college students was the first in the relationship between "virtue" and "talent." More than half of the students believed that the "virtue" of building morality and cultivating people was mainly civic morality, and about 30% of the students thought that the main value was loving the party and patriotism, which meant that most students believed that the main value of building morality and cultivating people was to establish morality. [ABSTRACT FROM AUTHOR]

This paper firstly introduces the background of the research on neural network and anomaly identification screening and mineralization prediction under semisupervised learning, then introduces supervised learning, semisupervised learning, unsupervised learning, and reinforcement learning, analyzes and compares their advantages and disadvantages, and concludes that unsupervised learning is the best way to process the data. In the research method, this paper classifies the obtained geochemical data by using semisupervised learning and then trains the obtained samples using the convolutional neural network model to obtain the mineralization prediction model and check its correctness, which finally provides the direction for the subsequent mineralization prediction research. [ABSTRACT FROM AUTHOR]

Natural Language Understanding (NLU) and Natural Language Generation (NLG) are the general methods that support machine understanding of text content. They play a very important role in the text information processing system including recommendation and question and answer systems. There are many researches in the field of NLU such as Bag of words, N-Gram, and neural network language model. These models have achieved a good performance in NLU and NLG tasks. However, since they require lots of training data, it is difficult to obtain rich data in practical applications. Thus, pretraining becomes important. This paper proposes a semisupervised way to deal with math word problem (MWP) tasks using unsupervised pretraining and supervised tuning methods, which are based on the Unified pretrained Language Model (UniLM). The proposed model requires fewer training data than traditional models since it uses model parameters of tasks that have been learned before to initialize the model parameters of new tasks. In this way, old knowledge helps new models successfully perform new tasks from old experiences instead of from scratch. Moreover, in order to help the decoder make accurate predictions, we combine the advantages of AR and AE language models to support one-way, sequence-to-sequence, and two-way predictions. Experiments, carried out on MWP tasks with 20,000+ mathematical questions, show that the improved model outperforms the traditional models with a maximum accuracy of 79.57%. The impact of different experiment parameters is also studied in the paper and we found that a wrong arithmetic order leads to incorrect solution expression generation. [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]

Today's society is a society of Internet information, especially the rapid development of new media that is the most distinctive project today. New media has changed the way traditional media are disseminated, and the advertising industry has made new developments with the rapid development of new media, especially with the rise of online advertising, making the dissemination of advertising more influential. In modern advertising information, online advertising has become very influential. This study aims at how to achieve user-matching push and set the right price is the two major problems of Internet advertising push, through the analysis of the emergence and development of network advertising and visual elements in network advertising, starting from the problems appearing in modern online advertisements, and studying the visual communication design in online advertisements. In this paper, a neural network model and a fuzzy comprehensive evaluation decision model are designed; so as to make our online advertisements rich in national characteristics and really cause online users' active clicks. The experimental results show that the proposed method achieves good results and verifies the effectiveness of the model. [ABSTRACT FROM AUTHOR]

In the context dominated by Internet communication, people's various emotions can be clearly reflected through network public opinion, whether it is the view of political affairs, the preference for entertainment, or the demand for life. This also allows management or providers to meet their needs more specifically. Based on today's need to understand the trend of Internet public opinion, this paper describes a deep neural network (DNN). A deep neural network is a machine learning model that is the foundation of deep learning and has a strong ability to mine potential information in data. By improving the loss function of the neural network, this paper reduces the influence of unbalanced data on the classification results and improves the classification effect of the model on a small number of categories. Aiming at the different lengths of Internet text, a more robust model of text sentiment classification is proposed, which makes the HCB-Att model better extract the local information and contextual information of the text. Finally, through comparative experiments, the optimization model used in this paper is proved to be effective for the analysis of network public opinion sentiment. [ABSTRACT FROM AUTHOR]

We conduct an in-depth study on the model construction of deep neural networks and design a model of painter's psychology and anime creation style to realize the study of the relationship between painter's psychology and anime creation style based on deep neural networks. This paper proposes an animation creation psychology classification algorithm that integrates human cognitive deep network structure optimization. The algorithm analyzes the connection between different convolutional layer features and animation characteristics through animation creation style CNN feature visualization. It interactively uses the knowledge of animation creation psychology expression techniques to optimize the network structure. This paper proposes a scene animation network based on spectral difference perception style. By analyzing the characteristics and differences in the spectrum between realistic and anime domain images, the generator is guided to learn the mapping relationships better to fit the style distribution of anime domain images. This paper uses a fully convolutional structure; the network is more lightweight and supports image inputs of arbitrary size, which can keep the semantic system of the background unchanged while highly deforming the five facial features, moving toward the goal of human-scene fusion for the animation task. [ABSTRACT FROM AUTHOR]

Dance is constantly discovering truth, goodness, and beauty in human social life, spreading truth, goodness, and beauty, and fully expressing the artistic pursuit of dance beauty. It shapes different dance images, expresses the aesthetic consciousness and feelings of dance, and resonates with the audience to meet their aesthetic needs through various forms of movement. Because the RBF neural network model is good at approximating functions, many researchers have begun to use the RBNN approximation model for engineering design. Due to the limited dance data available for research, this paper uses radial basis function neural network model to study the aesthetic characteristics of dance in the context of few-shot learning. When the time index reaches 50, the average ratio of the L-MBP algorithm is 33.4 percent, 32.5 percent for the RBNN algorithm, and 46.3 percent for this method. As can be seen, this method has the highest ratio of the three algorithms, giving it a distinct advantage in terms of dance aesthetics. As a result, this paper establishes a neural network model, trains and simulates the network model, studies and analyzes the influence of changes in influencing factors on the aesthetic characteristics of dance, and provides a new idea for the prediction of the aesthetic characteristics of dance and a reference for optimizing the design of the aesthetic system of dance using the prediction ability of radial basis function neural networks. [ABSTRACT FROM AUTHOR]

This paper addresses data mining and neural network model construction and analysis to design a data interaction process model based on data mining and topology visualization. This paper performs preprocessing data operations such as data filtering and cleaning of the collected data. A typical multichannel convolutional neural network (MCNN) in deep learning techniques is applied to alert students' academic performance. In addition, the network topology of the CNN is optimized to improve the performance of the model. The CNN has many hyperparameters that need to be tuned to construct an optimal model that can effectively interact with the data. In this paper, we propose a method to visualize the network topology within unstable regions to address the current problem of lacking an effective way to layout the network topology into specified areas. The technique transforms the network topology layout problem within the unstable region into a circular topology diffusion problem within a convex polygon, ensuring a clear, logical topology connection, and dramatically reducing the gaps in the area, making the layout more uniform beautiful. This paper constructs a real-time data interaction model based on JSON format and database triggers using message queues for reliable delivery. A platform-based real-time data interaction solution is designed by combining the timer method with the original key. The solution designed in this paper considers the real-time accuracy, security and reliability of data interaction. It satisfies the platform's initial and newly discovered requirements for data interaction. [ABSTRACT FROM AUTHOR]

In this paper, the algorithm of the deep convolutional neural network is used to conduct in-depth research and analysis of sports health big data, and an intelligent analysis system is designed for the practical process. A convolutional neural network is one of the most popular methods of deep learning today. The convolutional neural network has the feature of local perception, which allows a complete image to be divided into several small parts, by learning the characteristic features of each local part and then merging the local information at the high level to get the full representation information. In this paper, we first apply a convolutional neural network for four classifications of brainwave data and analyze the accuracy and recall of the model. The model is then further optimized to improve its accuracy and is compared with other models to confirm its effectiveness. A demonstration platform of emotional fatigue detection with multimodal data feature fusion was established to realize data acquisition, emotional fatigue detection, and emotion feedback functions. The emotional fatigue detection platform was tested to verify that the proposed model can be used for time-series data feature learning. According to the platform requirement analysis and detailed functional design, the development of each functional module of the platform was completed and system testing was conducted. The big data platform constructed in this study can meet the basic needs of health monitoring for data analysis, which is conducive to the formation of a good situation of orderly and effective interaction among multiple subjects, thus improving the information service level of health monitoring and promoting comprehensive health development. [ABSTRACT FROM AUTHOR]

In this paper, a multimodal knowledge mapping approach is used to digitize enterprise carbon assets, and a corresponding neural network model is designed for use in the practical process. Rich textual entity labels associated with images are obtained using an entity annotation system. A topology-based data fusion method is also designed based on the hierarchical relationship between WordNet and DBpedia to fuse the knowledge obtained from image visualization and text description mining. Existing neural network-based entity linking methods ignore the semantic gap between the context of sequential entity denotative items and the context of graph-structured entities, thus affecting the accuracy of entity linking. It is observed that the importance of words in the context of entity denotative items is different, and the importance of content in the entity context is also different. To solve the above problems, this paper proposes an entity linking method that combines a common attention mechanism with a graph convolutional neural network. Secondly, based on the basic theory of value assessment, the characteristics of classical asset valuation methods and their inapplicability to the valuation of carbon assets are analyzed, and thus the real option valuation method and its two classical models are introduced; after demonstrating the real option characteristics of carbon assets of power enterprise projects, a real option model-based carbon asset valuation model for power enterprise projects is constructed and its applicability is verified with case studies. Through analyzing the current situation and problems of carbon asset valuation work in power enterprises, targeted practical suggestions are put forward to further strengthen and enhance the carbon asset valuation work in power enterprises in the future. [ABSTRACT FROM AUTHOR]

In this paper, the residual convolutional neural network is used to extract the note features in the music score image to solve the problem of model degradation; then, multiscale feature fusion is used to fuse the feature information of different levels in the same feature map to enhance the feature representation ability of the model. A network composed of a bidirectional simple loop unit and a chained time series classification function is used to identify notes, parallelizing a large number of calculations, thereby speeding up the convergence speed of training, which also makes the data in the dataset no longer need to be strict with labels. Alignment also reduces the requirements on the dataset. Aiming at the problem that the existing cross-modal retrieval methods based on common subspace are insufficient for mining local consistency within modalities, a cross-modal retrieval method fused with graph convolution is proposed. The K-nearest neighbor algorithm is used to construct modal graphs for samples of different modalities, and the original features of samples from different modalities are encoded through a symmetric graph convolutional coding network and a symmetric multilayer fully connected coding network, and the encoded features are fused and input. We jointly optimize the intramodal semantic constraints and intermodal modality-invariant constraints in the common subspace to learn highly locally consistent and semantically consistent common representations for samples from different modalities. The error value of the experimental results is used to illustrate the effect of parameters such as the number of iterations and the number of neurons on the network. In order to more accurately illustrate that the generated music sequence is very similar to the original music sequence, the generated music sequence is also framed, and finally the music sequence spectrogram and spectrogram are generated. The accuracy of the experiment is illustrated by comparing the spectrogram and the spectrogram, and genre classification predictions are also performed on the generated music to show that the network can generate music of different genres. [ABSTRACT FROM AUTHOR]

In this paper, we conduct an in-depth study and analysis of the automatic image processing algorithm based on a multimodal Recurrent Neural Network (m-RNN) for light environment optimization. By analyzing the structure of m-RNN and combining the current research frontiers of image processing and natural language processing, we find out the problem of the ineffectiveness of m-RNN for some image generation descriptions, starting from both the image feature extraction part and text sequence data processing. Unlike traditional image automatic processing algorithms, this algorithm does not need to add complex rules manually. Still, it evaluates and filters through the training image collection and finally generates image automatic processing models by m-RNN. An image semantic segmentation algorithm is proposed based on multimodal attention and adaptive feature fusion. The main idea of the algorithm is to combine adaptive and feature fusion and then introduce data enhancement for small-scale multimodal light environment datasets by extracting the importance between images through multimodal attention. The model proposed in this paper can span the semantic differences of different modalities and construct feature relationships between different modalities to achieve an inferable, interpretable, and scalable feature representation of multimodal data. The automatic processing of light environment images using multimodal neural networks based on traditional algorithms eliminates manual processing and greatly reduces the time and effort of image processing. [ABSTRACT FROM AUTHOR]

The change of urban cultural space layout is a multi-variable, multi-objective, and restricted research process. The optimization of urban cultural space construction and layout is a multi-objective decision-making problem that needs to be solved urgently. Based on the forward three-layer neural network theory, this paper constructs an optimization model for the construction and layout of urban cultural space evaluation of the layout of cultural space. This paper first analyzes the feasibility of combining the forward three-layer neural network model with the optimization and adjustment of cultural space layout structure. Taking the three-layer feedforward network as an example, the structure optimization model based on the forward three-layer neural network is selected, and the established model is used to reflect the internal environment of the objective world. Structure and perform dynamic simulation. In the process of simulation modeling, from the aspects of system description, model structure, logical analysis, reasoning, and interpretation, two effective computer dynamic simulation methods, namely, forward three-layer neural network model and system dynamics SD model, were carried out for theoretical comparison and identification. The experimental results show that the feasibility and calculation error of the application of the optimization model are relatively good, reaching 0.897 and 6.21%, respectively. The number of newly added cultural spaces and the expansion speed show an increasing trend, expanding at an average annual speed of about 35 km2, effectively increasing the quality of regional planning and construction layout. [ABSTRACT FROM AUTHOR]

This paper adopts a blockchain fusion neural network algorithm to conduct an in-depth study on the model of agricultural cold chain coordination. We aim to enable HDFS to meet the demand of storing many small files of various types generated by various stages of agricultural cold chain coordination and then propose an improved balanced merging and index caching strategy based on file types and size grouping. The main three modules are the file preprocessing module, file balanced merging module, and index caching module. The experimental results show that this method can significantly improve the overall performance of HDFS when storing and reading large amounts of small files. Simulation experiments using the UCI test dataset show that the improved spectral clustering algorithm not only reduces the error rate but also significantly reduces the time spent on the clustering process, demonstrating the effectiveness and feasibility of the improved spectral clustering algorithm. The improved spectral clustering algorithm of this paper is used to cluster and analyze nearly one thousand cold chain coordination-related data, and the optimal city is successfully selected as the construction point of a large cold storage transit station. This study can effectively improve the efficiency of cold chain coordination resources and their time utilization and maximize the profit creation for cold chain coordination enterprises, selecting data features for prediction, experimenting with different models and parameters to optimize accuracy, and embedding the resulting learning system for prediction and further operations. The two models of coordination market demand forecasting models and methods are analyzed separately. Finally, after analyzing the prediction results of the two different prediction methods, it is found that it conforms to the actual situation of coordination development in Jiangxi Province. It shows that the coordination market prediction model established in this paper is meaningful and the prediction analysis made has some practical value. [ABSTRACT FROM AUTHOR]