Search

Internet of Things (IoT) has been used in smart cities, agriculture, weather forecasting, smart grids, and waste management. The IoT has huge potential but needs refinement. The paper focuses on lowering IoT sensor power consumption to improve network life. This work selects the best IoT cluster header (CH) to maximize energy consumption. The suggested technique uses particle swarm optimization (PSO) with artificial neural networks (ANNs). The optimal CH in an IoT network cluster was identified by taking into account the number of active nodes, the load, the residual energy, and the cost function. This work compares the suggested method with artificial bee colony, genetic, and adaptive gravity search algorithms. The hybrid solution beats conventional methods. [ABSTRACT FROM AUTHOR]

Small samples are prone to overfitting in the neural network training process. This paper proposes an optimization approach based on L2 and dropout regularization called a hybrid improved neural network algorithm to overcome this issue. The proposed model was evaluated based on the Modified National Institute of Standards and Technology (MNIST, grayscale-28 × 28 × 1) and Canadian Institute for Advanced Research 10 (CIFAR10, RGB - 32 × 32 × 3) as the training data sets and data applied to the LeNet-5 and Autoencoder neural network architectures. The evaluation is conducted based on cross-validation; the result of the model prediction is used as the final measure to evaluate the quality of the model. The results show that the proposed hybrid algorithm can perform more effectively, avoid overfitting, improve the accuracy of network model prediction in classification tasks, and reduce the reconstruction error in the unsupervised domain. In addition, employing the proposed algorithm without increasing the time complexity can reduce the effect of noisy data and bias and improve the training time of neural network models. Quantitative and qualitative experimental results show that the accuracy of using the proposed algorithm in this paper with the MNIST test set has an improvement of 2.3% and 0.9% compared to L2 regularization and dropout regularization, respectively, and based on the CIFAR10 data set, the accuracy improvement of 0.92% compared with L2 regularization and 1.31% concerning dropout regularization. The reconstruction error of using the proposed algorithm in this paper with the MNIST data set has an improvement of 0.00174 and 0.00398 compared to L2 regularization and dropout regularization, respectively, and based on the CIFAR10 data set, the accuracy improvement of 0.00078 compared with L2 regularization and 0.00174 concerning dropout regularization. [ABSTRACT FROM AUTHOR]

This paper proposes the design of a controller using the artificial neural network (ANN) for a solar photovoltaic (PV)-fed cascaded multilevel inverter (CMLI) to enhance the power quality. The objective of this presented ANN controller is to obtain a maximum output voltage with no filter components. This paper also investigates and eliminates the voltage harmonics that occurred in a solar-fed cascaded 3-stage inverter using various techniques such as pulse width modulation (PWM), digital logic control (DLC), fuzzy logic controller (FLC), and ANN, and the results are compared. Based on the results, the proposed ANN-based controller efficiently reduces harmonics and improves the power quality. This is achieved by solving the harmonic equations and thereby changing the switching angles of each semiconductor to a minimum value. The ANN is trained by a dataset consisting of varying input voltage and switching angles. The simulation was performed using MATLAB/Simulink for different types of controllers like PWM, DLC, FLC, and ANN for the 3-stage inverter. The simulated results are compared with the results obtained from a 3 kWp photovoltaic plant connected to the CMLI. Finally, on the basis of performance analysis, it was confirmed that the ANN-based controller effectively eliminates harmonics and improves the power quality. [ABSTRACT FROM AUTHOR]

Macroeconomic situation is the overall performance of the economic situation of a country and region. Making accurate forecasts of macroeconomic trends is of great significance for analyzing the success or failure of macroeconomic control policies, evaluating the quality of economic system operation, and correctly formulating future development planning strategies. The macroeconomic system is a nonlinear system, the environment is constantly changing, and additional disturbing factors directly affect the operation of the macroeconomic system, which has a great impact on the forecast results. The historical information required for macroeconomic modeling is unstable, unclear, and incomplete, which makes it very difficult to solve such problems with traditional forecasting methods. In response to the multivariate and nonlinear characteristics of macroeconomic forecasting, this paper proposes the application of artificial neural networks for forecasting. This paper introduces the recurrent neural network into the field of economic forecasting to solve the problems of the traditional BP (back propagation) neural network method. The experimental data are verified and the experimental results prove that the studied scheme based PSO-GRU improve the performance of economic forecasting. [ABSTRACT FROM AUTHOR]

Contextual representation recommendation directly uses contextual prefiltering technology when processing user contextual data, which is not the integration of context and model in the true sense. To this end, this paper proposes a context-aware recommendation model based on probability matrix factorization. We design a music genre style recognition and generation network. In this network, all the sub-networks of music genres share the explanation layer, which can greatly reduce the learning of model parameters and improve the learning efficiency. Each music genre sub-network analyzes music of different genres, realizing the effect of multitasking simultaneous processing. In this paper, a music style recognition method using a combination of independent recurrent neural network and scattering transform is proposed. The relevant characteristics of traditional audio processing methods are analyzed, and their suitable application scenarios and inapplicability in this task scenario are expounded. Starting from the principle of scattering transform, the superiority and rationality of using scattering transform in this task are explained. This paper proposes a music style recognition method combining two strategies of scattering transform and independent recurrent neural network. In the case that the incremental data set is all labeled, this paper introduces the solution of the convex hull vector, which reduces the training time of the initial sample. According to the error push strategy, an incremental learning algorithm based on convex hull vector and error push strategy is proposed, which can effectively filter historical useful information and at the same time eliminate useless information in new samples. Experiments show that this method improves the accuracy of music style recognition to a certain extent. Music style recognition based on independent recurrent neural network can achieve better performance. [ABSTRACT FROM AUTHOR]

With the rapid development of industry, the production and demand of automobiles have increased significantly. The generation of cars often requires tens of thousands of processes, consisting of hundreds of assembly lines to complete. Chain conveyors are widely used in the transportation of automobile assembly lines. The existing conveyor chains are roller chains. With the improvement of the requirements for conveyor reliability, synchronization, and environmental friendliness, the characteristics of roller conveyor chains restrict the further development of chain conveyors. It is urgent to study a new conveyor chain system to improve the synchronization, reliability, and environmental friendliness of chain conveyors on hundreds of assembly lines. In this paper, the mathematical modeling, meshing analysis, reliability, and environmental friendliness of the parameters of the components of the double pitch time-sharing meshing toothed conveyor chain system are studied. In addition, a novel fault diagnosis method of double pitch time-sharing meshing toothed conveyor chain transmission system based on neural network model is proposed in this paper. [ABSTRACT FROM AUTHOR]

It is very important for power grid development research and related technical improvement to obtain the disaster situation of fine-scale distribution network, such as the transportation condition evaluation of distribution network and the wind waterlogging disaster prediction of distribution network. Among them, the wind waterlogging disaster prediction of distribution network is the main one, and the prediction of its disaster degree often determines whether the distribution network can be prevented before and rescued after the disaster. Therefore, in view of the above problems, combined with the actual transmission situation of the distribution network, after collecting the measured disaster data of the distribution network in relevant areas, combined with the multi-source data fusion technology and neural network modeling technology, this paper analyzes the disaster degree indicators of different distribution networks and constructs the relevant fuzzy matrix through the fuzzy theory to evaluate the disaster degree, which is verified by the measured data. This distribution network disaster loss prediction model can effectively implement the disaster loss prediction of distribution network and compare its prediction results with the other two different common models. The comparison results show that the prediction accuracy of the multi-source data fusion prediction model constructed in this paper is more than 0.95 compared with the other two models, while the prediction accuracy of the other two models is not more than 0.9, which proves that the model constructed in this paper has smaller errors. It has the advantages of higher accuracy and faster convergence speed. [ABSTRACT FROM AUTHOR]

Human action recognition is a hotspot in the fields of computer vision and pattern recognition. Human action recognition technology has created huge social value and considerable economic value for the society. Meeting people's needs and understanding people's expressions are the current research focus. Aiming at the problem that the movement cannot be continuously identified and due to a lack of detailed features in the action decomposition pattern recognition in the traditional Wushu routine decomposition process, it is proposed to use Kinect technology to identify the Wushu routine movement decomposition process in the Wushu routine movement decomposition process. This paper analyzes the principle of skeleton tracking and skeleton extraction performed by the Kinect human sensor and uses the Kinect sensor with the Visual Studio 2015 development platform to collect and process the skeleton data of limb movements and defines eight static limb motion samples and four dynamic limbs. The study uses a deep learning neural network algorithm to train and identify the established database of static body movements and uses the same template matching algorithm and K-NN. The recognition effects of the algorithms were compared and analyzed, and it was concluded that the static body motion recognition rates of the three algorithms were all above 90%. In this paper, recognition experiments are carried out on the MSR action 3D database. The influence of different integrated decision-making methods on the recognition results is further discussed and analyzed, and the average method integrated decision-making, which is most suitable for the algorithm model in this paper, is proposed. The results show that the recognition accuracy of the algorithm reaches 98.1%, which proves the feasibility of the preprocessing algorithm. [ABSTRACT FROM AUTHOR]

English is the most widely used language in the world, and at the same time, English translation is becoming increasingly important. However, the traditional English translation model still has some problems, such as poor translation effect, repeated translation, translation solidification, and translation limitations caused by regional language differences in the application process of language communication. Differential equations refer to relational expressions containing unknown functions and their derivatives. Solving differential equations is to find unknown functions. Differential equations are a branch of mathematics developed along with calculus. In order to solve the problem, this paper puts forward the use of differential equation thinking research English translation language propagation dynamic model, the differential equation combines the neural network language model (NNLM) to form the language dynamic propagation using the differential equation and NNLM model algorithm of the dynamic model for the actual translation effect test (the database includes more than 3,000 indexed, abstract journals and newspapers, including nearly 3,000 full-text journals. The database covers topics, such as international business, economics, economic management, finance, accounting, labor and personnel, and banking. It is suitable for professionals in economics, business administration, financial banking, labor, and personnel management, etc.), and analyze the average code length and the first word hit rate of general language model (GLM), NNLM model, and domain model in the dynamic model in different fields. Then its statistics the overall recall rate and accuracy rate of the dynamic model. The results show that the average code length of the NNLM model is relatively shorter at 2.2 bits. The first word hit rate is higher than the other two models. The first word hit rate of the NNLM model is 85% under non-professional phrases, while 90% under professional phrases. The overall accuracy of the English translation of the dynamic model of language propagation in this paper is 86.37%, and the recall rate is 79.67. It shows that the dynamic model of language propagation combined with neural network and differential equation has a good translation effect and is feasible. [ABSTRACT FROM AUTHOR]

In recent years, my country's economic development has entered a new stage, and economic development has entered a new height. Gross Domestic Product (GDP) is the most important and commonly used measure to evaluate social and economic development. It can comprehensively reflect the operating conditions of the economy. At the 2014 APEC summit, Xi Jinping focused on the characteristics of the new normal of China's economy. The economic growth has undergone major changes, and the economic structure has become more and more complete and has been continuously optimized. Therefore, the driving force of innovation plays an important role in the future social and economic development. The high-tech demonstration zone is the main hub of my country's economic innovation and scientific and technological progress, and the development of the high-tech demonstration zone is particularly important in the development of the innovative economy. By predicting the economic growth, the development structure can be adjusted in time, and the coordinated allocation of resources can be achieved, which is conducive to the stable and rapid development of the economy. Therefore, it is very necessary to predict my country's economic growth. At present, more and more scholars have invested more energy in the establishment of economic forecasting models. Previously, the prediction effect of traditional models was not satisfactory. At present, the field of target camera machine learning has been transferred. The application of the powerful nonlinear fitting ability of the machine learning model can improve the prediction effect. Based on this, this paper studies the use of artificial neural network with improved BP algorithm to predict social and economic development, and analyzes social and economic development trends and changing laws. The results show that the forecasting model constructed in this paper has a good forecasting effect, and the research results can provide a theoretical basis for scientifically formulating macro-control policies. [ABSTRACT FROM AUTHOR]

Manufacturing industry has always occupied this very important proportion in the national economy. However, with the economic problems in the past two years, some enterprises began to have a deficit crisis. In order to fundamentally alleviate and solve the financial problems of enterprises, this paper studies the financial model prediction of enterprises based on principal component analysis and support vector machine. The multiple financial analysis trajectories are constructed by using PCA-SVM model, and the results are compared with those of logistic model, BP neural network model, and single support vector machine. Experiments show that the prediction level of PCA-SVM model is the best, and the accuracy of the second model is not as good as this model. The error rates of the second type are 14.81%, 14.54%, and 7.67%, respectively, which are higher than those of PCA-SVM model. After comparing the models in this paper, it is found that the components of the model should be extracted first, and then the data operation should be carried out. Such calculation trajectory has a high level of accuracy. The research of this paper provides reference value for solving the financial problems of manufacturing enterprises. [ABSTRACT FROM AUTHOR]

The operating environment of mechanical equipment is complex, and it is in high-intensity working conditions for a long time. The condition monitoring and fault diagnosis of equipment are very important. As a kind of precision part commonly used in mechanical equipment, the healthy operation of the rolling bearing is a necessary condition to ensure the reliable operation of the whole equipment. This research takes rolling bearing as the research object and is devoted to mechanical fault diagnosis analysis and system design. This paper studies the structure and working principle of rolling bearing, analyzes the types and locations of rolling bearing faults, and puts forward the overall framework of the fault diagnosis system and the workflow of the fault diagnosis system. This paper also studies the relevant theory of deep learning, proposes a neural network model framework for rolling bearing fault diagnosis, and preprocesses the rolling bearing vibration data. It uses a dropout algorithm and GRU to reduce the model parameter size and reduce the risk of overfitting. [ABSTRACT FROM AUTHOR]

In recent years, the tourism industry has grown rapidly around the world as an emerging force, especially in China, which has become the world's leading tourism country in recent years, and its tourism revenue also occupies a good weight in the country's total income. The number of tourists every year shows a very high growth rate, which not only improves the tourism economy but also brings great pressure to the management of various tourist attractions. In this context, many tourist attractions are actively developing innovative applications of new technologies, hoping to effectively improve their management efficiency. In these experiments, the speech big data analysis technology has achieved remarkable achievements, so it is necessary to combine the speech big data analysis technology with the demand analysis of the tourism industry. The purpose of this paper is to study the effective model of tourism consumer demand prediction using big data analysis. In the process of completing this paper, we consulted a large number of research results of big data analysis technology, tourism-related books, and demand prediction models in HowNet, VIP, and other network databases as well as campus libraries, summarized the related concepts of tourism, and used big data analysis technology to predict the demand of tourism consumers. Understand the needs of tourism consumers on major tourism websites, and extract the indicators that will affect the forecast results of consumer demand, establish a demand forecast model based on the indicators, and analyze its forecast effects through comparative analysis to understand its advantages and disadvantages, in order to establish a tourism demand forecast models providing actionable advice. Through the practical application case of the demand forecasting model, this paper puts forward the development strategy of tourism. The experimental results show that the mean square error of the neural network model is less than 2.5, which is more suitable for predicting the number of tourists, indicating that different models are suitable for predicting different indicators. The main contribution of this research lies in the modeling and analysis of regional tourism characteristics and tourists' willingness, so as to achieve accurate prediction of tourists in scenic spots and formulate targeted plans. [ABSTRACT FROM AUTHOR]

In real recommendation systems, implicit feedback data is more common and easier to obtain, and recommendation algorithms based on such data will be more applicable. However, implicit feedback data cannot directly express user preferences. Meanwhile, data sparsity caused by massive data is still an urgent problem to be solved in recommendation system. In response to this phenomenon, this paper proposes a deep collaborative filtering algorithm. In the perspective of implicit feedback, this method uses the advantages of convolutional neural network for effective learning of the nonlinear interaction of users and items and the characteristics of collaborative filtering algorithm for modeling the linear interaction of users and items and combines the two methods for recommendation. Finally, the baseline method is set up and the comparative experiment and parameter adjustment is carried out. The experimental results show that the proposed algorithm has significantly improved the recommendation accuracy on public dataset (Yahoo! Movie). The parameter adjustment results show that, under the condition of uniformly collecting negative feedback data and setting a certain number of convolution layers, the sparser the data is, the better the recommendation performs. As a result, this paper has made some progress in solving the problem of data sparsity and enriching the research of recommendation system. [ABSTRACT FROM AUTHOR]

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

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

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]

Influenced by cultural background, economic development, social system, education system, and other factors, there is still a big gap between Chinese institutions and developed countries in flute teaching, even with our neighbors, South Korea and Japan. Under the influence of cultural background, economic development, social system, and educational system, there is still a very big gap between Chinese colleges and universities and developed countries in flute teaching, even with our neighbors, South Korea and Japan. Because of its local perception and weight-sharing structure, the convolutional neural network is closer to the biological neural network in the real world. The weight-sharing structure reduces the complexity of the neural network, which can avoid the complexity of feature extraction and classification process in data reconstruction. This paper studies the analysis and optimization of flute playing and teaching system based on a convolutional neural network. By applying local perception field and parameter sharing in a convolutional neural network at the same time and adding multiple filters, it can not only effectively reduce the number of parameters but also extract features layer by layer. In the process of convolution, the parameters of the characteristic map obtained by each layer decrease layer by layer, but the number increases gradually. Based on the analysis of the problems faced by the flute performance teaching, this paper puts forward the corresponding solutions in order to promote the flute performance teaching in China to achieve better results. [ABSTRACT FROM AUTHOR]

The preparation of C4 olefins from ethanol has become a research hotspot in the field of chemical product production. Based on the test data of given catalyst combination at different temperatures, a neural network prediction model for the effect of different catalyst combination and temperature on C4 olefin yield is proposed in this paper. Firstly, taking the catalyst combination and temperature as independent variables, the C4 olefin yield is analyzed by multiple regression analysis and evaluated by R-square index. Secondly, on the basis of this experiment, the BP neural network model for predicting the yield of coupling reaction is reconstructed, and the adaptive genetic operator is added to the BP neural network to optimize its threshold, weight, and convergence speed, so as to improve the accuracy of yield prediction. Finally, the prediction results of BP and GA-BP models are compared from five aspects: SSE, MAE, MSE, RMSE, and MAPE. The experimental results show that the improved model has good global optimization ability and high yield prediction accuracy in the prediction of coupling reaction yield. Therefore, the multiple control variable model proposed in this paper has a certain positive significance for predicting the effect of catalyst combination on yield in coupling reaction. [ABSTRACT FROM AUTHOR]

The roof water hazard has severely weakened the safety of coal mine production. As the most direct data on the state of the roof, the monitoring big data of the hydraulic support is of great importance to coal mine safety. This paper proposes a neural network model based on multitimescale feature extraction for predicting the pressure-bearing data of hydraulic supports in coal mine working faces, drawing on the idea of multiscale time feature extraction in the empirical modal decomposition algorithm. The proposed model can excel in capturing temporal features at multiple scales in the time series, thus improving the prediction accuracy of the neural network model. We collected 80 days of monitoring data from 17 hydraulic supports in the working face and carried out comparative experiments with several existing models. The experiments show that the neural network model proposed in this paper is stable and reliable, with small prediction errors, and can provide important help for the prevention and control of the roof water hazard. [ABSTRACT FROM AUTHOR]

Software development effort and cost estimation (SDECE) is one of the most important tasks in the field of software engineering. A large number of research papers have been published on this topic in the last five decades. Investigating research trends using a systematic literature review when such a large number of research papers are published is a very tedious and time-consuming task. Therefore, in this research paper, we propose a generic automated text mining framework to investigate research trends by analyzing the title, author's keywords, and abstract of the research papers. The proposed framework is used to investigate research trends by analyzing the title, keywords, and abstract of select 1015 research papers published on SDECE in the last five decades. We have identified the most popular SDECE techniques in each decade to understand how SDECE has evolved in the past five decades. It is found that artificial neural network, fuzzy logic, regression, analogy-based approach, and COCOMO methods are the most used techniques for SDECE followed by optimization, use case point, machine learning, and function point analysis. The NASA and ISBSG are the most used dataset for SDECE. The MMRE, MRE, and PRED are the most used accuracy measures for SDECE. Results of the proposed framework are validated by comparing it with the outcome of the previously published review work and we found that the results are consistent. We have also carried out a detailed bibliometric analysis and metareview of the review and survey papers published on SDECE. This research study is significant for the development of new models for cost and effort estimations. [ABSTRACT FROM AUTHOR]

Note recognition technology has very important applications in instrument tuning, automatic computer music recognition, music database retrieval, and electronic music synthesis. This paper addresses the above issues by conducting a study on acoustic quality evaluation and its note recognition based on artificial neural networks, taking the lute as an example. For the acoustic quality evaluation of musical instruments, this paper uses the subjective evaluation criteria of musical instruments as the basis for obtaining the results of the subjective evaluation of the acoustic quality of the lute, similar to the acoustic quality evaluation, extracts the CQT and MFCC note signal features, and uses the single and combined features as the input to the Softmax regression BP neural network multiclassification recogniser; the classification coding of standard tones is used as the target for supervised network learning. The algorithm can identify 25 notes from bass to treble with high accuracy, with an average recognition rate of 95.6%; compared to other recognition algorithms, the algorithm has the advantage of fewer constraints, a wider range of notes, and a higher recognition rate. [ABSTRACT FROM AUTHOR]

The cross-regional spread of COVID-19 had a huge impact on the normal global social order. This paper aims to build an improved dynamic transmission rate model based on the conjugate gradient neural network predicting and analyzing the global COVID-19 epidemic. First, we conduct an exploratory analysis of the COVID-19 epidemic from Canada, Germany, France, the United States, South Korea, Iran, Spain, and Italy. Second, a two-parameter power function is used for the nonlinear fitting of the dynamic transmission rate on account of data-driven approaches. Third, we correct the residual error and construct an improved nonlinear dynamic transmission rate model utilizing the conjugate gradient neural network. Finally, the inflection points of the global COVID-19 epidemic and new outbreaks, as well as the corresponding existing cases are predicted under the optimal sliding window period. The experimental results show that the model presented in this paper has higher prediction accuracy and robustness than some other existing methods. [ABSTRACT FROM AUTHOR]

With the development of information technology and the deepening of government system reform, e-government has become one of the key points of government construction. However, both in developing countries and developed countries, there is a common phenomenon that the success rate of e-government projects is not high. Although various e-government evaluation index systems have been proposed in both theoretical research and application aspects, which provide strong support for the development of e-government, these index systems still have limitations. This paper comprehensively applies BP neural network and adaptive matching tracking, and constructs an e-government performance evaluation model based on adaptive matching neural network. We conduct simulation experiments on the constructed model to verify the accuracy and rationality of the model evaluation. It is feasible to introduce the Balanced Scorecard to construct the E-government performance evaluation index system. The constructed index system has strong objectivity, operability, comprehensiveness, guidance and sustainability, and can be applied to the practice of e-government performance evaluation. It is feasible to use the adaptive matching tracking neural network model to evaluate the performance of e-government. The model has the ability to self-learn the evaluation samples, and can grasp the mapping relationship between the evaluation indicators and the evaluation results, so as to imitate the evaluation of experts, and overcome the evaluation randomness and subjective uncertainty that are difficult to get rid of in traditional manual evaluation. In order to better evaluate the operation status of the e-government system and its adaptability to future needs, and find out its existing problems, this paper fully considers the interaction process between the government and enterprises, and establishes a corresponding responsive government. The application of adaptive matching tracking neural network model for e-government performance evaluation has better optimization results than the original BP neural network model. The specific performance is that the convergence speed is fast, the training will not fall into a local minimum, and the error of the evaluation results is small. [ABSTRACT FROM AUTHOR]

Modeling and forecasting of Total Electron Content (TEC) values by an Artificial Neural Network model (ANNm) have high agreement on November 2003, 2004 superstorms. The work discusses Solar Wind Parameters (SWp) from OMNI (Operating Missions as a Node on the Internet) and TEC (TECU) data (International Reference Ionosphere) IRI-2012, IRI-2016 on November 20, 2003 (Dst = –422 nT) and on November 08, 2004 (Dst = –374 nT) Geomagnetic Storms (GSs). The paper commences with a 120-hour GS exhibition of SWp and proceeds with the correlation data of the variables, their hierarchical tracks, and inner dispersions. The ANNm with SWp as the input and TEC data as the output are introduced. The performance of the ANNm for 2003 and 2004 superstorms is adequate. The Correlation Coefficient (R) and Root Mean Square Error (RMSE) of the ANNm are 97.5%, 1.17 TECU (IRI-2012), and 97.9%, 1.09 TECU (IRI-2016) for the 2003 GS and 97.0%, 0.89 TECU (IRI-2012), and 98.0%, 1.61 TECU (IRI-2016) for 2004 GS. Parameters effect of the R constant of TEC data points out to the dynamic pressure (nPa), the magnetic field Bz component (nT), the flow speed (km/s), and the proton density (1/cm3). Besides, the absolute total error and the variance of the predicted TEC data for November 2003 and November 2004 GSs are 0.06 (0.30%) with 0.013 variance (IRI-2012), 0.09 (0.49%) with 0.016 variance (IRI-2016) for 2003 storm and 0.13 (0.73%) with 0.033 variance (IRI-2012), and 0.11 (1.06%) with 0.035 variance (IRI-2016) for 2004. It means that the paper models TEC data with considerable consistency over the SWp. [ABSTRACT FROM AUTHOR]

The fault signals of planetary gears are nonstationary and nonlinear signals. It is difficult to extract weak fault features under strong background noise. This paper adopts a new filtering method, fractional Wavelet transform (FRWT). Compared with the traditional fractional Fourier transform (FRFT), it can improve the effect of noise reduction. This paper adopts a planetary gear fault diagnosis method combining fractional wavelet transform (FRWT) and two-dimensional convolutional neural network (2D-CNN). Firstly, several intrinsic mode component functions (IMFs) are obtained from the original vibration signal by AFSA-VMD decomposition, and the two components with the largest correlation coefficient are selected for signal reconstruction. Then, the reconstructed signal is filtered in fractional wavelet domain. By analyzing the wavelet energy entropy of the filtered signal, a two-dimensional normalized energy characteristic matrix is constructed and the two-dimensional features are input into the two-dimensional convolution neural network model for training. The simulation results show that the training effect of this method is better than that of FRFT-2D-CNN. Through the verification of the test set, we can know that the fault diagnosis of planetary gears can be realized accurately based on FRWT and 2D-CNN. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has already been applied to English lip-reading. However, Chinese lip-reading starts late and lacks relevant dataset, and the recognition accuracy is not ideal. Therefore, this paper proposes a new hybrid neural network model to establish a Chinese lip-reading system. In this paper, we integrate the attention mechanism into both CNN and RNN. Specifically, we add the convolutional block attention module (CBAM) to the ResNet50 neural network, which enhances its ability to capture the small differences among the mouth patterns of similarly pronounced words in Chinese, improving the performance of feature extraction in the convolution process. We also add the time attention mechanism to the GRU neural network, which helps to extract the features among consecutive lip motion images. Considering the effects of the moments before and after on the current moment in the lip-reading process, we assign more weights to the key frames, which makes the features more representative. We further validate our model through experiments on our self-built dataset. Our experiments show that using convolutional block attention module (CBAM) in the Chinese lip-reading model can accurately recognize Chinese numbers 0–9 and some frequently used Chinese words. Compared with other lip-reading systems, our system has better performance and higher recognition accuracy. [ABSTRACT FROM AUTHOR]

Accurate prediction of airborne equipment failure rate can provide correct repair and maintenance decisions and effectively establish a health management mechanism. This plays an important role in ensuring the safe use of the aircraft and flight safety. This paper proposes an optimal combination forecasting model, which mixes five single models (Multiple Linear Regression model (MLR), Gray model GM (1, N), Partial Least Squares model (PLS), Artificial Neural Network model (BP), and Support Vector Machine model (SVM)). The combined model and its single model are compared with the other three algorithms. Seven classic comparison functions are used for predictive performance evaluation indicators. The research results show that the combined model is superior to other models in terms of prediction accuracy. This paper provides a practical and effective method for predicting the airborne equipment failure rate. [ABSTRACT FROM AUTHOR]

The precision of traditional deformation resistance model is limited, which leads to the inaccuracy of the existing rolling force model. In this paper, the back propagation (BP) neural network model was established according to the industrial big data to accurately predict the deformation resistance. Then, a new rolling force model was established by using the BP neural network model. During the establishment of the neural network model, the data set of deformation resistance was established, which was calculated back from the actual rolling force data. Based on the data set after normalization, the BP neural network model of deformation resistance was established through the optimization of algorithm and network structure. It is shown that both the prediction accuracy of the neural network model on the training set and the test set are high, indicating that the generalization ability of the model is strong. The neural network model of the deformation resistance is compared with the theoretical one, and the maximum error is only 3.96%. Furthermore, by comparison with the traditional rolling force model, it is found that the prediction accuracy of the rolling force model imbedding with the present neural network model is improved obviously. The maximum error of the present rolling force model is just 3.86%. The research in this paper provides a new way to improve the prediction accuracy of rolling force model. [ABSTRACT FROM AUTHOR]

The constructive English learning approach provides better technical support and learning concepts for English learners. This paper firstly introduces the theoretical basis of constructive English learning and the status of domestic and international research and analyzes the advantages over the traditional English learning approach. This paper also analyzes the RBF neural network model, especially the locally adjusted RBF algorithm, and considers the training model and construction method of the RBF neural network to simulate the learning pattern of good learners of English in grades 4 and 6 to better realize the effect of the English learning platform and allow the English learning platform to play a supportive role in the learning process of students, and through the evaluation of the design, we can better improve the software design, which can be improved by evaluating the design. In the subsequent research, we should add a more user-friendly interface design, use larger model samples to train the model to make it more universal in accuracy and ensure good stability in operation, and integrate multiple algorithms to improve the training algorithm of RBF neural network to obtain a more general algorithm model. The rich teaching methods can cultivate students' independent inquiry ability, and the construction of this English learning platform can help improve students' ability to use information technology. By making students become the main subject of teaching and learning, we can stimulate students' enthusiasm and a good awareness of English learning. Using the radial basis function (RBF) algorithm and the constructive pod language teaching method, this English learning platform is a good and positive aid for students to learn English, and as a better English education tool, it has certain research value and positive significance for the study of English teaching. [ABSTRACT FROM AUTHOR]

Air traffic control is an important tool to ensure the safety of civil aviation. For the departments that do the work of air traffic control, reducing the percentage of unsafe event is the core task of safety management. If the relationship between the percentage of unsafe event and their influencing factors can be effectively clarified, then the probability of unsafe event in some control department can be predicted. So, it is of great importance to improve the level of safety management. To quantitatively estimate the probability of unsafe event, a three-layer BP neural network model is introduced in this paper. First, a probabilistic representation of unsafe event related to air traffic control department is made, and then, the probability of different classes of unsafe events and safe events is taken as the outputs of the BP neural network, the factors influencing occurrence of unsafe event connected with air traffic control is taken as inputs, and the sigmoid function is chosen as activation function for the hidden layer. Based on the error function of neural network, it is proved that the general BP neural network has two drawbacks when used for the training of small probability events, which are as follows: the pattern does not ensure that the sum of probability of all events is equal to one and the relative error between the actual outputs and desired outputs is very large after the training of neural network. The reason proved in this paper is that the occurrence rate of the unsafe event is much smaller than that of the safe event, resulting in each weight in the hide layer being subjected to the desired outputs of the safe event when using the gradient descent method for network training. To address this issue, a new mapping method is put forward to reduce the large difference of the desired outputs between the safe event and unsafe event. It is theoretically proved that the mapping method proposed in this paper can not only improve the training accuracy but also ensure that the sum of probability is equal to one. Finally, a numeric example is given to demonstrate that the method proposed in this paper is effective and feasible. [ABSTRACT FROM AUTHOR]

In order to solve the complicated process and low efficiency and low accuracy of solving a class of matrix equations, this paper introduces the linear saturated system model neural network architecture to solve the bisymmetric solution of a class of matrix equations. Firstly, a class of matrix equations is constructed to determine the key problems of solving the equations. Secondly, the linear saturated system model neural network structure is constructed to determine the characteristic parameters in the process of bisymmetric solution. Then, the matrix equations is solved by using backpropagation neural network topology. Finally, the class normalization is realized by using the objective function of bisymmetric solution, and the bisymmetric solution of a class of matrix equations is realized. In order to verify the solving effect of the method in this paper, three indexes (accuracy, correction accuracy, and solving time) are designed in the experiment. The experimental results show that the proposed method can effectively reduce the solving time, can improve the accuracy and correction effect of the bisymmetric solution, and has high practicability. [ABSTRACT FROM AUTHOR]

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

To avoid unnecessary energy waste due to the single temperature setpoints of the heating, ventilation, and air conditioning (HVAC) system during the seasonal variation period, this study proposed a long-short-term memory (LSTM) neural network to predict and control the temperature setpoint. The thermal comfort, cooling rate, and heating rate were predicted with outdoor environment parameters and occupant count. A large scale of operation data was collected from the EnergyPlus simulation, which was previously developed based on the characteristics of a real case study house. Different kinds of input characteristics were offered to test the stable use of LSTM and other artificial neural networks. This paper discusses the development of a Matlab EnergyPlus co-simulation to predict and control the temperature setpoints of a variable air volume system, especially the relationship between temperature setpoint and energy consumption. The simulation results indicate the advantages of the LSTM prediction of energy consumption and the potential for energy saving with predictive control. [ABSTRACT FROM AUTHOR]

Option pricing based on data-driven methods is a challenging task that has attracted much attention recently. There are mainly two types of methods that have been widely used, respectively, the neural network method and the ensemble learning method. The option pricing model based on the neural network has high complexity, and a large number of hyper-parameters will be generated during training, resulting in difficult model adjustment. Furthermore, a lot of training data are needed. The option pricing model based on ensemble learning is not ideal for data feature extraction, because each calculation of the ensemble learning method is mainly to reduce the final residual. Therefore, this paper adopts a learning framework that embeds the modular ensemble learning methods into the network learning structure, and an option pricing model based on deep ensemble learning is proposed. The model is mainly composed of two parts: features reorganization based on random forest, used to calculate the importance of features, combined with the original data as training input; the multilayer ensemble data training structure is based on network learning structure and embeds two ensemble learning methods as network modules, and it also designs a stop algorithm to automatically determine the number of layers. This enables the model to retain the effect of data feature extraction and adapt to small and medium data sets without generating many hyper-parameters. Moreover, in order to make the model fully absorb the advantages of the two ensemble learning methods, we adopt cross-training for data. From the experimental results, it can be concluded that compared with the current optimal method, the prediction performance of the proposed model is improved by 36% in the root mean square error (RMSE), which proves the superiority of the proposed model from the quantitative direction. [ABSTRACT FROM AUTHOR]

In this paper, an optimal control neural network algorithm is used to conduct an in-depth study and analysis of the evaluation of elementary school urban-rural exchange teachers, and an optimal control neural network evaluation model is designed and applied to the actual elementary school urban-rural exchange process. A tracking controller is designed to track the target trajectory for a typical second-order nonlinear system where the system model is partially unknown and the internal state of the system is unpredictable. The neural network observer is first designed using the input and output information of the system to approximate the drift dynamics of the system on the one hand and estimate the internal state information of the system on the other hand; then, based on the estimated system state, the sliding mode tracking controller is designed to achieve tracking of the established target trajectory. It is found that the policy implementation is not in place, teachers are not motivated to exchange and most of them aim at position promotion, their sense of responsibility is weak, and they cannot treat the matter of exchange seriously and correctly, thus cannot play their role in the exchange school. The ways to prevent and alleviate the conflict of interest among policy subjects are mainly divided into subjective and objective aspects: first, from the subjective level to arouse the value recognition of multiple interest subjects, including the government, should play the function of policy guidance and exhortation and clarify the value orientation of the policy; schools should cultivate the moral quality and professionalism of teachers and strengthen ideological mobilization; and teachers should improve their ideological awareness and consciously change their willingness to exchange. Establishing teachers' status as civil servants, paying attention to teachers' professional development and safeguarding the interests of rotating teachers to increase teachers' motivation to exchange and rotate, creating a good social opinion environment, providing a good inflow school support environment for rotating teachers, and building a cloud platform for sharing high-quality educational resources create a good policy implementation environment to ensure that the teacher exchange and rotation policy is effectively implemented to achieve its goals of balancing teacher deployment. Although gradient-based methods can achieve high training accuracy, the performance of the backpropagation algorithm may be unsatisfactory when applied to test data, that is, if the size of the training data is not large enough. [ABSTRACT FROM AUTHOR]

Low-carbon economy has become a topic of great concern to the international community. Sea level rise caused by climate change, flood disasters, biodiversity reduction, global famine, and other issues have begun to threaten the normal survival of human beings, and the climate problem needs to be solved urgently. While ensuring rapid economic development, in order to better control the total amount of greenhouse gas emissions, this paper, based on the theory of low-carbon economy, takes control of total carbon emissions for low-carbon economic development, as a perspective, and selects the optimization for the development of low-carbon economy. From the aspects of structural emission reduction technology and emission reduction, a carbon emission control optimization index system for low-carbon economic development based on total carbon emission control is constructed. Under the framework of the index system, construct an optimization model for the total amount of carbon emissions in a low-carbon economy and use the BP neural network model to seek the balance point between economic development, energy consumption, and carbon emissions so as to promote the rational and scientific development of the low-carbon economy. Planning and development. First of all, on the premise of maintaining the same economic growth rate, the optimization plan will reduce carbon emissions. Secondly, on the premise of keeping the cost of energy consumption unchanged, it is more reasonable to adjust the energy consumption structure. Taking the optimization plan as the suggestion for the development direction of the low-carbon economy, it provides scientific and feasible technical support for achieving the emission reduction target of reducing the unit greenhouse gas emission to 40%. [ABSTRACT FROM AUTHOR]

Forecasting electricity load demand is critical for power system planning and energy management. In particular, accurate short-term load forecasting (STLF), which focuses on the lead time horizon of few minutes to one week ahead, can help in better load scheduling, unit commitment, and cost-effective operation of smart power grids. In the last decade, different artificial intelligence (AI)-based techniques and metaheuristic algorithms have been utilized for STLF by the researchers and scientists with varying degrees of accuracy and efficacy. Despite the benefits of implemented methods for STLF, many drawbacks and associated problems have also been observed and reported by the researchers. This paper provides a comprehensive review of hybrid deep learning models based on nature-inspired metaheuristic techniques for STLF with respect to the analysis of the results and accuracy. Moreover, it also provides the research findings and gaps that will assist the researchers to have an early awareness of all important benefits and drawbacks of these integrated STLF methods scientifically and systematically. Especially, the hybrid forecast models using artificial intelligence-based methods for smart grids are focused. Several performance indices are used to compare and report the accuracy of these techniques including mean absolute percentage error (MAPE). Multiple other parametric and exogenous variable details have also been focused to figure out the potential of the intelligent load forecasting techniques from the perspective of smart power grids. [ABSTRACT FROM AUTHOR]

The fault diagnosis of urban rail transit gearboxes has the characteristics of complex vibration signals and large amounts of data. The daily scheduled maintenance cannot meet the needs of gearbox maintenance, so it is necessary to predict the fault types in advance. In this paper, a method of gearbox fault prediction based on sparse principal component analysis and a generalized regression neural network is presented, and the result of fault prediction can provide a reference for making maintenance plans. Based on principal component analysis (PCA), a sparse principal component is obtained by adding the LASSO penalty term, which reduces the risk of overfitting of PCA while obtaining a sparse solution. Then, the sparse reduced dimension principal component is input into the generalized regression neural network model for fault diagnosis. The results show that the fault diagnosis method based on the sparse principal component-generalized regression neural network model has high accuracy and is time-consuming. [ABSTRACT FROM AUTHOR]

As an important part of financial market, stock market price volatility analysis has been the focus of academic and industry attention. Candlestick chart, as the most widely used indicator for evaluating stock market price volatility, has been intensively studied and explored. With the continuous development of computer technology, the stock market analysis method based on candlestick chart is gradually changed from manual to intelligent algorithm. However, how to effectively use stock market graphical indicators to analyze stock market price fluctuations has been pending solution, and deep learning algorithms based on structured data such as deep neural networks (DNN) and recurrent neural networks (RNNs) always have the problems of making it difficult to capture the laws and low generalization ability for stock market graphical indicators data processing. Therefore, this paper proposes a quantification method of stock market candlestick chart based on Hough variation, using the graph structure embedding method to represent candlestick chart features and multiple attention graph neural network for stock market price fluctuation prediction. The experimental results show that the proposed method can interpret the candlestick chart features more accurately and has superiority performance over state-of-the-art deep learning methods, including SVM, CNN, LSTM, and CNN-LSTM. Relative to these algorithms, the proposed method achieves an average performance improvement of 20.51% in terms of accuracy and further achieves at least 26.98% improvement in strategy returns in quantitative investment experiments. [ABSTRACT FROM AUTHOR]

The homogenization of hotel competition has become a key problem faced by the hotel enterprises. According to the marketing theory, one perfect marketing strategy will improve hotel performance. Based on nonlinear random matrix and neural network model, this paper empirically analyzes the relationship between different marketing strategies and hotel performance from the aspects of hotel demand collection, strategy support, performance matching, and strategy regulation. The results show that the hotel performance based on product marketing strategies is the best, which has improving hotel performance about 5%. In contrast, the hotel performance based on people-oriented marketing strategies is the worst. Therefore, we believe that the hotel enterprises should pay attention to the adjustment of product structure and the role of people in hotel operation and management. Adjusting product structure, strengthening hotel information construction, and paying attention to talent cultivation play an important role in improving hotel performance, in order to provide useful reference for the development of other hotels. [ABSTRACT FROM AUTHOR]

In this paper, the random matrix method of a fuzzy comprehensive evaluation is used to conduct in-depth research and analysis on the assessment of foreign language translation levels in universities, and the corresponding model is designed for the automatic assessment of translation levels in university education. The Hessian matrix of the second-order optimization method is used to analyze the geometric properties of the loss plane of the deep neural network, and it is proved that the Hessian matrix can be constructed as a sample covariance matrix, which is the classical Wishart matrix in the random matrix theory. Using the study on the asymptotic distribution properties of the Wishart matrix in random matrix theory, the Hessian matrix is analyzed and the limiting spectral distribution, the eigenvalue extreme value distribution, and the standard conditional number distribution of the matrix are given. And the scores are basically distributed above 240, but some indicators have relatively low scores, all below 120 points, so the indicators distributed above 240 points are selected as reserved indicators. The analysis considers that the evaluation of foreign language translation in learning in colleges and universities is itself very fuzzy and constructs an evaluation model based on the fuzzy comprehensive judgment method, which provides a research strategy for the future evaluation of foreign language translation in learning in colleges and universities. Finally, the evaluation system of foreign language translation in learning in colleges and universities is designed and implemented. It is feasible to apply the fuzzy comprehensive judgment to the comprehensive evaluation of college students, the combined judgment of multiple methods has higher reliability and stability, and its research conclusions have certain reference value in the specific education management practice. The value, objectivity, feasibility, and wide adaptability of the fuzzy comprehensive evaluation method are analyzed, and some references are expected to be provided for translation quality assessment research and translation practice. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy and stability of English translation, this paper proposes an English translation model based on recurrent neural network. Based on the end-to-end encoder-decoder architecture, a recursive neural network (RNN) English machine translation model is designed to promote machine autonomous learning features, transform the distributed corpus data into word vectors, and directly map the source language and target language through the recurrent neural network. Selecting semantic errors to construct the objective function during training can well balance the influence of each part of the semantics and fully consider the alignment information, providing a strong guidance for the training of deep recurrent neural networks. The experimental results show that the English translation model based on recurrent neural network has high effectiveness and stability. Compared with the baseline system, it has improved about 1.51–1.86 BLEU scores. Conclusion. The model improves the performance and quality of English machine translation model, and the translation effect is better. [ABSTRACT FROM AUTHOR]

The learning environment is an important support condition for learning and an important variable affecting learning, so it is an important research content of learning theory, and the understanding of the learning environment is also developing and changing as the educational theory continues to develop. The new curriculum standards for college English demand teachers alter the original conventional way of pedagogy and optimize students' mode of learning. In teaching practice, the learning atmosphere exerts an extremely influential influence on the smooth implementation of teaching activities and the healthy development of students' minds and bodies. Deep learning is a hot research topic among machine learning areas in recent years, and deep belief networks as a pioneer in constructing such deep structures. Also, deep neural network (DNN) has been a hot research topic in the field of artificial intelligence and big data analysis in recent years. A DNN-based English learning environment optimization design is put forward in this paper, focusing on the problems of the English learning environment in colleges and the causes of the problems, and exploring strategies to optimize the English learning environment in colleges in order to promote the normal development of English teaching in colleges. The experimental results show that the DNN can improve the overall recognition rate of fault identification and fault location by 13% and 25% on average compared with the other two algorithms, so the deep learning can extract features directly from the original samples and overcome the defect that the neural network is easy to fall into local optimum, and obtain better results. The optimization of the learning method will help to realize the education concept of "human-oriented and comprehensive development," and help to stimulate students' enthusiasm, initiative, and exploration in learning. [ABSTRACT FROM AUTHOR]

Generative adversarial network (GAN) is a deep learning model that is widely applied to image generation, semantic segmentation, superresolution tasks, and so on. CycleGAN is a new model architecture that is used for various applications in image translation. This paper mainly focuses on the CycleGAN algorithm model. To improve the network model's capacity of extracting image features, the generator model uses the neural network of Unet, which consists of eight down-sampling and eight up-sampling layers, to extract image features. We use the Markov discriminator of PatchGAN since it has high-resolution and high-detail characteristics in the image style transfer. In order to improve the running efficiency, the depthwise separable convolution and standard convolution are combined in the Markov discriminator. The experimental results show that it can effectively shorten the running time. Then, we compare the image with the generative image that uses the L1 loss function, the L2 loss function, and the smooth L1 loss function. The experimental results show that the CycleGAN neural network can effectively complete the image style transfer. The L1 loss model can well retain the details of the original image. The L2 loss model is clear in the distant part of the natural photo generated by Monet painting, and the color tone is more similar to the original image. The image generated by the smoothL1 loss model is smoother. The L1 loss model and the smooth L1 loss model have some miscoloring in the natural photos generated by the Monet painting. In general, the L2 loss model is more stable, and the generative image is better. [ABSTRACT FROM AUTHOR]

With the rise of deep learning technology, due to the superior performance of the deep neural network, its application in the digital economy has attracted extensive attention of scholars. Since the beginning of the 21st century, my country's digital economy has developed rapidly, and its universalization and other characteristics have created favorable conditions for the optimal allocation of resources in underdeveloped regions and the exertion of comparative advantages. The digital economy will play a key role in poverty alleviation, promoting coordinated regional development, narrowing regional gaps, and improving the spatial layout of my country's reform and opening up. This paper studies the factors that the digital economy based on deep neural networks has on regional economic growth. Simulation experiment conclusions are as follows: (1) the digital economy of Guizhou, Beijing, Chongqing, Anhui, and Tibet is growing rapidly. The central and western regions are in a period of rapid growth. For the gap between major industrial provinces, the coefficient of variation reached about 1 before 2013, then it declined rapidly, and slowed down, and steadily declined after 2019, indicating that the gap in the digital economy in various regions is narrowing in general. (2) From the national level, the digital economy index coefficient is 1.24, that is, for every 1% increase in digital economy investment, GDP will increase by about 0.24%. The labor force increased by 1% and the GDP increased by about 0.22%. This promotion effect is also very obvious. (3) Judging from the above data, the western region urgently needs to promote the construction of the digital economy and introduce high-tech digital economy talents. The talent effect in the Midwest has a significant effect on GDP. (4) From the perspective of the whole country and other regions, the parameter coefficients and signs have not changed significantly, so the original model is robust, and so the conclusion is desirable. [ABSTRACT FROM AUTHOR]

As one of the hotspots in music information extraction research, music recognition has received extensive attention from scholars in recent years. Most of the current research methods are based on traditional signal processing methods, and there is still a lot of room for improvement in recognition accuracy and recognition efficiency. There are few research studies on music recognition based on deep neural networks. This paper expounds on the basic principles of deep learning and the basic structure and training methods of neural networks. For two kinds of commonly used deep networks, convolutional neural network and recurrent neural network, their typical structures, training methods, advantages, and disadvantages are analyzed. At the same time, a variety of platforms and tools for training deep neural networks are introduced, and their advantages and disadvantages are compared. TensorFlow and Keras frameworks are selected from them, and the practice related to neural network research is carried out. Training lays the foundation. Results show that through the development and experimental demonstration of the prototype system, as well as the comparison with other researchers in the field of humming recognition, it is proved that the deep-learning method can be applied to the humming recognition problem, which can effectively improve the accuracy of humming recognition and improve the recognition time. A convolutional recurrent neural network is designed and implemented, combining the local feature extraction of the convolutional layer and the ability of the recurrent layer to summarize the sequence features, to learn the features of the humming signal, so as to obtain audio features with a higher degree of abstraction and complexity and improve the performance of the humming signal. The ability of neural networks to learn the features of audio signals lays the foundation for an efficient and accurate humming recognition process. [ABSTRACT FROM AUTHOR]

Evaluation of the carrying capacity of mineral resources is one of the important research content in the implementation of sustainable development. Based on analyzing the metallogenic geological characteristics, distribution, and resource status of mineral resources in southern Shaanxi, this paper establishes an analysis model of mineral resources and mineral advantages based on the analytic hierarchy process and applies them to evaluate the advantages of mineral resources. To provide optimal and efficient results, an improved model of an artificial neural network based on the bat optimization algorithm has been utilized. Through model analysis, the potential value and carrying capacity of mineral resources in three major prefecture-level cities in southern Shaanxi are comprehensively evaluated and analyzed. The results show that the main dominant minerals in southern Shaanxi are gold, lead zinc, and molybdenum ore. There are three grades of mineral resources carrying capacity: Shangluo City is an excellent grade, Hanzhong City is a good grade, and Ankang City is a general grade. [ABSTRACT FROM AUTHOR]

With the non-stop merchandising and improvement of bodily training instructing reform, the institution of a couple of getting to know the assessment and evaluation mannequin of bodily training different educating impact can stimulate the enthusiasm and activity in sports activities and learning, which is useful to domesticate students' cognizance of lifelong bodily exercise. In the educating process, through the integration technique of varied instructing methods, this paper explores the issues present in the method of bodily training reform, places ahead a diverse bodily training instructing impact assessment mannequin that meets the wants of customized intelligence education and construction, and makes use of the technique of empirical evaluation to affirm the comparison model. In order to reflect the effect of physical training effectively and accurately, a convolution neural neighbourhood model based totally completely on the random multivariate matrix is proposed to reflect on consideration on the excellent of bodily education. The overall performance of the prediction accuracy assessment mannequin is evaluated via the simulation test and in contrast with the standard approach model. The experimental information is that the average assessment accuracy of the single convolution neural community mannequin is 82.15%, whilst the common comparison accuracy of the convolution neural community mannequin based totally on the random multivariate matrix is 97.58%, and the prediction accuracy is increased by means of 15.43%. The common prediction error of the single convolution neural community mannequin is 0.97, and the common error is 0.91, the common error is decreased by using 0.05. It shows that the random multivariate matrix convolution neural neighbourhood model can efficaciously realize the evaluation of instructing quality. [ABSTRACT FROM AUTHOR]

Garbage is an underutilized resource, and garbage classification is one of the effective ways to make full use of these resources. In order to realize the automation of garbage classification, some deep learning models are used for garbage images recognition. A novel garbage image recognition model Garbage Classification Net (GCNet) based on transfer learning and model fusion is proposed in this paper. After extracting garbage image features, EfficientNetv2, Vision Transformer, and DenseNet, respectively, are combined to construct the neural network model of GCNet. Data augmentation is used to expand the dataset and 41,650 garbage images are contained in the new dataset. Compared with other models through experiments, the results show that the proposed model has good convergence, high recall rate and accuracy, and short recognition time. [ABSTRACT FROM AUTHOR]