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This paper introduced a novel olfactory visualization freshness sensor array prepared by dropping pH indicators on UV lithography hydrophilic–hydrophobic paper under room temperature. The sensor array was applied in Grass carp spoilage classification. The sensor array can generate different discoloration patterns that respond to the pH change of the fish spoil; therefore, it can distinguish Grass carp freshness using pattern recognition. Classification models were built with linear discriminant analysis (LDA) and backpropagation artificial neural network (BP‐ANN) after extracting the sensor array's feature color information with principal component analysis (PCA). The performance of the BP‐ANN model was superior to that of the LDA model, with an optimum recognition rate of 95.45% (training set) and 90.90% (testing set). In conclusion, with a suitable algorithm choice and rational pH indicator arrangement, this olfactory visualization freshness sensor array can potentially monitor food freshness. We prepared Grass carp freshness sensor array through dropping pH‐indicators on hydrophilic–hydrophobic filter paper that fabricated by UV lithography under room temperature. The hydrophobic edges of the filter paper can maintain the sensor spots' shape and provide stable and consistent colorimetric change for Grass carp freshness recognition. Colorimetric classification models are built using LDA and BP‐ANN based on pattern recognition, and the recognition rate of the BP‐ANN model is above 90%. Novelty impact statement: We prepared Grass carp freshness sensor array through dropping pH‐indicators on hydrophilic‐hydrophobic filter paper that fabricated by UV lithography under room temperature. The hydrophobic edges of the filter paper can maintain the sensor spots' shape and provide stable and consistent colorimetric change for Grass carp freshness recognition. Colorimetric classification models are built using LDA and BP‐ANN based on pattern recognition, and the recognition rate of the BP‐ANN model is above 90 %. [ABSTRACT FROM AUTHOR]

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [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]

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]

Most international academic papers are written in English, and the use of tenses in English academic papers often follows some conventional rules. Automatically extracting and analyzing English tenses in scientific papers have begun to attract researchers' attention for the global environment. In the analysis of the English tense of scientific papers, consider that the neural network model that combines attention mechanism and sequential input network such as Long Short-Term Memory (LSTM) network has a long training time, low extraction accuracy, and cannot parallelize text input. We propose an environmental affection-driven English tense analysis model, which includes an attention mechanism and LSTM model and conducts a temporal analysis of English texts based on an affective computing model. In this paper, our proposed method is verified based on the self-built healthcare exercise-based corpus over public English environment. By comparison, the experimental results show that the method proposed in this paper has better performance than ordinary Convolutional Neural Network (CNN), Support Vector Machine (SVM), and LSTM based on attention mechanism. [ABSTRACT FROM AUTHOR]

Stroke is a high morbidity and mortality disease that poses a serious threat to people's health. Early recognition of the various warning signs of stroke is necessary so that timely clinical intervention can help reduce the severity of stroke. Deep neural networks have powerful feature representation capabilities and can automatically learn discriminant features from large amounts of data. This paper uses a range of physiological characteristic parameters and collaborates with deep neural networks, such as the Wasserstein generative adversarial networks with gradient penalty and regression network, to construct a stroke prediction model. Firstly, to address the problem of imbalance between positive and negative samples in the stroke public data set, we performed positive sample data augmentation and utilized WGAN‐GP to generate stroke data with high fidelity and used it for the training of the prediction network model. Then, the relationship between observable physiological characteristic parameters and the predicted risk of suffering a stroke was modeled as a nonlinear mapping transformation, and a stroke prediction model based on a deep regression network was designed. Finally, the proposed method is compared with commonly used machine learning‐based classification algorithms such as decision tree, random forest, support vector machine, and artificial neural networks. The prediction results of the proposed method are optimal in the comprehensive measurement index F. Further ablation experiments also show that the designed prediction model has certain robustness and can effectively predict stroke diseases. [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]

The article introduces a new approach to the ANN-based behavioral modeling of nonlinear RF/microwave systems or subsystems. The proposed model accounts for both the linear and the nonlinear distortion of modulated signals. The former is introduced by the two-port frequency-dependent admittance matrix. The latter is described by the means of a couple of describing functions (which may also be frequency-dependent), relating the two-port linear and the nonlinear responses. The describing functions are approximated by artificial neural networks (ANN) trained by harmonic-balance results. The resulting model is fast to compute, can accurately handle broadband modulated signals, and is fully bilateral. This means that the model can be routinely used to analyze systems where bidirectional signal flow takes place, e.g., due to mismatches between the interconnected subsystems, or even due to the presence of filters operating in the stopband. © 2002 John Wiley & Sons, Inc. Int J RF and Microwave CAE 12: 51–70, 2002. [ABSTRACT FROM AUTHOR]

Classical large-signal device models are indirectly derived from small-signal S-parameter measurements. Due to the availability of the nonlinear network measurement system (NNMS), models can be based directly upon full two-port large-signal measurements, resulting in higher model accuracy. In this article, we discuss three large-signal measurement-based modelling approaches. ©2000 John Wiley & Sons, Inc. Int J RF and Microwave CAE 10: 6–18, 2000. [ABSTRACT FROM AUTHOR]

Trash classification is an effective measure to protect the ecological environment and improve resource utilization. With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. In order to classify the trash of the TrashNet dataset, which consists of six classes of garbage images, this paper proposes a hybrid deep learning model based on deep transfer learning, which includes upper and lower streams. Firstly, the upper stream divides the input garbage image into category MPP (metal, paper, and plastic class) or category CGT (cardboard, glass, and trash class). Then, the lower stream predicts the exact class of trash according to the results of the upper stream. The proposed hybrid deep learning model achieves the best result with 98.5 % than that of the state-of-the-art approaches. Through the verification of CAM (class activation map), the proposed model can reasonably use the features of the image for classification, which explains the reason for the superior performance of this model. [ABSTRACT FROM AUTHOR]

In order to solve the industrial fake film problem produced by repeated imaging in the field of nondestructive testing, this paper proposes an image similarity comparison algorithm of X-ray film based on the Siamese neural network. Firstly, in order to accurately locate and detect weld areas from high-resolution X-ray film, a robust location and detection algorithm based on the Gaussian function is proposed. Next, in order to solve the problem that the deep learning model system extracts features according to squares by default, which results in the disappearance of film features with high horizontal and vertical features, effective weld regions are used and processed in blocks. Then, a small sample data set for weld similarity discrimination is established by using each block region, and an industrial fake film discrimination model based on twin neural networks is constructed. Finally, the corresponding weights are set for different fake feature information areas, and a similarity comparison system for industrial fake film detection is proposed. The experiment shows that the method in this paper can not only accurately locate the weld area but also accurately detect the fake film in the industry, avoiding the influence of the geometric transformation of the image and the insufficient number of data sets in the process of similarity comparison, and has a good detection effect. [ABSTRACT FROM AUTHOR]

Regional poverty is one of the most serious challenges facing the world today. Poverty, antipoverty, and poverty alleviation are the focus of the attention of scholars and the public. This paper takes China's counties as the research unit, selects the influencing factors of poverty from natural and socio-economic factors, establishes an evaluation index system, simulates the natural poverty index and socio-economic poverty eradication index of each county, and clarifies the distribution characteristics of spatial poverty using GIS spatial analysis and BP artificial neural network. The results indicate that natural factors are the main cause of poverty in Chinese counties, with 710 counties having a high natural poverty index, accounting for nearly 30% of the total number of counties in the country. The national county-level natural poverty index shows a clear strip distribution pattern along latitude and longitude, with a strip distribution from north to south and from west to east; socio-economic factors have played a certain role in poverty alleviation, with as many as 1521 counties with low socio-economic poverty alleviation indices, accounting for approximately 64% of the total number of counties in the country. The spatial distribution of the county-level socio-economic poverty alleviation index is relatively fragmented. Through spatial scanning statistics, a total of 44 county poverty pressure index risk clusters reached a statistical significance level, involving 243 counties and districts. In poverty reduction practice, the internal counties and districts of contiguous poverty-stricken areas should strengthen cooperation and exchange. In the process of poverty alleviation and development, targeted poverty alleviation and economic development should be carried out based on the poverty-dominant type and self-development ability of the county, in order to improve efficiency. Regions that are relatively prosperous and have taken the lead in poverty reduction should play a leading and exemplary role in strengthening the radiation power of regional central cities. The prominent feature of this study is the comprehensive utilization of multisource data and the use of new spatial analysis methods (flexible spatial scanning method is widely used in the field of infectious disease prevention and control research). By constructing a multidimensional poverty measurement system that includes natural and social factors, it distinguishes the differences between the factors that cause poverty and the factors that eliminate poverty in regional poverty. At the same time, the flexible spatial scanning detection method was used to detect the differentiation mechanism of poverty spatial patterns. [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]

Examination is a particularly important part of the students' whole learning process. Therefore, this paper designs and develops an online examination system. This paper studies an examination recommendation algorithm based on a machine learning neural network model. The algorithm can recommend problems for students according to students' error history, error prone, and knowledge poor problems. Experiments also show that the recommendation algorithm studied in this paper improves the recommendation effect compared with several other similar algorithms. [ABSTRACT FROM AUTHOR]

The classification process of MRI (magnetic resonance imaging) is frequently used for making medical diagnoses for conditions including pituitary, glioma, meningioma, and no tumor. For this reason, determining the type of MRI and its quantity are significant and valuable measurements that reveal the brain's state of health. To segment and classify brain analysis, laboratory personnel employ manual examination via screen; this requires a lot of labour and time. On the other hand, the devices used by specialists are not practical or inexpensive for every doctor or institution. In recent years, a variety of computational algorithms for segmentation and classification have been developed with improved results to get around the issue. Artificial neural networks (ANNs) have the capability and promise to classify in this regard. The purpose of this paper is to create and put into practice a system for classifying different types of MRI images of brain tumor samples. As a result, this paper concentrated on the tasks of segmentation, feature extraction, classifier building, and classification into four categories using various machine learning algorithms. The authors used VGG‐16, ResNet‐50, and AlexNet models based on the transfer learning algorithm for three models to classify images as an ensemble model. As a result, MRI brain tumor segmentation is more precise because each spatial feature point can now refer to all other contextual data. In the specifics, our models outperformed every other published modern ensemble model in the official deep learning challenge without any postprocessing. The ensemble model achieved an accuracy of 99.16%, a sensitivity of 98.47%, a specificity of 98.57%, a precision of 98.74%, a recall of 98.49%, and an F1‐score of 98.18%. These results significantly surpass the accuracy of other methods such as Naive Bayes, decision tree classifier, random forest, and DNN models. [ABSTRACT FROM AUTHOR]

This comprehensive review delves into the intersection of ensemble machine learning models and interpretability techniques for biomass and waste gasification, a field crucial for sustainable energy solutions. It tackles challenges like feedstock variability and temperature control, highlighting the need for deeper understanding to optimize gasification processes. The study focuses on advanced modeling techniques like random forests and gradient boosting, alongside interpretability methods like the Shapley additive explanations and partial dependence plots, emphasizing their importance for transparency and informed decision‐making. Analyzing diverse case studies, the review explores successful applications while acknowledging challenges like overfitting and computational complexity, proposing strategies for practical and robust models. Notably, the review finds ensemble models consistently achieve high prediction accuracy (often exceeding R2 scores of 0.9) for gas composition, yield, and heating value. These models (34% of reviewed papers) are the most applied method, followed by artificial neural networks (26%). Heating value (12%) was the most studied performance metric. However, interpretability is often neglected during model development due to the complexity of techniques like permutation and Gini importance. The paper calls for dedicated research on utilizing and interpreting ensemble models, especially for co‐gasification scenarios, to unlock new insights into process synergy. Overall, this review serves as a valuable resource for researchers, practitioners, and policymakers, offering guidance for enhancing the efficiency and sustainability of biomass and waste gasification. [ABSTRACT FROM AUTHOR]

In recent years, China's economy has been developing rapidly, and people's life and quality of life have been improving; more importantly, people's habits and living habits have also developed from the previous "unhygienic" and "not very careful" to the current "Healthier, more hygienic, greener and more sophisticated" direction. In the process of this development, due to the rapid development of the economy and the industrialization of cities, the incidence of heart disease is also increasing year by year. According to relevant studies, China's urbanization process has been unprecedented, the number of urbanized people in China has exploded in recent years, and the ratio of urban to rural population has increased from about 20 percent to about 75 percent today. The effects of the population and the urbanization of urban architecture are affecting people's physical and mental health, both consciously and unconsciously, causing both positive and negative physical and mental effects on the psychological and physical levels. In this paper, the concept of deep learning is fully utilized to train the CNN neural network model and apply it to the ECG abnormality recognition and prediction examination. In order to fully validate the application and significance of deep learning in ECG abnormality identification and prediction, the whole project was completed through subjective and objective experiments. The experimental results show that, from the subjective aspect, the ECG examination has been accepted by most people for different age groups, and the analysis results of the ECG examination with the deep learning model in this paper are more satisfactory; from the objective aspect, the CNN-ECG abnormality recognition prediction network model trained in this paper has high. The accuracy of the model for ECG abnormality recognition prediction can reach 86% when the learning rate is set to 0.0001 and the batch size is set to 120, and the model can reduce the burden of medical personnel to a certain extent. [ABSTRACT FROM AUTHOR]

With the continuous development of information technology and the arrival of the era of big data, music appreciation has also entered the digital development. Big data essence is highlighted by comparison with traditional data management and processing technologies. Under different requirements, the required time processing range is different. Music appreciation is an essential and important part of music lessons, which can enrich people's emotional experience, improve aesthetic ability, and cultivate noble sentiments. Data processing of music information resources will greatly facilitate the management, dissemination, and big data analysis and processing of music resources and improve the ability of music lovers to appreciate music. This paper aims to study the digital development of music in the environment of big data, making music appreciation more convenient and intelligent. This paper proposes an intelligent music recognition and appreciation model based on deep neural network (DNN) model. The use of DNN allows this study to have significant improvement over the traditional algorithm. This paper proposes an intelligent music recognition and appreciation model based on the DNN model and improves the traditional algorithm. The improved method in this paper refers to the Dropout method on the traditional DNN model. The DNN is trained on the database and tested on the data. The results show that, in the same database, the traditional DNN model is 114 and the RNN model is 120. The PPL of the improved DNN model in this paper is 98, i.e., the lowest value. The convergence speed is faster, which indicates that the model has stronger music recognition ability and it is more conducive to the digital development of music appreciation. [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]

In recent years, with the acceleration of urbanization and the implementation of compulsory education, the pressure on students' study and life has increased, and the phenomenon of psychological and behavioral problems has become increasingly prominent. Therefore, the school has regarded students' mental health education as the top priority in teaching work. Effective expression classification can assist psychology researchers to study psychology and other disciplines and analyze children's psychological activities and mental states by classifying expressions, thereby reducing the occurrence of psychological behavior problems. Most of the current mainstream methods focus on the exploration of text explicit features and the optimization of representation models, and few works pay attention to deeper language expressions. Metaphors, as language expressions often used in daily life, are closely related to an individual's emotion, cognition, and psychological state. This paper studies children's smiling face recognition based on deep neural network. In order to obtain a better identification effect of mental health problems of children, this paper attempts to use multisource data, including consumption data, access control data, network logs, and grade data, and proposes a multisource data-based mental health problem identification algorithm. The main research focus is feature extraction, trying to use one-dimensional convolutional neural network (1D-CNN) to mine students' online patterns from online behavior sequences, calculate abnormal scores based on students' consumption data in the cafeteria, and describe the dietary differences among students. At the same time, this paper uses the students' psychological state data provided by the psychological center as a label to improve the deficiencies caused by the questionnaire. This paper uses the training set to train five common classification algorithms, evaluates them through the validation set, and selects the best classifier as our algorithm and uses it to identify students with mental health problems in the test set. The experimental results show that precision reaches 0.68, recall reaches 0.56, and F 1 -measure reaches 0.67. [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]

Benefited from deep convolutional neural networks, various license plate detection methods based on deep networks have been proposed and achieved significant improvements compared with traditional methods. However, the high computational cost due to complex structures prevents these methods from being deployed in real-world applications. This paper proposes an efficient license plate detection method based on lightweight deep convolutional neural networks for improving the detection speed. To extract high-level features from input images, this paper designs a lightweight feature pyramid generation module based on a lightweight architecture and depth-wise convolutions. To further enhance feature pyramid, an efficient feature enhancement module is designed to fuse features generated by the region proposal network with backbone features. In the detection network, a light head structure based on fully connected layers is employed to further reduce the computational cost of the model. In experiments, floating point operations and detection ratio are used to evaluate the efficient of the proposed method. Experimental results on public datasets show that the proposed method achieves the best trade-off between speed and accuracy. [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]

In view of the issue that the features of the images in the shallow layer cannot be fully utilized when the image description is generated and the target association of the image cannot be sufficiently obtained, a generation method for the description of the acquisition of attention images is put forward in this paper. The proportions of the features of images at various depths are autonomously assigned based on the content data of the language model, and the images thus generated are all pictures with image features with attention. In this way, the effect of description generation of images has been improved. After the testing of the database, the results indicate that the calculation method of the algorithm put forward in this paper is more accurate than the top-down multimedia image algorithm generated by a single attention. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms underlying ADHD remain inadequately understood, and current approaches do not well link neural networks and attention networks within brain networks. Our objective is to investigate the neural mechanisms related to attention and explore neuroimaging biological tags that can be generalized within the attention networks. In this paper, we utilized resting-state functional magnetic resonance imaging data to examine the differential functional connectivity network between ADHD and typically developing individuals. We employed a graph convolutional neural network model to identify individuals with ADHD. After classification, we visualized brain regions with significant contributions to the classification results. Our results suggest that the frontal, temporal, parietal, and cerebellar regions are likely the primary areas of dysfunction in individuals with ADHD. We also explored the relationship between regions of interest and attention networks, as well as the connection between crucial nodes and the distribution of positively and negatively correlated connections. This analysis allowed us to pinpoint the most discriminative brain regions, including the right orbitofrontal gyrus, the left rectus gyrus and bilateral insula, the right inferior temporal gyrus and bilateral transverse temporal gyrus in the temporal region, and the lingual gyrus of the occipital lobe, multiple regions of the basal ganglia and the upper cerebellum. These regions are primarily involved in the attention executive control network and the attention orientation network. Dysfunction in the functional connectivity of these regions may contribute to the underlying causes of ADHD. [ABSTRACT FROM AUTHOR]

The low-bit quantization can effectively reduce the deep neural network storage as well as the computation costs. Existing quantization methods have yielded unsatisfactory results when being applied to lightweight networks. Additionally, following network quantization, the differences in data types between the operators can cause issues when deploying networks on Field Programmable Gate Arrays (FPGAs). Moreover, some operators cannot be accelerated heterogeneously on FPGAs, resulting in frequent switching between the Advanced RISC Machine (ARM) and FPGA environments for computation tasks. To address these problems, this paper proposes a custom network quantization approach. Firstly, an improved PArameterized Clipping Activation (PACT) method is employed during the quantization aware training to restrict the value range of neural network parameters and reduce the loss of precision arising from quantization. Secondly, the Consecutive Execution Of Convolution Operators (CEOCO) strategy is utilized to mitigate the resource consumption caused by the frequent environment switching. The proposed approach is validated on Xilinx Zynq Ultrascale+MPSoC 3EG and Virtex UltraScale+XCVU13P platforms. The MobileNetv1, MobileNetv3, PPLCNet, and PPLCNetv2 networks were utilized as testbeds for the validation. Moreover, experimental results are on the miniImageNet, CIFAR-10, and OxFord 102 Flowers public datasets. In comparison to the original model, the proposed optimization methods result in an average decrease of 1.2% in accuracy. Compared to conventional quantization method, the accuracy remains almost unchanged, while the frames per second (FPS) on FPGAs improves by an average of 2.1 times. [ABSTRACT FROM AUTHOR]

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

Among many sports, badminton is one of the most popular events and it is deeply loved by people. However, there is relatively little research on pose recognition and prediction of badminton, so this paper uses video image analysis to perform badminton action recognition prediction and action classification. In order to better realize the lower limb movement pattern recognition and motion posture prediction of badminton players, this paper chooses BP neural network algorithm to establish a badminton motion posture recognition and prediction model based on video image analysis. The simulation results of the models are compared, and it is found that the recognition of the motion pose and the prediction model established by the neural network are accurate, and they almost coincide with the actual motion trend. In this paper, a face detection and recognition framework is established. The algorithm is used to filter the pictures of the input recognition network. Through calculation, the average accuracy rate of the face detection algorithm realized in this paper can reach 92.6%. This shows that the face detection algorithm implemented in this paper basically meets the standards. In this paper, a single inertial sensor is used to classify and recognize badminton movements using sensors located on the right wrist, left wrist, waist, and right ankle. This shows that the right wrist is the best position and achieves different strokes. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to assess the vulnerability of reinforced concrete (RC) educational buildings in Dhaka city to seismic activity by utilizing machine learning (ML) algorithms. There are three main stages in traditional seismic vulnerability assessment: rapid visual assessment (RVA), preliminary engineering assessment (PEA), and detailed engineering assessment (DEA). The conventional three‐step evaluation process for determining the seismic vulnerability of existing buildings is time‐consuming and expensive, especially when dealing with a large building stock or a city. This study focuses on using an ML‐based approach to evaluate seismic vulnerability, specifically in terms of the story shear ratio (SSR), which serves as the risk index. The main concept is the utilization of RVA data to obtain analytical results (SSR). The dataset utilized in this study comprises RVA data for 268 buildings and corresponding PEA data for the same 268 buildings. The RVA data include the construction year, condition, typical floor area, number of stories, total floor area, additions, alterations, redundancy, pounding, and irregularities. The PEA data comprise SSR, which was generated from linear dynamic analysis. These data were collected from the Urban Resilience Project of Rajdhani Unnayan Kartripakkha (RAJUK), which is the development authority of Dhaka. Random forest regression (RFR), support vector regression (SVR), and artificial neural networks (ANNs) are employed to determine the SSR of existing educational RC buildings. A comparative analysis for each model is also made. From the analysis results, it shows that RFR, ANN, and SVR achieved coefficient of determination (R2) of 20%, 25%, and 35%, respectively. Based on the findings from the three separate model analyses, it can be concluded that SVR produced the highest performance among the considered models. [ABSTRACT FROM AUTHOR]

This paper established a novel approach for developing simplified yet accurate models using artificial neural networks (ANNs) in industrial environments. It demonstrates that combining nonlinear regression with neural network modeling enhances predictive accuracy while maintaining the inherent simplicity of ANNs. Industrial sectors are increasingly adopting environmentally friendly practices, driven by the recognition that sustainable initiatives can lead to significant and lasting financial benefits rather than merely a sense of ecological duty. Integrating energy efficiency practices offers potential advantages in waste reduction and resource conservation, which can decrease operating expenses over time. This contributes significantly to pollution mitigation by reducing overall energy consumption cost‐effectively. Numerical simulations based on experimental results validate the proposed method, addressing the complexity and accuracy challenges in business models within the energy sector. [ABSTRACT FROM AUTHOR]

The rapid development of e-commerce international trade has driven the rapid growth of the economic system of international trade enterprises. This also means that industry competition is gradually intensifying, which also makes performance evaluation the key to cross-border e-commerce international trade. At present, my country's research on the performance evaluation of cross-border e-commerce international trade is in a blank state. Therefore, this paper takes the international trade performance evaluation of cross-border e-commerce as the research object and, based on the deep neural network model, develops a cross-border international trade performance evaluation model, changes trade strategies, and improves trade performance. This paper first analyzes various neural network models, such as artificial neural network, "BP" neuron model, and LSTM neural network. This paper summarizes a deep neural network model that is conducive to the development of cross-border e-commerce and points out the problems in the current performance evaluation of cross-border e-commerce international trade: the e-commerce market supervision system is not perfect; the second is the inconsistent evaluation indicators; the third is the evaluation system. There are some differences with the actual. Finally, this paper puts forward relevant suggestions for the performance evaluation of cross-border e-commerce international trade and points out the advantages and disadvantages of various neural networks, as well as their roles in cross-border e-commerce performance evaluation, and compares these neural networks through experiments. Experiments show that among these neural network models, the deep neural network model is the best and has the highest accuracy and stability in e-commerce trade performance evaluation. In the later stage, we will improve the global logistics system, strengthen the application of big data technology, and improve the overall performance of global operations. First, a set of indicators is designed to evaluate the performance of e-commerce systems, using the enterprise key factor model concept. In addition, this evaluation method is different from the commonly used expert evaluation method and physical evaluation method in evaluating the construction quality, cost, education and growth ability, and performance level of the international business system of cross-border e-commerce. [ABSTRACT FROM AUTHOR]

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

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]

The incidence of oral cancer is still increasing. It has become very common in patients with malignant tumors, which has forced medical personnel to continuously explore its treatment methods. What kind of method can effectively and correctly diagnose the disease in the early stage and improve the survival rate has become one of the research topics that have attracted much attention. Aiming at this problem, it has great research significance for the field of oral precancerous lesions diagnosis. With the in-depth research on oral precancerous diagnosis, the research on artificial neural network (ANN) in medical diagnosis is gradually carried out. Its performance advantage is of great significance to solve the problem of early and correct disease diagnosis. This paper aimed to investigate the application of ANN-assisted cancer risk prediction method in risk prediction of oral precancerous lesions. Through the analysis and research of ANN and oral cancer, the construction of oral cancer risk prediction model was applied to solve the problem of improving the survival rate of oral cancer patients. In this paper, ANN and oral precancerous lesions were analyzed, the performance of the algorithm was experimentally analyzed, and the relevant theoretical formulas were used to explain. The results showed that the method had higher accuracy than traditional forecasting methods. When N = 2 , the output accuracy was above 90%. It can be seen that the algorithm can meet the needs of the diagnosis of high-risk groups of oral cancer lesions, and the diagnosis efficiency and patient survival rate has been greatly improved. [ABSTRACT FROM AUTHOR]

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 study of pragmatic failure in cross-cultural communication is a subject of great theoretical significance and practical value in contemporary linguistic research. This paper takes pragmatic failures in cross-cultural communication as the research object, and tries to make the discussion systematic, theoretical, and scientific. With this feature, the complexity of the convolutional network will be greatly reduced. In the convolution layer, the convolution operation can make the features of the initial speech more obvious, and it can also have certain effect on the noise reduction of speech. It is of great theoretical and practical significance to use convolutional neural network to study the intelligent analysis strategies of pragmatic failure in cross-cultural communication. In this paper, the intelligent analysis strategy of pragmatic failure in cross-cultural communication based on deep convolution neural network is taken as the research object, and an optimized end-to-end deep convolution neural network model is proposed. Experimental results show that the overall recognition rate of this algorithm is improved by 59.8%. Especially, the efficiency of obtaining results is basically maintained at 61.8%. The intelligent analysis strategy of pragmatic failure in cross-cultural communication based on convolutional neural network reduces redundant calculation and shortens training time to some extent, and this algorithm can better reflect the advantages of accelerating network convergence compared with simple network. [ABSTRACT FROM AUTHOR]

With the rise of Erhu teaching in recent years, a large number of people have joined the team to learn Erhu playing. However, due to the high cost of teaching and the unique one-to-one teaching mode between teachers and students, Erhu education resources are very scarce. Learning Erhu performance has become a luxury activity. Nowadays, with the rise of artificial intelligence, computer music is developing rapidly. Music has two important aspects: composition and performance. Different kinds of instruments convey different styles, and players inject different rhythms and dynamics into their performance, thus producing rich expressive force. The development of image style conversion, which opens people's evaluation of music performance, is an important issue in many fields of artificial intelligence (it is also known as intelligence, machine intelligence, referring to the intelligence shown by the machine made by people. Usually, artificial intelligence refers to the technique of presenting human intelligence through ordinary computer programs). For an Erhu song, there are various factors that affect its effectiveness, and there are many indexes to evaluate it, such as sense of rhythm, expressive force, musical sense, style, and so on. Using a computer to simulate the evaluation process is essential to find out the mathematical relationship between the factors that affect the performance of music and the evaluation indexes. Neural network is a kind of mathematical model proposed by simulating the way of thinking of human brain in artificial intelligence. It has the advantages of not having strict requirements on data distribution, nonlinear data processing method, strong robustness, and dynamics and is very suitable for the mathematical model of evaluation system. In addition, the neural network also has a strong theoretical basis, and their application in various industries has developed basically mature. This paper tries to introduce a deep neural network mathematical model into the evaluation system of Erhu performance, and the experimental results prove the reliability and practicality of the method in this paper. It can provide a method basis and theoretical reference for evaluation of Erhu performance effect. [ABSTRACT FROM AUTHOR]

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

At present, the economic development of the world's major economies is showing a positive and positive state. Driven by the development of related industries, the development of the financial field is also changing with each passing day. Various activities in the financial industry are in full swing, and the forecasts of related prospects are also full of uncertainties. Summarizing the laws of financial activities through technical means and making accurate predictions of future trends and trends is a hot research direction that relevant researchers pay attention to. Accurate financial forecasts can provide reference for financial activities and decision-making to a certain extent, promote the steady development of the market, and improve the conversion rate of financial profits. As an algorithm model that can simulate the biological visual system, the convolutional neural network can predict the numerical trend of the next period of time based on known data. Therefore, this paper integrates the support vector machine with the established model by establishing a convolutional neural network model and applies the prediction model to the prediction of financial time series data. The experimental results show that the model proposed in this paper can more accurately predict the trend of the stock index. [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]

The use of deep learning to improve English speaking has seen tremendous development in recent years. This study evaluates the noise that is present in the English speech environment, employs a two-way search method to select the optimum feature set, and applies a quick correlation filter to remove redundant features in order to increase the accuracy of English voice feature identification. In addition, this article designs a low-pass filter in the complex cepstrum domain to filter the room impulse response in order to obtain the estimated value of the complex cepstrum of the original speech signal. After doing so, the authors transform this estimated value into the time domain in order to obtain the estimated value of the original speech signal. In addition, this paper proposes a corresponding noise elimination model for the purpose of eliminating noise from English speech in a reverberant environment. It also designs a complex cepstrum domain filter in order to conduct simulation research on the different characteristics of the reverberation signal and the pure speech signal in the complex cepstrum domain. In conclusion, this study develops an English voice feature recognition model that is founded on a deep neural network. Furthermore, this paper uses experimental research to validate the validity of the algorithm model that was developed in this study. [ABSTRACT FROM AUTHOR]

A critical element of accident reconstruction technology is the driver reaction time, which is also a key aspect of evaluating driver takeover time in autonomous driving. There are various factors affecting driver reaction time, including driver psychological factors, nondriving tasks, external environment, and other reasons. It is necessary to record and predict the driver reaction time to support the takeover time in the degraded takeover study because this paper is designed to conduct a degraded takeover study in the human-machine codriving stage with safety of the intended functionality. Therefore, a model based on PSO-BP neural network algorithm to predict driver reaction time is developed. A wavelet transform algorithm is used to denoise the signal first in order to improve the convergence speed and prediction accuracy of the model. Meanwhile, the BP neural network prediction model based on the PSO is established to optimize the weights and thresholds of the BP neural network to achieve the prediction of the driver reaction time. A total of six main feature parameters of driver's HRV in the time and frequency domains were selected as input indicators and substituted into the input signal of PSO-BP neural network model for training and testing. The prediction results obtained from the PSO-BP neural network model were compared with that of the BP neural network prediction, and it demonstrated that the prediction results obtained in this paper have smaller error values, verifying the reasonableness and validity of the model. [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]

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 this paper, adaptive tracking control is applied to improve performances of an underactuated quadrotor helicopter with respect to attitude and position control. Firstly, the dynamic model is presented. Then a new trajectory tracking algorithm is designed by using the sigma-pi neural network and backstepping. The paper designs the sigma-pi neural network compensation control law and gives the Lyapunov-type stability analysis. Then the corresponding numerical simulations are performed by using MATLAB. Simulation results are shown to demonstrate the effectiveness of the proposed control strategy, which could reduce tracking error, decrease tracking time, and improve the anti-interference ability of the system. [ABSTRACT FROM AUTHOR]

With the development of the economy and real-time embedded systems and the progress of science and technology, people's economic income forms have undergone tremendous changes, and the concept of financial management has become clearer in people's property income arrangements. Project investment is one of the most popular financial management methods in the era of big data. Both large enterprise groups and individual petty bourgeoisie groups have begun to pay attention to the risks and benefits brought by the new financial management method of project investment. This paper's goal is to develop a fuzzy comprehensive evaluation (FCE) model for project investment risk based on computer vision technology and explore the application of computer vision technology in project investment risk evaluation. This article first uses a real-time embedded system to understand the basic process of project investment and select 10 investment experts for risk assessment, risks, and causes of the risks through literature research and case analysis. Then, this paper establishes a model of fuzzy comprehensive evaluation of project investment risk through computer vision technology, real-time embedded systems, and neural network models in big data and artificial intelligence technology to realize the analysis and prediction of project investment risk. The fuzzy comprehensive evaluation method and analytic hierarchy process (AHP) are used in this evaluation model to evaluate and forecast project investment risks. In addition, this paper also trains and tests the risk evaluation model of this research through the support vector machine classification algorithm, the real-time embedded system, and the average random consistency index. The research shows that the fuzzy comprehensive evaluation model of this study has higher accuracy for project investment risk evaluation than other risk evaluation methods. For example, for the investment risk of chemical fiber projects, this research model evaluated the factors such as organization, management, technology, and economy and found that the risks were all higher than 21.36%, which concluded that the overall investment risk of chemical fiber projects was relatively high. [ABSTRACT FROM AUTHOR]

This paper presents a structural health monitoring method based on artificial neural networks (ANNs) capable of detecting, locating, and quantifying damage in a single stage. The proposed framework employs a supervised neural network model that uses input factors calculated by modal parameters (natural frequencies or mode shapes), and output factors that represent the damage situation of elements or regions in a structural system. Unlike many papers in the literature that test damage detection methods only in numerical examples or simple experimental tests, this work also assesses the presented method in a real structure showing that it has potential for applications in real practical situations. Three different cases are evaluated through the methodology: numerical simulations, an experimental lab structure, and a real bridge. Initially, a cantilever beam and a 10-bar truss were numerically analyzed under ambient vibrations with different damage scenarios and noise levels. Afterward, the method is assessed in an experimental beam structure and in the Z24 bridge benchmark. The numerical simulations showed that the methodology is promising for identifying, locating, and quantifying single and multiple damages in a single stage, even with noise in the acceleration signals and changes in the first vibration mode of 0.015%. In addition, the Z24 bridge study confirmed that the damage detection method can localize damage in real civil structures considering only natural frequencies in the input factors, despite a mean difference of 4.08% between the frequencies in the healthy and damaged conditions. [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]

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]