Joint embedding VQA model based on dynamic word vector

Opinion Evolution with Information Quality of Public Person and Mass Acceptance Threshold

Public persons are nodes with high attention to public events, and their opinions can directly affect the development on events. However, because of rationality, the followers' acceptance to the public persons' opinions will depend on the information trait on public persons' opinions and own comprehension. To study how different opinions of the public persons guide different followers, we build an opinion dynamics model, which would provide a theoretical method for public opinion management. Based on the classical bounded confidence model, we extract the information quality variables and individual trust threshold and introduce them to construct our two-stage opinion evolution model. And then in the simulation experiments, we analyze the different effects of opinion information quality, opinion release time, and frequency on public opinion by adjusting the different parameters. Finally, we added a case to compare real data, the data from classical model simulation and the data from improved model simulation to verify the effectiveness on our model. The research found that the more sufficient the argument and the more moderate the attitude, the more likely to guide the public opinion. If public person holds different opinions and different information quality, he should choose different time to present his opinion to achieve ideal guide effect. When public person holds neutral opinion and the information quality is relatively general, he/she can intervene in public opinion as soon as possible to control final public opinion; when public person holds extreme opinion and the information quality is relatively high, he/she can choose to express opinion after a certain period on public opinion evolution, which is conducive to improve the guidance effect on public opinion. The frequency of releasing opinions of public person consistently has a positive impact on the final public opinion.

Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network

Using information technology to extract emergency decision-making knowledge from emergency plan documents is an essential means to enhance the efficiency and capacity of emergency management. To address the problems of numerous terminologies and complex relationships faced by emergency knowledge extraction of water diversion project, a multi-feature graph convolutional network (PTM-MFGCN) based on pre-trained model is proposed. Initially, through the utilization of random masking of domain-specific terminologies during pre-training, the model's comprehension of the meaning and application of such terminologies within specific fields is enhanced, thereby augmenting the network's proficiency in extracting professional terminologies. Furthermore, by introducing a multi-feature adjacency matrix to capture a broader range of neighboring node information, thereby enhancing the network's ability to handle complex relationships. Lastly, we utilize the PTM-MFGCN to achieve the extraction of emergency entity relationships in water diversion project, thus constructing a knowledge graph for water diversion emergency management. The experimental results demonstrate that PTM-MFGCN exhibits improvements of 2.84% in accuracy, 4.87% in recall, and 5.18% in F1 score, compared to the baseline model. Relevant studies can effectively enhance the efficiency and capability of emergency management, mitigating the impact of unforeseen events on engineering safety.

Deep Hybrid Multimodal Biometric Recognition System Based on Features-Level Deep Fusion of Five Biometric Traits

The need for information security and the adoption of the relevant regulations is becoming an overwhelming demand worldwide. As an efficient solution, hybrid multimodal biometric systems utilize fusion to combine multiple biometric traits and sources with improving recognition accuracy, higher security assurance, and to cope with the limitations of the uni-biometric system. In this paper, three strategies for dealing with a feature-level deep fusion of five biometric traits (face, both irises, and two fingerprints) derived from three sources of evidence are proposed and compared. In the first two proposed methodologies, each feature vector is mapped from the feature space into the reproducing kernel Hilbert space (RKHS) separately by selecting the appropriate reproducing kernel. In this higher space, where the result is the conversion of nonlinear relations to linear ones, dimensionality reduction algorithms (KPCA, KLDA) and quaternion-based algorithms (KQPCA, KQPCA) are used for the fusion of the feature vectors. In the third methodology, the fusion of feature spaces based on deep learning is administered by combining feature vectors in in-depth and fully connected layers. The experimental results on 6 databases in the proposed hybrid multibiometric system clearly show the multimodal template obtained from the deep fusion of feature spaces; while being secure against spoof attacks and making the system robust, they can use the low dimensionality of the fused vector to increase the accuracy of a hybrid multimodal biometric system to 100%, showing a significant improvement compared with uni-biometric and other multimodal systems.

The multi-modal fusion in visual question answering: a review of attention mechanisms

Visual Question Answering (VQA) is a significant cross-disciplinary issue in the fields of computer vision and natural language processing that requires a computer to output a natural language answer based on pictures and questions posed based on the pictures. This requires simultaneous processing of multimodal fusion of text features and visual features, and the key task that can ensure its success is the attention mechanism. Bringing in attention mechanisms makes it better to integrate text features and image features into a compact multi-modal representation. Therefore, it is necessary to clarify the development status of attention mechanism, understand the most advanced attention mechanism methods, and look forward to its future development direction. In this article, we first conduct a bibliometric analysis of the correlation through CiteSpace, then we find and reasonably speculate that the attention mechanism has great development potential in cross-modal retrieval. Secondly, we discuss the classification and application of existing attention mechanisms in VQA tasks, analysis their shortcomings, and summarize current improvement methods. Finally, through the continuous exploration of attention mechanisms, we believe that VQA will evolve in a smarter and more human direction.

VIOLA jones algorithm with capsule graph network for deepfake detection

DeepFake is a forged image or video created using deep learning techniques. The present fake content of the detection technique can detect trivial images such as barefaced fake faces. Moreover, the capability of current methods to detect fake faces is minimal. Many recent types of research have made the fake detection algorithm from rule-based to machine-learning models. However, the emergence of deep learning technology with intelligent improvement motivates this specified research to use deep learning techniques. Thus, it is proposed to have VIOLA Jones's (VJ) algorithm for selecting the best features with Capsule Graph Neural Network (CN). The graph neural network is improved by capsule-based node feature extraction to improve the results of the graph neural network. The experiment is evaluated with CelebDF-FaceForencics++ (c23) datasets, which combines FaceForencies++ (c23) and Celeb-DF. In the end, it is proved that the accuracy of the proposed model has achieved 94.

Diversity Learning Based on Multi-Latent Space for Medical Image Visual Question Generation

Auxiliary clinical diagnosis has been researched to solve unevenly and insufficiently distributed clinical resources. However, auxiliary diagnosis is still dominated by human physicians, and how to make intelligent systems more involved in the diagnosis process is gradually becoming a concern. An interactive automated clinical diagnosis with a question-answering system and a question generation system can capture a patient's conditions from multiple perspectives with less physician involvement by asking different questions to drive and guide the diagnosis. This clinical diagnosis process requires diverse information to evaluate a patient from different perspectives to obtain an accurate diagnosis. Recently proposed medical question generation systems have not considered diversity. Thus, we propose a diversity learning-based visual question generation model using a multi-latent space to generate informative question sets from medical images. The proposed method generates various questions by embedding visual and language information in different latent spaces, whose diversity is trained by our newly proposed loss. We have also added control over the categories of generated questions, making the generated questions directional. Furthermore, we use a new metric named similarity to accurately evaluate the proposed model's performance. The experimental results on the Slake and VQA-RAD datasets demonstrate that the proposed method can generate questions with diverse information. Our model works with an answering model for interactive automated clinical diagnosis and generates datasets to replace the process of annotation that incurs huge labor costs.

A comprehensive and systematic literature review on the big data management techniques in the internet of things

The Internet of Things (IoT) is a communication paradigm and a collection of heterogeneous interconnected devices. It produces large-scale distributed, and diverse data called big data. Big Data Management (BDM) in IoT is used for knowledge discovery and intelligent decision-making and is one of the most significant research challenges today. There are several mechanisms and technologies for BDM in IoT. This paper aims to study the important mechanisms in this area systematically. This paper studies articles published between 2016 and August 2022. Initially, 751 articles were identified, but a paper selection process reduced the number of articles to 110 significant studies. Four categories to study BDM mechanisms in IoT include BDM processes, BDM architectures/frameworks, quality attributes, and big data analytics types. Also, this paper represents a detailed comparison of the mechanisms in each category. Finally, the development challenges and open issues of BDM in IoT are discussed. As a result, predictive analysis and classification methods are used in many articles. On the other hand, some quality attributes such as confidentiality, accessibility, and sustainability are less considered. Also, none of the articles use key-value databases for data storage. This study can help researchers develop more effective BDM in IoT methods in a complex environment.

An accurate estimation algorithm for structural change points of multi-dimensional stochastic models

In order to improve the estimation accuracy of structural change points of multi-dimensional stochastic model, the accurate estimation algorithm of structural change points of multi-dimensional stochastic model is studied. A multi-dimensional stochastic Graphical Modeling model based on multivariate normal hypothesis is constructed, and the relationship between the Graphical Gaussian model and the linear regression model is determined. The parameters of the multi-dimensional stochastic model are estimated by using the parameter estimation algorithm of the multi-dimensional stochastic model containing intermediate variables. According to the parameter estimation results of the multi-dimensional stochastic model, the structural change point estimation results of the multi-dimensional stochastic model are obtained by using the accurate estimation algorithm of the structural change point based on the MLE identification local drift time. The experimental results show that the proposed algorithm has higher estimation accuracy of structural change points than the control algorithms, which shows that it can effectively estimate the structural change points of multi-dimensional random models and has higher practicability.

Binary Classification Quantum Neural Network Model Based on Optimized Grover Algorithm

We focus on the problem that the Grover algorithm is not suitable for the completely unknown proportion of target solutions. Considering whether the existing quantum classifier used by the current quantum neural network (QNN) to complete the classification task can solve the problem of the classical classifier, this paper proposes a binary quantum neural network classifical model based on an optimized Grover algorithm based on partial diffusion. Trial and error is adopted to extend the partial diffusion quantum search algorithm with the known proportion of target solutions to the unknown state, and to apply the characteristics of the supervised learning of the quantum neural network to binary classify the classified data. Experiments show that the proposed method can effectively retrieve quantum states with similar features. The test accuracy of BQM retrieval under the depolarization noise at the 20th period can reach 97% when the depolarization rate is 0.1. It improves the retrieval accuracy by about 4% and 10% compared with MSE and BCE in the same environment.

Modeling analysis of the correlation between duality innovation and multinational enterprise performance

In this study, we investigate how the influence of dual innovation affects the productivity of multinational enterprises (MNEs). Because of the rise of the knowledge-based economy, the capacity of multinational corporations (MNCs) to innovate technologically has become an increasingly important component in determining the extent to which they can compete in the global market. Models of Duality Innovation and Multinational Enterprise Performance with a Measurement of Corporate Risks from 2000 to 2015 were developed using corporate finance literature and data. The models show positive relationships between duality innovations and multinational enterprise performance. Furthermore, there has been an increasing level of corporate risks over the years when measured by both the duality innovation and multinational enterprise performance metrics. This article discusses the findings of this research project. It explains how they can help understand international enterprise performance while also explaining how to determine a potential risk profile for an individual or multiple companies. This knowledge is valuable because it helps us understand why some corporations succeed while others fail.

Interactive Display of Images in Digital Exhibition Halls under Artificial Intelligence and Mixed Reality Technology

The attractiveness of traditional exhibition halls to young people is gradually decreasing. Combining modern digital technology to improve the display effect of the exhibition hall can effectively enhance the effect of cultural publicity. This article introduces the technology of image interaction and mixed reality (MR) to improve the historical and cultural propaganda level of the Shaanxi exhibition hall. The advantages of MR technology in applying digital exhibition halls are theoretically expounded. A theoretical plan for Shaanxi history and culture-related display areas is designed using artificial intelligence combined with  MR  technology. In addition, the survey respondent's evaluation of the effect of the new exhibition hall is obtained using a questionnaire survey. The survey results show that 97% of people like the history and culture of Shaanxi but only 13% of the people say they know or know very well about the history and culture of Shaanxi. In addition, 60% of the tourists say they are satisfied with the cultural experience of Shaanxi, and only 27% of the tourists are very satisfied. Also, 96% of tourists are willing to experience Shaanxi's history and culture through digital exhibition halls, and 93% are willing to participate in cultural experience activities based on MR technology. The survey results prove that tourists are satisfied with the effect of the new exhibition hall. Tourists want to add a distinctive form of cultural experience to the exhibition hall. They are willing to accept digital exhibition halls incorporating MR technology and are very happy to participate in the exhibition method of image interaction. This shows that the use of image interactive display based on MR technology in the layout of the exhibition hall is recognized by people and has strong feasibility. This article has reference significance for the digital upgrade of the exhibition hall and the development of the cultural tourism industry.

Security Analysis of Social Network Topic Mining Using Big Data and Optimized Deep Convolutional Neural Network

This research aims to conduct topic mining and data analysis of social network security using social network big data. At present, the main problem is that users’ behavior on social networks may reveal their private data. The main contribution lies in the establishment of a network security topic detection model combining Convolutional Neural Network (CNN) and social network big data technology. Deep Convolution Neural Network (DCNN) is utilized to complete the analysis and search of social network security issues. The Long Short-Term Memory (LSTM) algorithm is used for the extraction of Weibo topic information in the memory wisdom. Experimental results show that the recognition accuracy of the constructed model can reach 96.17% after 120 iterations, which is at least 5.4% higher than other models. Additionally, the accuracy, recall, and F1 value of the intrusion detection model are 88.57%, 75.22%, and 72.05%, respectively. Compared with other algorithms, the model’s accuracy, recall, and F1 value are at least 3.1% higher than other models. In addition, the training time and testing time of the improved DCNN network security detection model are stabilized at 65.86 s and 27.90 s, respectively. The prediction time of the improved DCNN network security detection model is significantly shortened compared with that of the models proposed by other scholars. The experimental conclusion is that the improved DCNN has the characteristics of lower delay under deep learning. The model shows good performance for network data security transmission.

On the Critical Heat Flux Assessment of Micro- and Nanoscale Roughened Surfaces

The boiling crisis or critical heat flux (CHF) is a very critical constraint for any heat-flux-controlled boiling system. The existing methods (physical models and empirical correlations) offer a specific interpretation of the boiling phenomenon, as many of these correlations are considerably influenced by operational variables and surface morphologies. A generalized correlation is virtually unavailable. In this study, more physical mechanisms are incorporated to assess CHF of surfaces with micro- and nano-scale roughness subject to a wide range of operating conditions and working fluids. The CHF data is also correlated by using the Pearson, Kendal, and Spearman correlations to evaluate the association of various surface morphological features and thermophysical properties of the working fluid. Feature engineering is performed to better correlate the inputs with the desired output parameter. The random forest optimization (RF) is used to provide the optimal hyper-parameters to the proposed interpretable correlation and experimental data. Unlike the existing methods, the proposed method is able to incorporate more physical mechanisms and relevant parametric influences, thereby offering a more generalized and accurate prediction of CHF (R² = 0.971, mean squared error = 0.0541, and mean absolute error = 0.185).

Financial Risk Early Warning Model of Listed Companies Under Rough Set Theory Using BPNN

In order to reduce the risk of enterprise management, the financial risk early warning methods of listed companies are mainly studied. The financial risk characteristics of listed companies are analysed. With the help of rough set theory, the financial risk indicators are selected, and the financial risk early warning index system is established. The financial risk early warning model is constructed by using back propagation neural network (BPNN) algorithm based on deep learning. Finally, the accuracy and feasibility of the constructed neural network model are verified. The results show that rough set theory can be used to screen financial risk indicators and select important indicators, which can simplify the data and reduce the complexity of calculation. BPNN can calculate the simplified data and identify and evaluate the financial risk. Empirical analysis shows that the proposed method can shorten the training time of the model to a certain extent, and improve the accuracy of financial risk prediction.

Improved color image defogging algorithm based on dark channel prior

An improved algorithm of image defogging was proposed based on dark channel prior In order to solve the low efficiency and color distortion in the bright area using original algorithm. If the image contains large areas of bright areas such as sky, white clouds or partial white objects and water surface, we can know that the dark channel prior theory does not apply to these areas. Firstly, it is necessary to clear the bright area of the image. According to principle that he adjacent pixel attributes have similarity, the image transmittance of the local region also has similarity, Block function is Consruted. Applied the dark channel prior, judging whether each block includes a bright area by the absolute value of difference of atmospheric intensity and dark channel, the dark and bright areas of the image are obtained. So the estimation value of the adaptive space transmittance are also obtained. Secondly, the transmittance of bright region is small and it causes deviation, so the enhancement formula is used to modify it dynamically. In order to preserve the edge details after image restoration, for bright areas, using texture function to optimize transmittance independently, for others, using gradient and texture function together. Finally, it restored the fog-free image applying the atmospheric scattering model. The experimental results showed that the restored image had obvious details and rich color and fast processing speed through the proposed algorithm. The algorithm can also be applied to outdoor visual systems, such as video surveillance, intelligent traffic and so on.

Arm Movement Analysis Technology of Wushu Competition Image Based on Deep Learning

In order to improve the recognition accuracy of action poses for athletes in martial arts competitions, it is considered that a single frame pose does not have the temporal features required for sequential actions. Based on deep learning, this paper proposes an image arm movement analysis technology in martial arts competitions. The motion features of the arm are extracted from the bone sequence. Taking human bone motion information as temporal dynamic information, combined with RGB spatial features and depth map, the spatiotemporal features of arm motion data are formed. In this paper, we set up a slow frame rate channel and a fast frame rate channel to detect sequential motion of images. The deep learning model takes 16 frames from each video as samples. The softmax classifier is used to get the classification result of which action category the human action in the video belongs to. The test results show that the accuracy and recall rate of the arm motion analysis technology based on deep learning in martial arts competitions are 95.477% and 92.948%, respectively, with good motion analysis performance.

Analysis on the Influencing Factors and Training Strategies of Young Talents in Hospitals under the Background of Big Data

Under the background of big data, young talents in hospitals across my country are constantly striving to exert their personal scientific research and technical capabilities in terms of scientific research performance. At the same time, it also exposed many problems that need to be solved urgently. Under the background of the new era, each hospital needs to exert and increase its training in terms of scientific research and technology research and the use of young talents. By building a scientific platform and establishing an effective scientific research performance evaluation management system, the backbone of outstanding young medical talents can be realized. In the context of big data, this paper studies and analyzes the influencing factors of hospital young talent scientific research and performance management. After the preliminary data collection work, the chi-square verification was carried out on the survey data of young medical scientific research talents. It is confirmed that the research conclusions based on big data have certain basis for improving the scientific research management level of each hospital based on the research conclusions, thus providing a certain theoretical basis for enhancing the scientific research performance of young talents in the hospital.

Computational Analysis of Variational Inequalities Using Mean Extra-Gradient Approach

An improved variational inequality strategy for dealing with variational inequality in a Hilbert space is proposed in this article as an alternative; if Hilbert space is used as the domain of interest, the original extra-gradient method is proposed for resolving variational inequality. This improved variational inequality strategy can be used as a substitute for the original extra-gradient method in some situations. Mann’s mean value method, coupled with the widely used sub-gradient extra-gradient strategy, makes it possible to update all of the previous iterations in a single step, thus saving time and effort. All of this is made feasible via the use of Mann’s mean value technique in conjunction with the convex hull of all prior iterations of the algorithm. It is guaranteed that the mean value iteration will result in an acceptable resolution of a variational inequality issue as long as one or more of the criteria for the averaging matrix are fulfilled. Numerous experiments were performed in order to demonstrate the correctness of the theoretical conclusion obtained.

Improvement of Monitoring Technology for Corrosive Pollution of Marine Environment under Cloud Computing Platform

In view of the increasingly serious problem of marine ecological environmental pollution, the traditional marine environmental corrosive pollution monitoring technology has poor monitoring accuracy and poor monitoring timeliness, and the improvement of the marine environmental corrosive pollution monitoring technology under the cloud computing platform is proposed. The research significance and corrosion influence factors of steel corrosion in the marine environment are described, and the research progress of corrosion mechanism in five different zones of the marine environment is reviewed. Cloud computing parallelizes the processing of corrosive pollution data in the marine environment through virtualization and distributed technology, which greatly improves the efficiency of the algorithm. This paper studies the existing cloud computing platform and ocean monitoring system architecture, uses the distributed architecture to design a cloud computing-oriented ocean monitoring system and meets the design requirements in data collection and data processing. The experimental results show that the precision of marine environmental corrosion pollution monitoring technology proposed in this paper is 96% on average, and the completion rate of monitoring images is 82% on average, which can effectively realize marine environmental corrosion pollution monitoring.

Machine learning applications for COVID-19 outbreak management

Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.

The Impact of Corporate Capital Structure on Financial Performance Based on Convolutional Neural Network

Capital structure is an important indicator to measure the source, composition, and proportion of a company's equity and debit capital. It is not only related to the internal operating environment of listed companies but also related to the rights and obligations of shareholders and is closely related to the company's future development direction, decision-making bodies, and changes in governance structure. This study aims to study the impact of corporate capital structure on financial performance based on convolutional neural network. Based on the relevant theories of capital structure, by constructing a convolutional neural network model, taking a listed company as the research object, this study analyzes the company's capital structure, liabilities, and other financial conditions. Finally, it is concluded that short-term liabilities can meet the company's sustainable development and enhance the competitiveness of the industry, thereby increasing the company's operating income. However, a poor capital structure can negatively impact a company's finances. By improving the corporate governance structure of listed companies, strengthening the adjustment of the financing structure of listed companies, and strengthening the management of listed company's operating risks, the company's capital structure can be improved so that the company's financial situation can be sustainable and healthy.

Control of Time Delay Force Feedback Teleoperation System With Finite Time Convergence

In order to make the teleoperation system more practical, it is necessary to effectively control the tracking error convergence time of the teleoperation system. By combining the terminal sliding mode control method with the neural network adaptive control method, a bilateral continuous finite time adaptive terminal sliding mode control method is designed for the combined teleoperation system. The Lyapunov theory is used to analyze the stability of the closed-loop system, and the position tracking error is able to effectively converge in time. Finally, the effectiveness of the proposed control scheme is verified by MATLAB Simulink numerical simulation, and the numerical analysis of the results shows that the method has better system performance. Compared with the traditional two-sided control method (TPDC) of PD time-delay teleoperation system, the control method in this paper has good performance, improves stability, and makes steady-state errors smaller and better tracking.

Pharmaceutical Reagent Inventory Strategy Based on Contract Shelf Life and Patient Demand

As the function and R&D level of in vitro diagnostic reagents continue to improve, the need for hospitals for in vitro diagnostic reagents in clinical diagnosis also keeps increasing. However, under the influence of management, process, technology, equipment, materials, employees, and other unexpected disturbing factors, the output of reagents often has random uncertainty, and it is difficult to provide the finished products required by orders on time, in quality and quantity. A secondary supply chain consisting of reagent manufacturers, distributors, and hospitals is constructed, and the inventory control models of in vitro diagnostic reagent supply chain under three strategies of centralized decision-making, hospital-owned inventory, and reagent distributor-managed inventory are established, respectively, and the maximum expected returns of the supply chain system under different strategies are analyzed to achieve the optimal production decision of reagent manufacturers and the optimal procurement decision of hospitals. The results show that reducing the random output probability and patient demand uncertainty has a significant effect on improving the expected return of in vitro diagnostic reagent supply chain, and as the random output probability of reagent manufacturers and patient consumption demand uncertainty increase, the strategy of managing inventory by distributors in collaboration is always better than the strategy of managing inventory by hospitals' own warehouses, which can achieve higher expected return and better inventory quantity, but when the out-of-stock cost of hospitals is too high above a certain threshold, the hospital will tend to adopt the self-inventory strategy.

Characterization inference based on joint-optimization of multi-layer semantics and deep fusion matching network

The whole sentence representation reasoning process simultaneously comprises a sentence representation module and a semantic reasoning module. This paper combines the multi-layer semantic representation network with the deep fusion matching network to solve the limitations of only considering a sentence representation module or a reasoning model. It proposes a joint optimization method based on multi-layer semantics called the Semantic Fusion Deep Matching Network (SCF-DMN) to explore the influence of sentence representation and reasoning models on reasoning performance. Experiments on text entailment recognition tasks show that the joint optimization representation reasoning method performs better than the existing methods. The sentence representation optimization module and the improved optimization reasoning model can promote reasoning performance when used individually. However, the optimization of the reasoning model has a more significant impact on the final reasoning results. Furthermore, after comparing each module's performance, there is a mutual constraint between the sentence representation module and the reasoning model. This condition restricts overall performance, resulting in no linear superposition of reasoning performance. Overall, by comparing the proposed methods with other existed methods that are tested using the same database, the proposed method solves the lack of in-depth interactive information and interpretability in the model design which would be inspirational for future improving and studying of natural language reasoning.

Analysis of Safe Storage of Network Information Data and Financial Risks Under Blockchain Combined With Edge Computing

To discuss the control of financial risks (FRs) under blockchain (BC) and improve network information security (NIS) and data security, edge computing (EC) combined with BC is proposed to control the risks of the big data (BD) financial system. Firstly, the BC-based financial system is introduced, and the characteristics of BC such as decentralization, tamper-resistant, and smart contract are analyzed. Secondly, the development status of NIS and the characteristics of marginal computing are explained, and the control model of NIS is established. Then, EC is used to encrypt the identity authentication system to ensure data security, and the BC-based FR evaluation model is established. Finally, a questionnaire is designed regarding the NIS model, and the results are analyzed. A simulation experiment is conducted regarding the index evaluation of the BC-based FR evaluation model. The experimental results indicate that network personnel control, environment, and technology have positive effects on NIS, and the impact factors are 0.26, 0.24, and 0.33, respectively.

Platformance-Based Cross-Border Import Retail E-Commerce Service Quality Evaluation Using an Artificial Neural Network Analysis

The transaction scale of cross-border import e-commerce has grown rapidly around the world. Platform-style cross-border e-commerce does not control the quality, source and transaction process of goods strictly and comprehensively. In terms of customer service quality, the seller's customer service often ignores the customer's problems, and some customer service solutions cannot solve the customer's problems. Serving customers through the network has changed the traditional offline service form without distance, and the service process has a time and space distance. This paper constructs an evaluation index system based on the development of cross-border e-commerce. Through questionnaires, relevant data were obtained and analyzed. Analyze the results based on the collected data on the factors that affect the quality of cross-border import e-commerce services. Responsiveness is the most important factor found by artificial neural networks. The descending order of importance of other factors is fulfillment, diversity, privacy, reliability, compensation, and ease of use.

Estimating the density of deep eutectic solvents applying supervised machine learning techniques

Deep eutectic solvents (DES) are recently synthesized to cover limitations of conventional solvents. These green solvents have wide ranges of potential usages in real-life applications. Precise measuring or accurate estimating thermophysical properties of DESs is a prerequisite for their successful applications. Density is likely the most crucial affecting characteristic on the solvation ability of DESs. This study utilizes seven machine learning techniques to estimate the density of 149 deep eutectic solvents. The density is anticipated as a function of temperature, critical pressure and temperature, and acentric factor. The LSSVR (least-squares support vector regression) presents the highest accuracy among 1530 constructed intelligent estimators. The LSSVR predicts 1239 densities with the mean absolute percentage error (MAPE) of 0.26% and R² = 0.99798. Comparing the LSSVR and four empirical correlations revealed that the earlier possesses the highest accuracy level. The prediction accuracy of the LSSVR (i.e., MAPE = 0. 26%) is 74.5% better than the best-obtained results by the empirical correlations (i.e., MAPE = 1.02%).

Earnings Management Behavior of Enterprise Managers Based on Evolutionary Game Theory

Today, earnings mismanagement in China's enterprises has become a serious problem as managers conduct financial fraud by means of earnings management, hindering China's overall economic development. Upon shareholders' requirements and investors' concerns, managers should disclose real financial information. The essay analyzes the revenue function generated by the manager and the shareholder through an evolutionary theory model where the managers team of the enterprise and shareholders are both game parties. After building the model, the essay utilizes Python to stimulate the theoretical model to analyze both parties' behavior to explain the process of evolutionary game theory.

Intelligent Design of Multi-Machine Power System Stabilizers (PSSs) Using Improved Particle Swarm Optimization

In this paper, an improved version of the particle swarm optimization algorithm is proposed for the online tuning of power system stabilizers in a standard four-machine two-area power system to mitigate local and inter-area mode oscillations. Moreover, an innovative objective function is proposed for performing the optimization, which is a weight function of two functions. The first part of fitness is the function of the angular velocity deviation of the generators, and the other part is a function based on the percentage of undershoot and maximum overshoot, and also the damping time of the power system oscillations. The performance of the proposed stabilization method is compared with the genetic algorithm and bacteria foraging algorithm results. Simulations are made in three different power system operation conditions by changing the system load. The simulation results indicate the superiority of the proposed method over the genetic algorithm and bacteria foraging algorithm. In all the scenarios, power system oscillations are damped faster and with lower amplitude when the power system stabilizers coordinate with the proposed optimization method.

The Analysis of Global RMB Exchange Rate Forecasting and Risk Early Warning Using ARIMA and CNN Models

The purposes are to predict exchange rate fluctuations more accurately and enhance Chinese enterprises’ ability to avoid exchange rate risks. Renminbi (RMB) exchange rate fluctuation’s prediction methods are studied based on data mining technology. The Auto-Regressive Integrated Moving Average (ARIMA) model is introduced first using a modeling method that combines linear and nonlinear models. The linear prediction is obtained by the ARIMA model’s application in the RMB exchange rate’s dynamic fluctuation analysis. The nonlinear residual prediction is obtained by integrating the ARIMA model with the convolutional neural network (CNN) algorithm. The RMB exchange rate fluctuations’ influence mechanism on China’s economic growth is explored by theoretical analysis and empirical research. The US dollar’s daily central parity rate (USD) data against the RMB from September 2015 to March 2019 are selected for model verification, obtaining the exchange rate’s logarithmic return sequence (RUSD).

The Pharmacological Mechanism of the Effect of Plant Extract Compound Drugs on Cancer Pain Based on Network Pharmacology

Objective

We systematically analyzed the mechanism of plant-derived drugs alleviating cancer pain in our hospital through network pharmacology, so as to provide the possibility of further application of traditional Chinese medicine in the treatment of cancer pain.

Methods

We used TCMSP, ETCM, and TCMID databases to mine the active ingredients of plant-derived drugs. We combined OMIM, GeneCards, and DrugBank databases to mine and match the common targets of plant-derived drugs for cancer pain. We used the STRING platform and Cytoscape software to analyze and screen out the core targets. We used GO and KEGG methods to analyze the biological processes, molecular functions, cellular composition, and signaling pathways involved in the reduction of cancer pain by plant-derived drugs.

Results

We found 153 active ingredients from botanical drugs by TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, TCMSP), ETCM (The Encyclopedia of Traditional Chinese Medicine), and TCMID (Traditional Chinese Medicine Integrated Database) databases, covering 341 protein targets in human body. Combined with OMIM (Online Mendelian Inheritance in Man), GeneCards, and DrugBank databases, we excavated and matched 141 targets of plant-derived drugs and cancerous pain diseases. Through the analysis of the STRING platform and Cytoscape software, 19 core targets including TNF, MAPK1, JUN, and IL-6 were screened out. Go and KEGG enrichment showed that plant-derived drugs alleviated cancer pain processes involving 193 biological processes, 47 molecular functions, 22 cell components, and 118 signaling pathways. By screening genes involved in KEGG signaling pathway, it was found that plant-derived drugs were mainly associated with PI3K-Akt signaling pathway, tumor necrosis factor signaling pathway, MAPK signaling pathway, Toll-like receptor signaling pathway, and HIF-1 signaling pathway in alleviating cancer pain.

Conclusion

These results indicate that botanical drugs can positively affect the expression of inflammatory factors and apoptotic factors in the process of treatment and relief of cancer pain, which is expected to have a potential therapeutic effect on the relief of cancer pain.

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran

We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping.

Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness

One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline. In the proposed structure, a dual-energy gamma source consisting of barium-133 and cesium-137 isotopes emit photons, one detector recorded transmitted photons and a second detector recorded the scattered photons. After simulating the mentioned structure using Monte Carlo N-Particle (MCNP) code, time characteristics named 4th order moment, kurtosis and skewness were extracted from the recorded data of both the transmission detector (TD) and scattering detector (SD). These characteristics were considered as inputs of the multilayer perceptron (MLP) neural network. Two neural networks that were able to determine volume percentages with high accuracy, as well as classify all flow regimes correctly, were trained.

Haze Prediction Model Using Deep Recurrent Neural Network

In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of PM2.5/PM10 concentration. Since PM2.5 and PM10 concentration data are time series, their time characteristics should be considered in their prediction. However, the traditional neural network is limited by its own structure and has some weakness in processing time related data. Recurrent neural network is a kind of network specially used for sequence data modeling, that is, the current output of the sequence is correlated with the historical output. In this paper, a haze prediction model is established based on a deep recurrent neural network. We obtained air pollution data in Chengdu from the China Air Quality Online Monitoring and Analysis Platform, and conducted experiments based on these data. The results show that the new method can predict smog more effectively and accurately, and can be used for social and economic purposes.

Grey Correlation Analysis of Haze Impact Factor PM2.5

In recent years, frequent severe haze weather has formed in China, including some of the most populated areas. We found that these smog-prone areas are often relatively a “local climate” and aim to explore this series of scientific problems. This paper uses remote sensing and data mining methods to study the correlation between haze weather and local climate. First, we select Beijing, China and its surrounding areas (East longitude 115°20′11″–117°40′35″, North latitude 39°21′11″–41°7′51″) as the study area. We collected data from meteorological stations in Beijing and Xianghe from March 2014 to February 2015, and analyzed the meteorological parameters through correlation analysis and a grey correlation model. We study the correlation between the six influencing factors of temperature, dew point, humidity, wind speed, air pressure and visibility and PM2.5, so as to analyze the correlation between haze weather and local climate more comprehensively. The results show that the influence of each index on PM2.5 in descending order is air pressure, wind speed, humidity, dew point, temperature and visibility. The qualitative analysis results confirm each other. Among them, air pressure (correlation 0.771) has the greatest impact on haze weather, and visibility (correlation 0.511) is the weakest.

Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface

At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the human body, restore the three-dimensional scene of the area to be operated on, and track the motion of the soft tissue surface. This enables doctors to have a clearer understanding of the location depth of the surgical area, greatly reducing the negative impact of 2D image defects and ensuring smooth operation. In this study, firstly, the 3D coordinates of each feature point are calculated by using the parameters of the parallel binocular endoscope and the spatial geometric constraints. At the same time, the discrete feature points are divided into multiple triangles using the Delaunay triangulation method. Then, the 3D coordinates of feature points and the division results of each triangle are combined to complete the 3D surface reconstruction. Combined with the feature matching method based on convolutional neural network, feature tracking is realized by calculating the three-dimensional coordinate changes of the same feature point in different frames. Finally, experiments are carried out on the endoscope image to complete the 3D surface reconstruction and feature tracking.

A Haze Prediction Model in Chengdu Based on LSTM

Air pollution with fluidity can influence a large area for a long time and can be harmful to the ecological environment and human health. Haze, one form of air pollution, has been a critical problem since the industrial revolution. Though the actual cause of haze could be various and complicated, in this paper, we have found out that many gases’ distributions and wind power or temperature are related to PM2.5/10’s concentration. Thus, based on the correlation between PM2.5/PM10 and other gaseous pollutants and the timing continuity of PM2.5/PM10, we propose a multilayer long short-term memory haze prediction model. This model utilizes the concentration of O3, CO, NO2, SO2, and PM2.5/PM10 in the last 24 h as inputs to predict PM2.5/PM10 concentrations in the future. Besides pre-processing the data, the primary approach to boost the prediction performance is adding layers above a single-layer long short-term memory model. Moreover, it is proved that by doing so, we could let the network make predictions more accurately and efficiently. Furthermore, by comparison, in general, we have obtained a more accurate prediction.

Adaptive Neural Network Control of Time Delay Teleoperation System Based on Model Approximation

A bilateral neural network adaptive controller is designed for a class of teleoperation systems with constant time delay, external disturbance and internal friction. The stability of the teleoperation force feedback system with constant communication channel delay and nonlinear, complex, and uncertain constant time delay is guaranteed, and its tracking performance is improved. In the controller design process, the neural network method is used to approximate the system model, and the unknown internal friction and external disturbance of the system are estimated by the adaptive method, so as to avoid the influence of nonlinear uncertainties on the system.

Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model

As a kind of air pollution, haze has complex temporal and spatial characteristics. From the perspective of time, haze has different causes and levels of pollution in different seasons. From the perspective of space, the concentration of haze in adjacent areas will affect each other, showing some correlation. In this paper, we construct a multi-convolution haze-level prediction model for predicting haze levels in different areas of Beijing, which uses the remote sensing satellite image of the Beijing divided into nine regions as input and the haze pollution level as output. We categorize the predictions into four seasons in chronological order and use frequency histograms to analyze haze levels in different regions in different seasons. The results show that the haze pollution in the southern regions is significantly different from that in the northern regions. In addition, the haze tends to be clustered in adjacent areas. We use Global Moran’s I to analyze the predictions and find that haze is related to the geographical location in summer and autumn. We also use Local Moran’s I, Moran scatter plot, and Local Indicators of Spatial Association (LISA) to study the spatial characteristics of haze in adjacent areas. The results show, for the spatial distribution of haze in Beijing, that the southern regions present a high-high agglomeration, while the northern regions exhibit a ‘low-low agglomeration. The temporal evolution of haze on the seasonal scale, according to the chronological order of winter, spring, and summer to autumn, shows that the haze gradually becomes agglomerated. The main finding is that the haze pollution in southern Beijing is significantly different from that of northern regions, and haze tends to be clustered in adjacent areas.

A Haze Prediction Method Based on One-Dimensional Convolutional Neural Network

In recent years, more and more people are paying close attention to the environmental problems in metropolitan areas and their harm to the human body. Among them, haze is the pollutant that people are most concerned about. The demand for a method to predict the haze level for the public and academics keeps rising. In order to predict the haze concentration on a time scale in hours, this study built a haze concentration prediction method based on one-dimensional convolutional neural networks. The gated recurrent unit method was used for comparison, which highlights the training speed of a one-dimensional convolutional neural network. In summary, the haze concentration data of the past 24 h are used as input and the haze concentration level on the next moment as output such that the haze concentration level on the time scale in hours can be predicted. Based on the results, the prediction accuracy of the proposed method is over 95% and can be used to support other studies on haze prediction.

Research on image classification method based on improved multi-scale relational network

Small sample learning aims to learn information about object categories from a single or a few training samples. This learning style is crucial for deep learning methods based on large amounts of data. The deep learning method can solve small sample learning through the idea of meta-learning "how to learn by using previous experience." Therefore, this paper takes image classification as the research object to study how meta-learning quickly learns from a small number of sample images. The main contents are as follows: After considering the distribution difference of data sets on the generalization performance of measurement learning and the advantages of optimizing the initial characterization method, this paper adds the model-independent meta-learning algorithm and designs a multi-scale meta-relational network. First, the idea of META-SGD is adopted, and the inner learning rate is taken as the learning vector and model parameter to learn together. Secondly, in the meta-training process, the model-independent meta-learning algorithm is used to find the optimal parameters of the model. The inner gradient iteration is canceled in the process of meta-validation and meta-test. The experimental results show that the multi-scale meta-relational network makes the learned measurement have stronger generalization ability, which further improves the classification accuracy on the benchmark set and avoids the need for fine-tuning of the model-independent meta-learning algorithm.