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Today, computer graphics and graphic image processing techniques have been widely used in daily life and industrial production. Due to the development of computers, computer graphics has brought more convenience to our daily life. In order to give full play to the value of computers, this paper takes the Hakka paper-cut art with local characteristics as the starting point, first of all its development history, artistic characteristics, compositional forms, expression techniques, cultural connotations, Hakka paper-cut patterns, and the symbolic meaning of folk customs, and then we design a visualization system for the paper-cut works of Gannan Hakka based on computer graphics. In addition, the system provides a solution for the integration of Gannan Hakka paper-cut art and Jiangxi native product packaging design and provides a reference for the theory and practice of modern native product packaging design., Competing Interests: The author declares that he has no conflicts of interest., (Copyright © 2022 Xingping Li.)

In this paper, we propose a multiphase semistatic training method for swarm confrontation using multi-agent deep reinforcement learning. In particular, we build a swarm confrontation game, the 3V3 tank fight, based on the Unity platform and train the agents by a MDRL algorithm called MA-POCA, coming with the ML-Agent toolkit. By multiphase learning, we split the traditional single training phase into multiple consecutive training phases, where the performance level of the strong team for each phase increases in an incremental way. On the other hand, by semistatic learning, the strong team in all phases will stop learning when fighting against the weak team, which reduces the possibility that the weak team keeps being defeated and learns nothing at all. Comprehensive experiments prove that, in contrast to the traditional single-phase training method, the multiphase semistatic training method proposed in this paper can significantly increase the training efficiency, shedding lights on how the weak could learn from the strong with less time and computational cost., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 He Cai et al.)

Dementia is increasing day-by-day in older adults. Many of them are spending their life joyfully due to smart home technologies. Smart homes contain several smart devices which can support living at home. Automated assessment of smart home residents is a significant aspect of smart home technology. Detecting dementia in older adults in the early stage is the basic need of this time. Existing technologies can detect dementia timely but lacks performance. In this paper, we proposed an automated cognitive health assessment approach using machines and deep learning based on daily life activities. To validate our approach, we use CASAS publicly available daily life activities dataset for experiments where residents perform their routine activities in a smart home. We use four machine learning algorithms: decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and multilayer perceptron (MLP). Furthermore, we use deep neural network (DNN) for healthy and dementia classification. Experiments reveal the 96% accuracy using the MLP classifier. This study suggests using machine learning classifiers for better dementia detection, specifically for the dataset which contains real-world data., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Shtwai Alsubai et al.)

With an increasing number of network attacks using encrypted communication, the anomaly detection of encryption traffic is of great importance to ensure reliable network operation. However, the existing feature extraction methods for encrypted traffic anomaly detection have difficulties in extracting features, resulting in their low efficiency. In this paper, we propose a framework of encrypted traffic anomaly detection based on parallel automatic feature extraction, called deep encrypted traffic detection (DETD). The proposed DETD uses a parallel small-scale multilayer stack autoencoder to extract local traffic features from encrypted traffic and then adopts an L1 regularization-based feature selection algorithm to select the most representative feature set for the final encrypted traffic anomaly detection task. The experimental results show that DETD has promising robustness in feature extraction, i.e., the feature extraction efficiency of DETD is 66% higher than that of the conventional stacked autoencoder, and the anomaly detection performance is as high as 99.998%, and thus DETD outperforms the deep full-range framework and other neural network anomaly detection algorithms., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Gang Long and Zhaoxin Zhang.)

The sparrow search algorithm (SSA) is a novel swarm intelligence optimization algorithm. It has a fast convergence speed and strong global search ability. However, SSA also has many shortcomings, such as the unstable quality of the initial population, easy to fall into the local optimal solution, and the diversity of the population decreases with the iterative process. In order to solve these problems, this paper proposes an improved sparrow search algorithm (ISSA). ISSA uses Chebyshev chaotic map and elite opposition-based learning strategy to initialize the population and improve the quality of the initial population. In the process of producer location update, dynamic weight factor and Levy flight strategy are introduced to avoid falling into a local optimal solution. The mutation strategy is applied to the scrounger location update process, and the mutation operation is performed on individuals to increase the diversity of the population. In order to verify the feasibility and effectiveness of ISSA, it is tested on 23 benchmark functions. The results show that compared with other seven algorithms, ISSA has higher convergence accuracy, faster convergence speed, and stronger stability. Finally, ISSA is used to optimize the sound field of high-intensity focused ultrasound (HIFU). The results show that ISSA can effectively improve the focusing performance and reduce the influence of sound field sidelobe, which is of great benefit for HIFU treatment., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2023 Yihao Yang et al.)

With the development of artificial intelligence (AI) in the field of drug design and discovery, learning informative representations of molecules is becoming crucial for those AI-driven tasks. In recent years, the graph neural networks (GNNs) have emerged as a preferred choice of deep learning architecture and have been successfully applied to molecular representation learning (MRL). Up-to-date MRL methods directly apply the message passing mechanism on the atom-level attributes (i.e., atoms and bonds) of molecules. However, they neglect latent yet significant hyperstructured knowledge, such as the information of pharmacophore or functional class. Hence, in this paper, we propose Hyper-Mol, a new MRL framework that applies GNNs to encode hypergraph structures of molecules via fingerprint-based features. Hyper-Mol explores the hyperstructured knowledge and the latent relationships of the fingerprint substructures from a hypergraph perspective. The molecular hypergraph generation algorithm is designed to depict the hyperstructured information with the physical and chemical characteristics of molecules. Thus, the fingerprint-level message passing process can encode both the intra-structured and inter-structured information of fingerprint substructures according to the molecular hypergraphs. We evaluate Hyper-Mol on molecular property prediction tasks, and the experimental results on real-world benchmarks show that Hyper-Mol can learn comprehensive hyperstructured knowledge of molecules and is superior to the state-of-the-art baselines., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Shicheng Cui et al.)

Pansharpening technology is used to acquire a multispectral image with high spatial resolution from a panchromatic (PAN) image and a multispectral (MS) image. The detail injection model is popular for its flexibility. However, the accuracy of the injection gain and the extracted details may greatly influence the quality of the pansharpened image. This paper proposes an adaptive injection model to solve these problems. For detail extraction, we present a Gaussian filter estimation algorithm by exploring the intrinsic character of the MS sensor and convolving the PAN image with the filter to adaptively optimize the details to be consistent with the character of the MS image. For the adaptive injection coefficient, we iteratively adjust the coefficient by balancing the spectral and spatial fidelity. By multiplying the optimized details and injection gain, the final HRMS is obtained with the injection model. The performance of the proposed model is analyzed and a large number of tests are carried out on various satellite datasets. Compared to some advanced pansharpening methods, the results prove that our method can achieve the best fusion quality both subjectively and objectively., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Qun Song et al.)

A nonlinear error correction model (ECM) is developed to fit nonlinear relationships between the nonstationary time series in a cointegration relationship. Different from the previous parametric methods, this paper constructs a hybrid neural network to learn the nonlinear error correction model by combining a linear recurrent neural network with a multilayer BP network. The network learning algorithm is given by using the gradient descent method and error back propagation. Based on the principle of data-driven, all network parameters can be obtained through the network learning and training. The daily data of gold price and the US dollar index in 2021 were used to verify this proposed nonlinear ECM neural learning method and the results were compared by the likelihood ratio Chi-square test. Simulation results show that the proposed data-driven nonlinear error correction neural learning method can improve goodness of fit statistical significantly of complex nonlinear relationship between time series., Competing Interests: The authors declare that there are no conflicts of interest regarding this work., (Copyright © 2023 Xi Fang and Nan Yang.)

Face recognition technology is a powerful means to capture biological facial features and match facial data in existing databases. With the advantages of noncontact and long-distance implementation, it is being used in more and more scenarios. Affected by factors such as light, posture, and background environment, the face images captured by the device are still insufficient in the recognition rate of existing face recognition models. We propose an AB-FR model, a convolutional neural network face recognition method based on BiLSTM and attention mechanism. By adding an attention mechanism to the CNN model structure, the information from different channels is integrated to enhance the robustness of the network, thereby enhancing the extraction of facial features. Then, the BiLSTM method is used to extract the timing characteristics of different angles or different time photos of the same person so that convolutional blocks can obtain more face detail information. Finally, we used the cross-entropy loss function to optimize the model and realize the correct face recognition. The experimental results show that the improved network model indicates better identification performance and stronger robustness on some public datasets (such as CASIA-FaceV5, LFW, MTFL, CNBC, and ORL). Besides, the accuracy rate is 99.35%, 96.46%, 97.04%, 97.19%, and 96.79%, respectively., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2023 Xiaobo Qi et al.)

Breast cancer is the most common and deadly type of cancer in the world. Based on machine learning algorithms such as XGBoost, random forest, logistic regression, and K-nearest neighbor, this paper establishes different models to classify and predict breast cancer, so as to provide a reference for the early diagnosis of breast cancer. Recall indicates the probability of detecting malignant cancer cells in medical diagnosis, which is of great significance for the classification of breast cancer, so this article takes recall as the primary evaluation index and considers the precision, accuracy, and F 1-score evaluation indicators to evaluate and compare the prediction effect of each model. In order to eliminate the influence of different dimensional concepts on the effect of the model, the data are standardized. In order to find the optimal subset and improve the accuracy of the model, 15 features were screened out as input to the model through the Pearson correlation test. The K-nearest neighbor model uses the cross-validation method to select the optimal k value by using recall as an evaluation index. For the problem of positive and negative sample imbalance, the hierarchical sampling method is used to extract the training set and test set proportionally according to different categories. The experimental results show that under different dataset division (8 : 2 and 7 : 3), the prediction effect of the same model will have different changes. Comparative analysis shows that the XGBoost model established in this paper (which divides the training set and test set by 8 : 2) has better effects, and its recall, precision, accuracy, and F 1-score are 1.00, 0.960, 0.974, and 0.980, respectively., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Hua Chen et al.)

This article proposes a cuckoo algorithm (GFCS) based on the global feedback strategy and innovatively introduces a "re-fly" mechanism. In GFCS, the process of the algorithm is adjusted and controlled by a dynamic global variable, and the dynamic global parameter also serves as an indicator of whether the algorithm has fallen into a local optimum. According to the change of the global optimum value of the algorithm in each round, the dynamic global variable value is adjusted to optimize the algorithm. In addition, we set new formulas for the other main parameters, which are also adjusted by the dynamic global variable as the algorithm progresses. When the algorithm converges prematurely and falls into a local optimum, the current optimum is retained, and the algorithm is initialized and re-executed to find a better value. We define the previous process as "re-fly." To verify the effectiveness of GFCS, we conducted extensive experiments on the CEC2013 test suite. The experimental results show that the GFCS algorithm has better performance compared to other algorithms when considering the quality of the obtained solution., Competing Interests: The authors declare that they have no known conflicts of financial interest or personal relationships that could have influenced the work reported in this paper., (Copyright © 2023 Xingyu Liu et al.)

With the increasing frequency of autonomous driving, more and more attention is paid to personalized path planning. However, the path selection preferences of users will change with internal or external factors. Therefore, this paper proposes a personalized path recommendation strategy that can track and study user's path preference. First, we collect the data of the system, establish the relationship with the user preference factor, and get the user's initial preference weight vector by dichotomizing the K -means algorithm. The system then determines whether user preferences change based on a set threshold, and when the user's preference changes, the current preference weight vector can be obtained by redefining the preference factor or calling difference perception. Finally, the road network is quantized separately according to the user preference weight vector, and the optimal path is obtained by using Tabu search algorithm. The simulation results of two scenarios show that the proposed strategy can meet the requirements of autopilot even when user preferences change., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Pengzhan Chen et al.)

The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed., Competing Interests: The authors declare that they have no conflicts of interest to report regarding the present study., (Copyright © 2022 Zhihong Gao et al.)

To provide decision support to the commander, it is necessary to calculate shipborne vehicles' sortie mission reliability during the formulation of the layout plan. Therefore, this paper presents the sortie mission network model and reliability calculation method for shipborne vehicles. Firstly, the shipborne vehicle layout and sortie task characteristics are used to establish the sortie mission network model. The shipborne vehicles' sortie mission reliability problem is transformed into a two-terminal network reliability problem. Secondly, the minimal path set method is used to calculate the two-terminal network reliability. An improved tabu search algorithm based on a strategy of breaking up the whole into parts is proposed to search for the minimal path set that matches the length. Finally, the sum of disjoint products is used to process the minimal path set to obtain the shipborne vehicles' sortie mission reliability calculation formula. A numerical analysis of two simplified shipborne vehicles' layouts is given to illustrate the calculation process of the method. This study provides a new evaluation index and an effective quantitative basis for the evaluation system of shipborne vehicles' layout. It also provides theoretical support for the development of decision-making related to the sortie mission of shipborne vehicles., Competing Interests: The authors declare that they have no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Han Shi et al.)

An essential step in surface wave exploration is the inversion of dispersion curves. By inverting dispersion curves, we can effectively establish the shear-wave velocity model and obtain reliable subsurface stratigraphic information. The inversion of dispersion curves is an inversion problem with multiple parameters and multiple poles, and obtaining a high precision solution is difficult. Among the methods of inversion of dispersion curves, local search methods are prone to fall into local extremes, and global search methods such as particle swarm optimization (PSO) and genetic algorithm (GA) present the disadvantages of slow convergence speed and low precision. Deep learning models with strong nonlinear mapping capability can effectively solve nonlinear problems. Therefore, we propose a method called PSO-optimized long short-term memory (LSTM) network (PSO-LSTM) to invert the dispersion curves in order to improve the effect of inversion of dispersion curves. The method is based on the LSTM network, and PSO is used to optimize the LSTM network structure and other parameters that need to be given manually to improve the prediction of the network. Two theoretical geological models are used in the paper: Model A and Model B to test the PSO-LSTM. The tests include the noisy data test and noise-free data test. Model A was tested without noise, and Model B was tested with noise. In addition, PSO and LSTM were tested on model A to compare the performance of PSO-LSTM. In Model A , the maximum relative errors of PSO and LSTM are 20.76% and 5.85%, respectively, and the maximum standard deviations of PSO and LSTM are 57.37 and 1.97, respectively. For PSO-LSTM, the maximum relative errors of Model A and Model B in the inverse results are 2.05% and 2.09%, and the maximum standard deviations of Model A and Model B in the inverse results are 1.23 and 3.87, respectively. The test results of Model A show that the inversion performance of PSO-LSTM is better than those of LSTM and PSO, and the performance of the network can be improved after PSO is used to optimize the network parameters. The inverse results from Model B show that the PSO-LSTM is robust and can invert the dispersion curves well even after adding noise to the model. Finally, the PSO-LSTM is used to invert the actual data from Wyoming, USA, which demonstrates that the PSO-LSTM can be used for the quantitative interpretation of Rayleigh wave dispersion curves., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Yu Fu et al.)

Accurate image feature point detection and matching are essential to computer vision tasks such as panoramic image stitching and 3D reconstruction. However, ordinary feature point approaches cannot be directly applied to fisheye images due to their large distortion, which makes the ordinary camera model unable to adapt. To address such a problem, this paper proposes a self-supervised learning method for feature point detection and matching on fisheye images. This method utilizes a Siamese network to automatically learn the correspondence of feature points across transformed image pairs to avoid high annotation costs. Due to the scarcity of the fisheye image dataset, a two-stage viewpoint transform pipeline is also adopted for image augmentation to increase the data variety. Furthermore, this method adopts both deformable convolution and contrastive learning loss to improve the feature extraction and description of distorted image regions. Compared with traditional feature point detectors and matchers, this method has been demonstrated with superior performance on fisheye images., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Wei Tian et al.)

As a key technology for highly reliable communication in the fifth generation mobile communication for railway (5G-R) high-speed railway wireless communication system, once the handover fails, it will pose a serious risk to the safe operation of high-speed railway. As the speed of high-speed trains continues to increase, the handover will become more frequent, and how to improve the success rate of the handover is a key problem that needs to be solved. In this paper, we proposed an optimization algorithm based on the interval type 2 feature selection recurrent fuzzy neural network (T2RFS-FNN), which is a recurrent fuzzy neural network with interval type 2 feature selection, to address the problem of fixed hysteresis threshold and single consideration for the handover algorithm between the control plane and the user plane of the high-speed railway under 5G-R. The algorithm integrates reference signal receiving power (RSRP). Reference signal receiving quality (RSRQ) and throughput to optimise the hysteresis threshold. First, a feedforward neural network structure is designed to implement fuzzy logic inference, and an interval type-two Gaussian subordination function is used to improve the nonlinear expressiveness of the model. Then, a feature selection layer is added to determine the output of the affiliation function, which completes the optimization of the hysteresis threshold and overcomes the drawback of the fixed hysteresis threshold of the handover algorithm. Finally, simulation analysis of the control-plane and user-plane handover algorithms is carried out separately. The results show that the proposed method can effectively improve the success rate and reduce the ping-pong handover rate compared to the comparison algorithms. The results provide a theoretical reference for the speedup of high-speed railway trains and the evolution of the global system for mobile communications for railway (GSM-R) to 5G-R., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Yong Chen et al.)

This paper investigates the theoretical bound to reduce the parameter uncertainty in Bayesian adaptive estimation for psychometric functions and proposes an exploration-exploitation (E-E) approach to improve the computation efficiency for parameter estimations. When the experimental trial goes on, the uncertainty of the parameters decreases dramatically and the space between the maximal mutual information and the theoretical bound gets narrower, so the advantage of classical Bayesian adaptive estimation algorithm diminishes. This approach tries to trade off the exploration (parameter posterior uncertainty) and the exploitation (parameter mean estimation). The experimental results show that the proposed E-E approach estimates parameters for psychometric functions with same convergence and reduces the computation time by more than 34.27%, compared with the classical Bayesian adaptive estimation., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Mingyao Li and Juanping Zhu.)

The traditional Deep Deterministic Policy Gradient (DDPG) algorithm has been widely used in continuous action spaces, but it still suffers from the problems of easily falling into local optima and large error fluctuations. Aiming at these deficiencies, this paper proposes a dual-actor-dual-critic DDPG algorithm (DN-DDPG). First, on the basis of the original actor-critic network architecture of the algorithm, a critic network is added to assist the training, and the smallest Q value of the two critic networks is taken as the estimated value of the action in each update. Reduce the probability of local optimal phenomenon; then, introduce the idea of dual-actor network to alleviate the underestimation of value generated by dual-evaluator network, and select the action with the greatest value in the two-actor networks to update to stabilize the training of the algorithm process. Finally, the improved method is validated on four continuous action tasks provided by MuJoCo, and the results show that the improved method can reduce the fluctuation range of error and improve the cumulative return compared with the classical algorithm., Competing Interests: The authors declare that there are no conflicts of interest to report regarding the present study., (Copyright © 2022 Haifei Zhang et al.)

Image sentiment recognition has attracted considerable attention from academia and industry due to the increasing tendency of expressing opinions via images and videos online. Previous studies focus on multilevel representation from global and local views to improve recognition performance. However, it is insufficient to research the importance and relationship of visual regions for image sentiment recognition. This paper proposes an attention-based sentiment region importance and relationship (ASRIR) analysis method, including important attention and relation attention for image sentiment recognition. First, we extract spatial region features using a multilevel pyramid network from the image. Second, we design important attention to exploring the sentiment semantic-related regions and relation attention to investigating the relationship between regions. In order to release the excessive concentration of attention, we employ a unimodal function as the objective function for regularization. Finally, the region features weighted by the attention mechanism are fused and input into a fully connected layer for classification. Extensive experiments on various commonly used image sentiment datasets demonstrate that our proposed method outperforms the state-of-the-art approaches., Competing Interests: The authors declare that they have no conflicts of interest or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Shanliang Yang et al.)

Digitalizing and translating a scanned document image entails detecting the characters using a detector and translating the characters in the order they were detected with a translator. However, it is impossible to translate these characters correctly because the detector often detects them in any order. As a result, since it is critical to organize the recognized characters for proper translation, we propose ordering characters from documents with multiple variations using the strength of the learning-based model that learns the necessary operations from the data. In this task, it is difficult to order the characters written on antique handwritten documents that have deviations such as a bent or split line, as opposed to official records that have lines placed uprightly one by one. Because dealing with these many variants using a human-designed algorithm is problematic, we arrange characters printed on papers with diverse variations by taking advantage of a training model that can learn the appropriate function from data. Our method outputs both line id and y -axis and combines them to assign the sequential index. It is difficult to train using simply local regions because sequential character indexes in a large range include long-range dependencies. To solve this problem, we use network architecture to expand the receptive field as wide as possible. The network must learn to give various indexes to characters in similar places for each document because the number and area of characters vary for each document. We offer the ground truth assign method based on the absolute position to assign similar indexes to characters in similar places. Furthermore, even if the network uses absolute ground truth, the network may assign the incorrect line if the center coordinates of characters are biased in one direction. As a result, we employed the Region of Interest (ROI) from the pretrained coordinate layer, which contains position and trend information. We used the modified edit distance to compare the similarity of character indexes from the ground truth and our technique. In addition, we computed the modified fisher criterion to assess the degree of the clustering line. Consequently, our edit distance is just 0.43 times that of the human-designed algorithm, and our fisher criterion is 1.46 times that of the human-designed algorithm, improving the performance of human-designed algorithm., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Hyeonjin Lee et al.)

In this paper, the regulation stability problem of the human arm continuous movement is investigated based on Markovian jumping parameters. In particular, the intermittent control mechanism is adopted in the arm movement regulation procedure to model the human intermittent motor control strategy. Furthermore, by taking into account the Markovian jumping parameters with different modes, the asynchronous regulation issue is proposed to model mode mismatch between the motor control and arm movement. On the basis of model transformation, sufficient stability conditions are established during the arm movements, and the desired regulation gain can be obtained by the convex optimization method. In the end, an illustrative example is presented to show the applicability and effectiveness of our developed model and optimized regulation approach., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2022 Chao Ma and Wei Wu.)

Aiming at the 1vs1 confrontation problem in a complex environment where obstacles are randomly distributed, the DDPG (deep deterministic policy gradient) algorithm is used to design the maneuver decision-making method of UAVs. Traditional methods generally assume that all obstacles are known globally. In this paper, a UAV airborne lidar detection model is designed, which can effectively solve the problem of obstacle avoidance when facing a large number of unknown obstacles. On the basis of the designed model, the idea of transfer learning is used to transfer the strategy trained by one UAV in a simple task to a new similar task, and the strategy will be used to train the strategy of the other UAV. This method can improve the intelligence of the UAVs in both sides alternately and progressively. The simulation results show that the transfer learning method can speed up the training process and improve the training effect., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 Jindong Zhu et al.)

Aiming at the difficulty of feature extraction in the case of multicomponent and strong noise in the traditional rolling bearing fault diagnosis method, this paper proposes a bearing fault diagnosis network with double attention mechanism. The original signal with noise is decomposed into a series of intrinsic mode functions (IMFs) by the Empirical Mode Decomposition method. The Pearson correlation coefficient is discussed to filter the IMFs components for signal reconstruction. The spatial features of the reconstructed signal are extracted by attention convolutional networks. Then, time series features are extracted based on the long short-term memory method. Furthermore, the importance of temporal features is measured through a temporal attention mechanism. The Softmax layer of the constructed network is used as the classifier for fault diagnosis. Comparing this method with the existing methods of experiments, the proposed method has not only better diagnosis accuracy but also stronger antiinterference ability and generalization ability, which can accurately diagnose and classify the bearing fault types. The fault diagnosis accuracy rate for each load is above 99%., Competing Interests: On behalf of all authors, the corresponding authors state that there are no conflicts of interest., (Copyright © 2022 QingE Wu et al.)

Traffic sign detection is a challenging problem in the field of unmanned driving, particularly important in complex environments. We propose a method, based on the improved You only look once (YOLO) v4, to detect and recognize multiscale traffic signs in complex environments. This method employs an image preprocessing module that can classify and denoize images of complex environments and then input the images into the improved YOLOv4. We also design an improved feature pyramid structure to replace the original feature pyramid of YOLOv4. This structure uses an adaptive feature fusion module and a multiscale feature transfer mechanism to reduce putative information loss in the feature map generation process and improve the information transfer between deep and shallow features, enhancing the representation ability of feature pyramids. Finally, we use EIOU LOSS and Cluster-NMS to further improve the model performance. The experimental results on the fusion of Tsinghua-Tencent 100 K and our collected dataset show that the proposed method achieves an mAP of 81.78%. Compared to existing methods, our method demonstrates its superiority with regard to traffic sign detection., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Yongjie Wang et al.)

0-1 Knapsack problem (KP) is NP-hard. Approximate solution is vital for solving KP exactly. In this paper, a fast polynomial time approximate solution (FPTAS) is proposed for KP. FPTAS is a local search algorithm. The best approximate solution to KP can be found in the neighborhood of the solution of upper bound for exact k -item knapsack problem (E- k KP) where k is near to the critical item s . FPTAS, in practice, often achieves high accuracy with high speed in solving KP. The computational experiments show that the approximate algorithm for KP is valid., Competing Interests: The authors declare that they have no known conflicts financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Zhengyuan Wang et al.)

With the development of neuromorphic computing, more and more attention has been paid to a brain-inspired spiking neural network (SNN) because of its ultralow energy consumption and high-performance spatiotemporal information processing. Due to the discontinuity of the spiking neuronal activation function, it is still a difficult problem to train brain-inspired deep SNN directly, so SNN has not yet shown performance comparable to that of an artificial neural network. For this reason, the spike-based approximate backpropagation (SABP) algorithm and a general brain-inspired SNN framework are proposed in this paper. The combination of the two can be used for end-to-end direct training of brain-inspired deep SNN. Experiments show that compared with other spike-based methods of directly training SNN, the classification accuracy of this method is close to the best results on MNIST and CIFAR-10 datasets and achieves the best classification accuracy on sonar image target classification (SITC) of small sample datasets. Further analysis shows that compared with artificial neural networks, our brain-inspired SNN has great advantages in computational complexity and energy consumption in sonar target classification., Competing Interests: The authors declare that they have no conflicts of interests., (Copyright © 2022 Yang Liu et al.)

Convolutional neural network (CNN) training often necessitates a considerable amount of computational resources. In recent years, several studies have proposed for CNN inference and training accelerators in which the FPGAs have previously demonstrated good performance and energy efficiency. To speed up the processing, CNN requires additional computational resources such as memory bandwidth, a FPGA platform resource usage, time, power consumption, and large datasets for training. They are constrained by the requirement for improved hardware acceleration to support scalability beyond existing data and model sizes. This paper proposes a procedure for energy efficient CNN training in collaboration with an FPGA-based accelerator. We employed optimizations such as quantization, which is a common model compression technique, to speed up the CNN training process. Additionally, a gradient accumulation buffer is used to ensure maximum operating efficiency while maintaining gradient descent of the learning algorithm. To validate the design, we implemented the AlexNet and VGG-16 models on an FPGA board and laptop CPU along side GPU. It achieves 203.75 GOPS on Terasic DE1 SoC with the AlexNet model and 196.50 GOPS with the VGG-16 model on Terasic DE-SoC. Our result also exhibits that the FPGA accelerators are more energy efficient than other platforms., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Guta Tesema Tufa et al.)

Balancing machine is a general equipment for dynamic balance verification of rotating parts, whether it breaks down or does not determine the accuracy of dynamic balance verification. In order to solve the problem of insufficient fault diagnosis accuracy of balancing machine, a fault diagnosis method of balancing machine based on the Improved Sparrow Search Algorithm (ISSA) optimized Extreme Learning Machine (ELM) was proposed. Firstly, iterative chaos mapping and Fuch chaos mapping were introduced to initialize the population and increase the population diversity. Secondly, the adaptive dynamic factor and Levy flight strategy were also introduced to update the individual positions and improve the model convergence speed. Finally, the fault feature vector was input to the ISSA-ELM model with the fault type as the output. The experiment showed that the fault diagnosis accuracy of ISSA-ELM is as high as 99.17%, which is 1.67%, 2.50%, 7.50%, and 17.50% higher than that of SSA-ELM, HHO-ELM, PSO-ELM, and ELM, respectively, further improving the prediction accuracy of the operation state of the balancing machine., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Lei Li et al.)

This paper presents a new approach to solve multi-objective decision-making (DM) problems based on neural networks (NN). The utility evaluation function is estimated using the proposed group method of data handling (GMDH) NN. A series of training data is obtained based on a limited number of initial solutions to train the NN. The NN parameters are adjusted based on the error propagation training method and unscented Kalman filter (UKF). The designed DM is used in solving the practical problem, showing that the proposed method is very effective and gives favorable results, under limited fuzzy data. Also, the results of the proposed method are compared with some similar methods., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Xiaofeng Hong et al.)

Feature selection provides the optimal subset of features for data mining models. However, current feature selection methods for high-dimensional data also require a better balance between feature subset quality and computational cost. In this paper, an efficient hybrid feature selection method (HFIA) based on artificial immune algorithm optimization is proposed to solve the feature selection problem of high-dimensional data. The algorithm combines filter algorithms and improves clone selection algorithms to explore the feature space of high-dimensional data. According to the target requirements of feature selection, combined with biological research results, this method introduces the lethal mutation mechanism and the Cauchy operator to improve the search performance of the algorithm. Moreover, the adaptive adjustment factor is introduced in the mutation and update phases of the algorithm. The effective combination of these mechanisms enables the algorithm to obtain a better search ability and lower computational costs. Experimental comparisons with 19 state-of-the-art feature selection methods are conducted on 25 high-dimensional benchmark datasets. The results show that the feature reduction rate for all datasets is above 99%, and the performance improvement for the classifier is between 5% and 48.33%. Compared with the five classical filtering feature selection methods, the computational cost of HFIA is lower than the two of them, and it is far better than these five algorithms in terms of the feature reduction rate and classification accuracy improvement. Compared with the 14 hybrid feature selection methods reported in the latest literature, the average winning rates in terms of classification accuracy, feature reduction rate, and computational cost are 85.83%, 88.33%, and 96.67%, respectively., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Yongbin Zhu et al.)

An improved Ghost-YOLOv5s detection algorithm is proposed in this paper to solve the problems of high computational load and undesirable recognition rate in the traditional detection methods of pavement diseases. Ghost modules and C3Ghost are introduced into the YOLOv5s network to reduce the FLOPs (floating-point operations) in the feature channel fusion process. Mosaic data augmentation is also added to improve the feature expression performance. A public road disease dataset is reconstructed to verify the performance of the proposed method. The proposed model is trained and deployed to NVIDIA Jetson Nano for the experiment, and the results show that the average accuracy of the proposed model reaches 88.17%, increased by 4.01%, and the model FPS (frames per second) reaches 12.51, increased by 184% compared with the existing YOLOv5s. Case studies show that the proposed method satisfies the practical application requirements of pavement disease detection., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Yinze Chu et al.)

At present, the image inpainting method based on deep learning has achieved a better inpainting effect than traditional methods, but the inpainting results still have problems such as local structure disorder and blurred texture when the images involving a large defect area are processed. This paper proposes a second-order generative image inpainting model based on edge and feature self-arrangement constraints. The model consists of two parts: edge repair network and image repair network. Based on the self-encoder, the edge repair network generates the edges in the defect area according to the known information of the image and improves the edge repair effect by minimizing the adversarial loss and feature matching loss. The image inpainting network fills the defect area with the edge repair result as a priori condition. On the basis of U-Net, the feature self-arrangement module (FSM) is proposed to reconstruct the coding features of a specific scale, and the reconstructed feature skips to connect the decoding layer of the same scale, and it is fused with the upper layer underlying features for decoding. Meanwhile, guide loss, adversarial loss, and reconstruction loss are introduced to narrow the difference between the repaired image and the original image. The experimental results show that the inpainting results of the proposed model have stronger structural connectivity and clearer textures and the performance of PSNR, SSIM, and mean L1 loss in the Celeba, Facade, and Places2 is better than other inpainting methods, indicating that the algorithm can produce an inpainting effect with highly connected structure, reasonable semantics, and fine details., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 Fan Yao and Yanli Chu.)

The rationality and timeliness of the comprehensive results of English course learning quality are increasingly important in the process of modern education. There are some problems in the scientific evaluation of English course learning quality and teachers' own English course learning, such as the need for proper adjustment and improvement. Based on the improved network theory of genetic algorithm, this paper takes an online English course learning quality evaluation model and uses MATLAB 7.0 to write the graphical user interface of the neural set network English course learning quality prediction model. The model uses the genetic algorithm of adaptive mutation to optimize the initial weights and values of the neural set network and solves the problems of prediction accuracy and convergence speed of English course learning quality evaluation results. Simulation experiments show that the neural set network has a strong dependence on the initial weights and thresholds. Using the improved genetic algorithm to optimize the initial weights and thresholds of the neural set network reduced the time for the neural set network to find the weights and thresholds that meet the training termination conditions, the prediction accuracy was increased to 0.897, the prediction accuracy was 78.85%, and the level prediction accuracy was 84.62%, which effectively promoted the development of online English course learning in colleges and the continuous improvement of teachers' English course learning level., Competing Interests: The author declares that there are no conflicts of interest or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Qiuping Song.)

Remote-sensing image scene data contain a large number of scene images with different scales. Traditional scene classification algorithms based on convolutional neural networks are difficult to extract complex spatial distribution and texture information in images, resulting in poor classification results. In response to the above problems, we introduce the vision transformer network structure with strong global modeling ability into the remote-sensing image scene classification task. In this paper, the parallel network structure of the local-window self-attention mechanism and the equivalent large convolution kernel is used to realize the spatial-channel modeling of the network so that the network has better local and global feature extraction performance. Experiments on the RSSCN7 dataset and the WHU-RS19 dataset show that the proposed network can improve the accuracy of scene classification. At the same time, the effectiveness of the network structure in remote-sensing image classification tasks is verified through ablation experiments, confusion matrix, and heat map results comparison., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Wei Wang et al.)

The existing interpretation models for the time spectrum of impulse neutron oxygen activation require interpreters to select the peak range or background range manually from the time spectrum curve, and there is no adaptive interpretation model that can determine the peak range or background range. In this paper, an adaptive selection rule for background segment is proposed, and a semiautomatic interpretation model is constructed by combining background segment interpretation model. Firstly, the interpretation operator selects the time spectrum curve, then the algorithm program adaptively determines the background segment according to the rules, and then calculates and displays the transit time and volume flow according to the background segment interpretation model. The processing results of the measured data show that the interpretation model in this paper not only retains the interpretation precision of the background interpretation model, but also reduces the labor intensity of the interpretation operator, realizing the semiautomatic interpretation of the time spectrum., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Yong Dong et al.)

With the continuous improvement of the network hardware environment, people turn the demand target to the network application environment and the construction of information resources. How to build a network teaching platform for general undergraduate teaching to ensure the stability of the system and high-quality services during operation especially large-scale concurrent access will inevitably lead to the increase in the business volume of each core part of the network, the number of visits, and data traffic. With the growth of the network, the corresponding processing power and computing intensity also increase rapidly, which causes problems such as overloading of core network equipment, network bottlenecks, and network congestion. Simply pursuing high-performance hardware to solve problems will undoubtedly result in high cost investment; moreover, equipment with excellent performance cannot meet the needs of the current rapidly growing business volume. According to the design goal of the dance online teaching platform, to meet the online teaching load requirements of many people at the same time, the pressure of the web server cluster must be great. Because many people in online at the same time put too much pressure on the web server, this part of the network cannot be processed in time, which leads to the phenomenon that the performance of this part and even the whole network is degraded. In severe cases, it will even cause network communication services to come to a standstill, that is, the so-called deadlock phenomenon. If the protocol software cannot detect congestion and reduce the packet sending rate, the network will be paralyzed due to congestion. This situation will cause the problem of movement delay for online dance teaching, which will seriously affect the quality of teaching. Therefore, the dance online course system should be continuously improved, the quality of online courses should be continuously improved, and the study of the practical application of load balancing technology in the network teaching environment has become an important means to solve the relationship between supply and demand of network teaching. According to the experimental analysis, when the number of Worker' actuators is fixed, the execution time span of MakeSpan increases with the increase of tasks, while the time required by the optimized load balancing algorithm proposed in this paper increases by 1.32 s on average with the increase of tasks, and the time required by heuristic algorithm and bee colony algorithm increases by 3.68 s and 3.45 s on average with the increase of tasks. On the whole, the optimized load balancing algorithm proposed in this paper has obvious advantages., Competing Interests: The author declares that there are no conflicts of interest., (Copyright © 2022 Qirong Yang.)

In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure are analyzed, and both have positive effects on knowledge sharing in the actual development process, but the degree of them also possesses certain variability. A model of the factors influencing the quality of knowledge shared by users is constructed to explore the relationship between the dimensions in social capital and how they affect the community users' perceptions of knowledge quality. Exploring the strong influencing factors of product repurchase rate has key implications for promoting product sales and sales forecasting. The scale of this paper has undergone several minor revisions, and the content validity is very good. Criterion validity is generally reflected by Person correlation coefficient, and construct validity includes exploratory factor analysis and confirmatory factor analysis. First, the values were clustered, and the optimal variables were selected using stepwise regression and fitted with Poisson regression models to explore the relationship between the repurchase rate of different products and the factors that strongly influence the repurchase rate in the case. To predict the sales of goods, the advantages of the BP neural network, LSTM neural network, and Verhulst gray model are combined: BP neural network can predict sales by combining the current data corresponding to independent variables, LSTM neural network can explore the influence of historical data on sales, and Verhulst model can predict sales based on the growth trend of variables. A BP-LSTM-Verhulst nested neural network based on the AP algorithm is constructed to predict sales volume, and the accuracy of this method is proved by example. Finally, it is found that the proposed sales prediction method has higher accuracy than exponential regression and shallow neural networks. The deep learning prediction method combining unstructured data such as images proposed in the paper not only provides a more accurate sales prediction method for short life cycle products in e-commerce but also provides an effective deep learning method for management practices. The KMO value obtained after the test is less than 0.5, which is not suitable for factor analysis. Using SPSS22.0 to expand the KMO value and Bartlett's sphericity test, the KMO value is significantly greater than the minimum requirement of 0.5, the KMO value of self-efficacy is 0.826, and the KMO value of expected return is 0.870., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Li Tian and Xiumei Wang.)

The reptile search algorithm (RSA) is a swarm-based metaheuristic algorithm inspired by the encirclement and hunt mechanisms of crocodiles. Compared with other algorithms, RSA is competitive but still suffers from low population diversity, unbalanced exploitation and exploration, and the tendency to fall into local optima. To overcome these shortcomings, a modified variant of RSA, named MRSA, is proposed in this paper. First, an adaptive chaotic reverse learning strategy is employed to enhance the population diversity. Second, an elite alternative pooling strategy is proposed to balance exploitation and exploration. Finally, a shifted distribution estimation strategy is used to correct the evolutionary direction and improve the algorithm performance. Subsequently, the superiority of MRSA is verified using 23 benchmark functions, IEEE CEC2017 benchmark functions, and robot path planning problems. The Friedman test, the Wilcoxon signed-rank test, and simulation results show that the proposed MRSA outperforms other comparative algorithms in terms of convergence accuracy, convergence speed, and stability., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Qihang Yuan et al.)

Feature selection is an important way to optimize the efficiency and accuracy of classifiers. However, traditional feature selection methods cannot work with many kinds of data in the real world, such as multi-label data. To overcome this challenge, multi-label feature selection is developed. Multi-label feature selection plays an irreplaceable role in pattern recognition and data mining. This process can improve the efficiency and accuracy of multi-label classification. However, traditional multi-label feature selection based on mutual information does not fully consider the effect of redundancy among labels. The deficiency may lead to repeated computing of mutual information and leave room to enhance the accuracy of multi-label feature selection. To deal with this challenge, this paper proposed a multi-label feature selection based on conditional mutual information among labels (CRMIL). Firstly, we analyze how to reduce the redundancy among features based on existing papers. Secondly, we propose a new approach to diminish the redundancy among labels. This method takes label sets as conditions to calculate the relevance between features and labels. This approach can weaken the impact of the redundancy among labels on feature selection results. Finally, we analyze this algorithm and balance the effects of relevance and redundancy on the evaluation function. For testing CRMIL, we compare it with the other eight multi-label feature selection algorithms on ten datasets and use four evaluation criteria to examine the results. Experimental results illustrate that CRMIL performs better than other existing algorithms., Competing Interests: The author declares no conflicts of interest., (Copyright © 2022 Xiujuan Wang and Yuchen Zhou.)

Considering that in the process of job scheduling, the cluster load should be prebalanced rather than remedied when the load is seriously unbalanced; therefore, in this paper, the task scheduling flow of the Hadoop cluster is analyzed deeply. On the Hadoop platform, a self-dividing algorithm is proposed for load balancing. An intelligent optimization algorithm is used to solve load balance. A dynamic feedback load balancing scheduling method is proposed from the point of view of task scheduling. In order to solve the shortcoming of the fair scheduling algorithm, this paper proposes two ways to improve the resource utilization and overall performance of Hadoop. When the mapping task is completed and the tasks to be reduced are assigned, the task assignment is based on the performance of the nodes to be reduced. It gives full play to the advantages of the ant colony algorithm and the hive colony algorithm so that the fusion algorithm can better deal with load balance. Then, three existing scheduling algorithms are introduced in detail: single queue scheduling, capacity scheduling, and fair scheduling. On this basis, an improved task scheduling strategy based on genetic algorithm is proposed to allocate and execute application tasks to reduce task completion time. The experiment verifies the effectiveness of the algorithm. The LBNP algorithm greatly improves the efficiency of reducing task execution and job execution. The delay capacity scheduling algorithm can ensure that most tasks can achieve localization scheduling, improve resource utilization, improve load balance, and speed up job completion time., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Weiyu Fu and Lixia Wang.)

The development of big data technology makes the feature selection technology gradually perfect. The advantages of different feature selection technologies are different. Among them, random forest algorithm belongs to integrated feature selection algorithm. Integrated processing of classification results can screen out the most representative feature impact. Based on this background and random forest algorithm, this paper analyzes the evaluation of motion effect. After the measurement, this paper obtains the body data before and after the training. After the calculation, the change data of the body index are determined. The random forest feature selection method is used as the carrier to determine the corresponding index attribute set. In the process of data set input, the corresponding whole input data set is formed through data classification. The completion of training, through the comparative experiment, is conducive to clear the degree of influence of physical indicators and then complete the exercise effect evaluation. The research shows that the random forest algorithm has significant advantages in the evaluation of sports effect and can effectively improve the accuracy of classification., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Jin Dong and Dengxiao Dong.)

Clustering analysis is an unsupervised learning method, which has applications across many fields such as pattern recognition, machine learning, information security, and image segmentation. The density-based method, as one of the various clustering algorithms, has achieved good performance. However, it works poor in dealing with multidensity and complex-shaped datasets. Moreover, the result of this method depends heavily on the parameters we input. Thus, we propose a novel clustering algorithm (called the MST-DC) in this paper, which is based on the density core. Firstly, we employ the reverse nearest neighbors to extract core objects. Secondly, we use the minimum spanning tree algorithm to cluster the core objects. Finally, the remaining objects are assigned to the cluster to which their nearest core object belongs. The experimental results on several synthetic and real-world datasets show the superiority of the MST-DC to Kmeans, DBSCAN, DPC, DCore, SNNDPC, and LDP-MST., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Qiang Gao et al.)

The more frequent occurrence of network security incidents has an impact on network security. Through the research on network security situational awareness, this paper constructs a multilevel network security situation evaluation index system from various aspects and uses the Elman neural network optimized by the genetic algorithm to evaluate network security situation. Aiming at the disadvantage of subjective dependence in the traditional assignment method of basic probability assignment function, Elman neural network is used to obtain the basic probability assignment function to increase its objectivity, and it is optimized with the PSD algorithm. In addition, the neural network is further improved by the genetic algorithm. In the traditional D-S evidence theory, an evidence correction step is added to optimize the situation that the final judgment result is incorrect due to evidence conflict. Finally, the fusion rules of the D-S evidence theory are used to fuse the support degrees of the four first-level situations to different security levels to obtain the final network security situation assessment result. The results show that the prediction accuracy of the GA-Elman neural network model is as high as 80%, which is significantly higher than that of the traditional D-S model, indicating that the model proposed in this paper has improved the accuracy of the assessment and prediction results. In conclusion, this study provides feasible theoretical prediction guidance for the accurate assessment of network security posture, reveals the improvement ideas for network security development, and is of great significance for the maintenance of network environment security., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Jianxun Li et al.)

Big data has the traits such as "the curse of dimensionality," high storage cost, and heavy computation burden. Self-representation-based feature extraction methods cannot effectively deal with the image-level structural noise in the data, so how to character a better relationship of reconstruction representation is very important. Recently, sparse representation with smoothed matrix multivariate elliptical distribution (SMED) using structural information to handle low-rank error images caused by illumination or occlusion has been proposed. Based on SMED, we present a new method named SMEDP for feature extraction. SMEDP firstly utilizes SMED to automatically construct an adjacency graph and then obtains an optimal projection matrix by maximizing the ratio of the local scatter matrix and the total scatter matrix in the PCA subspace. Experiments on the COIL-20 object database, ORL face database, and CMU PIE face database prove that SMEDP works well and can achieve considerable visual and recognition performance than the relevant methods., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Hong Qiu et al.)

This paper proposes a performance evaluation method of public administration departments based on the improved DEA algorithm, which solves the quality problem of performance evaluation of public administration departments and lays a foundation for the long-term development and performance improvement of public administration departments. The DEA model is the core to sort out the performance evaluation methods of public administration departments. From the perspective of DEA algorithm, whether DEA is effective is an important content that public administration departments must consider when carrying out performance administration, and public service satisfaction is the core parameter obtained by DEA algorithm. That is to say, by optimizing public services and improving service satisfaction, the public administration department can further improve the quality of performance evaluation. Therefore, we can carry out performance evaluation of public administration departments from the satisfaction of public services. The empirical research conclusion shows that according to the effective judgment theorem of DEA, it can be concluded that among the eight social security departments, there are five departments that can achieve DEA effectiveness, namely department 2, department 3, department 4, department 5, and department 6. There are three non-DEA valid ones, namely, department 1, department 7 and department 8. The public satisfaction and the total service cost will affect the performance quality to a certain extent, and only by balancing various influencing factors can the performance evaluation quality of the public administration department be maximized., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Qing Guan et al.)

This paper combines principal component analysis, a BP neural network, and a simulated annealing algorithm, to construct a PCA-SA-BPNN risk forecast model to evaluate and predict the RMB internationalization risk status of China. First, we analyze the risk of RMB internationalization and its transmission mechanism from the perspective of the economic characteristics of neighboring countries and trading partner countries. Second, we use the FASP index system construction method for reference to construct a forecast index system for macro- and microrisks brought about by RMB internationalization. Then, the weight of each index is determined through index common degree analysis and principal component analysis, and the risk of RMB internationalization is divided. On this basis, the risks of RMB internationalization in China from 2000 to 2019 are divided into four categories. Based on the BP neural network algorithm optimized by the simulated degradation algorithm, the PCA-SA-BPNN model of RMB internationalization risk forecast is constructed. Finally, the validity of the model is verified by experimental verification, and the risk status of RMB internationalization in 2020 is simulated and predicted. The research results show that the risk status of RMB internationalization in 2020 is basically safe, and the risks of RMB internationalization mainly come from macroeconomic growth risks and systemic risks of the financial system., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Shili Hu and Jie Du.)

This paper investigates the bifurcation issue of fractional-order four-neuron recurrent neural network with multiple delays. First, the stability and Hopf bifurcation of the system are studied by analyzing the associated characteristic equations. It is shown that the dynamics of delayed fractional-order neural networks not only depend heavily on the communication delay but also significantly affects the applications with different delays. Second, we numerically demonstrate the effect of the order on the Hopf bifurcation. Two numerical examples illustrate the validity of the theoretical results at the end., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Yu Fei et al.)

Under the modern environment, the reconstruction of enterprise's core competitiveness depends not only on capital and technical strength, but also on the overall strength of its human resources. At the same time, effective allocation and rational use of talents are needed to create good performance for enterprises. Enterprise human resource management is the key part of the whole enterprise management. At the same time, it is also a necessary preparation for the continuous development and innovation of enterprises. In the whole process of human resource management, the core work is person-post matching. Only by promoting the reasonable implementation of person-post matching can other management work be carried out smoothly. This paper expounds two major elements in human resource management, namely, the concept and measurement of person-post matching and the principle of person-post matching. And the factors in the matching of people and posts are analyzed. This paper probes into the implementation of person-post matching in enterprise human resource management. Based on this, this paper puts forward a depth model of accurate matching between people and posts based on ability perception. On the basis of studying the optimization of human resource scheduling, this paper takes into account three factors: resource constraints, heterogeneity of employee efficiency and time sequence relationship, and uses integer linear programming theory to model the system with the shortest construction period as the goal. The research shows that the accuracy of this algorithm can reach about 94%, which is about 8% higher than the traditional algorithm. It has certain superior performance. This will provide some reference for related researchers., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Shaoze Zhang.)