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Traditional short‐term traffic volume forecasting approaches make it difficult to predict the highly spatiotemporally coupled short‐time traffic. To tackle the problem, this paper first proposes a variational modal algorithm (GWO‐VMD) based on the optimization of the gray wolf search algorithm. It aims to decompose and reduce the noise of short‐time traffic flows. Meanwhile, it reduces the intricacy of data sequences and enhances the regularity pattern. To address the insufficient utilization of spatiotemporal features, this paper presents an innovative deep‐learning traffic prediction framework based on the stacking of multiple temporal trend‐aware graph attention (TGA) layers and gated temporal convolution (GTC) layers, which are called trend‐aware temporal graph neural network (TTGAN). TGA dynamically models the space‐time relationships of traffic data, and GTC models the temporal characteristics of traffic data. The experimental findings demonstrate that the MAPE model, as presented, achieves a reduction of 9% and 2% compared to the AGCRN and GWNET models, respectively, in the domain of deep spatiotemporal graph modeling. Data decomposition and noise reduction are necessary to achieve accurate results. This model has superior performance in terms of mean absolute error (MAE), coefficient of determination (R2), and explained variance score (EVAR). [ABSTRACT FROM AUTHOR]

Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms. [ABSTRACT FROM AUTHOR]

In recent years, commercial banks and rural financial institutions in and below the county area have developed rapidly, and the number of outlets has increased rapidly. But for the vast rural areas of the country, the number of outlets is still limited. At present, there are still a large number of areas with zero financial coverage in rural the country. The development of the financial industry in a region not only affects the level of local economic growth but also has a good effect on improving the income level of local residents and changing the way of local economic development. To better develop rural finance and county economy, this paper constructs an artificial neural network model and FA model to predict the rural finance industry, and four indicators are selected to test the performance measurement of the model. The hit rates of the five models are 54.65%, 53.75%, 59.78%, 65.1%, and 73.08%, respectively. The model proposed in this paper has the highest hit rate and can predict rural finance more accurately as well, allowing for clearer changes in county economic and industrial growth. [ABSTRACT FROM AUTHOR]

In recent years, with changing conditions in global markets and competition between supply chains, organizations have realized that in order to grow and survive in today's turbulent environment, they must make every effort to create and maintain a sustainable competitive advantage. Therefore, in this research, a multiobjective, multilevel, multiperiod, and multicommodity mathematical model is presented. The proposed model has five levels in the direct direction and three levels in the reverse direction. This research is one of the few studies that has considered the sustainable closed-loop supply chain of cellulosic-based products (paper and corrugated board) in the model and has considered it at all levels of the chain. Also, the quality of returned products in the reverse supply chain and its impact on the collection and recycling of products and returning to the supply chain are other contributions of this research. The results show, by increasing the transportation costs, the number of established distribution centers increases to a certain extent and finally remains constant. As it is shown, the reduction of transportation costs to the point of establishing four centers in the recession scenario and five centers in the boom scenario becomes saturated and is no longer able to minimize the number of established centers. [ABSTRACT FROM AUTHOR]

Rural tourism has become an important force in implementing the rural revitalisation strategy and accelerating rural economic development. The hectic pace of life has made more and more city dwellers yearn for rural life, and travelling in the countryside has become their weekend choice. However, the current level of rural tourism informationization is low, the publicity is insufficient, the tourists' awareness is low, and the source of customers is seriously insufficient. To this end, this paper proposes a relatively novel multidata source fusion tourism recommendation algorithm, which adopts the idea of tensor orthogonal decomposition and fuses multisource data models to predict the target domain's for rating. The integrated consideration of multiple data sources under the do-it-yourself approach assists the target domain to discover the target user neighbourhood users more quickly and to discover the user's interest degree more accurately. It is worth pointing out that the recommendation algorithm proposed in this paper under the fusion of multiple data sources is not necessarily applicable to data sources with weak correlation, such as travel data sources and music data sources, which are relatively weakly correlated, and the algorithm is slightly weak in making predictions of user preferences. [ABSTRACT FROM AUTHOR]

Obstacle avoidance path planning capability, as one of the key capabilities of UAV (Unmanned Aerial Vehicle) to achieve safe autonomous flight, has always been a hot research topic in UAV research filed. As a commonly used obstacle avoidance path planning algorithm, RRT (Rapid-exploration Random Tree) algorithm can carry out obstacle avoidance path planning in real time and online. In addition, it can obtain the asymptotically optimal obstacle avoidance path on the premise of ensuring the completeness of probability. However, it has some problems, such as high randomness, slow convergence speed, long transit time, and curved flight trajectory, so that it cannot meet the flight conditions of the actual UAV. To solve these problems, the paper proposes an improved RRT algorithm. In the process of extending the random tree, ACO (ant colony optimization) is introduced to make the planning path asymptotically optimal. The optimized algorithm can set pheromones on the path obtained by RRT and select the next extension point according to the pheromone concentration. And then through a certain number of iterations, it converges to an ideal path scheme. In addition, this paper also uses MATLAB to verify the effectiveness and superiority of the algorithm: Although RRT is easy to fall into local optimization, since the optimization method in this paper can almost certainly converge to the optimal solution, when it is necessary to preplan the path before UAV takeoff, it can be used. [ABSTRACT FROM AUTHOR]

The communication recognition of mobile phone core is a test of the development of machine vision. The size of mobile phone core is very small, so it is difficult to identify small defects. Based on the in-depth study of the algorithm, combined with the actual needs of core identification, this paper improves the algorithm and proposes an intelligent algorithm suitable for core identification. In addition, according to the actual needs of core wire recognition, this paper makes an intelligent analysis of the core wire recognition process. In addition, this paper improves the traditional communication image recognition algorithm and analyzes the data of the recognition algorithm according to the shape and image characteristics of the mobile phone core. Finally, after constructing the functional structure of the system model constructed in this paper, the system model is verified and analyzed, and on this basis, the performance of the improved core recognition algorithm proposed in this paper is verified and analyzed. From the results of online monitoring and recognition, the statistical accuracy of mobile phone core video recognition is about 90%, which has higher accuracy in mobile phone core image recognition than traditional recognition algorithms. The core line recognition algorithm based on deep learning and machine vision is effective and has a good practical effect. [ABSTRACT FROM AUTHOR]

This paper makes use of the characteristics of initial sensitivity and randomness of the chaotic map to design an image encryption algorithm based on the sine map and the tent map. The sine map is used to improve the tent map; then, the improved sine-tent map is proposed. The traditional tent map proposed in this paper has an expanded control parameter range and better chaos. In this algorithm, bit rearrangement is adopted to further improve the improved sine-tent map, which can reconstruct the output value and expand the chaotic characteristics of the map. In this algorithm, the image parameters are connected with the algorithm to generate the key. In the encryption step, a method of replacing the most significant bit and scrambling-diffusion algorithm is designed to encrypt the plaintext image. Finally, the algorithm is simulated with the experiment and evaluated with analysis; then, the experimental results are given. The evaluation results show that the ciphertext of the algorithm has high randomness, strong robustness, and better resistance to differential attacks after comparison. The correlation of the ciphertext image pixels is very low, and the algorithm is highly secure as a conclusion. [ABSTRACT FROM AUTHOR]

Cost management is an important part of enterprise management and is fundamental to the survival and development of enterprises. Cost management is of great significance to promote production and cost-saving, strengthen cost accounting, improve management, and enhance the overall management level of enterprises. In this paper, different factors affecting mapping are combined with a group intelligence algorithm to study virtual network mapping, which dissects enterprise cost management in a new perspective and analyzes the relationship between enterprises in each supply chain and the association between the processes of each link in the supply chain. The algorithm uses node CPU, node degree, and neighboring bandwidth resources as advisors' strategies, respectively, and customers consult multiple advisors in the process of constructing mapping solutions, and the advisors give suggestions to customers according to their strategies. CGS-VNE uses an advisor-guided search mechanism to ensure the diversity of mapping solutions, which can fully explore the solution space and derive high-quality enterprise costing solutions through iteration. Whether in evaluating enterprise cost control or enhancing enterprise cost control capability, the value chain theory can be practically applied to clarify the current situation of enterprise cost control under the premise of combining theory and practice and truly achieve the fundamental purpose of helping enterprises to fully implement cost control. [ABSTRACT FROM AUTHOR]

With the research of machine learning technology and big data intelligent processing technology in engineering application becoming more and more mature, people gradually combine machine learning technology and big data intelligent processing technology. Aiming at the problem of innovative employment in colleges and universities, this paper proposes a dynamic decision tree algorithm based on these two technologies and constructs a dynamic model of graduates' behavior. Through the analysis of dynamic decision tree algorithm, a big data analysis system is formed. Finally, simulation experiments verify whether the dynamic model can correctly reflect the behavior of college graduates. The results show that the big data integration system based on big data and dynamic decision tree algorithm has high adaptability. Incremental adaptive optimization of the traditional decision tree model can significantly improve the prediction effect and prediction time of dynamic data and provide theoretical support for the industrialization and social significance of big data technology. The dynamic decision tree algorithm of college employment proposed in this paper has good predictability and provides a certain theoretical reference for college graduates' entrepreneurship. [ABSTRACT FROM AUTHOR]

In the mobile crowdsensing task assignment, under the premise that the data platform does not know the user's perceived quality or cost value, how to establish a suitable user recruitment mechanism is the critical issue that this article needs to solve. It is necessary to learn the user's perceived quality in the execution p. It also needs to try its best to ensure the efficiency and profit maximization of the mobile group intelligence perception platform. Therefore, this paper proposes a mobile crowdsensing user recruitment algorithm based on Combinatorial Multiarmed Bandit (CMAB) to solve the recruitment problem with known and unknown user costs. Firstly, the user recruitment process is modeled as a combined multiarm bandit model. Each rocker arm represents the selection of different users, and the income obtained represents the user's perceived quality. Secondly, it proposes the upper confidence bound (UCB) algorithm, which updates the user's perceptual quality according to the completion of the task. This algorithm sorts the users' perceived quality values from high to low, then selects the most significant ratio of perceived quality to recruitment costs under the budget condition, assigns tasks, and updates their perceived quality. Finally, this paper introduces the regret value to measure the efficiency of the user recruitment algorithm and conducts many experimental simulations based on real data sets to verify the feasibility and effectiveness of the algorithm. The experimental results show that the recruitment algorithm with known user cost is close to the optimal algorithm, and the recruitment algorithm with unknown user cost is more than 75% of the optimal algorithm result, and the gap tends to decrease as the budget cost increases, compared with other comparisons. The algorithm is 25% higher, which proves that the proposed algorithm has good learning ability and can independently select high-quality users to realize task assignments. [ABSTRACT FROM AUTHOR]

HRMS is a very critical tool for companies. The recruitment text contains rich information that can provide strong information support for the company's recruitment work and also improve the efficiency of job seekers in finding job opportunities. To this end, for the problem of multilabel text classification of recruitment information, this paper provides two algorithms for multilayer classification based on supported SVM. First, the same learning subclass method is used for text sorting subclass acquisition, and then, the class of the text is determined. Second, the hemispherical support SVM is used to find the smallest hypersphere in the feature space that contains the most text of that class and segment the text of that class from other texts. For the text to be classified, the distance from it to the center of each hypersphere is used to determine the class of the text. Experimental results on recruitment data demonstrate that the algorithm in this paper has a high check-all rate, check-accuracy rate, and F1. And, the relationship between HRM activities and corporate performance is discussed. [ABSTRACT FROM AUTHOR]

The cultivation of strategic emerging industries is strategic to the economic transformation and upgrading in Fujian, a southeastern province of China. It is of great significance to study its selection model. In this paper, according to the characteristics of strategic emerging industries, drawing on the double helix model, this paper sets up an evaluation system for strategic emerging industries. Then, the entropy-VIKOR algorithm was introduced to evaluate and rank the backbone enterprises in Fujian, based on the data on 21 listed enterprises of strategic emerging industries in the province from 2017 to 2019. The empirical research results are consistent with the actual situation of the development of strategic emerging industries in Fujian Province, which proves the feasibility of this research method. Finally, according to the empirical analysis results, this study puts forward some countermeasures for the development of strategic emerging industries in Fujian province. [ABSTRACT FROM AUTHOR]

In intelligent warehouse, the problem of transporting goods in intelligent warehouse is becoming increasingly complex, and the traditional way of automatically guiding vehicles (AGVs) is inefficient, so automated robot systems are introduced into intelligent warehouses. In this paper, a task assignment model for robots is presented with the transportation problem of robots in intelligent warehouse as the research background. To solve the robot task assignment problem in intelligent warehouse, a novel Pareto-based multiobjective optimization algorithm (MOEA) is proposed, and the aggregation function is invoked to replace the crowding distance; the brain storm operator is used for crossover and mutation. Finally, the ability of the algorithm to solve the benchmark test problem suite and real-world problems is experimentally confirmed. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to find the duration of maximum time connectivity of sensor nodes under uncertainty utilizing the prespecified voltage/power of each sensor node. Wireless sensor networks (WSNs) are composed of nodes that transmit data between each other over routing. A variety of routing protocols and algorithms exist, each related to a particular set of conditions. There are a variety of routing algorithms available, some of which can be used in WSNs for routing. The goal of the fastest distance routing algorithms in a WSN is to use the least amount of energy possible. In a WSN, Dijkstra's algorithm is typically used for shortest path routing. The Floyd–Warshall's algorithm is used to compute the shortest paths between distinct nodes in a regular graph, but due to the absence of a communication mode, this algorithm is not ideal for routing in wireless networks. In this research work, we have considered a WSN to find out the maximum connectivity time utilizing optimum voltage. On the other hand, duration of connectivity and energy/voltage are two vital parameters that are difficult to manage. Because of limited resources and safety concerns, safety implementation is limited. Also, due to the irregular/hazardous environmental situations, the distance between sensor nodes and its voltage to link up the nodes are totally unpredictable. In this work, we employ triangular fuzzy numbers to express unpredictability. Then, utilizing defuzzification of fuzzy numbers, the associated WSN problem was transformed into a crisp one. The widely used signed distance approach has been applied for the defuzzification of fuzzy numbers in this case. To determine the best outcome and to illustrate the usefulness of the suggested technique, a numerical example has been solved using the modified Floyd–Warshall's algorithm. Finally, concluding remarks on the proposed approach as well as future studies have been provided. [ABSTRACT FROM AUTHOR]

In recent years, with the development of Big Data and dynamic modeling technology, people have gradually deepened the research on the efficient construction of music audio database. Based on this background, this paper first realizes the construction of multilevel music audio database using Big Data technology and collaborative filtering algorithm. Then, through the Big Data analysis technology based on collaborative filtering algorithm, the QRE (quick reaction estimate) model is constructed and the data query system is formed. Finally, experiments are designed to verify whether the music audio database based on collaborative filtering algorithm can correctly retrieve the target music audio data. The experimental results show that compared with the traditional single ∗ input stacked database, the music and audio database constructed by the discrete Big Data dynamic modeling technology based on collaborative filtering algorithm has faster search rate and higher accuracy and can accurately locate the data nodes in the database. This research uses Big Data and collaborative filtering algorithm technology to establish a new multilevel music audio database with discrete dynamic modeling characteristics of complex systems, which can greatly improve the data structure of each level of the database, so as to improve the efficiency and accuracy of retrieving various types of music and audio files in the database. [ABSTRACT FROM AUTHOR]

With the development of information technology, computer networks have become a part of people's lives and work. However, computer viruses and malicious network attacks make network security face huge challenges, and more accurate detection of attacks has become the focus of attention to current computer fields. This paper proposes an intrusion detection model, which is mainly based on the XGBoost (Extreme Gradient Boosting), and uses the WOA (Whale Optimization Algorithm) to find the best parameters for it. The collected network data are first preprocessed by the PCA (Principal Component Analysis) dimensionality reduction method, and then, the preprocessed data are imported into the WOA-XGBoost algorithm so that the overall model has better intrusion detection capabilities for data after training. The experimental results are applied to the well-known KDD CUP 99 data in the computer network field, and compared with the accuracy of the results obtained by parameter adjustment in the traditional way, it shows that the intrusion detection model under this method has better accuracy. [ABSTRACT FROM AUTHOR]

Dynamic bus scheduling refers to adjusting the departure time according to the latest time-varying information or adjusting bus speed in the process of operation. These control strategies can prevent bus bunching and alleviate traffic pressure. The paper studies the multiline bus dynamic scheduling with consideration of departure time and speed meanwhile. The hyperheuristic algorithm is proposed, and low-level heuristics (LLH) operators are designed. The simulation experiment is performed for the passenger flow distribution of different strengths and types of different scenarios. By comparing the experimental results of genetic algorithm (GA) and hyperheuristic algorithm in solving different scenarios, the results show that in smooth, increasing, decreasing, and multiconvex passenger flow mode, the performance of the hyperheuristic algorithm is higher than that of GA. The promotion rate reaches 18∼28%, and especially the average value of the hyperheuristic algorithm designed under multiconvex passenger flow is up to 28.62%, significantly reducing passengers' waiting time. By comparing the stability of the three passenger flow modes, the results illustrate that the stability of the hyperheuristic algorithm is lower than that of GA. For the smooth passenger flow mode, the stability of medium and lower density of GA is higher than that of the hyperheuristic algorithm. In comparison, the high-density stability of the hyperheuristic algorithm is better than that of GA. [ABSTRACT FROM AUTHOR]

With the continuous development of China's social economy, the competitiveness of brand market is gradually increasing. In order to improve their own level in brand building, major enterprises gradually explore and study visual communication design. Brand visual design has also received more and more attention. Building a complete and rich visual design system can improve the brand level and attract users to consume. Based on the abovementioned situation, this paper proposes to use collaborative filtering algorithm to analyze and study brand visual design. Firstly, a solution is proposed to solve the problem of low accuracy of general recommendation algorithm in brand goods. Collaborative filtering algorithm is used to analyze the visual communication design process of enterprise brand. Research on personalized image design according to consumers' trust and recognition of brand design is conducted. In traditional craft brand visual design, we mainly study the impact of image design on consumer behavior. The brand loyalty model is used to predict and analyze the visual design effect. Also, the user's evaluation coefficient is taken as the expression of brand visual design recognition. Finally, the collaborative filtering algorithm is optimized to improve the consumer similarity based on the original algorithm. The results show that the brand visual design using collaborative filtering algorithm can help enterprises obtain greater benefits in their own brand construction. It provides effective data help in the development of traditional craft brands. [ABSTRACT FROM AUTHOR]

Trains can be optimally spread over the period of the cyclic timetable. By integrating sequencing issue with headway time together, this paper studies the structure optimization of mixed-speed train traffic for a cyclic timetable. Firstly, by taking it as a job-shop problem with sequence-dependent setup times on one machine, in the type of infinite capacity resource with headway (ICR + H), the problem is transformed to alternative graph (AG) and then recast to the mixed-speed train traffic planning (MSTTP) model. For the multiobjective in MSTTP, three indicators are optimized, i.e., heterogeneity, cycle time, and buffer time, which correspond to diversity of train service toward passenger, capacity consumption of rail network, and stability of train operation, respectively. Secondly, the random-key genetic algorithm (RKGA) is proposed to tackle the sequence and headway simultaneously. Finally, RKGA is coded with visual studio C# and the proposed method is validated with a case study. The rail system considered is a line section encompassing a territory of 180 km with 15 mixed-speed trains in each cycle of the timetable. Results indicate the comprehensively balanced train plan for all stakeholders from random variations of train sequence and headway time. Both the quantitative proportion of heterogeneity/homogeneity (e.g., 2.5) about the optimized distribution of the mixed train traffic and the link between train headway time and the sequence for each traffic scenario are found. All the findings can be used to arrange the mixed-speed train traffic more scientifically. [ABSTRACT FROM AUTHOR]

Technical movement analysis requires specialized domain knowledge and processing a large amount of data, and the advantages of AI in processing data can improve the efficiency of data analysis. In this paper, we propose a feature pyramid network-based temporal action detection (FPN-TAD) algorithm, which is used to solve the problem that the action proposal module has a low recall rate for small-scale temporal target action regions in the current video temporal action detection algorithm research. This paper is divided into three parts. The first part is an overview of the algorithm; the second part elaborates the network structure and the working principle of the FPN-TAD algorithm; and the third part gives the experimental results and analysis of the algorithm. [ABSTRACT FROM AUTHOR]

The biggest view of the whole world on science and technology and sports is that science and technology and sports both represent national strength. At present, the integration of sports and science and technology has not reached a certain height, especially in the prediction of sports behavior and injury assessment, and the investment in science and technology is still lacking. This leads to a high number of injuries caused by sports every year. However, swarm intelligence algorithm has made few breakthrough achievements in the past few years, and the combination of sports behavior and swarm intelligence algorithm can just solve this problem. It is very important to choose the algorithm for predicting and assessing sports behavior. We should choose an efficient algorithm with high stability, high convergence speed, and optimization ability. In this paper, the IPSGWO algorithm is proposed to realize this application. IPSGWO algorithm is based on the GWO algorithm, with appropriate strategies and ideas, to maximize the improvement. In this paper, the convergence curve of PSO, GWO, and IPSGWO is tested to determine whether the IPSGWO algorithm has more stable and higher performance, and the simulation experiment is used to determine whether the IPSGWO algorithm is suitable for prediction and injury assessment compared with the other two. From the experimental results, the IPSGWO algorithm does have higher performance; because of this, it is more accurate for prediction and injury assessment. [ABSTRACT FROM AUTHOR]

The decomposition-based algorithm, for example, multiobjective evolutionary algorithm based on decomposition (MOEA/D), has been proved effective and useful in a variety of multiobjective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX) operator with differential evolution (DE) operator, which is used to enhance the diversity of the solutions more effectively. However, the amplification factor and the crossover probability are fixed in MOEA/D-DE, which would lead to a low convergence rate and be more likely to fall into local optimum. To overcome such a prematurity problem, this paper proposes three different adaptive operators in DE with crossover probability and amplification factors to adjust the parameter settings adaptively. We incorporate these three adaptive operators in MOEA/D-DE and MOEA/D-PaS to solve MOPs and many-objective optimization problems (MaOPs), respectively. This paper also designs a sensitive experiment for the changeable parameter η in the proposed adaptive operators to explore how η would affect the convergence of the proposed algorithms. These adaptive algorithms are tested on many benchmark problems, including ZDT, DTLZ, WFG, and MaF test suites. The experimental results illustrate that the three proposed adaptive algorithms have better performance on most benchmark problems. [ABSTRACT FROM AUTHOR]

The development of information technology has led to the rapid development of modern book packaging design. Book packaging design is different from painting. It is a kind of design process that combines artistry and practicality and has double characteristics. With the continuous progress of society, people's requirements for book packaging design have become higher and higher, and modern book packaging design has become an important topic in the field of art design. To this end, this paper introduces the machine learning algorithms used in this paper, including the AdaBoost algorithm and the SVR algorithm. Specifically, it includes the principles and specific implementation steps for AdaBoost classification algorithm and SVR regression algorithm, as well as evaluation indexes of AdaBoost classification and SVR regression analysis. Realization of physical books reflects artistry, creativity, professionalism, popularity, and vitality of books in the packaging and design of books in the electronic information era. The stimulation effect of this paper starts from packaging design, inspection mechanism, and brand psychology to get the superior design in modern book packaging design. [ABSTRACT FROM AUTHOR]

This paper proposes a nonconvex model (called LogTVSCAD) for deblurring images with impulsive noises, using the log-function penalty as the regularizer and adopting the smoothly clipped absolute deviation (SCAD) function as the data-fitting term. The proposed nonconvex model can effectively overcome the poor performance of the classical TVL1 model for high-level impulsive noise. A difference of convex functions algorithm (DCA) is proposed to solve the nonconvex model. For the model subproblem, we consider the alternating direction method of multipliers (ADMM) algorithm to solve it. The global convergence is discussed based on Kurdyka–Lojasiewicz. Experimental results show the advantages of the proposed nonconvex model over existing models. [ABSTRACT FROM AUTHOR]

In the blockchain social network, the traditional influence maximization algorithm has the problem of insufficient accuracy of the influence spread. To solve the above problem, a BCLT model including the characteristics of the blockchain is established based on the linear threshold model. The BC-RIS algorithm is proposed based on the reverse reachable set. The BC-RIS algorithm's influence spread and running time and the traditional algorithm is compared using the real blockchain social network data set. The experimental results show that the BC-RIS algorithm can obtain a larger influence spread range, which is more in line with the influence propagation law of the blockchain social network. [ABSTRACT FROM AUTHOR]

Smart tourism can provide high-quality and convenient services for different tourists, and tourism itinerary planning system can simplify tourists' tourism preparation. In order to improve the limitation of the recommendation dimension of traditional travel planning system, this paper designs a mixed user interest model on the premise of traditional user interest modeling and combines various attributes of scenic spots to form personalized recommendation of scenic spots. Then, it uses heuristic travel planning cost-effective method to construct the corresponding travel planning system for travel planning. In terms of the accuracy rate of travel planning recommendation, the accuracy rate of multidimensional hybrid travel recommendation algorithm is 0.984, and the missing rate is 0. When the travel cost and travel time are the same and the number of scenic spots is 20–30, the memory occupation of MH algorithm is only 1/2 of that of TM algorithm. The results show that the multidimensional hybrid travel recommendation algorithm can improve the personalized travel planning of users and the travel time efficiency ratio. The results of this study have a certain reference value in improving user satisfaction with the travel planning system and reducing user interaction. [ABSTRACT FROM AUTHOR]

In the past few decades, metaheuristic algorithms (MA) have been developed tremendously and have been successfully applied in many fields. In recent years, a large number of new MA have been proposed. Slime mould algorithm (SMA) is a novel swarm-based intelligence optimization algorithm. SMA solves the optimization problem by imitating the foraging and movement behavior of slime mould. It can effectively obtain a promising global optimal solution. However, it still suffers some shortcomings such as the unstable convergence speed, the imprecise search accuracy, and incapability of identifying a local optimal solution when faced with complicated optimization problems. With the purpose of overcoming the shortcomings of SMA, this paper proposed a multistrategy enhanced version of SMA called ESMA. The three enhanced strategies are chaotic initialization strategy (CIS), orthogonal learning strategy (OLS), and boundary reset strategy (BRS). The CIS is used to generate an initial population with diversity in the early stage of ESMA, which can increase the convergence speed of the algorithm and the quality of the final solution. Then, the OLS is used to discover the useful information of the best solutions and offer a potential search direction, which enhances the local search ability and raises the convergence rate. Finally, the BRS is used to correct individual positions, which ensures the population diversity and enhances the overall search capabilities of ESMA. The performance of ESMA was validated on the 30 IEEE CEC2014 functions and three IIR model identification problems, compared with other nine well-regarded and state-of-the-art algorithms. Simulation results and analysis prove that the ESMA has a superior performance. The three strategies involved in ESMA have significantly improved the performance of the basic SMA. [ABSTRACT FROM AUTHOR]

The key technology of online travel recommendation system has been widely concerned by many Internet experts. This paper studies and designs a scenario aware service model in online travel planning system and proposes an online travel planning recommendation model which integrates collaborative filtering and clustering personalized recommendation algorithm. At the same time, the algorithm performance test method and model evaluation index are given. The results show that CTTCF algorithm can find more neighbor users than UCF algorithm, and the smaller the search space is, the more significant the advantage is. The number of neighbors is 5, 10, 15, 20, and 25, respectively, and the corresponding average absolute error values are about 0.815, 0.785, 0.765, 0.758, and 0.755, respectively. The scores of the six emotional travel itinerary recommendation schemes are all higher than 142 points. Only the two schemes have no obvious rendering effect. The proposed online travel itinerary planning scheme has potential value and important significance in the application of follow-up recommendation system. It solves the problem of low scene perception satisfaction in the key technologies of online tourism planning system. [ABSTRACT FROM AUTHOR]

With the exponential increasement of 3D models, 3D model classification is crucial to the effective management and retrieval of model database. Feature descriptor has important influence on 3D model classification. Voxel descriptor expresses surface and internal information of 3D model. However, it does not contain topological structure information. Shape distribution descriptor expresses geometry relationship of random points on model surface and has rotation invariance. They can all be used to classify 3D models, but accuracy is low due to insufficient description of 3D model. This paper proposes a 3D model classification algorithm that fuses voxel descriptor and shape distribution descriptor. 3D convolutional neural network (CNN) is used to extract voxel features, and 1D CNN is adopted to extract shape distribution features. AdaBoost algorithm is applied to combine several Bayesian classifiers to get a strong classifier for classifying 3D models. Experiments are conducted on ModelNet10, and results show that accuracy of the proposed method is improved. [ABSTRACT FROM AUTHOR]

Influence maximization (IM) is fundamental to social network applications. It aims to find multiple seed nodes with an enormous impact cascade to maximize these nodes' spread of influence in social networks. Traditional methods for solving influence maximization of the social network, such as the distance method, greedy method, and PageRank method, may suffer from issues of low calculation accuracy and high computational cost. In this paper, we propose a new bacterial foraging optimization algorithm to solve the IM problem based on the complete-three-layer-influence (CTLI) evaluation model. In this algorithm, a novel grid-based reproduction strategy and a direction-adjustment-based chemotaxis strategy are devised to enhance the algorithm's searchability. Finally, we conduct comprehensive experiments on four social network cases to verify the effectiveness of the proposed algorithm. The experimental results show that our proposed algorithm effectively solves the social network's influence maximization. [ABSTRACT FROM AUTHOR]

Cooperative coevolution (CC) is an effective framework for solving large-scale global optimization (LSGO) problems. However, CC with static decomposition method is ineffective for fully nonseparable problems, and CC with dynamic decomposition method to decompose problems is computationally costly. Therefore, a two-stage decomposition (TSD) method is proposed in this paper to decompose LSGO problems using as few computational resources as possible. In the first stage, to decompose problems using low computational resources, a hybrid-pool differential grouping (HPDG) method is proposed, which contains a hybrid-pool-based detection structure (HPDS) and a unit vector-based perturbation (UVP) strategy. In the second stage, to decompose the fully nonseparable problems, a known information-based dynamic decomposition (KIDD) method is proposed. Analytical methods are used to demonstrate that HPDG has lower decomposition complexity compared to state-of-the-art static decomposition methods. Experiments show that CC with TSD is a competitive algorithm for solving LSGO problems. [ABSTRACT FROM AUTHOR]

At present, the existing prediction algorithm of a port cargo throughput neglects the correction of the initial value of the cargo series data model, which leads to a large error in a port cargo throughput prediction. Therefore, a prediction algorithm of a port cargo throughput based on the ES-Markov model is designed. A decompose function is used to decompose the time series of a port cargo throughput, and the trend elements of a port cargo throughput are divided into long-term trend, seasonal trend, fluctuation trend, and irregular trend. In this study, the ES-Markov model is introduced, and the initial prediction is obtained by using the cubic exponential smoothing method, and the state transition matrix is obtained by the Markov principle. Based on the results of the time-series analysis and the ES-Markov model, the prediction algorithm of a port cargo throughput is designed. In the experimental design, the Elman neural network is used to construct an experimental sample data model. The monthly cargo throughput data of a certain port for eight months from May 2020 to December 2020 are collected and sorted according to the time series. The experimental results show that the prediction results of the proposed algorithm are closer to the actual value and the fluctuation of the prediction results is less than that of the reference. [ABSTRACT FROM AUTHOR]

Traffic flow is chaotic due to nonstationary realistic factors, and revealing the internal nonlinear dynamics of chaotic data and making high-accuracy predictions is the key to traffic control and inducement. Given that high-quality phase space reconstruction is the foundation of predictive modeling. Firstly, an improved C-C method based on the fused norm search domain is proposed to address the issue that the C-C method in the phase space reconstruction algorithm does not meet the Euclidean metric accuracy and reduces the reconstruction quality when the infinite norm metric is used. Secondly, to address the problem of insufficient learning ability of traditional convolutional combinatorial modeling for complex phase space laws of chaotic traffic flow, the high-dimensional phase space features are extracted using the layer-by-layer pretraining mechanism of convolutional deep belief networks (CDBNs), and the temporal features are extracted by combining with long short-term memory (LSTM). Finally, an improved probabilistic dynamic reproduction-based genetic algorithm (PDRGA) is proposed to address the problem of the hybrid model falling into a local optimum when learning the phase space law. Experiments are conducted in three aspects: phase space reconstruction quality analysis, comparison of optimization algorithm convergence, and prediction model performance comparison. The experimentation with two data sets demonstrates that the improved C-C method combines the advantages of the high accuracy metric of the L2 norm with the low operational complexity of the infinite norm, achieving a balance between reconstruction quality and algorithm efficiency. The proposed PDRGA optimization algorithm is a lightweight improvement of the traditional genetic algorithm (GA) and solves the problem that the model tends to fall into a local optimum by optimizing the initial weights of CDBN. Meanwhile, the five error evaluation indexes of the proposed PDRGA-CDBN-LSTM hybrid model are lower than those of the baseline model, providing a new modeling idea for chaotic traffic flow prediction. [ABSTRACT FROM AUTHOR]

To achieve effective connection between each node of the Internet of Things (IoT), there is a great demand of precise positioning. The traditional firefly localization algorithm only considers part of the ranging information, and the positioning effect is not good. Therefore, an improved algorithm is proposed to take advantage of the ranging information between unknown nodes to achieve cooperative location. First, all ranging information is considered to construct a new objective function. The firefly optimization approach is then utilized to discover the best solution based on the new goal function. The starting location created by the random technique has a significant impact on the new algorithm's localization performance, thus the traditional firefly localization method's initial position is employed to increase cooperative localization performance. The simulation results demonstrate that the novel approach outperforms the classic firefly technique in terms of placement accuracy. [ABSTRACT FROM AUTHOR]

Stroke is one of the fatal diseases worldwide, and its primary mechanism is produced by cerebrovascular stenosis, blockages, or embolisms. Computer-aided diagnosis can assist clinical practitioners in identifying cerebrovascular anomalies, elucidating the precise lesions' location in the patients, and providing guidance for clinical therapy. Due to different portions of the cerebrovascular possessing diverse morphological properties and the limited narrow area, the detection effect is unsatisfactory. A retrained two-stage algorithm for detecting cerebral arterial stenosis in CTA images is proposed to solve these problems by further fusing image features and improving the quality of regions of interest. In Faster R-CNN and Libra R-CNN, the backbone network was Resnet50, with deformable convolutional and nonlocal neural networks introduced in the third, fourth, and fifth stages of the backbone network. Deformable convolutional networks learned offsets to extract morphological features of blood vessels in different tomographic planes. Nonlocal neural networks fused global information and extracted global features from location information of feature maps. A cascade detector refined object classification and bounding box regression before prediction. The experimental results show that the retained algorithm increases mAP by 7.3% and 7.5%, respectively, compared with Faster R-CNN and Libra R-CNN. Deformable convolutional networks, nonlocal neural networks, and cascade detectors are incorporated into further feature fusion; thus, semantic information about the cerebrovascular structure is learned, demonstrating more accurate stenotic region detection and demonstrating generalizability across different two-stage algorithms. [ABSTRACT FROM AUTHOR]

Community discovery plays a crucial role in understanding the structure of networks. In recent years, the application of clustering algorithms in the community-discovery tasks of complex networks has been studied frequently. In this study, we proposed a balance factor of node density and node-degree centrality for the core-node selection problem in community discovery. We also proposed a new community-discovery algorithm based on the balance factor, adaptability, and modularity increment, which is based on the balance factor (BComd). First, the proposed method was able to identify the core nodes in a community. Second, we used node-degree centrality, node density, and adaptability to detect overlaps between communities and then we removed these overlaps from the network to obtain a subnetwork with a clear community structure. Third, we obtained the preliminary community divisions by clustering the subnetworks, and these preliminary communities were usually the core parts of the communities they belonged to. Finally, each preliminary community was compressed into a new node, and then, the new network was clustered using the Louvain algorithm. The experimental results showed that the algorithm identified the core nodes in communities well, effectively discovered overlaps between communities, and had superior performance in large-scale networks. [ABSTRACT FROM AUTHOR]

At present, the fast-paced work and life make people under great pressure, and people's enthusiasm for fitness is getting higher and higher, which is in contradiction with the shortage of existing stadiums. So it is considerably significant to open shared stadiums near where citizens live for booking. Therefore, how to allow citizens to book a suitable stadium according to their own needs through mobile phones or computers is an urgent problem to be solved. The booking of the shared stadium can be regarded as a mobile edge computing (MEC) scenario, and the problem can be transformed into task scheduling research under MEC through intelligent scheduling method. When using edge computing (EC) technology for service calculation, the mobile terminal needs to offload the service to the edge computing server. After the server completes the calculation, the calculation results will be sent back to the mobile terminal. Therefore, the calculation time and system energy consumption in the calculation process can be further reduced through task scheduling to improve user satisfaction. In this study, joint scheduling of service caching and task algorithm is proposed to reduce the latency of booking shared stadium request and improve user experience. The simulation results show that the proposed algorithm with edge cooperation idea can achieve lower average system latency at lower load level and can significantly reduce the cloud offloading ratio under low and middle pressure. In addition, the proposed algorithm uses the secondary transfer of more tasks to reduce the pressure of local task running. Finally, the quality of experience (QoE) satisfaction rate under low pressure is guaranteed. [ABSTRACT FROM AUTHOR]

With the rapid development of 3D (three-dimensional) printing technology, it has been widely used in the field of ceramic arts and crafts. However, due to the complexity of 3D printing technology, it will face complex modeling and calculation when designing ceramic art crafts. To this end, the artificial intelligence algorithm is introduced, and using the data measured by the built-in modeling instruction of LAMMPS of the artificial intelligence algorithm, the program is used to reset its coordinates, length, width, height, and focal length. The obtained data are modified by postprocessing to correct its coordinates and the size of the simulation frame, so that the nanopowder model is placed in the center, forming a solid ellipsoidal aluminum nanopowder and cutting it into a three-dimensional model of teapot, which is transformed into the STL file of two-dimensional cross section, and the finished product is printed out to the 3D printer. Finally, the RTM model is used to test the quality of tourist souvenirs. The results show that the homogeneity of variance is much greater than 0.10. It can be inferred that the tourist souvenirs of pottery teapots have met the requirements of national technological quality standards. [ABSTRACT FROM AUTHOR]

The non-linear market microstructure (MM) model for financial time series modeling is a flexible stochastic volatility model with demand surplus and market liquidity. The estimation of the model is difficult, since the unobservable surplus demand is a time-varying stochastic variable in the return equation, and the market liquidity arises both in the mean term and in the variance term of the return equation in the MM model. A fast and efficient Markov Chain Monte Carlo (MCMC) approach based on an efficient simulation smoother algorithm and an acceptance-rejection Metropolis–Hastings algorithm is designed to estimate the non-linear MM model. Since the simulation smoother algorithm makes use of the band diagonal structure and positive definition of Hessian matrix of the logarithmic density, it can quickly draw the market liquidity. In addition, we discuss the MM model with Student-t heavy tail distribution that can be utilized to address the presence of outliers in typical financial time series. Using the presented modeling method to make analysis of daily income of the S&P 500 index through the point forecast and the density forecast, we find clear support for time-varying volatility, volatility feedback effect, market microstructure theory, and Student-t heavy tails in the financial time series. Through this method, one can use the estimated market liquidity and surplus demand which is much smoother than the strong stochastic return process to assist the transaction decision making in the financial market. [ABSTRACT FROM AUTHOR]

One of the main challenges in microgrid system energy management is dealing with uncertainties such as the power output from renewable energy sources. The classic two-stage robust optimization (C-TSRO) method was proposed to cope with these uncertainties. However, this method is oriented to the worst-case scenario and is therefore somewhat conservative. In this study, focusing on the energy management of a typical islanded microgrid and considering uncertainties such as the power output of renewable energy sources and the power demand of loads, an expected-scenario-oriented two-stage robust optimization (E-TSRO) method is proposed to alleviate the conservative tendency of the C-TSRO method because the E-TSRO method chooses to optimize the system cost according to the expected scenario instead, while ensuring the feasibility of the first-stage variables for all possible scenarios, including the worst case. According to the structural characteristics of the proposed model based on the E-TSRO method, a column-and-constraint generation (C & CG) algorithm is utilized to solve the proposed model. Finally, the effectiveness of the E-TSRO model and the solution algorithm are analysed and validated through a series of experiments, thus obtaining some important conclusions, i.e., the economic efficiency of system operation can be improved at about 6.7% in comparison with the C-TSRO results. [ABSTRACT FROM AUTHOR]