The gradual rise of precision medicine,such as mining cardiopulmonary exercise time series of young hypertensive patients,can understand the response of different individuals to aerobic exercise training.This helps to improve the efficiency of hypertension management plan and achieve aerobic exercise intervention more effectively.One of the bottlenecks in this study is that it is difficult to obtain sufficient sample data.To solve the above problem,this paper adopts the weighted dynamic-time-warping barycenter averaging algorithm(WDBA) to realize data augmentation of time series,focusing on the barycenter selection and the weight assignment.In this paper,the concept of active barycenter is introduced for the first time,and the selection strategies of representative barycenter and diversity barycenter are proposed to improve the effect of data augmentation.Furthermore,aiming at the shortcomings of the existing weight assignment strategies,a random strategy with decreasing distance is proposed to further improve the generalization ability of the model by avoiding the synthesis of duplicate samples.Experimental results show that the accuracy of predicting the efficacy of aerobic exercise intervention in young hypertensive patients can be further improved by considering both the barycenter selection and the weight assignment for data augmentation in the background of this study.

In recent years,the research of sequential recommendation has developed rapidly in the recommendation field,existing methods are good at capturing users’ sequential behavior to achieve preference prediction.Among them,some advanced methods integrate users’ sentiment information to guide behavior mining.However,the advanced sentiment-based models do not consider mining relations between multi-category user sentiment sequences.Moreover,such methods cannot intuitively explain the contribution of temporal sentiments to user preferences.To make up for the above shortcomings,this paper first attempts to store temporal sentiments in the form of memory and impose constraints on them.Specifically,this research proposes two mechanisms including sentiment self-constraint and sentiment mutual-constraint to explore the associations between multiple categories of sentiments and assist user behaviors in completing sequential recommendations.Furthermore,the proposed memory framework is able to record users’ temporal sentiment attention,so that it can provide a certain degree of intuitive explanation on the basis of accurately predicting users’ temporal preference.Experimental results show that our approach outperforms existing state-of-the-art sequential methods,and it has better explainable effects than the sentiment-based sequential recommendation models.

In order to establish an accurate multibeam sonar elevation data prediction model and solve the problem of the accuracy of air-squared prediction of artificial reefs,a multibeam sonar elevation data prediction method based on a combined model of improved convolutional neural network(CNN) and BP neural network is proposed.First,the improved CNN is used to extract topographic trend features by full convolutional operation of the elevation data,and then input to BP to further explore the internal topographic trend change pattern,so as to achieve the prediction of multibeam sonar elevation data.Experiments are conducted with multibeam sonar elevation data from a submarine ranch and cross-validated using the null square volume of artificial reefs.Finally,it is compared with the traditional kriging,BP,GA-BP,and PSO-BP models.The results show that the improved CNN-BP model performs the best prediction results on multibeam sonar elevation data and artificial reef air-square volume,which verifies the feasibility,reliability and high accuracy of the proposed method.

In recent years,academia has gradually accumulated a large amount of data.As an effective method for representing and analyzing big data,network structure has rich dimensions and can model a large amount of data in real life.Graph online analytic processing(Graph OLAP) technology inherits the related ideas of traditional OLAP technology,allowing users to analyze multi-dimensional network data from different angles and granularities.However,most of the existing graph OLAP technologies revolve around the construction of data cubes,and most of the related operations are simple extensions of traditional OLAP technologies on graph data,and the built models have weak ability to mine the topology of the network itself.To this end,the aca-demic network constellation model and related graph OLAP analysis algorithms are firstly designed,which more clearly highlights the topological structure information of academic networks and improves the analysis ability of graph OLAP.Secondly,the corresponding materialization strategy is proposed,which effectively improves the efficiency of graph OLAP analysis.

Community structure is an important feature in complex networks,and the goal of local community detection is to query a community subgraph containing a set of seed nodes.Traditional local community detection algorithms usually use the topology of the network for community query,ignoring the rich node attribute information in the network.A local community detection algorithm based on multi-objective particle swarm optimization is proposed for realistic and widespread attribute networks.Firstly,attribute relationship edges are constructed based on the attribute similarity between nodes and their multi-order neighbours,and topological relationship edges are obtained by weighting the network structure based on the motif information,followed by sampling the two relationship edges around the core nodes using a random walk algorithm to obtain alternative node sets.Based on this,the alternative node sets are iteratively filtered by a multi-objective particle swarm optimization algorithm to obtain a topologically tight and attribute-homogeneous community structure.Experimental results on real datasets show that the proposed method improves the performance of local community detection.

Traffic prediction is an important research component of urban intelligent transportation systems to make our travel more efficient and safer.Accurately predicting traffic flow remains a huge challenge due to complex temporal and spatial depen-dencies.In recent years,graph convolutional network(GCN) has shown great potential for traffic prediction,but GCN-based mo-dels tend to focus on capturing temporal and spatial dependencies,ignoring the dynamic correlation between temporal and spatial dependencies and failing to integrate them well.In addition,previous approaches use real-world static traffic networks to construct spatial adjacency matrices,which may ignore the dynamic spatial dependencies.To overcome these limitations and improve the performance of the model,a novel spatial-temporal Graph-CoordAttention network(STGCA) is proposed.Specifically,the spatial-temporal synchronization module is proposed to model the spatial-temporal dependence of the crossing relations at different moments.Then,a dynamic graph learning scheme is proposed to mine potential graph information based on data correlation between traffic flows.Compared with the existing baseline models on four publicly available datasets,STGCA exhibits excellent perfor-mance.

Feature mining is a key step to learn the interaction between users and items in the recommendation algorithm model,which is of great significance to improve the accuracy of the recommendation model.Among the existing feature mining models,although the linear logistic regression model is simple and can achieve good fitting effect,its generalization ability is weak,and the model has a large demand for feature parameters.Deep & Cross network can effectively realize the cross extraction of features,but its representation ability of data features is still insufficient.Therefore,by introducing the idea of multiple residual structure and cross coding,an improved recommendation model of Deep & Cross network based on decision tree is proposed.Firstly,it designs a tree structure based on GBDT algorithm to construct enhanced features,which strengthens the deep mining of the model on potential features.Secondly,the input parameter dimension of the embedded layer of the model is amplified and optimized.Finally,the improved Deep & Cross network recommendation model is used for recommendation prediction.This design can not only break the limitations of existing models in generalization ability,but also keep the feature parameters simple and strengthen their representation ability,so as to effectively mine the hidden associations of users and improve the accuracy of recommendation.Experimental results based on the public test data set show that the prediction effect of the proposed model is better than the exis-ting feature interaction methods.

The traditional density clustering algorithms do not consider the attribute difference between data points in the clustering process,but treat all data points as homogenous points.Based on the traditional DBSCAN algorithm,a dynamic neighborhood－－density based spatial clustering of applications with noise(DN-DBSCAN) is proposed.When it is working,each point’s neighborhood radius is determined by the properties of itself,so the neighborhood radius is dynamic changing.Thus,different influences on datasets produced by points with different properties is reflected in the clustering results,making the density clustering algorithm has more practical meaning and can be more reasonable to solve practical problems.On the basis of example analysis,the DN-DBSCAN algorithm is applied to solve the urban agglomeration division problem in the Yangtze river delta,and the results of DBSCAN algorithm,OPTICS algorithm and DPC algorithm are compared and analyzed.The results show that DN-DBSCAN algorithm can reasonably classify urban agglomerations in the Yangtze river delta according to the different attributes of each city with an accuracy of 95%,which is much higher than the accuracy of 85%,85% and 88% of the other three algorithms respectively,indicating that it has a better ability to solve practical problems.

Temporal hierarchical data is a kind of hierarchical data characterized by time dimension description and is used to model the hierarchical structure that changes over time.Compared with management methods for common hierarchical data,there are still problems in temporal hierarchical data management such as the complexity of storage scheme design and inefficiency of query and update.To solve the above problems,a temporal hierarchical data management method based on nested intervals scheme is proposed.4 types of change in hierarchical data are firstly analyzed from the perspective of the node change,based on which the storage and query capabilities of multi-version nodes in a rational database are then realized by extending the time labels.Finally,the abundantly gapped nested intervals scheme(AGNIS) is put forward to solve the problem of data insertion inefficiency in common nested intervals scheme.Experiments based on the data of Chinese administrative division and its adjustment from 2021 to 2022 show that the proposed method can implement the storage of historical hierarchical data and the query of hie-rarchical snapshot at any time,with a high efficiency in data query and update operation.

Credit evaluation is a key problem in finance,which predicts whether a user is at risk of defaulting and thus reduces bad debt losses.One of the key challenges in credit evaluation is the presence of a large number of invalid or redundant features in the dataset.To solve this problem,an improved feature selection using forest optimization algorithm(IFSFOA) is proposed.It addresses the shortcomings of the original algorithm FSFOA by using a cardinality check-based initialization strategy instead of randomized initialization in the initialization phase to improve the algorithm’s search capability;using a multi-level variation strategy in the local seeding phase to optimize the local search capability and solve the problems of restricted search space and localization of FSFOA;using a greedy selection strategy to select high-quality trees and eliminate low-quality trees when updating the candidate forest.In updating the candidate forest,we use the greedy selection strategy to select high-quality trees and eliminate low-quality trees,and converge the search dispersion process.Finally,the results show that IFSFOA outperforms FSFOA and more efficient feature selection algorithms proposed in recent years in terms of classification ability and dimension reduction ability,and validates the effectiveness of IFSFOA by setting up comparison experiments on public credit evaluation datasets covering low,medium and high dimensions.

In the era of big data,data access speed is an important indicator to measure the performance of large-scale storage systems.Index is one of the main technologies to improve data access performance in database system.In recent years,learned index(LI) is proposed,which uses machine learning models instead of traditional B+-tree indexes,leverages pattern about the under-lying data distribution to train the models and optimize the indirect search of data query into the direct search of function calculation,learned index can speed up queries and reduce the size of an index.However,the fitting effect of LI is general,and it assumes that the data is static and read-only,it does not support modification operations such as insertion.This paper presents GDLIN,a novel form of a learned index,which uses gradient descent algorithm to fit the data.Gradient descent algorithm can reduce the error between the predict position and the actual position,which can reduce the cost of local research.Besides,GDLIN recursive calls the construction algorithm until only one model is created,which makes full use of keys’ distribution,and avoids the increase of the size of index with the data volume.In addition,GDLIN uses the sorted linked list to address the problem of data insertion.Experiment results demonstrate GDLIN improves the lookup throughput by 2.1× compared with the traditional B＋-trees without insertion.Besides,GDLIN improves the lookup performance by 1.08× compared with the LI when the factor of insertion is 0.5.

Traffic flow forecasting is of great importance to urban road planning,traffic safety issues and building smart cities.However,most existing traffic prediction models cannot capture the dynamic spatio-temporal correlation of traffic data well enough to obtain satisfactory prediction results.To address this problem,a dynamic spatio-temporal neural network-based city traffic flow prediction method is proposed to solve the traffic flow prediction problem.First,by modelling the nearest cycle dependence,daily cycle dependence and weekly cycle dependence of the traffic data,a 3D convolutional neural network is used on each component to extract the high-dimensional features of urban traffic.Then,an improved residual structure is used to capture the correlation between remote area pairs and the prediction area,and a fusion of spatial attention and temporal attention mechanisms is used to capture the dynamic correlation between traffic flows in different time periods in different areas.Finally,the outputs of the three components are weighted and fused using a parameter matrix-based approach to obtain the prediction results.Experiments on two publicly available datasets,TaxiBJ and BikeNYC,show that the proposed model outperforms the mainstream traffic forecasting models.

Effective anomaly detection of multivariate time series is important for data mining analysis.However,most of the exi-sting detection methods are based on single modality,they cannot effectively utilize the distribution information of time series in multi-modal space.For multi-modal features,there is no effective adaptive fusion method and extraction method of spatial-temporal dependence.In this paper,a time series anomaly detection method based on multi-modal feature fusion is proposed.The multi-modal feature adaptive fusion module is established,it can adaptively fuse the multi-modal features through convolution network and soft selection mode.The spatial-temporal attention module is proposed,it is composed of temporal attention and spatial attention.It extracts spatial-temporal dependence of the multi-modal features and outputs the spatial-temporal attention vector.Then the model prediction results are obtained based on the spatial-temporal attention vector.By learning the distribution of normal samples,anomaly detection result is obtained according to the error measure between the predicted values and the real values.The proposed method is compared with other state-of-the-art models on four public datasets,and results demonstrate its effectiveness.

Emotional information contained in massive texts on the Internet expresses public views and attitudes.How to identify and utilize emotional resources has become the focus of research in various fields.By combing the relevant theories and literature on fine-grained emotion recognition,this paper summarizes the classification methods and application scenarios,and discusses the technical challenges and practical gaps.Through analysis,it is found that fine-grained emotion recognition methods mainly include emotion lexicon,traditional machine learning and neural network learning,which are mostly used in business analysis and public opinion management.In view of the future research trend,firstly,the real-time updating of online emotion words,domain lexicon construction and semantic analysis technology can be studied.Secondly,how to improve the automatic classification of training data and build a semi-supervised learning model need to be further discussed.In addition,the research of business analysis and public opinion management can explore the integration of aspect extraction and emotion recognition.This paper summarizes and comments on emotion recognition technology and its application,which can provide a reference for the subsequent research.

To promote the development of health and medical big data and actively promote the safe sharing of medical data,this paper constructs a tripartite evolutionary game model of the system manager,data provider and data demander based on the blockchain architecture.Firstly,prospect theory is combined with evolutionary game,and the parameters of traditional evolutio-nary game are improved by the prospect value function.Secondly,the possibility of game equilibrium and its evolution trend are discussed.Finally,the influence of different factors on the decision-making of each participant in medical data sharing under blockchain architecture is discussed through numerical simulation.The results show that the choice of initial strategy has a signi-ficant influence on the stability of game strategy.The evolution of the system can be accelerated by improving the regulatory bene-fits of the system manager,reducing the perceived losses of the data provider,and improving the compensation of the data demander for actively reporting non-compliance behaviors,thus enhancing the trust of all participants and promoting the formation of trust relationships.