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Personalized recommendation systems learn user preference characteristics by analyzing behavioral data such as ratings and comments generated by users in the Internet, and provide precise recommendations for individual users accordingly. However, in real life, users often conduct group activities like group buying and traveling together. How to recommend for groups has become a heated research topic in recent years. Most existing group recommendation algorithms are recommended for given divided groups by collectively combining the preferences of members in the group. However, in most cases, users' group properties are fickle. As the results of group detection are decisive to the performance of group recommendation, group detection is particularly important to the group recommendation algorithm. After analyzing problems of existing group recommendation algorithms, this paper proposes the density peak clustering group detection algorithm based on GRU-CNN and the group recommendation algorithm based on the mechanism. With respect to group detection, most of the existing group detection algorithms suffer from certain deficiencies: First, depending solely on the users' static preference features while ignoring the variation of users' interest over time when finding the group structure in the network; second, group division based on users' topic features extracted from reviews is difficult to support further digging of the in-depth features in reviews. To address the above-mentioned problems, this paper proposes a density peak clustering group detection algorithm based on CNN-GRU. It would first extract representative keywords in the reviews with LDA topic model, and then model time series information based on GRU attaining users' dynamic topic features. Coupling with deeper characteristics cored out by CNN, density peak clustering algorithm completes its group detection finally. Experiments on real dataset indicate that the features mined by the fusion depth neural network model effectively capture users' dynamic preferences, and yield better results of group detection than that of existing algorithms. [ABSTRACT FROM AUTHOR]

Ontology user portraits describe the semantic structure of users' interests. It is very important to study the similar relationship between user portraits to find the communities with overlapping interests. The hierarchical characteristics of user interest can generate multiple similarity relations, which is conducive to the formation of interest clusters. This paper proposed a method of overlapping community detection combining the hierarchical characteristics of user interest and the module distribution entropy of node. First, a hierarchical user interest model was constructed based on the ontology knowledge base to measure the multi-granularity topic similarity of users. Then, a heterogeneous hypergraph was established by using the multi-granularity topic similarity and the following similarity of users to represent the interest network. Based on the mechanism of module distribution entropy of nodes, the community detection algorithm was applied to identify the interested community. The real performance of the proposed algorithm on multiple networks was verified by experiments. The experimental results show that the proposed algorithm is better than the typical overlapping community detection algorithm in terms of accuracy and recall rate. [ABSTRACT FROM AUTHOR]

With the deepening application of wearable devices in the field of indoor positioning, the mining and application of human behavior patterns based on new technologies such as mobile terminals, channel technologies and intelligent algorithms have gradually become a research hotspot in the field of intelligent positioning. At this stage, outdoor positioning satellite technology has been gradually improved, but it cannot work effectively in indoor environment, and indoor positioning technology has not formed a set of standard and effective scheme at this stage, which leads to a scene of a hundred contentions for indoor positioning technology. This paper proposes an indoor intelligent localization algorithm based on channel state information, which first forms the measurements of antenna subarrays by the fixed antenna subset arrays located in different paths to achieve the phase estimation based on channel state; then determines the AoA of the direct path from the target to the AP using ToF information and eliminates the STO noise of channel state information by unfolding the best linear fit of CSI phase; again the AoA and ToF estimates from multiple measurements are plotted in two-dimensional space, and the direct path possibility estimation is achieved by identifying the estimates through five clusters of Gaussian-averaged clustering algorithm; finally the coordinates of the target to be located are obtained by coupling direct propagation path, estimation clustering and least-squares estimation using three APs as the receiver and the target to be located as the transmitter. The simulation results show that the intelligent localization algorithm proposed in this paper is able to lack the localization accuracy at the decimeter level in the indoor localization environment with three fixed APs, and the average error is better than the localization algorithms of support vector machine, deep learning and limit learning. [ABSTRACT FROM AUTHOR]

Using wearable devices to realize the mining and application of human behavior patterns has become a hotspot in the field of intelligent positioning. Wearable devices provide an analyzable data foundation for indoor spatial distribution and human behavior pattern prediction. The development of the intelligent positioning system based on RSSI has encountered a bottleneck that it is difficult to improve the positioning accuracy. Therefore, some research works started emphasizing location technology based on channel state information (CSI). In this paper, the principle used by Wi-Fi channel state information to realize intelligent positioning is described, the characteristics of CSI are analyzed, and an intelligent positioning algorithm based on CSI is proposed. Specifically, the algorithm first estimates the angle of arrival (AoA) based on the MUSIC algorithm, separates the reflected paths in the multipath components, and accurately estimates the AoA of each path. Second, phase estimation with channel state information is achieved by forming different antenna subarray measurements under the consideration of a subset of antennas and subcarriers. Then, the phase response linear fitting of the data packet CSI is eliminated using the ToF purification algorithm to obtain the corrected phase response and realize the elimination of the STO noise of the channel state information. Finally, the target position is calculated by effectively filtering the reflection path through the likelihood value, and the accurate target positioning function is achieved. The experimental results demonstrate that the intelligent positioning algorithm proposed in this paper can achieve decimeter-level positioning accuracy under the condition of a fixed number of APs, and the average error is better than that of deep learning-based and SVM-based positioning algorithms. In other words, the accuracy of intelligent positioning is improved. [ABSTRACT FROM AUTHOR]

The rapid growth of contents on the Web in different languages increases the demand of Cross-Lingual Information Retrieval (CLIR). The accuracy of result suffers due to many problems such as ambiguity and drift issue in query. Query Expansion (QE) offers reliable solution for obtaining suitable documents for user queries. In this paper, we proposed an architecture for Hindi–English CLIR system using QE for improving the relevancy of retrieved results. In this architecture, for the addition of term(s) at appropriate position(s), we proposed a location-based algorithm to resolve the drift query issue in QE. User queries in Hindi language have been translated into document language (i.e. English) and the accuracy of translation is improved using Back-Translation. Google search has been performed and the retrieved documents are ranked using Okapi BM25 to arrange the documents in the order of decreasing relevancy to select the most suitable terms for QE. We used term selection value (TSV) for QE and for retrieving the terms, we created three test collections namely the (i) description and narration of the Forum for Information Retrieval Evaluation (FIRE) dataset, (ii) Snippets of retrieved documents against each query and (iii) Nearest-Neighborhood (NN) words against each query word among the ranked documents. To evaluate the system, 50 queries of Hindi language are selected from the FIRE-2012 dataset. In this paper, we performed two experiments: (i) impact of the proposed location-based algorithm on the proposed architecture of CLIR; and (ii) analysis of QE using three datasets, i.e. FIRE, NN and Snippets. In the first case, result shows that the relevancy of Hindi–English CLIR is improved by performing QE using the location-based algorithm and a 12% of improvement is achieved as compared to the results of QE obtained without applying the location-based algorithm. In the second case, the location-based algorithm is applied on three datasets. The Mean Average Precision (MAP) values of retrieved documents after QE are 0.5379 (NN), 0.6018 (FIRE) and 0.6406 (Snippets) for the three test collections, whereas the MAP before QE is 0.37102. This clearly shows the significant improvement of retrieved results for all three test collections. Among the three test collections, QE has been found most effective along with Snippets as indicated by the results with the improvements of 6.48% and 19.12% over FIRE and NN test collections, respectively. [ABSTRACT FROM AUTHOR]

As an important part of metal processing, welding is widely used in industrial manufacturing activities, and its application scenarios are very extensive. Due to technical limitations, the welding process always unavoidably leaves weld defects. Weld defects are extremely hazardous, and the work used must be guaranteed to be defect-free, regardless of the field. However, manual weld inspection has subjective factors such as inefficiency and easy missed detection, and although some automatic weld inspection methods have appeared, these traditional methods still do not meet actual demand in terms of detection time and detection accuracy. Therefore, there is a need for a higher quality weld image automatic detection method to replace the manual method and the traditional automatic detection method. In view of the above, this paper proposes a weld seam image recognition algorithm based on deep learning. The Adam adaptive moment estimation algorithm is chosen as the backpropagation optimization algorithm to accelerate the training of convolutional neural networks and design an independent adaptive learning rate. Through the simulation of the collected 4500 tube images, the adaptive threshold-based method is used for weld seam extraction. The algorithm proposed in this paper is compared with the weld seam recognition method based on image texture feature value distribution (ITFVD) and the SUSAN-based weld defect target detection method. The results show that the proposed method can identify weld defects in a short time on different sizes of weld images, and can further detect the type of weld defects. In addition, the method in this paper is better than the other two methods in the false detection rate, recall rate and overall recognition accuracy, which shows that the experimental results have achieved the expected results. [ABSTRACT FROM AUTHOR]

Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer's complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system. [ABSTRACT FROM AUTHOR]

Multi-furnace scheduling simultaneously is an important part to increase productivity and reduce the production cost in single crystal silicon enterprises. In the restrained power consumption requirements environment, the optimal sequencing of process operation start-time for single crystal furnaces is a challenging problem. To solve this problem, the scheduling model of multi-furnace scheduling is established in this paper to minimize the maximum completion time. Then, an improved DE algorithm called the multi-strategy individual adaptive mutation differential evolution algorithm (MSIADE) is presented to address the scheduling model. In the improved DE algorithm, the different dimensional and multi-strategy mutation operations are adopted to refrain the algorithm from the local optimal, then the different mutation factors are assigned to each individual through the rank of fitness function value to strengthen the exploration ability of the MSIADE algorithm. Simulation experiments results based on the standard test functions and the established scheduling model show the feasibility in the established model and the effectiveness in the proposed algorithm. [ABSTRACT FROM AUTHOR]

Various UNet architecture-based image change detection algorithms promote the development of image change detection, but there are still some defects. First, under the encoder–decoder framework, the low-level features are extracted many times in multiple dimensions, which generates redundant information; second, the relationship between each feature layer is not modeled so sufficiently that it cannot produce the optimal feature differentiation representation. This paper proposes a remote image change detection algorithm based on the multi-feature self-attention fusion mechanism UNet network, abbreviated as MFSAF UNet (multi-feature self-attention fusion UNet). We attempt to add multi-feature self-attention mechanism between the encoder and decoder of UNet to obtain richer context dependence and overcome the two above-mentioned restrictions. Since the capacity of convolution-based UNet network is directly proportional to network depth, and a deeper convolutional network means more training parameters, so the convolution of each layer of UNet is replaced as a separated convolution, which makes the entire network to be lighter and the model's execution efficiency is slightly better than the traditional convolution operation. In addition to these, another innovation point of this paper is using preference to control loss function and meet the demands for different accuracies and recall rates. The simulation test results verify the validity and robustness of this approach. [ABSTRACT FROM AUTHOR]

Earthquake prediction based on extreme imbalanced precursor data is a challenging task for standard algorithms. Since even if an area is in an earthquake-prone zone, the proportion of days with earthquakes per year is still a minority. The general method is to generate more artificial data for the minority class that is the earthquake occurrence data. But the most popular oversampling methods generate synthetic samples along line segments that join minority class instances, which is not suitable for earthquake precursor data. In this paper, we propose a Safe Zone Synthetic Minority Oversampling Technique (SZ-SMOTE) oversampling method as an enhancement of the SMOTE data generation mechanism. SZ-SMOTE generates synthetic samples with a concentration mechanism in the hyper-sphere area around each selected minority instances. The performance of SZ-SMOTE is compared against no oversampling, SMOTE and its popular modifications adaptive synthetic sampling (ADASYN) and borderline SMOTE (B-SMOTE) on six different classifiers. The experiment results show that the quality of earthquake prediction using SZ-SMOTE as oversampling algorithm significantly outperforms that of using the other oversampling algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we examine the main advances registered in the last ten years in Pattern Recognition methodologies based on graph matching and related techniques, analyzing more than 180 papers; the aim is to provide a systematic framework presenting the recent history and the current developments. This is made by introducing a categorization of graph-based techniques and reporting, for each class, the main contributions and the most outstanding research results. [ABSTRACT FROM AUTHOR]

With the explosively growing amount of online information, recommender system becomes an important tool to help users efficiently find their desired information. In this paper, we propose a Graph Neural Network Social Recommendation Based on Coupled Influence by analyzing the social influence of 2-level friends (CI-GNNSR). First, we mine the user's historical rating information and second-degree social information. Then, to learn the feature representation of users and movies, multiple Graph Attention Networks (GAT) are used to model the user-movie Graph and social network Graph. Our algorithm uses an attention-based memory network to learn the interest influence representation between users and their collaborative friends, which can distinguish the related factors among different users' friends. The experiment results show that CI-GNNSR enhances the accuracy of recommendation by considering users' social influence factors from multiple perspectives. [ABSTRACT FROM AUTHOR]

There has been a lot of research on edge-computing task offloading in deep reinforcement learning (DRL). Deep reinforcement learning is one of the important algorithms in the current AI field, but there is still room for improvement in the time cost and adaptive correction ability of the algorithm. This paper studies the application of DRL algorithms in edge-computing task offloading, and its key innovation is to propose an MADRLCO algorithm, which is based on the design idea of the Actor–Critic framework, uses the DNN model to act as an Actor, and can more accurately locate the initial decision through iterative training, and use the LSTM model to optimize the Critic, which can be more accurate. The optimal decision can be located in a short period of time. The main work of this paper is divided into three parts: (1) The AC algorithm of the Actor–Critic framework in DRL is proposed to be applied to edge-computing task offloading. (2) To address the weak generalization ability of the basic version of the Actor–Critic algorithm in multi-objective optimization, the sequential quantitative correction and adaptive correction parameter K method are used to optimize the Critic frame, thereby improving the generalization ability of the model in multi-objective decision-making and improving the rationality of decision-making results. (3) Aiming at the problem of large time cost in the critical framework of the model, a search algorithm for resource allocation-related parameters based on the time-series prediction method is proposed (time-series forecasting is a research branch of pattern recognition), which reduces the time overhead of the algorithm and improves the adaptive correction capability of the model. The algorithm in this paper can adapt to not only the time-varying network channel state, but also the time-varying number of device connections. Finally, it is proved by experiments that compared with the DRL calculation offloading algorithm based on DNN plus binary search, the MADRLCO algorithm reduces the model training time by 66.27%, and in the environment of the time-varying number of devices in the metasystem, the average model average. The standard calculation rate is 0.0403 higher than that of the current optimal algorithm. [ABSTRACT FROM AUTHOR]

Underwater bubble plume images contain a wealth of information on wave field and flow characteristics, which can provide valuable research data for marine development, environmental protection, and underwater surveys. However, based on fusing image features and wave field environment features, identifying accurately the underwater bubble plume is still very difficult. In order to improve the accuracy and robustness of bubble plume identification in complex underwater environments, an underwater bubble plume recognition algorithm based on multi-feature fusion understanding is proposed. In this paper, a weight-independent dual-channel residual convolutional neural network (CNN) for feature extraction of the original optical images and the nonsubsampled contourlet transform (NSCT) low-frequency images, and the multi-scale composite feature map groups are generated. Then adaptive fusion is performed based on the feature contribution of the target in different types of images. Next, logical region of interest (ROI) masks are generated by the attention mechanism and superimposed on the fused image to further highlight the target features. Finally, the multi-scale dual-channel fused feature maps containing ROI masks are used for underwater bubble plume target recognition. The experimental results show that the designed recognition network can effectively fuse the features of the original optical images and the NSCT low-frequency imagers, improve the depth of information fusion, and retain the target texture features and the morphological features while reducing the interference of the background information, and have good recognition accuracy and robustness for multi-scale bubble targets in the underwater environment. [ABSTRACT FROM AUTHOR]

Printed circuit boards (PCBs) play a critical role in electronic products. Ensuring these products' long-term reliability and consistent performance requires effective PCB defect detection. Although existing deep learning models for PCB defect detection are not highly accurate, they often neglect capability considerations. This paper introduces a precise, fast, and lightweight defect detection model, CCG-YOLO, based on an enhanced YOLOv5 model to address this issue. The enhancements in CCG-YOLO can be summarized as follows: (1) Improved Backbone network: The feature extraction ability of the Backbone network is enhanced by introducing a C3HB module, which fosters spatial interaction capabilities. (2) Lightweight feature fusion network: A lightweight convolution structure called Ghost-Shuffle Convolution is incorporated in the feature fusion network, remarkably reducing model parameters while maintaining performance. (3) Efficient residual networking: To enhance model performance further, a CNeB module is introduced based on the ConvNeXt network, which replaces the C3 module in the Neck. CNeB improves model detection accuracy and reduces the number of model parameters. The combination of these enhancements results in impressive performance. CCG-YOLO achieves mean average precision (mAP@0.5) of 99.5% and 88.75% in mAP@0.5:0.95 on the TDD-Net public dataset. Compared with the original YOLOv5s algorithm, CCG-YOLO offers a 4.24% improvement in mAP@0.5:0.95, a 1 MB reduction in model size, a 0.472 M decrease in the number of parameters, a 0.6G floating point operation reduction in computational complexity, and a 120 frames per second real-time inference speed. These experimental results underscore that the proposed model excels in accuracy and speed and has a compact size for PCB defect detection. Moreover, CCG-YOLO is easily deployable on low-end devices, making it well-suited for meeting the real-time requirements of industrial defect detection. [ABSTRACT FROM AUTHOR]

Deep learning classification is commonly used to assess biomedical data. Fetal heart rate (FHR) signal data monitor the health of both the mother and the fetus and prevent mobility and death in fetuses at risk of hypoxia. But, the data imbalance is a major problem and happens frequently. To overcome this problem, a Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm (HBA) is proposed in this paper for Fetal Arrhythmia Detection and Classification from ECG signals (DSDACNN-HBA-FAC). The input ECG signals are collected from the fetal ECG dataset. Then, the ECG data are pre-processed using coherence shock filtering (CSF), which eliminates artifacts, like baseline drift, contact noise, muscle artifacts, electrode motion, electromyography artifacts. Using the improved non-subs sampled Shearlet transform (INSST) method, the pre-processed ECG data recover the mean, standard deviation, root mean square, skewness, kurtosis, zero-crossing, autoregressive coefficients, estimated entropy, and Higuchi's fractal dimension. The DSDACNN classifier utilizes the features extracted to categorize the output as normal and arrhythmia. In general, DSDACNN does not agree on any optimization techniques to define the optimum parameters and to ensure accurate classification. Therefore, HBA is employed to optimize the DSDACNN weight parameters. The proposed method is implemented in MATLAB. The performance metrics, like sensitivity, precision, recall, f-measure, specificity, accuracy, computation time, RoC, and error rate are evaluated. The performance of DSDACNN-HBA-FAC achieves 3.101%, 7.12%, 7.73% high accuracy, 51.136%, 59.04%, 44.51% lower computation Time and 2.292%, 5.365%, 1.551% greater AUC compared with the existing techniques, like Fetal Arrhythmia Detection with Classification from ECG Signals: A NonInvasive Method (ANN-FAC), Semi-supervised active transfer learning for fetal ECG arrhythmia identification (DNN-FAC), fetal arrhythmia detection using adaptive single channel electrocardiogram extraction (CNN-FAC), respectively. [ABSTRACT FROM AUTHOR]

Under the background of accelerating urbanization and increasing stress of ecological environment, the construction of livable city has attracted extensive attention and become a hot spot in the study of urban problems in the world. The evaluation of livable city is a reference for the comparison of urban development and also one of the evaluation criteria for the comparison of urban competitiveness. This paper focuses on three different evaluation factors of ecological environment, economic development and public service to construct an evaluation model of environmental quality of livable cities. Then particle swarm optimization (PSO) is introduced to optimize the parameters of support vector machine (SVM), and a SVM algorithm based on PSO (PSO-SVM) is proposed to solve the livable city evaluation model. Finally, the spatial analysis combined with ArcGIS software obtained the livable city evaluation and division results of Hunan Province. The results show that PSO-SVM algorithm is superior to SVM, BA-SVM, GA-SVM, and has the advantages of faster speed and higher classification accuracy. [ABSTRACT FROM AUTHOR]

In haptic interaction technology, texture haptic display is an important part. In order to perceive color image texture better, the haptic display methods of color texture based on force feedback and tactile feedback are proposed in this work. On the one hand, through the study of the physiological and psychological perception characteristics of color information, a new force rendering method of color image texture based on force feedback device is presented. The experimental results of color texture force perception show that the color texture force rendering algorithm in this paper works well. On the other hand, a color texture vibration tactile model is established based on the designed vibrotactile device. Its effectiveness is verified by vibration tactile perception experiment. Finally, for the color texture image of the real object surface, the force and vibrotactile display methods of color texture in this paper are used to conduct perception experiments and compared. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, online learning and online education are becoming more and more popular. Intelligent learning diagnosis has become an effective means to guarantee the quality of online learning, and has become a research hotspot in the direction of education informatization. Concept map is an intuitive visual tool that can discover the concepts poorly mastered by students, and provide useful clues for identifying learning disabilities of students. This paper proposed a learning diagnosis method constructed based on the concept map. First, it groups learners, then uses direct hashing and pruning (DHP) to generate association rules between the different concepts, and finally uses the DHP algorithm to produce and construct the concept map automatically to discover the concepts poorly mastered by students, and realize the automatic diagnosis of learning problems. Case studies that have been done in college mathematics classes in some universities of Lianyungang have fully verified the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The new coronavirus spreads widely through droplets, aerosols and other carriers. Wearing a mask can effectively reduce the probability of being infected by the virus. Therefore, it is necessary to monitor whether people wear masks in public to prevent the virus from spreading further. However, there is no mature general mask wearing detection algorithm. Based on tiny YOLOv3 algorithm, this paper realizes the detection of face with mask and face without mask, and proposes an improvement to the algorithm. First, the loss function of the bounding box regression is optimized, and the original loss function is optimized as the Generalized Intersection over Union (GIoU) loss. Second, the network structure is improved, the residual unit is introduced into the backbone to increase the depth of the network and the detection of two scales is expanded to three. Finally, the size of anchor boxes is clustered based on k -means algorithm. The experimental results on the constructed dataset show that, compared with the tiny YOLOv3 algorithm, the algorithm proposed in this paper improves the detection accuracy while maintaining high-speed inference ability. [ABSTRACT FROM AUTHOR]

Nowadays, imbalanced data classification is a hot topic in data mining and recently, several valuable researches have been conducted to overcome certain difficulties in the field. Moreover, those approaches, which are based on ensemble classifiers, have achieved reasonable results. Despite the success of these works, there are still many unsolved issues such as disregarding the importance of samples in balancing, determination of proper number of classifiers and optimizing weights of base classifiers in voting stage of ensemble methods. This paper intends to find an admissible solution for these challenges. The solution suggested in this paper applies the support vector data descriptor (SVDD) for sampling both minority and majority classes. After determining the optimal number of base classifiers, the selected samples are utilized to adjust base classifiers. Finally, genetic algorithm optimization is used in order to find the optimum weights of each base classifier in the voting stage. The proposed method is compared with some existing algorithms. The results of experiments confirm its effectiveness. [ABSTRACT FROM AUTHOR]

The technique of human action recognition can be applied in a number of fields, such as medical rehabilitation, posture in the domain of sports, and emotion perception in counselling. Deep learning action recognition models that consider the continuous changes in the human skeleton can efficiently identify action states, such as rehabilitation processes, posture, and changes in emotion. However, the keypoints of the human skeleton are susceptible to occlusion due to body movement, resulting in a lower confidence level in their positions, which in turn affects the training and execution of human action recognition models. This paper proposes a keypoint correction algorithm based on continuous-time images of temporal variation that can predict and correct the skeleton keypoints with low confidence, based on the previous and subsequent multiple images. The proposed temporal-variation skeleton keypoint correction algorithm can provide accurate feature association training for the locations of the skeleton keypoints. The proposed method is shown to improve the accuracy of human action recognition by at least 30% over three alternative models in the literature: STV-GCN, GCN-NAS, and 2S-AGCN. The proposed algorithm is also improved by at least 30% compared with the above-mentioned methods in the literature, based on an action recognition model with a Gaussian filter. [ABSTRACT FROM AUTHOR]

In recent years, the study of social-based recommender systems has become an active research topic. We incorporate a combination of static and dynamic interest characteristics to predict users' real-time dynamic interests, which has rarely been considered in previous studies. In this paper, we propose a graph neural network social recommendation model that integrates static and dynamic feature relationships (FSDFR-GNNSR). The model uses a graph embedding algorithm to extract static features of users and movies, and takes the static features as input to gated recurrent unit (GRU), so that the model can take static features into consideration while modeling user dynamic behavior. Finally, we use graph attention networks to represent the dynamic influence of friends, simplify the update strategy of second-order neighbor nodes. We apply graph pooling operations to improve the generalization ability of the algorithm. Empirical analyses on real datasets show that the proposed approach achieves superior performance to existing approaches. [ABSTRACT FROM AUTHOR]

In order to solve the inaccuracy of age estimation dataset and the imbalance of age distribution, this paper proposes an age estimation model based on the structured sparse learning. Firstly, the Multi-label representation of facial images is performed by age, and the age estimation model is trained by solving the model matrix. Finally, the correlation with all age labels is calculated according to the facial images and age estimation model to be tested, and the most correlated age is taken as the predicted age. This paper sets up a series of verification experiments, and analyzes the structured sparse age estimation model from several perspectives. The proposed algorithm has achieved good results in the evaluation of indexes such as the mean absolute error, accumulation index curve and convergence rate, and has designed the demo system to put the model into use. Facts prove that the age estimation model proposed in this paper may achieve a good estimation effect. [ABSTRACT FROM AUTHOR]

As a meta-heuristic algorithm, the ant colony algorithm has been successfully used to solve various combinatorial optimization problems. However, the existing algorithm that takes the power of ants to solve distributed constraint optimization problems (ACO_DCOP) is easy to fall into local optima. To deal with this issue, this paper presents an adaptive ant colony algorithm based on local information entropy to solve distributed constraint optimization problems, named LIEAD. In LIEAD, the local information entropy is introduced to help agents adaptively select the pheromone update strategy and value selection strategy, which improves the convergence speed and the quality of the solution. Moreover, a restart mechanism is designed to break the accumulation state of pheromone, which increases the population diversity and helps the algorithm jump out of the local optima. The extensive experimental results indicate that LIEAD can significantly outperform ACO_DCOP and is competitive with the state-of-the-art DCOPs algorithms. [ABSTRACT FROM AUTHOR]

Microscopic images of bovine milk somatic cells are used to classify neutrophils, epithelial cells, macrophages and lymphocytes. Using pattern recognition technology, the problem of classification and recognition is solved from different nature, levels and spaces. The proposed RKSGA-SVM algorithm is used to realize somatic cell image recognition. First, color, morphological and texture features of four types of cells are extracted separately, including geometric and moment invariant features. Second, ReliefF algorithm is used to calculate the weights of all features. According to preset cumulative contribution rate, the preliminary feature set is obtained. Third, redundant features are eliminated by Kolmogorov–Smirnov (KS) test, and the high-level optimization is obtained. The selected feature sets have remarkable distinguishing ability. Finally, the weighted optimal feature sets are obtained by weighted coefficient method on the advanced optimal feature sets. The overall accuracy of RKSGA-SVM algorithm is 99.00%, and Kappa coefficient is 0.987. The proposed algorithm has the advantages of balancing classification accuracy, eliminating redundancy and reducing feature dimension. On the premise of ensuring high classification accuracy, the feature set can reduce feature dimension and the amount of data calculation, improve operation efficiency and save storage space. Experiments show that the feature selection method proposed in this paper is feasible and more suitable for extracting feature sets in the process of somatic cell classification. [ABSTRACT FROM AUTHOR]

Threshold segmentation has been widely used in recent years due to its simplicity and efficiency. The method of segmenting images by the two-dimensional maximum entropy is a species of the useful technique of threshold segmentation. However, the efficiency and stability of this technique are still not ideal and the traditional search algorithm cannot meet the needs of engineering problems. To mitigate the above problem, swarm intelligent optimization algorithms have been employed in this field for searching the optimal threshold vector. An effective technique of lightning attachment procedure optimization (LAPO) algorithm based on a two-dimensional maximum entropy criterion is offered in this paper, and besides, a chaotic strategy is embedded into LAPO to develop a new algorithm named CLAPO. In order to confirm the benefits of the method proposed in this paper, the other seven kinds of competitive algorithms, such as Ant–lion Optimizer (ALO) and Grasshopper Optimization Algorithm (GOA), are compared. Experiments are conducted on four different kinds of images and the simulation results are presented in several indexes (such as computational time, maximum fitness, average fitness, variance of fitness and other indexes) at different threshold levels for each test image. By scrutinizing the results of the experiment, the superiority of the introduced method is demonstrated, which can meet the needs of image segmentation excellently. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology and network technology, it has become possible to build a large-scale networked video surveillance system. The video surveillance system has become a new type of infrastructure necessary for modern cities. In this paper, the problem of foreground extraction and motion recognition in intelligent video surveillance is studied. The three key sub-problems, namely the extraction of motion foreground in video, the deblurring of motion foreground and the recognition of human motion, are studied and corresponding solutions are proposed. A background modeling technique based on video block is proposed. The background is modeled at the block level, which greatly reduces the spatial complexity of the algorithm. It solves the problem that the traditional Gaussian model (GMM) moving target enters the static state and is integrated into the background process. The target starts to move for a long time and there are ghosts and other problems, which reduce the processing efficiency of the lifting algorithm. The test results on the Weizmann dataset show that the proposed algorithm can achieve high human motion recognition accuracy and recognition with low computational complexity. The rate can reach 100%; the local constrained group sparse representation classification (LGSRC) model is used to classify it. The experimental results on Weizmann, KTH, UCF sports and other test datasets confirm the validity of the algorithm in this chapter. KNN, SRC voting classification accuracy. [ABSTRACT FROM AUTHOR]

Pose estimation is the basis and key of human motion recognition. In the two-dimensional human pose estimation based on image, in order to reduce the adverse effects of mutual occlusion among multiple people and improve the accuracy of motion recognition, a structurally symmetrical two-dimensional multi-person pose estimation model combined with face detection is proposed in this paper. First, transfer learning is used to initialize each sub-branch network model. Then, MTCNN is used for face detection to predict the number of people in the image. According to the number of people, the image is input into the improved two-branch OpenPose network. What is more, the double judgment algorithm is proposed to correct the false detection of MTCNN. The experimental results show that compared with TensorPose, which is the latest improved method based on OpenPose, the Average Precision (AP) (Intersection over Union (IoU) = 0. 5) on the validation set is 8.8 higher. Furthermore, compared with OpenPose, the mean AP (IoU = 0. 5 : 0. 9 5) is 1.7 higher on the validation set and is 1.3 higher on the Test-dev test set. [ABSTRACT FROM AUTHOR]

A recent paper posed the question: "Graph Matching: What are we really talking about?". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented and discussed. The first includes almost all the graph matching algorithms proposed from the lake seventies, and describes the different classes of algorithms. The second taxonomy considers the types of common applications of graph-based techniques in the Pattern Recognition and Machine Vision field. [ABSTRACT FROM AUTHOR]

In order to study the development trend of the Internet's role in supporting enterprise innovation, a new method based on deep learning algorithm and knowledge graph technology is proposed. Experiments show that the accuracy, F1 value, recall rate and precision of the algorithm are distinctly improved compared with the existing algorithms. A new algorithm is applied to analyze the innovation evolution of Chinese enterprises, using papers, patents and other documents between 1905 and 2020 as data sources. Based on the experimental results, the development stages can be divided into five stages. The research focus is on product R&D innovation, manufacturing innovation, marketing innovation, resource allocation innovation and organizational innovation. It can be seen that the development process of the Internet supporting enterprise innovation system is an evolutionary development process from point to line, to surface, to the body, and to the ecosystem. [ABSTRACT FROM AUTHOR]

Numerous surrogate-assisted expensive multi-objective optimization algorithms were proposed to deal with expensive multi-objective optimization problems in the past few years. The accuracy of the surrogate models degrades as the number of decision variables increases. In this paper, we propose a surrogate-assisted expensive multi-objective optimization algorithm based on decision space compression. Several surrogate models are built in the lower dimensional compressed space. The promising points are generated and selected in the lower compressed decision space and decoded to the original decision space for evaluation. Experimental studies show that the proposed algorithm achieves a good performance in handling expensive multi-objective optimization problems with high-dimensional decision space. [ABSTRACT FROM AUTHOR]

Van Genuchten (VG) equation is the most commonly used equation of soil moisture characteristic curve, and the accuracy of its parameters directly affects the calculation accuracy of soil moisture motion equation. In order to obtain the parameters of the equation more accurately, this paper establishes an optimization model of the VG equation parameters. This optimization model uses the advantages of the cuckoo search algorithm and the differential evolution algorithm to combine the two into a new hybrid cuckoo search algorithm, namely DECS algorithm, and uses this algorithm to solve the parameter optimization problem of VG equation of soil moisture characteristic curve. By collecting and analyzing the relevant experimental data of various soil qualities, the dehumidification and moisture absorption curves of three different soil qualities were selected for simulation calculation. The results show that in the experiment of solving the parameter estimation problem of the VG equation, the DECS hybrid algorithm has better exploration and development capabilities than the cuckoo search algorithm. The hybrid algorithm has relatively significant performance in terms of calculation accuracy and convergence. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) is the hottest network paradigm, which permits the Secondary Users (SUs) like wireless devices/users for intelligent accessing of unallocated radio spectrum. Such accessing happens by enabling interference-free transmission of Primary Users (PUs), who are allotted with some deserved radio spectrum portions. This radio communication paradigm has effective usage in vehicular networks, where communication should be established from vehicles to static stations (vehicle-to-infrastructure) or within vehicles (vehicle-to-vehicle), without allotting dedicated frequencies. Nevertheless, the major issue in designing CR is that it must be built to aid in efficient transmitting and sensing of data through the available radio spectrum channels. This paper proposes a Model Predictive Control (MPC)-based prediction model, via a Deep Learning approach. Here, a Deep Belief Network (DBN) allows in predicting the PU transmission state as idle or busy. Moreover, this paper comes out with a new optimization concept that achieves more accurate and precise prediction. The weight of DBN is optimally selected to pave way for effective performance. Further, a new hybrid algorithm named as Cuckoo Search-Grasshopper Optimization Algorithm (CS-GOA) is proposed. The performance of the proposed model is compared over the other conventional models, in terms of channel utilization and back off, and proved for supremacy. The throughput of the proposed model, even at 50 SUs is better, when compared to other methods. CS-GOA achieved better channel utilization and backoff rate, as compared to ProMAC and NN, when the numbers of SUs and PUs in the architecture are varied with time. [ABSTRACT FROM AUTHOR]

In this paper, an efficient interactive image segmentation method based on maximization of submodular function under user's scribble constraint is presented. The problem of interactive image segmentation is formulated as the maximum entropy rate under user's constraints. The objective function is submodular, and we solve the constrained submodular function maximization by incorporating a new data structure and some aggregating rules into the greedy algorithm. The main steps of our algorithm are as follows. First, the pixels scribbled by the user are clustered separately as target foreground and background clusters. Second, in the process of greedy algorithm, unscribbled pixels are aggregated to the corresponding target cluster according to the proposed aggregating rules. Finally, the segmentation result is presented by the two target clusters. The experiments and comparisons on three standard benchmarks show that our method has good performance. Our method is straightforward and efficient, and the time complexity of our method is between linear and polynomial. Furthermore, we analyze the influence of different scribbles, and propose some optimal scribble strategies. [ABSTRACT FROM AUTHOR]

In this paper, the artificial intelligence control algorithm for steering robot of steering wheel is studied. The steering movement of wheeled soccer robot is controlled by artificial intelligence control algorithm, and the steering movement is modeled and simulated. Firstly, the characteristics of artificial neurons are simulated and a similar control model is constructed to complete the simulation of football. The artificial intelligence control algorithm has a dynamic feedback item compared with the traditional intelligent model, which has a better effect on the steering control of the wheeled soccer robot. In this paper, artificial intelligence control algorithm is used to optimize the parameters of artificial intelligence control algorithm, and the output of control signal of each steering part of wheeled soccer robot is simulated in the experiment, and the control of the steering action of wheeled soccer robot by artificial intelligence control algorithm is verified by experiments. Then the artificial intelligence control algorithm forms the connection structure. This method provides a good reference for steering control of wheeled soccer robots. [ABSTRACT FROM AUTHOR]

This paper presents an end-to-end algorithm for solving circuit problems in secondary physics. A key challenge in solving circuit problems is to automatically understand circuit problems over the modals of both text and schematic. Existing methods have a limited capacity in problem understanding due to the they cannot deal with the numerous expressions of problems in natural language and the various circuit diagrams. In fact that this paper, a batch of methods is proposed to work against the challenge of solving circuit problems. The problem understanding is modeled as a problem of relation extraction and a scheme is proposed to extract relations from both text and schematic. A syntax–semantics model is adopted to extract explicit relations from text, whereas a unit-theorem-based method is proposed to extract implicit relations. And a mesh search method is proposed to extract relations from schematic. Based on the result of problem understanding, an algorithm is proposed to produce the solutions of circuit problems, in which the solutions are presented in a readable way. The experimental results demonstrate the effectiveness of the proposed algorithm in solving circuit problems. To the best of our knowledge, this paper is the first literature which reports the quantitative results in understanding and solving circuit problems. [ABSTRACT FROM AUTHOR]

With the limitation of air traffic and the rapid increase in the number of flights, flight delay is becoming more frequent. Flight delay leads to financial and time losses for passengers and increases operating costs for airlines. Therefore, the establishment of an accurate prediction model for flight delay becomes vital to build an efficient airline transportation system. The air transportation system has a huge amount of data and complex operation modes, which is suitable for analysis by using machine learning methods. This paper discusses the factors that may affect the flight delay, and presents a new flight delay prediction model. The five warning levels are defined based on flight delay database by using K-means clustering algorithm. After extracting the key factors related to flight operation by the grey relational analysis (GRA) algorithm, an improved machine learning algorithm called GRA — Genetic algorithm (GA) — back propagation neural network, GRA-GA-BP, is introduced, which is optimized by GA. The calculation results show that, compared with models before optimization and other two algorithms in previous papers, the proposed prediction model based on GRA-GA-BP algorithm shows a higher prediction accuracy and more stability. In terms of operation efficiency and memory consumption, it also has good performance. The analysis presented in this paper indicates that this model can provide effective early warnings for flight delay, and can help airlines to intervene in flights with abnormal trend in advance. [ABSTRACT FROM AUTHOR]

This paper focuses on the intelligent recognition problem of radar detection targets. Aiming at the low accuracy and slow speed of millimeter-wave radar clustering point cloud information, a feature algorithm of millimeter-wave radar suitable for detecting targets is proposed. In the detection of targets by millimeter-wave radar, distance is the biggest factor affecting the number and degree of sparsity. A method that combines the feature information of the point cloud with the KD tree proximity search algorithm and the DBSCAN clustering algorithm is proposed, which can adapt to the problems of uneven target point cloud, small amount of data and slow clustering speed. The improved algorithm can use the KD tree to quickly find adjacent points and calculate the distance between adjacent points. The corresponding number of thresholds is set according to the distance where the target is located, and the radius of the target area reflected by the millimeter-wave radar plus the distance of the last threshold point is used as the neighborhood radius of the improved algorithm. Therefore, fast and adaptive parameter adjustment of the millimeter-wave radar can be realized. Simulation tests show that the improved clustering algorithm has better parameters. The accuracy of the improved algorithm is increased by 4.2%, and it also greatly improves the clustering speed. [ABSTRACT FROM AUTHOR]

With the improvement of digital image display technology, the "secondary imaging" caused by digital cameras is also gradually popularized, and the quality of the recaptured image formed by this imaging is also getting higher and higher, and this kind of high-quality fake image has caused great threat to digital images security. We propose a neural network-based recaptured image identification algorithm and use the difference between two types of images to build the identification algorithm in the frequency domain. The algorithm uses filtering to obtain the feature images which are the high-frequency and low-frequency filtering images, in order to further distinguish the image differences, the direction of the filtered image obtained from high-frequency images, each direction of the filtered image contains high-frequency information at different angles, and the low-frequency image is downsampled. At the same time, the low-frequency image is downsampled to obtain a multi-scale filtered image. The algorithm extracts the features from previous images as the feature values for classification, and finally uses neural networks for classification to obtain the classification results, and these prove that the algorithm presented is able to differentiate the recaptured images effectively in this paper. [ABSTRACT FROM AUTHOR]

Attribute reduction is a part of the most basic and significant research contents in rough set theory. The so-called attribute reduction is the smallest independent subset that keeps certain properties of information table unchanged. In this paper, four interval-valued intuitionistic fuzzy probabilistic rough set models and their natures are given on the basis of the dominant-, inferior- and interval-valued intuitionistic fuzzy probabilistic rough set models. At the same time, the interval-valued intuitionistic fuzzy numbers are transformed by fuzzy degree, and the approximate accuracy and approximate classification quality under the dominance relation are used for reduction, at last, the feasibility is verified by an example. [ABSTRACT FROM AUTHOR]

The image copy–move forgery can achieve the realistic effects without leaving any obvious manipulation traces because the copied source and pasted target belong to the homogenous sources. Image copy–move forgery detection (CMFD) is still a serious challenge issue for researchers, e.g. computational efficiency, occlusive (smoothing) object detection, and partial object detection, under various post-processing and geometrical transformations. This paper proposes a novel CMFD scheme using the characteristics of local descriptors. First, the proposed method deeply analyzes the structure and mines the inherent characteristics of the local descriptor (SURF) for feature extraction in the coarse and smooth regions. Subsequently, the proposed method takes the kernel features as coarse feature matching to decrease matching costs. Then, the found candidate keypoints with a relatively small quantity are used with complete features to perform fine keypoint matching for getting suspicious candidate keypoint pairs. Furthermore, based on the deep mining of the inherent local descriptor characteristics, the proposed method can indicate the forgery region localizations through keypoint pairs, even the forgeries suffering from various geometrical transformations. Then, the proposed method proposes adaptive dual-filtering algorithms of K -means relying on keypoint characteristics, and spatial distance for hierarchical progress of feature filtering. Finally, Delaunay Triangulation relies on the true-positive keypoint pairs to perform the forgery region matting effectively and efficiently. The experimental results demonstrate that the proposed method can achieve the best F1 scores compared to the state-of-the-art methods in most cases, especially in anti-scaling transformations. [ABSTRACT FROM AUTHOR]

In this paper, a steganography algorithm has been proposed which is based on quantization table modification and image scrambling in the DCT domain. First, the algorithm homogenizes the energy by scrambling the cover image to improve the number of DCT coefficients suitable for information embedding. Second, the embedding capacity of the DCT block is determined by the value of the quantization table in the algorithm, different quantization tables get different embedding effects. In addition, this paper proposes an optimized modified quantization table. Extensive experiments show that the proposed algorithm achieves a great potential for confidential data and indiscernible image quality. [ABSTRACT FROM AUTHOR]

For the virtual reality 360 ∘ videos, equirectangular projection (ERP) is a commonly used projection format. However, its high resolution brings extraordinary huge computational complexity in encoding. In order to speed up the intra coding process, a fast coding unit (CU) partitioning algorithm based on regional decision tree is proposed in this paper. The frame image is divided into two regions from a statistical point of view, and the earlysplit and pruned decision trees are established using light weight sample attributes for each region. With the help of these decision trees, the CU partitioning process is accelerated. Compared with the original algorithm of HM16.20, the proposed algorithm can reduce the encoding time by 28%, while BD-rate only increases by 0.27%. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures. [ABSTRACT FROM AUTHOR]

Online transaction log records the relevant information of the users, commodities and transactions, as well as changes over time, which can help analysts understand commodities' sales. The existing visualization methods mainly analyze the purchase behavior from the perspective of users, while analyzing the sales trend of commodities can better help merchants to make business decisions. Based on the transaction log, this paper puts forward the visual analysis framework of commodity sales trend and the corresponding data processing algorithm. The concepts of volatility and dynamic performance of sales trend are proposed, through which the multi-dimensional sales data of time-oriented are displayed in two-dimensional space. The "Feature Ring" is designed to display the detailed sales information of the products. Based on the above methods, a visual analysis system is designed and implemented. The usability and validity of the visualization methods are verified by using JD online transaction data. The visualization methods enable manufacturers to formulate production plans and carry out product research and develop better. [ABSTRACT FROM AUTHOR]

Most nonstationary and time-varying mixed source separation algorithms are based on the model of instantaneous mixtures. However, the observation signal is a convolutional mixed source in reverberation environment, such as mobile voice received by indoor microphone arrays. In this paper, a time-varying convolution blind source separation (BSS) algorithm for nonstationary signals is proposed, which can separate both time-varying instantaneous mixtures and time-varying convolution mixtures. We employ the variational Bayesian (VB) inference method with Gaussian process (GP) prior for separating the nonstationary source frame by frame from the time-varying convolution signal, in which the prior information of the mixing matrix and the source signal are obtained by the Gaussian autoregressive method, and the posterior distributions of parameters (source signal and mixing matrix) are obtained by the VB learning. In the learning process, the learned parameters and hyperparameters are propagated to the next frame for VB inference as the prior which is combined with the likelihood function to get the posterior distribution. The experimental results show that the proposed algorithm is effective for separating time-varying mixed speech signals. [ABSTRACT FROM AUTHOR]

The traditional Gaussian mixture background model failed to build a reasonable background in complex scenarios, so this paper proposes an improved self-adaptive Gaussian mixture background modeling which integrates the difference method and adaptive threshold segmentation to improve the traditional one. In the proposed model, the difference method is applied to achieve the segmentation of changing area and background area, and different weight update policies are used for different areas. Background area updates background model with a fixed update rate. The changing region is divided into moving target area and background show area with the fusion of adaptive threshold; the background show area has a large update rate, allowing the previously obscured parts to recover rapidly; the moving target area won't build new Gaussian components for the Gaussian mixture model. Experiments show that the video sequence algorithm with uncertainties of building background model has good adaptability. It helps to improve computing speed to a large extent and it can also respond to the change of the actual scene quickly. [ABSTRACT FROM AUTHOR]

Jigsaw puzzle algorithm is important as it can be applied to many areas such as biology, image editing, archaeology and incomplete crime-scene reconstruction. But, still, some problems exist in the process of practical application, for example, when there are a large number of similar objects in the puzzle fragments, the error rate will reach 30%-50%. When some fragments are missing, most algorithms fail to restore the images accurately. When the number of fragments of the jigsaw puzzle is large, efficiency is reduced. During the intelligent puzzle, mainly the Sum of Squared Distance Scoring (SSD), Mahalanobis Gradient Compatibility (MGC) and other metrics are used to calculate the similarity between the fragments. On the basis of these two measures, we put forward some new methods: 1. MGC is one of the most effective measures, but using MGC to reassemble the puzzle can cause an error image every 30 or 50 times, so we combine the Jaccard and MGC metric measure to compute the similarity between the image fragments, and reassemble the puzzle with a greedy algorithm. This algorithm not only reduces the error rate, but can also maintain a high accuracy in the case of a large number of fragments of similar objects. 2. For the lack of fragmentation and low efficiency, this paper uses a new method of SSD measurement and mark matrix, it is general in the sense that it can handle puzzles of unknown size, with fragments of unknown orientation, and even puzzles with missing fragments. The algorithm does not require any preset conditions and is more practical in real life. Finally, experiments show that the algorithm proposed in this paper improves not only the accuracy but also the efficiency of the operation. [ABSTRACT FROM AUTHOR]

The data are rapidly expanding nowadays, which makes it very difficult to analyze valuable information from big data. Most of the existing data mining algorithms deal with big data problems at large time and space costs. This paper focuses on the sampling problem of big data and puts forward an efficient heuristic Cluster Sampling Arithmetic, called CSA. Many of the former researchers adopted random method to extract early sample set from the original data and then made a variety of different processing of the sample in order to obtain the corresponding minimum sample set, which is regarded as a representation of the original big data set. However, the final processing results of big data will be severely affected by the random sampling process at the beginning, resulting in lower comprehensiveness and quality of the final data results and longer processing time. Based on this view, CSA introduces the idea of clustering to obtain minimum sample set of big data, which is in contrast to the random sampling method in the current literature. CSA makes cluster analysis of the original data set and selects the center of each class as centralized members of the minimum sample set. It aims at ensuring that the sample distribution accords with the characteristics of the original data, guarantees the data integrity and reduces the processing time. The max-min distance means that the pattern recognition has been integrated into the clustering process in order to get the clustering center and prevent algorithm from local optimum. The final experimental results show that, compared with the existing work, CSA algorithm can efficiently reflect the characteristics of the original data and reduce the time of data processing. The obtained minimum sample set has also achieved good effects in the classification algorithm. [ABSTRACT FROM AUTHOR]