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4th International Conference on Artificial Intelligence and big data, ICAIBD 2021

作者： Chen, Luyao Xu, Tao International Education College Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9780738131702

Housing transaction data from online housing platform contains many valuable information about development trends of real estate, but is also the first-hand reference for citizens to carry out housing transaction. However, how to find the key information hidden in housing transaction data from Internet is still a serious challenge. To solve it, based on web crawling technology and visual analysis technique, this paper proposes a novel scheme for gaining and analyzing housing transaction data. It includes data crawling, data prepossessing, data analysis and other steps. An empirical research is conducted with housing transaction data of Hangzhou, China. The results show the spatial distribution characters of housing price, and the most cost-effective type of real estate in Hangzhou. © 2021 IEEE.

关键词： Web crawler

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Graph-Enhanced Multi-Scale Contrastive Learning for Graph Anomaly Detection with Adaptive Diffusion Models

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IEEE Transactions on big data 2025年

作者： Xiao, Chunjing Tong, Yao Lu, Jiahui Xue, Yuxia Tai, Wenxin Cheng, Zhangtao Zhou, Fan The Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China The University of Electronic Science and Technology of China Chengdu610054 China

Graph anomaly detection has gained significant research interest across various domains. Due to the lack of labeled data, contrastive learning has been applied in detecting anomalies and various contrastive strategies have been initiated. However, these methods might force two instances (e.g., node-level and subgraph-level representations) with different category labels to be consistent during model training, which can adversely impact the model robustness. Also, they extract node-level representations only based on node attributes, which are inadequate in reflecting the information of the structural anomaly. To tackle this problem, we present a Graph-enhanced multi-scale Contrastive Learning framework for Anomaly Detection, GCLAD. In this framework, we design a diffusion probabilistic model-based graph enhancement module to adaptively manipulate neighbors to generate enhanced graphs, which can efficiently enhance subgraph-level representations and alleviate the inconsistent problem. Further, we present a multi-scale contrastive module where we introduce meta-paths to exploit a few relevant neighbors to boost node-level representations, and build the multi-scale contrastive losses to promote anomaly detection performance. Experimental results demonstrate the superiority of GCLAD compared with state-of-the-art baselines. © 2015 IEEE. All rights reserved.

关键词： Contrastive Learning

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Augmenting Prototype Network with TransMix for Few-shot Hyperspectral Image Classification

arXiv

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arXiv 2024年

作者： Liu, Chun Yang, Longwei Dong, Dongmei Li, Zheng Yang, Wei Han, Zhigang Wang, Jiayao School of Computer and Information Engineering Henan Key Laboratory of Big Data Analysis and Processing Henan Engineering Laboratory of Spatial Information Processing Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Beijing Electro-Mechanical Engineering Institute Beijing100074 China College of Geography and Environmental Science Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

Few-shot hyperspectral image classification aims to identify the classes of each pixel in the images by only marking few of these pixels. And in order to obtain the spatial-spectral joint features of each pixel, the fixed-size patches centering around each pixel are often used for classification. However, observing the classification results of existing methods, we found that boundary patches corresponding to the pixels which are located at the boundary of the objects in the hyperspectral images, are hard to classify. These boundary patchs are mixed with multi-class spectral information. Inspired by this, we propose to augment the prototype network with TransMix for few-shot hyperspectrial image classification(APNT). While taking the prototype network as the backbone, it adopts the transformer as feature extractor to learn the pixel-to-pixel relation and pay different attentions to different pixels. At the same time, instead of directly using the patches which are cut from the hyperspectral images for training, it randomly mixs up two patches to imitate the boundary patches and uses the synthetic patches to train the model, with the aim to enlarge the number of hard training samples and enhance their diversity. And by following the data agumentation technique TransMix, the attention returned by the transformer is also used to mix up the labels of two patches to generate better labels for synthetic patches. Compared with existing methods, the proposed method has demonstrated sate of the art performance and better robustness for few-shot hyperspectral image classification in our experiments. All the codes are available at https://***/HENULWY/APNT. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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Azimuth signal reconstruction for hrws-sar from uniform resampling 2

Azimuth signal reconstruction for hrws-sar from uniform resa...

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2nd International Conference on Artificial Intelligence, Information processing and Cloud Computing, AIIPCC 2021

作者： Zhang, Hanqing Wu, Lin Li, Ning Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China College of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9783800756162

Signal reconstruction is a key step in the azimuth multichannel high-resolution wide-swath synthetic aperture radar (HRWS-SAR) imaging algorithms. Krieger’s digital beam-forming (DBF) algorithm is currently one of the commonly used algorithms for processing HRWS-SAR data. However, this algorithm is only suitable for uniform space-time sampling and moderately non-uniform sampling. In the case of highly non-uniform sampling, the imaging performance of Krieger’s DBF algorithm will be seriously deteriorated. In this paper, a uniform resampling algorithm is presented to solve above problem. The proposed algorithm adopts modern sampling theory to reduce the approximate error. This algorithm consists of two key steps: 1. The given signal samples are projected onto a selected intermediate subspace, spanned by integer translations of a compactly supported kernel function. This process generates a sparse set of equations, which can be effectively solved by using a sparse equation solvers. 2. The results are projected onto the subspace where the sampled signal is known to reside. The orthogonal matching pursuit (OMP) algorithm is used to realize the second projection and reconstruction the signal effectively. Simulation results show that the newly proposed algorithm has a significant improvement in imaging performance than Krieger’s DBF algorithm. © 2021 AIIPCC 2021 - 2nd International Conference on Artificial Intelligence, Information processing and Cloud Computing. All rights reserved.

关键词： Synthetic aperture radar

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An Algorithm of data Classification Based on PCA and K-Means++

An Algorithm of Data Classification Based on PCA and K-Means...

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2021 IEEE Conference on Telecommunications, Optics and Computer Science, TOCS 2021

作者： Huang, Ruirui Cheng, Qianshuai Chen, Zhengquan Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9781665424981

Principal component analysis (PCA) and K-means++ algorithm are used to classify and analyze eight main variables of the average annual consumption expenditure of urban households in 31 Provinces and cities in China in a given year, so as to better build the reasonable consumption level of each area. In this paper, a data classification method based on PCA and improved K-means++ is proposed. In this method, PCA was used to extract eight variable characteristics of consumer expenditure, and K-means++ algorithm was used to classify the data. The experimental results show that this method reduces the dependence of the characteristic dimension of variable data and the k-means++ algorithm on the initial classification center of gravity, and improves the accuracy and stability of data classification results. © 2021 IEEE.

关键词： Principal component analysis

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An application-oriented multi-granularity vehicle trajectory data indexing structure based on concept lattice 5

An application-oriented multi-granularity vehicle trajectory...

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5th International Conference on Computer Information Science and Application Technology, CISAT 2022

作者： Xu, Tao Zhang, Zixiang Ma, Xiaqing Deng, Jiaming Zhao, Yingying Li, Xiang Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China International Education College Henan University Zhengzhou450046 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

ISBN: (纸本)9781510660076

Massive amount of vehicle trajectory data is an important data source and has been widely used in many research fields. However, due to the huge volume and variety of application scenarios, it is still not easy to achieve the application-oriented and efficient retrieval of vehicle trajectory. The paper proposes a multi-granularity vehicle trajectory index method based on the concept lattice model. The method identifies the multi-granularity feature regions for application requirements to segment vehicle trajectory and employs the concept lattice model to maintain the complex relationships between vehicle trip and the feature regions, to realize the vehicle travel trajectory data retrieval for the multi-granularity feature regions. In this experiment, shanghai city is divided into 21 feature regions according to the county-level area of Shanghai, and the concept lattice index structure of vehicle trips is constructed. The availability and efficiency of the proposed method are verified by the comparative experimental results. © 2022 SPIE.

关键词： Vehicles

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Design of Offline analysis System for Remote Sensing data Service Based on Hive

Design of Offline Analysis System for Remote Sensing Data Se...

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2021 International Conference on Digital Society and Intelligent Systems, DSInS 2021

作者： Zhang, Hongkuo Zhang, Jiaxiang Tian, Meng Qiao, Baojun Heanan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9781665406307

With the wide application of remote sensing data service platform, the platform accumulates more and more data, and the remote sensing users' demand for remote sensing data service becomes more and more diverse. Therefore, how to deal with remote sensing service data, make statistics and analyze its potential value, and meet the diverse needs of remote sensing users becomes particularly important. Based on big data technology, this paper firstly collects and stores remote sensing user behavior data and remote sensing service platform business data, then uses Hive to build offline data warehouse, performs dimensional modeling and hierarchical design of the data, and finally obtains relevant index data values by statistical analysis. Through the statistical analysis of remote sensing service data can not only provide data support for active service mode, but also provide support for the decision of remote sensing application department managers. © 2021 IEEE.

关键词： data warehouses

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Design and Research of Remote Sensing Process Language based on XML 2

Design and Research of Remote Sensing Process Language based...

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2nd IEEE International Conference on Information Technology, big data and Artificial Intelligence, ICIBA 2021

作者： Zuo, Xianyu Li, Heng Zhou, Liming Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781665428767

In the era of rapid development of remote sensing technology, in order to improve the production efficiency of remote sensing data processing flow and further optimize the editing of remote sensing processes in the visual interface. In this paper we have analyzed the characteristics and attributes of remote sensing processes and algorithms. We made the production of processes and algorithms into scripts, and realizes to create remote sensing processes by inputting scripting language in the visual interface. A remote sensing language based on XML is designed to read and store key information. This language will use XML to drive the production of remote sensing process. With the language we design, a remote sensing process customization system has been implemented. It can improve the editing and production efficiency of remote sensing data processing flow. © 2021 IEEE.

关键词： XML

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PG-YOLO: A Lightweight Vehicle Detection Method for Drones

PG-YOLO: A Lightweight Vehicle Detection Method for Drones

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International Symposium on Parallel and Distributed processing with Applications, ISPA

作者： Yang Liu Tengyu Fan Kun Mei Liming Zhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Department of Clinical Laboratory Huaihe Hospital of Henan University Kaifeng China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the rapid development of drone technology, object detection technology is widely applied in the field of drone intelligent transportation. In response to the problems of low accuracy and high computational complexity in vehicle detection tasks on resource limited drones, this paper proposes a lightweight and efficient improved model PG-YOLO for drone vehicle detection based on the YOLOv7-tiny model. Firstly, replace the LeakyReLU activation function with the SiLU activation function to compensate for the lack of nonlinear expression in the LeakyReLU activation function, and improve the convergence speed and generalization of the model during training; Secondly, the feature extraction and fusion modules were optimized to enhance the network’s perception and capture capabilities of the target, improve detection accuracy, and reduce the number of parameters; Then, lightweight convolution PConv and GSConv are introduced, and a lightweight module PPC based on PConv is proposed to improve the network’s Neck layer, reducing the number of parameters and computation while maintain detection performance; Finally, the CBAM attention mechanism is introduced to improve the detection performance for the problem of dense vehicle targets. The improved network achieved an average detection accuracy of 60.1%, an increase of 12.1 percentage points compared to the original network, a reduction of 73.6% in parameter count, a reduction of 15.9% in computational complexity, and a detection speed of 154 FPS. Compared to the original network, the network is more lightweight and achieves higher detection accuracy, which can more effectively perform unmanned aerial vehicle detection tasks.

关键词： Training Accuracy Attention mechanisms Vehicle detection Computational modeling Transportation Feature extraction Autonomous aerial vehicles Computational complexity Drones

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FC-NT: A crime prediction algorithm incorporating formal context and neural networks 3

FC-NT: A crime prediction algorithm incorporating formal con...

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3rd International Conference on Advanced Algorithms and Signal Image processing, AASIP 2023

作者： Zao, Feifei Lv, Lingling Ding, Feng Jin, Shixian Guo, Xiaoding Henan Province Engineering Research Center of Spatial Information Processing Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Zhengzhou450046 China School of Electrical Engineering North China University of Water Resources and Electric Power Zhengzhou450046 China School of Internet of Things Engineering Jiangnan University Wuxi214122 China Henan Provincial Educational Resources Guarantee Center Zhengzhou450003 China

ISBN: (纸本)9781510668522

The task of crime prediction is to predict the charges of a case based on the given description of the case, which has become a research hot spot. Most of the existing methods use neural networks and machine learning algorithms to predict. The prediction results have high accuracy, but the prediction results are often not well interpretable. In addition, some early traditional methods can explain the prediction results, but their accuracy is often very low. Formal concept analysis is an effective data extraction tool. Therefore, this paper combines formal concept analysis with neural network to improve the accuracy of the algorithm. The experimental results show that our method has higher prediction results and can better explain the prediction results than the original neural network model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Forecasting

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