Big Data最新文献_第3页

Kriging, Polynomial Chaos Expansion, and Low-Rank Approximations in Material Science and Big Data Analytics. 材料科学和大数据分析中的克里金法、多项式混沌展开和低域近似。

IF 2.6 4区计算机科学

Big Data Pub Date : 2024-08-01 Epub Date: 2023-04-24 DOI: 10.1089/big.2022.0124

Golsa Mahdavi, Mohammad Amin Hariri-Ardebili

引用次数: 0

Research on the Influence of Information Iterative Propagation on Complex Network Structure. 信息迭代传播对复杂网络结构的影响研究。

IF 2.6 4区计算机科学

Big Data Pub Date : 2024-07-27 DOI: 10.1089/big.2023.0016

Yinuo Qian, Fuzhong Nian, Zheming Wang, Yabing Yao

{"title":"Research on the Influence of Information Iterative Propagation on Complex Network Structure.","authors":"Yinuo Qian, Fuzhong Nian, Zheming Wang, Yabing Yao","doi":"10.1089/big.2023.0016","DOIUrl":"https://doi.org/10.1089/big.2023.0016","url":null,"abstract":"Dynamic propagation will affect the change of network structure. Different networks are affected by the iterative propagation of information to different degrees. The iterative propagation of information in the network changes the connection strength of the chain edge between nodes. Most studies on temporal networks build networks based on time characteristics, and the iterative propagation of information in the network can also reflect the time characteristics of network evolution. The change of network structure is a macromanifestation of time characteristics, whereas the dynamics in the network is a micromanifestation of time characteristics. How to concretely visualize the change of network structure influenced by the characteristics of propagation dynamics has become the focus of this article. The appearance of chain edge is the micro change of network structure, and the division of community is the macro change of network structure. Based on this, the node participation is proposed to quantify the influence of different users on the information propagation in the network, and it is simulated in different types of networks. By analyzing the iterative propagation of information, the weighted network of different networks based on the iterative propagation of information is constructed. Finally, the chain edge and community division in the network are analyzed to achieve the purpose of quantifying the influence of network propagation on complex network structure.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Fast Survival Support Vector Regression Approach to Large Scale Credit Scoring via Safe Screening. 通过安全筛选进行大规模信用评分的快速生存支持向量回归方法。

IF 2.6 4区计算机科学

Big Data Pub Date : 2024-07-23 DOI: 10.1089/big.2023.0033

Hong Wang, Ling Hong

引用次数: 0

Content-Aware Human Mobility Pattern Extraction. 内容感知的人类移动模式提取。

IF 2.6 4区计算机科学

Big Data Pub Date : 2024-07-10 DOI: 10.1089/big.2022.0281

Shengwen Li, Chaofan Fan, Tianci Li, Renyao Chen, Qingyuan Liu, Junfang Gong

{"title":"Content-Aware Human Mobility Pattern Extraction.","authors":"Shengwen Li, Chaofan Fan, Tianci Li, Renyao Chen, Qingyuan Liu, Junfang Gong","doi":"10.1089/big.2022.0281","DOIUrl":"https://doi.org/10.1089/big.2022.0281","url":null,"abstract":"Extracting meaningful patterns of human mobility from accumulating trajectories is essential for understanding human behavior. However, previous works identify human mobility patterns based on the spatial co-occurrence of trajectories, which ignores the effect of activity content, leaving challenges in effectively extracting and understanding patterns. To bridge this gap, this study incorporates the activity content of trajectories to extract human mobility patterns, and proposes acontent-aware mobility pattern model. The model first embeds the activity content in distributed continuous vector space by taking point-of-interest as an agent and then extracts representative and interpretable mobility patterns from human trajectory sets using a derived topic model. To investigate the performance of the proposed model, several evaluation metrics are developed, including pattern coherence, pattern similarity, and manual scoring. A real-world case study is conducted, and its experimental results show that the proposed model improves interpretability and helps to understand mobility patterns. This study provides not only a novel solution and several evaluation metrics for human mobility patterns but also a method reference for fusing content semantics of human activities for trajectory analysis and mining.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141565068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Intelligent Channel Estimation Algorithm Based on Extended Model for 5G-V2X. 基于 5G-V2X 扩展模型的智能信道估计算法。

IF 4.6 4区计算机科学

Big Data Pub Date : 2024-04-01 Epub Date: 2023-02-27 DOI: 10.1089/big.2022.0029

Jie Huang, Cheng Xu, Zhaohua Ji, Shan Xiao, Teng Liu, Nan Ma, Qinghui Zhou

引用次数: 0

Preemptive Epidemic Information Transmission Model Using Nonreplication Edge Node Connectivity in Health Care Networks. 在医疗网络中使用无复制边缘节点连接的抢先式流行病信息传输模型

IF 4.6 4区计算机科学

Big Data Pub Date : 2024-04-01 Epub Date: 2023-04-19 DOI: 10.1089/big.2022.0278

Chandu Thota, Constandinos X Mavromoustakis, George Mastorakis

{"title":"Preemptive Epidemic Information Transmission Model Using Nonreplication Edge Node Connectivity in Health Care Networks.","authors":"Chandu Thota, Constandinos X Mavromoustakis, George Mastorakis","doi":"10.1089/big.2022.0278","DOIUrl":"10.1089/big.2022.0278","url":null,"abstract":"The reliability in medical data organization and transmission is eased with the inheritance of information and communication technologies in recent years. The growth of digital communication and sharing medium imposes the necessity for optimizing the accessibility and transmission of sensitive medical data to the end-users. In this article, the Preemptive Information Transmission Model (PITM) is introduced for improving the promptness in medical data delivery. This transmission model is designed to acquire the least communication in an epidemic region for seamless information availability. The proposed model makes use of a noncyclic connection procedure and preemptive forwarding inside and outside the epidemic region. The first is responsible for replication-less connection maximization ensuring better availability of the edge nodes. The connection replications are reduced using the pruning tree classifiers based on the communication time and delivery balancing factor. The later process is responsible for the reliable forwarding of the acquired data using a conditional selection of the infrastructure units. Both the processes of PITM are accountable for improving the delivery of observed medical data, over better transmissions, communication time, and achieving fewer delays.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"141-154"},"PeriodicalIF":4.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9440721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image Smart Segmentation Analysis Against Diabetic Foot Ulcer Using Internet of Things with Virtual Sensing. 利用虚拟传感物联网对糖尿病足溃疡进行图像智能分割分析。

IF 4.6 4区计算机科学

Big Data Pub Date : 2024-04-01 Epub Date: 2023-06-08 DOI: 10.1089/big.2022.0283

Chandu Thota, Dinesh Jackson Samuel, Mustafa Musa Jaber, M M Kamruzzaman, Renjith V Ravi, Lydia J Gnanasigamani, R Premalatha

{"title":"Image Smart Segmentation Analysis Against Diabetic Foot Ulcer Using Internet of Things with Virtual Sensing.","authors":"Chandu Thota, Dinesh Jackson Samuel, Mustafa Musa Jaber, M M Kamruzzaman, Renjith V Ravi, Lydia J Gnanasigamani, R Premalatha","doi":"10.1089/big.2022.0283","DOIUrl":"10.1089/big.2022.0283","url":null,"abstract":"Diabetic foot ulcer (DFU) is a problem worldwide, and prevention is crucial. The image segmentation analysis of DFU identification plays a significant role. This will produce different segmentation of the same idea, incomplete, imprecise, and other problems. To address these issues, a method of image segmentation analysis of DFU through internet of things with the technique of virtual sensing for semantically similar objects, the analysis of four levels of range segmentation (region-based, edge-based, image-based, and computer-aided design-based range segmentation) for deeper segmentation of images is implemented. In this study, the multimodal is compressed with the object co-segmentation for semantical segmentation. The result is predicting the better validity and reliability assessment. The experimental results demonstrate that the proposed model can efficiently perform segmentation analysis, with a lower error rate, than the existing methodologies. The findings on the multiple-image dataset show that DFU obtains an average segmentation score of 90.85% and 89.03% correspondingly in two types of labeled ratios before DFU with virtual sensing and after DFU without virtual sensing (i.e., 25% and 30%), which is an increase of 10.91% and 12.22% over the previous best results. In live DFU studies, our proposed system improved by 59.1% compared with existing deep segmentation-based techniques and its average image smart segmentation improvements over its contemporaries are 15.06%, 23.94%, and 45.41%, respectively. Proposed range-based segmentation achieves interobserver reliability by 73.9% on the positive test namely likelihood ratio test set with only a 0.25 million parameters at the pace of labeled data.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"155-172"},"PeriodicalIF":4.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9595875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-Path Graph Neural Network with Adaptive Auxiliary Module for Link Prediction. 带自适应辅助模块的双路径图神经网络用于链路预测

IF 4.6 4区计算机科学

Big Data Pub Date : 2024-03-25 DOI: 10.1089/big.2023.0130

Zhenzhen Yang, Zelong Lin, Yongpeng Yang, Jiaqi Li

{"title":"Dual-Path Graph Neural Network with Adaptive Auxiliary Module for Link Prediction.","authors":"Zhenzhen Yang, Zelong Lin, Yongpeng Yang, Jiaqi Li","doi":"10.1089/big.2023.0130","DOIUrl":"https://doi.org/10.1089/big.2023.0130","url":null,"abstract":"Link prediction, which has important applications in many fields, predicts the possibility of the link between two nodes in a graph. Link prediction based on Graph Neural Network (GNN) obtains node representation and graph structure through GNN, which has attracted a growing amount of attention recently. However, the existing GNN-based link prediction approaches possess some shortcomings. On the one hand, because a graph contains different types of nodes, it leads to a great challenge for aggregating information and learning node representation from its neighbor nodes. On the other hand, the attention mechanism has been an effect instrument for enhancing the link prediction performance. However, the traditional attention mechanism is always monotonic for query nodes, which limits its influence on link prediction. To address these two problems, a Dual-Path Graph Neural Network (DPGNN) for link prediction is proposed in this study. First, we propose a novel Local Random Features Augmentation for Graph Convolution Network as a baseline of one path. Meanwhile, Graph Attention Network version 2 based on dynamic attention mechanism is adopted as a baseline of the other path. And then, we capture more meaningful node representation and more accurate link features by concatenating the information of these two paths. In addition, we propose an adaptive auxiliary module for better balancing the weight of auxiliary tasks, which brings more benefit to link prediction. Finally, extensive experiments verify the effectiveness and superiority of our proposed DPGNN for link prediction.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140289590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating the Co-Movement and Asymmetric Relationships of Oil Prices on the Shipping Stock Returns: Evidence from Three Shipping-Flagged Companies from Germany, South Korea, and Taiwan. 探究油价对航运股回报的共动和非对称关系：来自德国、韩国和台湾的三家航运滞后公司的证据。

IF 4.6 4区计算机科学

Big Data Pub Date : 2024-02-13 DOI: 10.1089/big.2023.0026

Jumadil Saputra, Kasypi Mokhtar, Anuar Abu Bakar, Siti Marsila Mhd Ruslan

{"title":"Investigating the Co-Movement and Asymmetric Relationships of Oil Prices on the Shipping Stock Returns: Evidence from Three Shipping-Flagged Companies from Germany, South Korea, and Taiwan.","authors":"Jumadil Saputra, Kasypi Mokhtar, Anuar Abu Bakar, Siti Marsila Mhd Ruslan","doi":"10.1089/big.2023.0026","DOIUrl":"https://doi.org/10.1089/big.2023.0026","url":null,"abstract":"In the last 2 years, there has been a significant upswing in oil prices, leading to a decline in economic activity and demand. This trend holds substantial implications for the global economy, particularly within the emerging business landscape. Among the influential risk factors impacting the returns of shipping stocks, none looms larger than the volatility in oil prices. Yet, only a limited number of studies have explored the complex relationship between oil price shocks and the dynamics of the liner shipping industry, with specific focus on uncertainty linkages and potential diversification strategies. This study aims to investigate the co-movements and asymmetric associations between oil prices (specifically, West Texas Intermediate and Brent) and the stock returns of three prominent shipping companies from Germany, South Korea, and Taiwan. The results unequivocally highlight the indispensable role of oil prices in shaping both short-term and long-term shipping stock returns. In addition, the research underscores the statistical significance of exchange rates and interest rates in influencing these returns, with their effects varying across different time horizons. Notably, shipping stock prices exhibit heightened sensitivity to positive movements in oil prices, while exchange rates and interest rates exert contrasting impacts, one being positive and the other negative. These findings collectively illuminate the profound influence of market sentiment regarding crucial economic indicators within the global shipping sector.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139736755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long- and Short-Term Memory Model of Cotton Price Index Volatility Risk Based on Explainable Artificial Intelligence. 基于可解释人工智能的棉花价格指数波动风险的长短期记忆模型。

IF 4.6 4区计算机科学

Big Data Pub Date : 2024-02-01 Epub Date: 2023-11-17 DOI: 10.1089/big.2022.0287

Huosong Xia, Xiaoyu Hou, Justin Zuopeng Zhang

{"title":"Long- and Short-Term Memory Model of Cotton Price Index Volatility Risk Based on Explainable Artificial Intelligence.","authors":"Huosong Xia, Xiaoyu Hou, Justin Zuopeng Zhang","doi":"10.1089/big.2022.0287","DOIUrl":"10.1089/big.2022.0287","url":null,"abstract":"Market uncertainty greatly interferes with the decisions and plans of market participants, thus increasing the risk of decision-making, leading to compromised interests of decision-makers. Cotton price index (hereinafter referred to as cotton price) volatility is highly noisy, nonlinear, and stochastic and is susceptible to supply and demand, climate, substitutes, and other policy factors, which are subject to large uncertainties. To reduce decision risk and provide decision support for policymakers, this article integrates 13 factors affecting cotton price index volatility based on existing research and further divides them into transaction data and interaction data. A long- and short-term memory (LSTM) model is constructed, and a comparison experiment is implemented to analyze the cotton price index volatility. To make the constructed model explainable, we use explainable artificial intelligence (XAI) techniques to perform statistical analysis of the input features. The experimental results show that the LSTM model can accurately analyze the cotton price index fluctuation trend but cannot accurately predict the actual price of cotton; the transaction data plus interaction data are more sensitive than the transaction data in analyzing the cotton price fluctuation trend and can have a positive effect on the cotton price fluctuation analysis. This study can accurately reflect the fluctuation trend of the cotton market, provide reference to the state, enterprises, and cotton farmers for decision-making, and reduce the risk caused by frequent fluctuation of cotton prices. The analysis of the model using XAI techniques builds the confidence of decision-makers in the model.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"49-62"},"PeriodicalIF":4.6,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136400257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0