Big Data最新文献_第7页

The incidence and prevalence of coeliac disease in the United Kingdom 英国乳糜泻的发病率和流行率

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-11-01 DOI: 10.1370/afm.22.s1.5051

Yvonne Nartey, C. Crooks, Joe West, Timothy R. Card, Laila J. Tata

引用次数: 0

Machine Learning Analysis of Serious Illness Conversations Predicts Patient Reports of Feeling Heard & Understood 重症患者对话的机器学习分析可预测患者关于被倾听和理解的报告

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-11-01 DOI: 10.1370/afm.22.s1.5279

Bob Gramling, Donna Rizzo, Margaret Eppstein, Bradford Demarest

引用次数: 0

Changes in Reasons for Visits to Primary Care as a Result of the COVID-19 Pandemic: by INTRePID COVID-19 大流行导致初级保健就诊原因的变化：按 INTRePID 分类

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-11-01 DOI: 10.1370/afm.22.s1.5425

Karen Tu, M. Lapadula

引用次数: 0

Breast cancer screening during the COVID-19 Pandemic in the United States: Results from real-world health records data 美国 COVID-19 大流行期间的乳腺癌筛查：来自真实世界健康记录数据的结果

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-11-01 DOI: 10.1370/afm.22.s1.4885

William Curry, Wen-Jan Tuan, Qiushi Chen, Andrew Chung

引用次数: 0

A Novel Method for Utilizing Electronic Health Record Data in Condition-specific Research 在特定病症研究中利用电子健康记录数据的新方法

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-11-01 DOI: 10.1370/afm.22.s1.4955

Tarin Clay, Melissa Filippi, Elise Robertson, Cory B. Lutgen, Elisabeth F. Callen

引用次数: 0

Harmonized Healthcare Database across Family Medicine Institutions 全科医疗机构的统一医疗保健数据库

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-11-01 DOI: 10.1370/afm.22.s1.5404

Chance R. Strenth, David Schneider, U. Sambamoorthi, Sravan Mattevada, Kimberly Fulda, Bhaskar Thakur, Anna Espinoza

引用次数: 0

Identifying the Factors Associated with the Accumulation of Diabetes Complications to Inform a Prediction Tool 确定糖尿病并发症累积的相关因素，为预测工具提供依据

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-11-01 DOI: 10.1370/afm.22.s1.5071

Winston R. Liaw, Ben King, Omolola E. Adepoju, Jiangtao Luo, Ioannis Kakadiaris, Todd Prewitt, Jessica Dobbins, Pete Womack

引用次数: 0

Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds. 服务级别协议监控即服务：云中服务级别协议的独立监控服务。

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-10-01 Epub Date: 2022-01-24 DOI: 10.1089/big.2021.0274

Afzal Badshah, Ateeqa Jalal, Umar Farooq, Ghani-Ur Rehman, Shahab S Band, Celestine Iwendi

{"title":"Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds.","authors":"Afzal Badshah, Ateeqa Jalal, Umar Farooq, Ghani-Ur Rehman, Shahab S Band, Celestine Iwendi","doi":"10.1089/big.2021.0274","DOIUrl":"10.1089/big.2021.0274","url":null,"abstract":"The cloud network is rapidly growing due to a massive increase in interconnected devices and the emergence of different technologies such as the Internet of things, fog computing, and artificial intelligence. In response, cloud computing needs reliable dealings among the service providers, brokers, and consumers. The existing cloud monitoring frameworks such as Amazon Cloud Watch, Paraleap Azure Watch, and Rack Space Cloud Kick work under the control of service providers. They work fine; however, this may create dissatisfaction among customers over Service Level Agreement (SLA) violations. Customers' dissatisfaction may drastically reduce the businesses of service providers. To cope with the earlier mentioned issue and get in line with cloud philosophy, Monitoring as a Service (MaaS), completely independent in nature, is needed for observing and regulating the cloud businesses. However, the existing MaaS frameworks do not address the comprehensive SLA for customer satisfaction and penalties management. This article proposes a reliable framework for monitoring the provider's services by adopting third-party monitoring services with clearcut SLA and penalties management. Since this framework monitors SLA as a cloud monitoring service, it is named as SLA-MaaS. On violations, it penalizes those who are found in breach of terms and condition enlisted in SLA. Simulation results confirmed that the proposed framework adequately satisfies the customers (as well as service providers). This helps in developing a trustworthy relationship among cloud partners and increases customer attention and retention.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"339-354"},"PeriodicalIF":4.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39857084","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: 6

HUTNet: An Efficient Convolutional Neural Network for Handwritten Uchen Tibetan Character Recognition. HUTNet:一种高效的卷积神经网络，用于乌琴藏文手写体识别。

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-10-01 Epub Date: 2023-01-19 DOI: 10.1089/big.2021.0333

Guowei Zhang, Weilan Wang, Ce Zhang, Penghai Zhao, Mingkai Zhang

{"title":"HUTNet: An Efficient Convolutional Neural Network for Handwritten Uchen Tibetan Character Recognition.","authors":"Guowei Zhang, Weilan Wang, Ce Zhang, Penghai Zhao, Mingkai Zhang","doi":"10.1089/big.2021.0333","DOIUrl":"10.1089/big.2021.0333","url":null,"abstract":"Recognition of handwritten Uchen Tibetan characters input has been considered an efficient way of acquiring mass data in the digital era. However, it still faces considerable challenges due to seriously touching letters and various morphological features of identical characters. Thus, deeper neural networks are required to achieve decent recognition accuracy, making an efficient, lightweight model design important to balance the inevitable trade-off between accuracy and latency. To reduce the learnable parameters of the network as much as possible and maintain acceptable accuracy, we introduce an efficient model named HUTNet based on the internal relationship between floating-point operations per second (FLOPs) and Memory Access Cost. The proposed network achieves a ResNet-18-level accuracy of 96.86%, with only a tenth of the parameters. The subsequent pruning and knowledge distillation strategies were applied to further reduce the inference latency of the model. Experiments on the test set (Handwritten Uchen Tibetan Data set by Wang [HUTDW]) containing 562 classes of 42,068 samples show that the compressed model achieves a 96.83% accuracy while maintaining lower FLOPs and fewer parameters. To verify the effectiveness of HUTNet, we tested it on the Chinese Handwriting Data sets Handwriting Database 1.1 (HWDB1.1), in which HUTNet achieved an accuracy of 97.24%, higher than that of ResNet-18 and ResNet-34. In general, we conduct extensive experiments on resource and accuracy trade-offs and show a stronger performance compared with other famous models on HUTDW and HWDB1.1. It also unlocks the critical bottleneck for handwritten Uchen Tibetan recognition on low-power computing devices.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"387-398"},"PeriodicalIF":4.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10543391","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

Prediction and Big Data Impact Analysis of Telecom Churn by Backpropagation Neural Network Algorithm from the Perspective of Business Model. 基于商业模型的反向传播神经网络算法对电信客户流失的预测与大数据影响分析。

IF 4.6 4区计算机科学

Big Data Pub Date : 2023-10-01 Epub Date: 2023-01-19 DOI: 10.1089/big.2021.0365

Jiabing Xu, Jiarui Liu, Tianen Yao, Yang Li

{"title":"Prediction and Big Data Impact Analysis of Telecom Churn by Backpropagation Neural Network Algorithm from the Perspective of Business Model.","authors":"Jiabing Xu, Jiarui Liu, Tianen Yao, Yang Li","doi":"10.1089/big.2021.0365","DOIUrl":"10.1089/big.2021.0365","url":null,"abstract":"This study aims to transform the existing telecom operators from traditional Internet operators to digital-driven services, and improve the overall competitiveness of telecom enterprises. Data mining is applied to telecom user classification to process the existing telecom user data through data integration, cleaning, standardization, and transformation. Although the existing algorithms ensure the accuracy of the algorithm on the telecom user analysis platform under big data, they do not solve the limitations of single machine computing and cannot effectively improve the training efficiency of the model. To solve this problem, this article establishes a telecom customer churn prediction model with the help of backpropagation neural network (BPNN) algorithm, and deploys the MapReduce programming framework on Hadoop platform. Using the data of a telecom company, this article analyzes the loss of telecom customers in the big data environment. The research shows that the accuracy of telecom customer churn prediction model in BPNN is 82.12%. After deploying large data sets, the learning and training time of the model is greatly shortened. When the number of nodes is 8, the acceleration ratio of the model remains at 60 seconds. Under big data, the telecom user analysis platform not only ensures the accuracy of the algorithm, but also solves the limitations of single machine computing and effectively improves the training efficiency of the model. Compared with that of the existing research, the accuracy of the model is improved by 25.36%, and the running time is shortened by about twice. This business model based on BPNN algorithm has obvious advantages in processing more data sets, and has great reference value for the digital-driven business model transformation of the telecommunications industry.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"355-368"},"PeriodicalIF":4.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10549823","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: 1