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Hyperledger Fabric Blockchain: Secure and Efficient Solution for Electronic Health Records

computers, Materials & Continua 2021年第8期68卷 2377-2397页

作者： Mueen Uddin M.S.Memon Irfana Memon Imtiaz Ali Jamshed Memon Maha Abdelhaq Raed Alsaqour Faculty of Science Universiti Brunei DarusslamGadongBE1410Negara Brunei Darussalam CSE Department QUESTNawabshahPakistan School of Computing and Mathematics Ulster UniversityJordanstown CampusNewtownabbeyBT370QBUK Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia

Background:Electronic Health Record(EHR)systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients ***,the existing EHR systems mostly lack in providing appropriate security,entrusted access control and handling privacy and secrecy issues and challenges in current hospital ***:To solve this delicate problem,we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR ***:In our EHR blockchain system,Peer nodes from various organizations(stakeholders)create a ledger network,where channels are created to enable secure and private communication between different stakeholders on the ledger *** patients and other stakeholders are identified and registered on the network by unique digital certificates issued by membership service provider(MSP)component of the fabric ***:We created and implemented different Chaincodes to handle the business logic for executing separate EHR transactions on the *** proposed fabric architecture provides a secure,transparent and immutable mechanism to store,share and exchange EHRs in a peer-to-peer network of different healthcare *** ensures interoperability,scalability and availability in adapting the existing EHRs for strengthening and providing an effective and secure method to integrate and manage patient records among medical institutions in the healthcare ecosystem.

关键词： Electronic health records blockchain hyperledger fabric patient data privacy private permissioned blockchain healthcare ecosystem

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Converging Paths in Divergent systems: A Comparative Analysis of Data science Education Strategies in China and the United States

Converging Paths in Divergent Systems: A Comparative Analysi...

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Frontiers in Education (FIE) Conference

作者： Duo Li Elizabeth Milonas Qiping Zhang Department of Big Data Management and Application Shenyang Institute of Technology Fushun China Computer Systems Technology New York City College of Technology (CUNY) Brooklyn USA Palmer School of Library and Information Science Long Island University Brookville USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research-to-practice full paper compares data science education strategies in China and the United States, exploring whether different approaches can achieve similar educational outcomes. In the U.S., data science programs are typically developed by individual schools to meet labor market demands, integrating data science as an interdisciplinary subject. In contrast, Chinese universities follow a uniform plan set by the education department, resulting in specialized fields like ‘Big Data Management and Application’ and ‘Data science and Big Data Technology.’ This research collected data on course offerings and university rankings in China and the U.S., analyzing curriculum content and program characteristics. Major findings indicate that in the U.S., Data science and Business Analytics programs focus on technical skills and are primarily found in top-ranking universities, while in China, Data science and Big Data Management programs emphasize management and are more widely distributed across different ranking groups. Despite these differences, both countries show similar geographic concentration patterns in data science programs. The study concludes that China and the U.S. adopt different educational strategies but achieve comparable effectiveness in data science education.

关键词： Economics Industries Technological innovation Analytical models Education Data science Big Data Market research Data models Business

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ETOP: Energy-Efficient Transmission Optimization Protocol in Sensor Networks of IoT 5th

ETOP: Energy-Efficient Transmission Optimization Protocol in...

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5th International Conference on New Trends in Information and Communications Technology Applications, NTICT 2021

作者： Idrees, Ali Kadhum Al-Mamory, Safaa O. Idrees, Sara Kadhum Couturier, Raphael Department of Computer Science University of Babylon Babylon Iraq College of Business Informatics University of Information Technology and Communications Baghdad Iraq FEMTO-ST Institute/CNRS The DISC Department Univ. Bourgogne Franche-Comte Belfort France

ISBN: (纸本)9783030934163

In the past few years, smart devices have been rapidly increased due to their ever-increasing use in different real-world applications. Most of these devices are sensor nodes that represents the basic element in the Internet of Things (IoT). This increasing number in sensor devices will lead to an increase in the size of transmitted sensed readings across the internet, spending energy of sensor nodes, and decreasing the lifetime of the network. Therefore, to tackle this problem, an Energy-efficient Transmission Optimization Protocol (ETOP) is proposed to optimize the transmission and the lifetime of Sensor Networks of IoT. ETOP achieves this mission by using a simple reduction algorithm-based correlation clustering at the sensor stage to remove the redundant data before transmitting it to the gateway or sink. The results are conducted using the OMNeT++ simulator which show that the ETOP protocol can optimize the transmission and the lifetime of Sensor networks better than other methods. © 2021, Springer Nature Switzerland AG.

关键词： Energy efficiency

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The joint effects of physical activity and sleep duration on risk of osteoporosis in Taiwanese adult population: The Taiwan Biobank Study

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Osteoporosis International 2024年第3期35卷 523-531页

作者： Chen, Kun-Hui Su, Chen-Ming Liu, Wei-Ju Tzeng, Huey-En Lee, Chia-Lin Tsai, Chun-Hao Department of Orthopedic Surgery Taichung Veterans General Hospital Taichung 407219 Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung 402204 Taiwan Department of Computer Science & Information Engineering College of Computing and Informatics Providence University Taichung City 43301 Taiwan Department of Sports Medicine China Medical University Taichung 406040 Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung 407219 Taiwan Intelligent Data Mining Laboratory Department of Medical Research Taichung Veterans General Hospital Taichung 407219 Taiwan School of Medicine Taipei Medical University Taipei 11031 Taiwan Division of Transfusion Medicine /Hematology - Oncology Taichung Veterans General Hospital Taichung 40705 Taiwan Department of Public Health College of Public Health China Medical University Taichung 406040 Taiwan Department of Medicine School of Medicine National Yang Ming Chiao Tung University Taipei 112304 Taiwan Department of Orthopedics China Medical University Hospital Taichung 406040 Taiwan College of Biomedical Engineering China Medical University Taichung 406040 Taiwan

Summary: Most studies investigating the association between physical activity and osteoporosis prevention only focused on specific types of physical activity. This study’s evidence regarding the combined effects or interaction of sleep duration and physical activity. The findings emphasize the role of sleep duration and physical activity in association with osteoporosis. Purpose: The associations between physical activity, sleep duration, and prevalent osteoporosis in Taiwanese adults were studied in this cross-sectional study. Methods: The Taiwan Biobank enrolled a community-based cohort of ~ 120,000 volunteers (as of April 30, 2020) between 30 and 76 years of age with no history of cancer. Amongst, bone mineral density (BMD) measures by dual-energy X-ray absorptiometry (DXA) were available in 22,402 participants. After excluding individuals who had no complete data of BMI (n = 23), MET score (n = 207), T-score (n = 8,826), and sleep duration (n = 16), 13,330 subjects were included as the primary cohort. Univariate and multivariable regression analyses were performed to determine the associations between the presence of osteoporosis, physical activity level, sleep duration, and other variables. Results: The results showed that after adjustment, subjects with physical activity < 20 METs/week and ≥ 20 METs/week (aOR = 1.017 and 0.767, respectively) were associated with risk of osteoporosis than those with zero MET. The odds of osteoporosis were not significantly lower in subjects who slept for ≥ 8 h/day (aOR = 0.934,p=0.266). In addition, compared to short sleepers with no physical activity, adults with increased physical activity ≥ 20 METs/week and sleep ≥ 8 h/day had a significantly lowest likelihood of osteoporosis (aOR = 0.702). Those with medium physical activity (< 20 METs/week) plus average sleep duration (6.5–8 h/day) did not have significant higher odds of osteoporosis (aOR = 1.129,p=0.151). Conclusion: The findings emphasize the joint role of sleep duration

关键词： Bone mineral density (BMD) Exercise habit Osteoporosis Sleep duration Taiwan biobank

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Deep Models for Visual Sentiment Analysis of Disaster-related Multimedia Content

Deep Models for Visual Sentiment Analysis of Disaster-relate...

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MediaEval 2021 Workshop, MediaEval 2021

作者： Ahmad, Khubaib Ayub, Muhammad Asif Ahmad, Kashif Al-Fuqaha, Ala Ahmad, Nasir Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

This paper presents a solutions for the MediaEval 2021 task namely"Visual Sentiment Analysis: A Natural Disaster Use-case". The task aims to extract and classify sentiments perceived by viewers and the emotional message conveyed by natural disaster-related images shared on social media. The task is composed of three sub-tasks including, one single label multi-class image classification subtask, and, two multi-label multi-class image classification subtasks. Both the multi-label classification tasks cover different sets of labels. In our proposed solutions, we mainly rely on two different state-of-the-art models namely, Inception-v3 and VggNet-19, pre-trained on ImageNet. Both the pre-trained models are fine-tuned for each of the three subtasks using different strategies. Overall encouraging results are obtained on all of the three subtasks. On the single-label classification subtask (i.e. subtask 1), we obtained the weighted average F1-scores of 0.540 and 0.526 for the Inception-v3 and VggNet-19 based solutions, respectively. On the multi-label classification tasks i.e., subtask 2 and subtask 3, the weighted F1-scores of our Inceptionv3 based solutions are 0.572 and 0.516, respectively. Similarly, the weighted F1-scores of our VggNet-19 based solution on the subtask 2 and subtask 3 are 0.584 and 0.495, respectively. Copyright 2021 for this paper by its authors.

关键词： Sentiment analysis

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SmartEye: Detecting COVID-19 Misinformation on Twitter for Mitigating Public Health Risk

SmartEye: Detecting COVID-19 Misinformation on Twitter for M...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Jinwei Liu Rui Gong Wei Zhou Department of Computer and Information Sciences Florida A&M University Tallahassee FL USA Department of Mathematics Science and Informatics Mercer University Macon GA USA College of Agriculture and Food Sciences Florida A&M University Tallahassee FL USA

The spread of COVID-19 misinformation (e.g., fake news) on social media poses a serious public health risk. It is critical to identify COVID-19 misinformation. In this paper, we propose SmartEye: a novel machine learning based (ML-based) approach for detecting COVID-19 misinformation on Twitter for mitigating public health risk. To test the approach, we conducted experiments based on the data collected from Twitter. Experimental results show the effectiveness of SmartEye.

关键词： COVID-19 Deep learning Social networking (online) Blogs Big Data Fake news Public healthcare

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SSRN

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SSRN 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nannig China Guangxi Key Laboratory of Multimedia Communications and Network Technology China China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China College of Systems Engineering National University of Defense Technology Changsha China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data tra

关键词： Profitability

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SurgicalGaussian: Deformable 3D Gaussians for High-Fidelity Surgical Scene Reconstruction

arXiv

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arXiv 2024年

作者： Xie, Weixing Yao, Junfeng Cao, Xianpeng Lin, Qiqin Tang, Zerui Dong, Xiao Guo, Xiaohu Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Guangdong Provincial Key Laboratory IRADS Department of Computer Science BNU-HKBU United International College Zhuhai China Department of Computer Science The University of Texas at Dallas Dallas United States

Dynamic reconstruction of deformable tissues in endoscopic video is a key technology for robot-assisted surgery. Recent reconstruction methods based on neural radiance fields (NeRFs) have achieved remarkable results in the reconstruction of surgical scenes. However, based on implicit representation, NeRFs struggle to capture the intricate details of objects in the scene and cannot achieve real-time rendering. In addition, restricted single view perception and occluded instruments also propose special challenges in surgical scene reconstruction. To address these issues, we develop SurgicalGaussian, a deformable 3D Gaussian Splatting method to model dynamic surgical scenes. Our approach models the spatio-temporal features of soft tissues at each time stamp via a forward-mapping deformation MLP and regularization to constrain local 3D Gaussians to comply with consistent movement. With the depth initialization strategy and tool mask-guided training, our method can remove surgical instruments and reconstruct high-fidelity surgical scenes. Through experiments on various surgical videos, our network outperforms existing method on many aspects, including rendering quality, rendering speed and GPU usage. The project page can be found at https://***. © 2024, CC BY-SA.

关键词： Tissue

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PRIVACY-PRESERVING DATA LINKAGE ACROSS PRIVATE AND PUBLIC DATASETS FOR COLLABORATIVE AGRICULTURE RESEARCH

arXiv

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arXiv 2024年

作者： Zafar, Osama González, Rosemarie Santa Wilkins, Gabriel Morales, Alfonso Ayday, Erman Department of Computer and Data Sciences Case Western Reserve University United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States College of Letters & Science University of Wisconsin–Madison United States

Digital agriculture—the use of technology in agricultural practices—plays an integral role in agricultural research, with the potential to improve crop yield, disease resilience, and long-term soil health. However, it raises privacy concerns and risks, such as adverse pricing, price discrimination, increased insurance costs, and manipulation of essential agricultural resources. These concerns deter farm owners and operators from sharing their data because it could be used against them. This study addresses the need for privacy-preserving frameworks that enables secure data sharing for digital agriculture practices. We propose a privacy-preserving framework designed to mitigate privacy risks while enabling comprehensive data analysis. The proposed tool allows the agricultural community to benefit from research driven policy making that establishes meaningful connections across public and private digital agriculture datasets. Our privacy-preserving algorithm behind the framework can be used to: (i) identify similar farmers based on specified attributes using multiple private datasets, (ii) provide aggregate information such as time and location, (iii) determine price and availability trends of target products, and (iv) correlate these trends with relevant public policy databases, such as food insecurity data, without compromising privacy. We validate the framework using real-life datasets from Farmer’s Market. Also, we demonstrate the framework’s efficacy and practicality by training various machine learning models on linked privacy-preserved private and public data. The numerical experiments showcase how the framework can support policymakers and researchers in implementing digital agriculture related to food insecurity and pricing. Our work addresses critical privacy concerns, thereby facilitating more effective and secure agricultural research. It represents a significant contribution to digital agriculture by enabling the secure integration and analysis of data, p

关键词： Differential privacy

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When data disappear: public health pays as US policy strays

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The Lancet Digital Health 2025年 100874页

作者： McAndrew, Thomas Lover, Andrew A Hoyt, Garrik Majumder, Maimuna S Department of Biostatistics and Health Data Science College of Health Lehigh University BethlehemPA United States Department of Biostatistics and Epidemiology School of Public Health and Health Sciences University of Massachusetts Amherst AmherstMA United States Department of Computer Science and Engineering PC Rossin College of Engineering and Applied Sciences Lehigh University BethlehemPA United States Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children's Hospital BostonMA United States

Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources;and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good. © 2025 The Author(s)

关键词： Health insurance

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