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Predicting Sociodemographic Attributes from Mobile Usage Patterns: Applications and Privacy Implications. 从移动使用模式预测社会人口属性:应用与隐私影响
IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-01-01 Epub Date: 2023-08-14 DOI: 10.1089/big.2022.0182
Rouzbeh Razavi, Guisen Xue, Ikpe Justice Akpan

When users interact with their mobile devices, they leave behind unique digital footprints that can be viewed as predictive proxies that reveal an array of users' characteristics, including their demographics. Predicting users' demographics based on mobile usage can provide significant benefits for service providers and users, including improving customer targeting, service personalization, and market research efforts. This study uses machine learning algorithms and mobile usage data from 235 demographically diverse users to examine the accuracy of predicting their sociodemographic attributes (age, gender, income, and education) from mobile usage metadata, filling the gap in the current literature by quantifying the predictive power of each attribute and discussing the practical applications and privacy implications. According to the results, gender can be most accurately predicted (balanced accuracy = 0.862) from mobile usage footprints, whereas predicting users' education level is more challenging (balanced accuracy = 0.719). Moreover, the classification models were able to classify users based on whether their age or income was above or below a certain threshold with acceptable accuracy. The study also presents the practical applications of inferring demographic attributes from mobile usage data and discusses the implications of the findings, such as privacy and discrimination risks, from the perspectives of different stakeholders.

当用户与他们的移动设备互动时,会留下独特的数字足迹,这些足迹可被视为预测性代理,揭示用户的一系列特征,包括他们的人口统计学特征。根据移动使用情况预测用户的人口统计学特征可为服务提供商和用户带来显著的好处,包括改善客户定位、服务个性化和市场研究工作。本研究利用机器学习算法和来自 235 位不同人口统计学特征用户的移动使用数据,研究了从移动使用元数据预测其社会人口属性(年龄、性别、收入和教育程度)的准确性,通过量化各属性的预测能力并讨论实际应用和隐私影响,填补了现有文献的空白。研究结果表明,从移动使用足迹中预测性别最为准确(平衡准确率 = 0.862),而预测用户的教育水平则更具挑战性(平衡准确率 = 0.719)。此外,分类模型还能根据用户的年龄或收入是否高于或低于某个阈值对其进行分类,准确率在可接受范围内。研究还介绍了从移动使用数据推断人口统计学属性的实际应用,并从不同利益相关者的角度讨论了研究结果的影响,如隐私和歧视风险。
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引用次数: 0
An Improved Influence Maximization Method for Online Advertising in Social Internet of Things. 社交物联网中网络广告影响力最大化的改进方法。
IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-01-01 Epub Date: 2023-08-02 DOI: 10.1089/big.2023.0042
Reza Molaei, Kheirollah Rahsepar Fard, Asgarali Bouyer

Recently, a new subject known as the Social Internet of Things (SIoT) has been presented based on the integration the Internet of Things and social network concepts. SIoT is increasingly popular in modern human living, including applications such as smart transportation, online health care systems, and viral marketing. In advertising based on SIoT, identifying the most effective diffuser nodes to maximize reach is a critical challenge. This article proposes an efficient heuristic algorithm named Influence Maximization of advertisement for Social Internet of Things (IMSoT), inspired by real-world advertising. The IMSoT algorithm consists of two steps: selecting candidate objects and identifying the final seed set. In the first step, influential candidate objects are selected based on factors, such as degree, local importance value, and weak and sensitive neighbors set. In the second step, effective influence is calculated based on overlapping between candidate objects to identify the appropriate final seed set. The IMSoT algorithm ensures maximum influence and minimum overlap, reducing the spreading caused by the seed set. A unique feature of IMSoT is its focus on preventing duplicate advertising, which reduces extra costs, and considering weak objects to reach the maximum target audience. Experimental evaluations in both real-world and synthetic networks demonstrate that our algorithm outperforms other state-of-the-art algorithms in terms of paying attention to weak objects by 38%-193% and in terms of preventing duplicate advertising (reducing extra cost) by 26%-77%. Additionally, the running time of the IMSoT algorithm is shorter than other state-of-the-art algorithms.

最近,一个基于物联网和社交网络概念整合的新课题--社交物联网(SIoT)被提出来。SIoT 在现代人类生活中越来越受欢迎,包括智能交通、在线医疗系统和病毒式营销等应用。在基于 SIoT 的广告中,如何识别最有效的扩散节点以最大限度地扩大覆盖范围是一个严峻的挑战。本文受现实世界广告的启发,提出了一种高效的启发式算法,名为社交物联网广告影响最大化算法(IMSoT)。IMSoT 算法包括两个步骤:选择候选对象和确定最终种子集。第一步,根据度、局部重要性值、弱敏感邻居集等因素选择有影响力的候选对象。在第二步中,根据候选对象之间的重叠计算有效影响,以确定合适的最终种子集。IMSoT 算法可确保影响最大、重叠最小,从而减少种子集造成的传播。IMSoT 的独特之处在于它注重防止重复广告,从而降低了额外成本,并考虑到弱对象,以最大限度地覆盖目标受众。在真实世界和合成网络中进行的实验评估表明,我们的算法在关注弱对象方面比其他一流算法高出 38%-193%,在防止重复广告(降低额外成本)方面比其他一流算法高出 26%-77%。此外,IMSoT 算法的运行时间也短于其他先进算法。
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引用次数: 0
Acknowledgment of Reviewers 2023. 鸣谢 2023 年审稿人。
IF 4.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-12-19 DOI: 10.1089/big.2023.29063.ack
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引用次数: 0
Secure Biomedical Document Protection Framework to Ensure Privacy Through Blockchain. 通过区块链确保隐私的生物医学文件安全保护框架。
IF 4.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-12-01 Epub Date: 2023-05-23 DOI: 10.1089/big.2022.0170
Ramkumar Jayaraman, Mohammed Alshehri, Manoj Kumar, Ahed Abugabah, Surender Singh Samant, Ahmed A Mohamed

In the recent health care era, biomedical documents play a crucial role, and they contain much evidence-based documentation associated with many stakeholders data. Protecting those confidential research documents is more difficult and effective, and a significant process in the medical-based research domain. Those bio-documentation related to health care and other relevant community-valued data are suggested by medical professionals and processed. Many traditional security mechanisms such as akteonline and Health Insurance Portability and Accountability Act (HIPAA) are used to protect the biomedical documents as they consider the problem of non-repudiation and data integrity related to the retrieval and storage of documents. Thus, there is a need for a comprehensive framework that improves protection in terms of cost and response time related to biomedical documents. In this research work, blockchain-based biomedical document protection framework (BBDPF) is proposed, which includes blockchain-based biomedical data protection (BBDP) and blockchain-based biomedical data retrieval (BBDR) algorithms. BBDP and BBDR algorithms provide consistency on the data to prevent data modification and interception of confidential data with proper data validation. Both the algorithms have strong cryptographic mechanisms to withstand post-quantum security risks, ensuring the integrity of biomedical document retrieval and non-deny of data retrieval transactions. In the performance analysis, Ethereum blockchain infrastructure is deployed BBDPF and smart contracts using Solidity language. In the performance analysis, request time and searching time are determined based on the number of request to ensure data integrity, non-repudiation, and smart contracts for the proposed hybrid model as it gets increased gradually. A modified prototype is built with a web-based interface to prove the concept and evaluate the proposed framework. The experimental results revealed that the proposed framework renders data integrity, non-repudiation, and support for smart contracts with Query Notary Service, MedRec, MedShare, and Medlock.

在最近的医疗保健时代,生物医学文件发挥着至关重要的作用,其中包含许多与利益相关者数据相关的循证文件。保护这些机密研究文件更加困难和有效,也是医学研究领域的一个重要过程。这些与医疗保健有关的生物文档和其他相关的社区价值数据都是由医疗专业人员建议和处理的。许多传统的安全机制,如akteonline 和《健康保险可携性和责任法案》(HIPAA),都被用来保护生物医学文档,因为它们考虑到了与文档检索和存储相关的不可抵赖性和数据完整性问题。因此,有必要建立一个综合框架,从成本和响应时间方面改善对生物医学文件的保护。在这项研究工作中,提出了基于区块链的生物医学文档保护框架(BBDPF),其中包括基于区块链的生物医学数据保护(BBDP)和基于区块链的生物医学数据检索(BBDR)算法。BBDP 和 BBDR 算法提供数据一致性,通过适当的数据验证防止数据被修改和机密数据被截取。这两种算法都具有强大的加密机制,能够抵御量子化后的安全风险,确保生物医学文献检索的完整性和数据检索交易的非否认性。在性能分析中,以太坊区块链基础设施部署了 BBDPF 和使用 Solidity 语言的智能合约。在性能分析中,根据请求数量确定请求时间和搜索时间,以确保数据完整性、不可抵赖性和智能合约的逐步增加。为了验证概念和评估所提出的框架,我们建立了一个基于网络界面的修改原型。实验结果表明,建议的框架提供了数据完整性、不可否认性,并支持与 Query Notary Service、MedRec、MedShare 和 Medlock 的智能合约。
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引用次数: 0
OzNet: A New Deep Learning Approach for Automated Classification of COVID-19 Computed Tomography Scans. OzNet:用于 COVID-19 计算机断层扫描自动分类的新型深度学习方法。
IF 4.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-12-01 Epub Date: 2023-03-16 DOI: 10.1089/big.2022.0042
Oznur Ozaltin, Ozgur Yeniay, Abdulhamit Subasi

Coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Therefore, the classification of computed tomography (CT) scans alleviates the workload of experts, whose workload increased considerably during the pandemic. Convolutional neural network (CNN) architectures are successful for the classification of medical images. In this study, we have developed a new deep CNN architecture called OzNet. Moreover, we have compared it with pretrained architectures namely AlexNet, DenseNet201, GoogleNet, NASNetMobile, ResNet-50, SqueezeNet, and VGG-16. In addition, we have compared the classification success of three preprocessing methods with raw CT scans. We have not only classified the raw CT scans, but also have performed the classification with three different preprocessing methods, which are discrete wavelet transform (DWT), intensity adjustment, and gray to color red, green, blue image conversion on the data sets. Furthermore, it is known that the architecture's performance increases with the use of DWT preprocessing method rather than using the raw data set. The results are extremely promising with the CNN algorithms using the COVID-19 CT scans processed with the DWT. The proposed DWT-OzNet has achieved a high classification performance of more than 98.8% for each calculated metric.

2019 年冠状病毒病(COVID-19)正在全球迅速蔓延。因此,计算机断层扫描(CT)扫描的分类可减轻专家的工作量,而在该疾病流行期间,专家的工作量大大增加。卷积神经网络(CNN)架构在医学图像分类方面取得了成功。在这项研究中,我们开发了一种名为 OzNet 的新型深度 CNN 架构。此外,我们还将其与经过预训练的架构(即 AlexNet、DenseNet201、GoogleNet、NASNetMobile、ResNet-50、SqueezeNet 和 VGG-16)进行了比较。此外,我们还比较了三种预处理方法与原始 CT 扫描的分类成功率。我们不仅对原始 CT 扫描图像进行了分类,还采用了三种不同的预处理方法,即离散小波变换 (DWT)、强度调整和红绿蓝图像灰度到彩色的转换,对数据集进行了分类。此外,众所周知,使用 DWT 预处理方法比使用原始数据集的架构性能更高。使用经 DWT 处理的 COVID-19 CT 扫描数据的 CNN 算法取得了非常理想的结果。所提出的 DWT-OzNet 在每个计算指标上都达到了 98.8% 以上的高分类性能。
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引用次数: 0
ODQN-Net: Optimized Deep Q Neural Networks for Disease Prediction Through Tongue Image Analysis Using Remora Optimization Algorithm. ODQN-Net:利用 Remora 优化算法通过舌头图像分析进行疾病预测的优化深度 Q 神经网络
IF 4.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-12-01 Epub Date: 2023-09-13 DOI: 10.1089/big.2023.0014
S V N Sreenivasu, P Santosh Kumar Patra, Vasujadevi Midasala, G S N Murthy, Krishna Chaitanya Janapati, J N V R Swarup Kumar, Pala Mahesh Kumar

Tongue analysis plays the major role in disease type prediction and classification according to Indian ayurvedic medicine. Traditionally, there is a manual inspection of tongue image by the expert ayurvedic doctor to identify or predict the disease. However, this is time-consuming and even imprecise. Due to the advancements in recent machine learning models, several researchers addressed the disease prediction from tongue image analysis. However, they have failed to provide enough accuracy. In addition, multiclass disease classification with enhanced accuracy is still a challenging task. Therefore, this article focuses on the development of optimized deep q-neural network (DQNN) for disease identification and classification from tongue images, hereafter referred as ODQN-Net. Initially, the multiscale retinex approach is introduced for enhancing the quality of tongue images, which also acts as a noise removal technique. In addition, a local ternary pattern is used to extract the disease-specific and disease-dependent features based on color analysis. Then, the best features are extracted from the available features set using the natural inspired Remora optimization algorithm with reduced computational time. Finally, the DQNN model is used to classify the type of diseases from these pretrained features. The obtained simulation performance on tongue imaging data set proved that the proposed ODQN-Net resulted in superior performance compared with state-of-the-art approaches with 99.17% of accuracy and 99.75% and 99.84% of F1-score and Mathew's correlation coefficient, respectively.

根据印度阿育吠陀医学,舌头分析在疾病类型预测和分类方面发挥着重要作用。传统上,阿育吠陀医学专家通过手动检查舌头图像来识别或预测疾病。然而,这不仅耗时,而且不精确。由于近来机器学习模型的进步,一些研究人员开始通过舌头图像分析来预测疾病。然而,这些研究未能提供足够的准确性。此外,提高准确性的多类疾病分类仍是一项具有挑战性的任务。因此,本文重点研究开发优化的深度 q 神经网络(DQNN),用于从舌头图像进行疾病识别和分类,以下简称 ODQN-Net。首先,本文引入了多尺度视网膜方法来提高舌头图像的质量,该方法同时也是一种去噪技术。此外,还使用局部三元模式来提取基于颜色分析的疾病特异性特征和疾病依赖性特征。然后,利用受自然启发的 Remora 优化算法从可用的特征集中提取最佳特征,并缩短计算时间。最后,使用 DQNN 模型根据这些预训练特征对疾病类型进行分类。在舌头成像数据集上获得的模拟性能证明,与最先进的方法相比,所提出的 ODQN-Net 具有更优越的性能,准确率为 99.17%,F1 分数和 Mathew 相关系数分别为 99.75% 和 99.84%。
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引用次数: 0
Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart. 在创新药物倡议中共享医疗大数据同时保护患者机密:来自BigData@Heart.
IF 4.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-12-01 Epub Date: 2023-10-27 DOI: 10.1089/big.2022.0178
Megan Schröder, Sam H A Muller, Eleni Vradi, Johanna Mielke, Yvonne M F Lim, Fabrice Couvelard, Menno Mostert, Stefan Koudstaal, Marinus J C Eijkemans, Christoph Gerlinger

Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries. Sharing IPD was not considered feasible due to legal requirements and the sensitive medical nature of these data. In addition, harmonizing the data sets for a federated data analysis was difficult due to capacity constraints and the heterogeneity of the data sets. An alternative option was to share summary statistics through contingency tables. Here it is demonstrated that this method along with anonymization methods to ensure patient anonymity had minimal loss of information. Although sharing IPD should continue to be encouraged and strived for, our approach achieved a good balance between data transparency while protecting patient privacy. It also allowed a successful collaboration between industry and academia.

共享个人患者数据(IPD)是一个简单的概念,但由于数据隐私和数据安全问题、指导方针不完善以及法律障碍,实现起来很复杂。在诸如Bigdata@Heart在创新药物倡议中,由于不同联盟成员之间的利益竞争。一个项目BigData@Heart,案例研究1,需要汇集来自七个异质数据集的数据:来自三个不同行业合作伙伴的五项随机对照试验,以及两个疾病登记处。由于法律要求和这些数据的敏感医学性质,共享IPD被认为是不可行的。此外,由于容量限制和数据集的异质性,统一联邦数据分析的数据集很困难。另一种选择是通过列联表共享汇总统计数据。这里证明了这种方法以及确保患者匿名性的匿名化方法具有最小的信息损失。尽管应该继续鼓励和努力共享IPD,但我们的方法在数据透明度和保护患者隐私之间取得了良好的平衡。它还促成了工业界和学术界之间的成功合作。
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引用次数: 0
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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-11-01 DOI: 10.1370/afm.22.s1.5051
Yvonne Nartey, C. Crooks, Joe West, Timothy R. Card, Laila J. Tata
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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-11-01 DOI: 10.1370/afm.22.s1.5279
Bob Gramling, Donna Rizzo, Margaret Eppstein, Bradford Demarest
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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-11-01 DOI: 10.1370/afm.22.s1.5425
Karen Tu, M. Lapadula
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引用次数: 0
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