Internet of Things-enabled Passive Contact Tracing in Smart Cities

Akhavan, Zeinab; Esmaeili, M.; Sikeridis, Dimitrios; Devetsikiotis, Michael

doi:10.1016/j.iot.2021.100397

Cited by 15 publications

(6 citation statements)

References 25 publications

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“…Simply put, XGBoost yields better results than conventional decision trees by utilizing classification and regression trees as base learners to consecutively blend various tree predictions with the use of gradient boosting as an error minimizer [28]. XGBoost also lowers the likelihood of over-fitting through regularization by employing a second-order Taylor series to approximate the value of the loss function [39,40,41]. All of this makes XGBoost a gradient boosting library optimized for efficiency, versatility and portability [42,43].…”

Section: Classifiermentioning

confidence: 99%

Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Abbasniya

Sheikholeslamzadeh²,

Nasiri³

et al. 2022

Computers and Electrical Engineering

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Section: Classifiermentioning

confidence: 99%

Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Abbasniya

Sheikholeslamzadeh²,

Nasiri³

et al. 2022

Computers and Electrical Engineering

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“…The LightGBM algorithm was first developed by part of Microsoft's R&D team [21]. Although many researchers used XGBoost in their research [22]- [28], LightGBM is a more accurate decision tree algorithm than the existing tree boosting algorithms, because LightGBM produces more complicated trees. Although producing complex trees, LightGBM is highspeed and uses low memory because it utilizes GOSS and EFB algorithms.…”

Section: Lightgbmmentioning

confidence: 99%

Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM

Nasiri

Kheyroddin

Dorrigiv

et al. 2022

2022 IEEE World AI IoT Congress (AIIoT)

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The COVID-19 disease was first discovered in Wuhan, China, and spread quickly worldwide. After the COVID-19 pandemic, many researchers have begun to identify a way to diagnose the COVID-19 using chest X-ray images. The early diagnosis of this disease can significantly impact the treatment process. In this article, we propose a new technique that is faster and more accurate than the other methods reported in the literature. The proposed method uses a combination of DenseNet169 and MobileNet Deep Neural Networks to extract the features of the patient's X-ray images. Using the univariate feature selection algorithm, we refined the features for the most important ones. Then we applied the selected features as input to the LightGBM (Light Gradient Boosting Machine) algorithm for classification. To assess the effectiveness of the proposed method, the ChestX-ray8 dataset, which includes 1125 X-ray images of the patient's chest, was used. The proposed method achieved 98.54% and 91.11% accuracies in the two-class (COVID-19, Healthy) and multi-class (COVID-19, Healthy, Pneumonia) classification problems, respectively. It is worth mentioning that we have used Gradient-weighted Class Activation Mapping (Grad-CAM) for further analysis.

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“…Mobile phone apps that capture situations where two mobile phones have been in close proximity for a sufficient time for the risk of infection to be high have been developed for digital contact tracing and have been used in several countries around the world [ 11 ]. Furthermore, smart cards are also increasingly utilized as identification credentials to control access to certain school areas and monitor attendance [ 12 ], and smart card data can be utilized to assist contact tracing efforts [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Views and Needs of Students, Parents, and Teachers on Closed-Circuit Television, Proximity Trackers, and Access Cards to Facilitate COVID-19 Contact Tracing in Schools: Thematic Analysis of Focus Groups and Interviews

Chantziara¹,

Craddock²,

McCallum³

et al. 2023

JMIR Form Res

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Background Contact tracing is considered a key measure in preventing the spread of infectious diseases. Governments around the world adopted contact tracing to limit the spread of COVID-19 in schools. Contact tracing tools utilizing digital technology (eg, GPS chips, Bluetooth radios) can increase efficiency compared to manual methods. However, these technologies can introduce certain privacy challenges in relation to retention, tracking, and the using and sharing of personal data, and little is known about their applicability in schools. Objective This is the second of two studies exploring the potential of digital tools and systems to help schools deal with the practical challenges of preventing and coping with an outbreak of COVID-19. The aim was to explore the views, needs, and concerns among secondary school stakeholders (parents, teachers, pupils) regarding the implementation of three digital tools for contact tracing: access cards, proximity tracking, and closed-circuit television (CCTV). Methods Focus groups and interviews were conducted with secondary school students, parents, and teachers. The topic guide was informed by the Unified Theory of Technology and Acceptance. Data-driven and theory-driven approaches were combined to identify themes and subthemes. Results We recruited 22 participants. Findings showed that there is no single solution that is suitable for all schools, with each technology option having advantages and limitations. Existing school infrastructure (eg, CCTV and smart/access cards technology) and the geography of each school would determine which tools would be optimal for a particular school. Concerns regarding the cost of installing and maintaining equipment were prominent among all groups. Parents and teachers worried about how the application of these solutions will affect students’ right to privacy. Parents also appeared not to have adequate knowledge of the surveillance technologies already available in schools (eg, CCTV). Students, who were mostly aware of the presence of surveillance technologies, were less concerned about any potential threats to their privacy, while they wanted reassurances that any solutions would be used for their intended purposes. Conclusions Findings revealed that there is not one tool that would be suitable for every school and the context will determine which tool would be appropriate. This study highlights important ethical issues such as privacy concerns, balancing invasions of privacy against potential benefits, transparency of communication around surveillance technology and data use, and processes of consent. These issues need to be carefully considered when implementing contact tracing technologies in school settings. Communication, transparency, and consent within the school community could lead to acceptance and engagement with the new tools.

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Internet of Things-enabled Passive Contact Tracing in Smart Cities

Cited by 15 publications

References 25 publications

Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM

Views and Needs of Students, Parents, and Teachers on Closed-Circuit Television, Proximity Trackers, and Access Cards to Facilitate COVID-19 Contact Tracing in Schools: Thematic Analysis of Focus Groups and Interviews

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