Artificial intelligence-enabled Internet of Things-based system for COVID-19 screening using aerial thermal imaging

Barnawi, Ahmed; Chhikara, Prateek; Tekchandani, Rajkumar; Kumar, Neeraj; Alzahrani, Bander A.

doi:10.1016/j.future.2021.05.019

Cited by 85 publications

(50 citation statements)

References 35 publications

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“…Thermal imaging with face detection is used in the first step of the proposed work [21]. The suspected patient is required to be tested to confirm the indefinite disease.…”

Section: Face Recognition Systemmentioning

confidence: 99%

“…If the body temperature is higher than 37 °C, then it takes that person's face and converts it to a 128-dimensional vector and sends it to the central server. The system compares the selected face with the existing database using machine learning algorithms, finds matches, and sends a notification to the suspected person to consult a doctor soon [21].…”

Section: Temperature Identification Systemmentioning

confidence: 99%

“…The model is advanced enough to recognize face-mask mode. The system collects accurate stats such as the number and percentage of persons wearing masks [21].…”

Section: Face Mask Detectionmentioning

confidence: 99%

“…Then, anomaly detection and automatic pattern recognition in the collected data can be performed with machine learning models. Machine learning technologies predict more accurately and faster than traditional business intelligence (BI) tools [21].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Imran

Zaman²,

Imran

et al. 2021

Electronics

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Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as `machine learning’, blockchain, `Internet of Things or IoT’, and keywords conjoined with `healthcare’ and `health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.

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“…Thermal imaging with face detection is used in the first step of the proposed work [21]. The suspected patient is required to be tested to confirm the indefinite disease.…”

Section: Face Recognition Systemmentioning

confidence: 99%

Section: Temperature Identification Systemmentioning

confidence: 99%

“…The model is advanced enough to recognize face-mask mode. The system collects accurate stats such as the number and percentage of persons wearing masks [21].…”

Section: Face Mask Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Imran

Zaman²,

Imran

et al. 2021

Electronics

View full text Add to dashboard Cite

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“…Novel coronavirus disease (known as COVID-19) has significantly impacted the world, with 30+ million confirmed cases and a million deaths globally. Early and precise diagnosis of COVID-19 is of high importance for restraining the spread of the disease and to reduce death [ 10 ]. In the current state of the pandemic COVID-19, this usage of UAVs can prove to help reduce the load on the hospitals and health care staff.…”

Section: Introductionmentioning

confidence: 99%

A CNN-based scheme for COVID-19 detection with emergency services provisions using an optimal path planning

et al. 2021

Self Cite

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Unmanned Air Vehicles (UAVs) are becoming popular in real-world scenarios due to current advances in sensor technology and hardware platform development. The applications of UAVs in the medical field are broad and may be shared worldwide. With the recent outbreak of COVID-19, fast diagnostic testing has become one of the challenges due to the lack of test kits. UAVs can help in tackling the COVID-19 by delivering medication to the hospital on time. In this paper, to detect the number of COVID-19 cases in a hospital, we propose a deep convolution neural architecture using transfer learning, classifying the patient into three categories as COVID-19 (positive) and normal (negative), and pneumonia based on given X-ray images. The proposed deep-learning architecture is compared with state-of-the-art models. The results show that the proposed model provides an accuracy of 94.92%. Further to offer time-bounded services to COVID-19 patients, we have proposed a scheme for delivering emergency kits to the hospitals in need using an optimal path planning approach for UAVs in the network.

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Real-time Social Distancing Detection Approach Using YOLO and Unmanned Aerial Vehicles

Merizalde

Morillo

2022

Smart Technologies, Systems and Applications

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Artificial intelligence-enabled Internet of Things-based system for COVID-19 screening using aerial thermal imaging

Cited by 85 publications

References 35 publications

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

A CNN-based scheme for COVID-19 detection with emergency services provisions using an optimal path planning

Real-time Social Distancing Detection Approach Using YOLO and Unmanned Aerial Vehicles

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