Monitoring Social Distancing Using Artificial Intelligence for Fighting COVID-19 Virus Spread

Alyami, Hashem; Alosaimi, Wael; Krichen, Moez; Alroobaea, Roobaea

doi:10.4018/ijossp.2021070104

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…The use of artificial intelligence (AI) [75][76][77][78] in software TST NG has been gaining popularity in recent years [79,80]. AI can help automate various tasks involved in software TST NG , such as test case generation, test execution, and result analysis.…”

Section: Use Of Ai In Software Testingmentioning

confidence: 99%

A Survey on Formal Verification and Validation Techniques for Internet of Things

Krichen

2023

Applied Sciences

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The Internet of Things (IoT) has brought about a new era of connected devices and systems, with applications ranging from healthcare to transportation. However, the reliability and security of these systems are critical concerns that must be addressed to ensure their safe and effective operation. This paper presents a survey of formal verification and validation (FV&V) techniques for IoT systems, with a focus on the challenges and open issues in this field. We provide an overview of formal methods and testing techniques for the IoT and discuss the state explosion problem and techniques to address it. We also examined the use of AI in software testing and describe examples of tools that use AI in this context. Finally, we discuss the challenges and open issues in FV&V for the IoT and present possible future directions for research. This survey paper aimed to provide a comprehensive understanding of the current state of FV&V techniques for IoT systems and to highlight areas for further research and development.

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Section: Use Of Ai In Software Testingmentioning

confidence: 99%

A Survey on Formal Verification and Validation Techniques for Internet of Things

Krichen

2023

Applied Sciences

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“…The reports are prepared during the digital mammography screening program as per the American College of Radiology Breast Imaging Reporting and Data System (ACR BIRADS) catalog. This catalog was last modified in November 2015 ( 12 , 13 ). As per the BIRADS, MBD gets classified into the four significant groups of mammographic breast density as class A, class B, class C, and class D, which reduces the sensitivity of digital mammography ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection

Pawar

Sharma

Sapate³

et al. 2022

Front. Public Health

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Percentage mammographic breast density (MBD) is one of the most notable biomarkers. It is assessed visually with the support of radiologists with the four qualitative Breast Imaging Reporting and Data System (BIRADS) categories. It is demanding for radiologists to differentiate between the two variably allocated BIRADS classes, namely, “BIRADS C and BIRADS D.” Recently, convolution neural networks have been found superior in classification tasks due to their ability to extract local features with shared weight architecture and space invariance characteristics. The proposed study intends to examine an artificial intelligence (AI)-based MBD classifier toward developing a latent computer-assisted tool for radiologists to distinguish the BIRADS class in modern clinical progress. This article proposes a multichannel DenseNet architecture for MBD classification. The proposed architecture consists of four-channel DenseNet transfer learning architecture to extract significant features from a single patient's two a mediolateral oblique (MLO) and two craniocaudal (CC) views of digital mammograms. The performance of the proposed classifier is evaluated using 200 cases consisting of 800 digital mammograms of the different BIRADS density classes with validated density ground truth. The classifier's performance is assessed with quantitative metrics such as precision, responsiveness, specificity, and the area under the curve (AUC). The concluding preliminary outcomes reveal that this intended multichannel model has delivered good performance with an accuracy of 96.67% during training and 90.06% during testing and an average AUC of 0.9625. Obtained results are also validated qualitatively with the help of a radiologist expert in the field of MBD. Proposed architecture achieved state-of-the-art results with a fewer number of images and with less computation power.

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“…In recent times, it is seen that researchers are widely using UVC to perform complex operations such as scientific applications for deactivating viruses; water purification to remove viruses and bacteria from the water; air purification which deactivates the viruses and germs present in the air of the atmosphere (9); sanitizing a room. Additionally, UVC has many applications for deactivating pathogens over different surfaces and mediums.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence Approach for Expurgating Edible and Non-Edible Items

Kumar

Bansal

Alroobaea

et al. 2022

Front. Public Health

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In the pandemic of COVID-19, it is crucial to consider the hygiene of the edible and nonedible things as it could be dangerous for our health to consume infected things. Furthermore, everything cannot be boiled before eating as it can destroy fruits and essential minerals and proteins. So, there is a dire need for a smart device that could sanitize edible items. The Germicidal Ultraviolet C (UVC) has proved the capabilities of destroying viruses and pathogens found on the surface of any objects. Although, a few minutes exposure to the UVC can destroy or inactivate the viruses and the pathogens, few doses of UVC light may damage the proteins of edible items and can affect the fruits and vegetables. To this end, we have proposed a novel design of a device that is employed with Artificial Intelligence along with UVC to auto detect the edible items and act accordingly. This causes limited UVC doses to be applied on different items as detected by proposed model according to their permissible limit. Additionally, the device is employed with a smart architecture which leads to consistent distribution of UVC light on the complete surface of the edible items. This results in saving the health as well as nutrition of edible items.

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Monitoring Social Distancing Using Artificial Intelligence for Fighting COVID-19 Virus Spread

Cited by 4 publications

References 40 publications

A Survey on Formal Verification and Validation Techniques for Internet of Things

A Survey on Formal Verification and Validation Techniques for Internet of Things

Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection

An Artificial Intelligence Approach for Expurgating Edible and Non-Edible Items

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