COVID-19 Imaging Detection in the Context of Artificial Intelligence and the Internet of Things

Gu, Xiaowei; Chen, Shuwen; Zhu, Huisheng; Brown, Mackenzie

doi:10.32604/cmes.2022.018948

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…The results show that the two models 16 and MobileNetV2 outperform the rest of the models according to the recognition accuracy, where the x-rays indicate whether the patient has COVID-19 or not. In [6][7][8], authors used ResNet model with segmentation on the computed tomography (CT) dataset of Covid patients. The results verified the performance behavior by giving high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

Al-Dulaimi¹,

Guneser²,

Hameed³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Recently, the demand for renewable energy has increased due to its environmental and economic needs. Solar panels are the mainstay for dealing with solar energy and converting it into another form of usable energy. Solar panels work under suitable climatic conditions that allow the light photons to access the solar cells, as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these photons. Thus, the panels are unable to work under these conditions. A layer of snow forms on the solar panels due to snowfall in areas with low temperatures. Therefore, it causes an insulating layer on solar panels and the inability to produce electrical energy. The detection of snow-covered solar panels is crucial, as it allows us the opportunity to remove snow using some heating techniques more efficiently and restore the photovoltaics system to proper operation. This paper presents five deep learning models, -16, -19, ESNET-18, ESNET-50, and ESNET-101, which are used for the recognition and classification of solar panel images. In this paper, two different cases were applied; the first case is performed on the original dataset without trying any kind of preprocessing, and the second case is extreme climate conditions and simulated by generating motion noise. Furthermore, the dataset was replicated using the upsampling technique in order to handle the unbalancing issue. The conducted dataset is divided into three different categories, namely; all_snow, no_snow, and partial snow. The five models are trained, validated, and tested on this dataset under the same conditions 60% training, 20% validation, and testing 20% for both cases. The accuracy of the models has been compared and verified to distinguish and classify the processed dataset. The accuracy results in the first case show that the compared models -16, -19, ESNET-18, and ESNET-50 give 0.9592, while ESNET-101 gives 0.9694. In the second case, the models outperformed their counterparts in the first case by evaluating performance, where the accuracy results reached 1.00, 0.9545, 0.9888, 1.00. and 1.00 for -16, -19, ESNET-18 and ESNET-50, respectively. Consequently, we conclude that the second case models outperformed their peers.

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Section: Introductionmentioning

confidence: 99%

Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

Al-Dulaimi¹,

Guneser²,

Hameed³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…The second paper "COVID-19 Imaging Detection in the Context of Artificial Intelligence and the Internet of Things" by Gu et al [2]. They introduced the segmentation of methods and applications.…”

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confidence: 99%

Introduction to the Special Issue on Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph

Wang¹,

Zhang²,

Huo³

2022

Computer Modeling in Engineering &Amp; Sciences

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Application of U-Net and Optimized Clustering in Medical Image Segmentation: A Review

Shao¹,

Chen²,

Zhou³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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COVID-19 Imaging Detection in the Context of Artificial Intelligence and the Internet of Things

Cited by 4 publications

References 38 publications

Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

Introduction to the Special Issue on Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph

Application of U-Net and Optimized Clustering in Medical Image Segmentation: A Review

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