Face Mask Detection Using Lightweight Deep Learning Architecture and Raspberry Pi Hardware: An Approach to Reduce Risk of Coronavirus Spread While Entrance to Indoor Spaces

Özyurt, Fatih; Mira, Ahmet; Çoban, Ayşe

doi:10.18280/ts.390227

Cited by 4 publications

(2 citation statements)

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“…Shufflenet is a deep model designed to provide effective results even on devices with low computing power. This model accepts 224×224 images [24,25]. Resnet50 architecture accepts 224×224 images just like Googlenet and Shufflenet architectures.…”

Section: Pre-trained Cnn Modelsmentioning

confidence: 99%

Hybrid Deep Model for Automated Detection of Tomato Leaf Diseases

Bayram¹,

Bingol²,

Alataş³

2022

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Tomatoes are preferred by farmers because of their high productivity. This fruit has a fibrous structure and contains plenty of vitamins. Tomato diseases are generally observed on stem, fruit, and leaves. Early diagnosis of the disease in plants is of vital importance for the plant. This is very important for farmers who expect economic gain from that plant. Because if the disease is not treated early, these tomatoes should be destroyed. For these reasons, systems to diagnose the disease early are very important. In this study, a tomato leaf diseases classification model developed with deep learning methods, which is one of the most popular artificial intelligence techniques, is proposed in order to eliminate the possibility of the human eye being mistaken. In this study, 6 different Convolutional Neural Network (CNN) architectures were used. In the first stage of this study, which consists of two stages, the classification process was carried out with the Alexnet, Googlenet, Shufflenet, Efficientb0, Resnet50, and Inceptionv3 architectures that were previously trained. In the second stage, feature maps of tomato leaf images in the dataset were obtained using the six pre-trained deep learning architectures. In the hybrid model proposed in this study, the feature maps extracted using the best two of the six deep learning models are concatenated. Then, the Neighborhood Component Analysis (NCA) method was applied to the extracted features in order to speed up the system, unnecessary features were removed and optimized. The optimized feature map is classified by traditional intelligent classification models. As a result of experimental studies, the average accuracy rate of the proposed model is 99.50 percent.

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Section: Pre-trained Cnn Modelsmentioning

confidence: 99%

Hybrid Deep Model for Automated Detection of Tomato Leaf Diseases

Bayram¹,

Bingol²,

Alataş³

2022

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“…Therefore, using rate constants measured in simple aqueous environments or the resulting models to guide real-world virus inactivation or using a single linear modeling approach to fit virus inactivation by UV irradiation with multiple influencing factors is challenging. Machine learning (ML) techniques have been applied in a wide range of fields, including environmental science, because of their strong ability to fit nonlinear multidimensional relationships between prediction targets and their influential factors. , Recently, ML has also been applied to aid in the fight against the COVID-19 pandemic for classifying ICU admissions and resource allocation, diagnosing and triaging cases of COVID-19 with chest X-ray images, detecting people who do not wear masks in public places, and predicting the effect of environmental chemicals on the gene transcription of SARS-CoV-2 . To the best of our knowledge, ML has not been systematically used in UV inactivation studies of coronaviruses.…”

Section: Introductionmentioning

confidence: 99%

Preparing for the Next Pandemic: Predicting UV Inactivation of Coronaviruses with Machine Learning

Huang,

Ma,

Huangfu

et al. 2023

Environ. Sci. Technol.

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The epidemic of coronaviruses has posed significant public health concerns in the last two decades. An effective disinfection scheme is critical to preventing ambient virus infections and controlling the spread of further outbreaks. Ultraviolet (UV) irradiation has been a widely used approach to inactivating pathogenic viruses. However, no viable framework or model can accurately predict the UV inactivation of coronaviruses in aqueous solutions or on environmental surfaces, where viruses are commonly found and spread in public places. By conducting a systematic literature review to collect data covering a wide range of UV wavelengths and various subtypes of coronaviruses, including severe acute respiratory syndrome 2 (SARS-CoV-2), we developed machine learning models for predicting the UV inactivation effects of coronaviruses in aqueous solutions and on environmental surfaces, for which the optimal test performance was obtained with R2 = 0.927, RMSE = 0.565 and R2 = 0.888, RMSE = 0.439, respectively. Besides, the required UV doses at different wavelengths to inactivate the SARS-CoV-2 to 1 Log TCID50/mL titer from different initial titers were predicted for inactivation in protein-free water, saliva on the environmental surface, or the N95 respirator. Our models are instructive for eliminating the ongoing pandemic and controlling the spread of an emerging and unknown coronavirus outbreak.

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