A Deep Neural Network Based on ResNet for Predicting Solutions of Poisson–Boltzmann Equation

Kwon, In Gyu; Jo, Gwanghyun; Shin, Kwang-Seong

doi:10.3390/electronics10212627

Cited by 3 publications

(2 citation statements)

References 39 publications

(64 reference statements)

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“…It operates in two ways. First, skip connections resolve the vanishing gradient problem by creating a different path for the gradient to use [20]. Secondly, it can also learn an identity function of the model, and thereby, ensure that the model's higher levels do not function any worse than its bottom layer.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Face Mask Classification with Convolutional Neural Network for Proper and Improper Face Mask Wearing

Azis¹,

Suhaimi²,

Abas³

2023

IJC

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Since the discovery of COVID-19, the wearing of a face mask has been recognized as an effective means of curbing the spread of most infectious respiratory diseases. A face mask must completely enclose the lips and nose properly for effective prevention of the disease. Some people still refuse to wear the mask, either out of annoyance or difficulty, or they are just wearing it incorrectly, which diminishes the mask's effectiveness and renders it worthless. The deep learning models described in this research provide a mechanism for assessing whether a face mask is being worn correctly or incorrectly using images. For both training and testing, the suggested method makes use of MaskedFace-Net dataset that contains annotated photos of an individual's face with proper and improper masks. Threshold optimizations are applied to produce significant results of prediction when comparing ResNet50, MobileNetV2 and DenseNet121 models. It is observed that better performance can be achieved with having accuracy as the target evaluation metric and reaching accuracy levels of 97.6%, 99.0%, and 99.8% for ResNet50, DenseNet121, and MobileNetV2, respectively after threshold optimization. As an outcome, DenseNet121 outperformed the other evaluated models when accuracy, recall, and precision metrics were used to assess the testing set. The face mask categorization can be used to automatically monitor face masks in real-time in public locations like hospitals, airports, shopping complexes and congested spaces to verify compliance with the published guidelines by the higher authorities in a country, making the results valuable for future use.

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Section: Introductionmentioning

confidence: 99%

Real-Time Face Mask Classification with Convolutional Neural Network for Proper and Improper Face Mask Wearing

Azis¹,

Suhaimi²,

Abas³

2023

IJC

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“…However, there have been huge developments in deep learning (DL) communities (see [7][8][9][10][11][12] and the references therein). One of the main advantages of DL-based methods is that once the networks are trained, they can produce predictions of the target variable in real time.…”

Section: Introductionmentioning

confidence: 99%

ResNet-Based Simulations for a Heat-Transfer Model Involving an Imperfect Contact

Wang

Shin

2022

JICCE

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Simulating the heat transfer in a composite material is an important topic in material science. Difficulties arise from the fact that adjacent materials cannot match perfectly, resulting in discontinuity in the temperature variables. Although there have been several numerical methods for solving the heat-transfer problem in imperfect contact conditions, the methods known so far are complicated to implement, and the computational times are non-negligible. In this study, we developed a ResNet-type deep neural network for simulating a heat transfer model in a composite material. To train the neural network, we generated datasets by numerically solving the heat-transfer equations with Kapitza thermal resistance conditions. Because datasets involve various configurations of composite materials, our neural networks are robust to the shapes of material-material interfaces. Our algorithm can predict the thermal behavior in real time once the networks are trained. The performance of the proposed neural networks is documented, where the root mean square error (RMSE) and mean absolute error (MAE) are below 2.47E-6, and 7.00E-4, respectively.

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D-ResNet-PVKELM: deep neural network and paragraph vector based kernel extreme machine learning model for multimodal depression analysis

Jacob

Mandava

2023

Multimed Tools Appl

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A Deep Neural Network Based on ResNet for Predicting Solutions of Poisson–Boltzmann Equation

Cited by 3 publications

References 39 publications

Real-Time Face Mask Classification with Convolutional Neural Network for Proper and Improper Face Mask Wearing

Real-Time Face Mask Classification with Convolutional Neural Network for Proper and Improper Face Mask Wearing

ResNet-Based Simulations for a Heat-Transfer Model Involving an Imperfect Contact

D-ResNet-PVKELM: deep neural network and paragraph vector based kernel extreme machine learning model for multimodal depression analysis

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