A Mask Detection Algorithm Based on Improved Yolov5s

Aiming at the severe form of new coronavirus epidemic prevention and control, a target detection algorithm is proposed to detect whether masks are worn in public places. The Ghostnet and SElayer modules with fewer design parameters replace the BottleneckCSP part in the original Yolov5s network, which reduces the computational complexity of the model and improves the detection accuracy. The bounding box regression loss function DIOU is optimized, the DGIOU loss function is used for bounding box regression, and the center coordinate distance between the two bounding boxes is considered to achieve a better convergence effect. In the feature pyramid, the depthwise separable convolution DW is used to replace the ordinary convolution, which further reduces the amount of parameters and reduces the loss of feature information caused by multiple convolutions. The experimental results show that compared with the yolov5s algorithm, the proposed method improves the mAP by 4.6% and the detection rate by 10.7 frame/s in the mask wearing detection. Compared with other mainstream algorithms, the improved yolov5s algorithm has better generalization ability and practicability. © 2022 ACM.

PINN training using biobjective optimization: The trade-off between data loss and residual loss

Physics informed neural networks (PINNs) have proven to be an efficient tool to represent problems for which measured data are available and for which the dynamics in the data are expected to follow some physical laws. In this paper, we suggest a multiobjective perspective on the training of PINNs by treating the data loss and the residual loss as two individual objective functions in a truly biobjective optimization approach. As a showcase example, we consider COVID-19 predictions in Germany and built an extended susceptibles-infected-recovered (SIR) model with additionally considered leaky-vaccinated and hospitalized populations (SVIHR model) to model the transition rates and to predict future infections. SIR-type models are expressed by systems of ordinary differential equations (ODEs). We investigate the suitability of the generated PINN for COVID-19 predictions and compare the resulting predicted curves with those obtained by applying the method of non-standard finite differences to the system of ODEs and initial data. The approach is applicable to various systems of ODEs that define dynamical regimes. Those regimes do not need to be SIR-type models, and the corresponding underlying data sets do not have to be associated with COVID-19.

A graph-based edge attention gate medical image segmentation method

For the purpose of solving the problems of missing edges and low segmentation accuracy in medical image segmentation, a medical image segmentation network (EAGC_UNet++) based on residual graph convolution UNet++ with edge attention gate (EAG) is proposed in the study. With UNet++ as the backbone network, the idea of graph theory is introduced into the model. First, the dropout residual graph convolution block (DropRes_GCN Block) and the traditional convolution structure in UNet++ are used as encoders. Second, EAGs are adopted so that the model pays more attention to image edge features during decoding. Finally, aiming at the imbalance problem of positive and negative samples in medical image segmentation, a new weighted loss function is introduced to enhance segmentation accuracy. In the experimental part, three datasets (LiTS2017, ISIC2018, COVID-19 CT scans) were used to evaluate the performances of various models;multiple groups of ablation experiments were designed to verify the effectiveness of each part of the model. The experimental results showed that EAGC_UNet++ had better segmentation performance than the other models under three quantitative evaluation indicators and better solved the problem of missing edges in medical image segmentation. © 2023 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Quantile regression for count data: jittering versus regression coefficients modelling in the analysis of credits earned by university students after remote teaching.

The extension of quantile regression to count data raises several issues. We compare the traditional approach, based on transforming the count variable using jittering, with a recently proposed approach in which the coefficients of quantile regression are modelled by parametric functions. We exploit both methods to analyse university students' data to evaluate the effect of emergency remote teaching due to COVID-19 on the number of credits earned by the students. The coefficients modelling approach performs a smoothing that is especially convenient in the tails of the distribution, preventing abrupt changes in the point estimates and increasing precision. Nonetheless, model selection is challenging because of the wide range of options and the limited availability of diagnostic tools. Thus the jittering approach remains fundamental to guide the choice of the parametric functions.

COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet.

Coronavirus disease 2019 (COVID-19) epidemic has devastating effects on personal health around the world. It is significant to achieve accurate segmentation of pulmonary infection regions, which is an early indicator of disease. To solve this problem, a deep learning model, namely, the content-aware pre-activated residual UNet (CAPA-ResUNet), was proposed for segmenting COVID-19 lesions from CT slices. In this network, the pre-activated residual block was used for down-sampling to solve the problems of complex foreground and large fluctuations of distribution in datasets during training and to avoid gradient disappearance. The area loss function based on the false segmentation regions was proposed to solve the problem of fuzzy boundary of the lesion area. This model was evaluated by the public dataset (COVID-19 Lung CT Lesion Segmentation Challenge-2020) and compared its performance with those of classical models. Our method gains an advantage over other models in multiple metrics. Such as the Dice coefficient, specificity (Spe), and intersection over union (IoU), our CAPA-ResUNet obtained 0.775 points, 0.972 points, and 0.646 points, respectively. The Dice coefficient of our model was 2.51% higher than Content-aware residual UNet (CARes-UNet). The code is available at https://github.com/malu108/LungInfectionSeg.

Enhanced Framework for COVID-19 Prediction with Computed Tomography Scan Images using Dense Convolutional Neural Network and Novel Loss Function.

Recent studies have shown that computed tomography (CT) scan images can characterize COVID-19 disease in patients. Several deep learning (DL) methods have been proposed for diagnosis in the literature, including convolutional neural networks (CNN). But, with inefficient patient classification models, the number of 'False Negatives' can put lives at risk. The primary objective is to improve the model so that it does not reveal 'Covid' as 'Non-Covid'. This study uses Dense-CNN to categorize patients efficiently. A novel loss function based on cross-entropy has also been used to improve the CNN algorithm's convergence. The proposed model is built and tested on a recently published large dataset. Extensive study and comparison with well-known models reveal the effectiveness of the proposed method over known methods. The proposed model achieved a prediction accuracy of 93.78%, while false-negative is only 6.5%. This approach's significant advantage is accelerating the diagnosis and treatment of COVID-19.

DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism.

Protein arginine methylation is an important posttranslational modification (PTM) associated with protein functional diversity and pathological conditions including cancer. Identification of methylation binding sites facilitates a better understanding of the molecular function of proteins. Recent developments in the field of deep neural networks have led to a proliferation of deep learning-based methylation identification studies because of their fast and accurate prediction. In this paper, we propose DeepGpgs, an advanced deep learning model incorporating Gaussian prior and gated attention mechanism. We introduce a residual network channel to extract the evolutionary information of proteins. Then we combine the adaptive embedding with bidirectional long short-term memory networks to form a context-shared encoder layer. A gated multi-head attention mechanism is followed to obtain the global information about the sequence. A Gaussian prior is injected into the sequence to assist in predicting PTMs. We also propose a weighted joint loss function to alleviate the false negative problem. We empirically show that DeepGpgs improves Matthews correlation coefficient by 6.3% on the arginine methylation independent test set compared with the existing state-of-the-art methylation site prediction methods. Furthermore, DeepGpgs has good robustness in phosphorylation site prediction of SARS-CoV-2, which indicates that DeepGpgs has good transferability and the potential to be extended to other modification sites prediction. The open-source code and data of the DeepGpgs can be obtained from https://github.com/saizhou1/DeepGpgs.

Custom Weighted Balanced Loss function for Covid 19 Detection from an Imbalanced CXR Dataset

In this paper, we have proposed a novel framework, that is ResNet-18 model along with Custom Weighted Balanced loss function, in order to automatically detect Covid-19 disease from a highly imbalanced Chest X-Ray (CXR) dataset. Covid 19 disease has become a global pandemic, for last two years. Early automatic detection of Covid-19, from CXR images has been the key to survive from this pandemic. In the recent advent, researchers have already proposed several Deep Learning (DL) models, which can detect Covid-19 disease (with higher accuracy) from CXR images. However, Covid-19 detection by DL models are fraught with the problem of class imbalance, since most of the available CXR datasets are found highly imbalanced. In this paper, we have worked in a new direction, that is, alleviating the class imbalance problem from CXR dataset by using novel loss function. First, we choose a challengeable CXR dataset in which there are four classes, they are Covid, Normal, Lung Opacity (LO) and Viral Pneumonia (VP). Later we have identified that real problem of this dataset is not only the class imbalance, but also, huge intra-class variance is observed in Covid class. Therefore, we have come up with a new idea, that is, modifying the bias weights in a Weighted Categorical Cross Entropy (WCCE), based on reducing both of the factors, i.e., class imbalance and intra-class variance from the dataset. For the experimentation, we have chosen a ResNet-18 model which is trained from scratch for a large Chexpert CXR dataset and thereafter it is pre-trained on the Covid CXR dataset. Experimental results suggest that ResNet-18 model along with proposed Custom Weighted Balanced loss function, have improved 2-4% accuracy, precision, recall, F1 score and AUC for four class CXR dataset. Furthermore, we have tested the same framework for three class Covid CXR dataset, after excluding LO class. We have achieved 96% accuracy, 97% precision, 96% recall, 97% F1 score and 97% AUC for three class classification task. This is significant (3-4%) improvement than the performance of ResNet-18 model with CCE. © 2022 IEEE.

Mask Wearing Detection Algorithm Based on Improved SSD

In recent years, the COVID-19, which involves a highly infectious virus, has spread worldwide.Wearing masks in public areas can reduce the transmission and hence the spread of the virus.Additionally, using computer vision technology to detect mask wearing behavior in public areas is crucial.To prevent and control epidemics, the correct form of wearing face masks must be identified.In an actual environment, the detection of mask wearing is complex and diverse.The scale of a face wearing a mask is different;furthermore, the difference between the correct and wrong forms of wearing a mask is subtle and hence difficult to detect.Therefore, a mask wearing detection algorithm based on an improved Single Shot Multibox Detector(SSD) algorithm is proposed herein.Based on the SSD network, the algorithm introduces a feature fusion network and an attention coordination mechanism, reconstructs the feature extraction network, and enhances the ability of learning and processing detailed information.In addition, the classification prediction score and IoU score of the algorithm are combined, whereas the Quality Focal Loss(QFL) function is used to adjust the weight of positive and negative samples.An experiment is performed on acustom-developed mask wearing test dataset.Experimental results show that the average accuracy of the algorithm is 96.28%, which is 5.62% higher than that of the original algorithm.The improved algorithm offers good accuracy and practicability for mask wearing detection, as well assatisfies the requirements for epidemic prevention and control. © 2022, Editorial Office of Computer Engineering. All rights reserved.

An Efficient Face Mask Wearing Detection Algorithm Based on Improved YOLOv3

In recent years, the Corona Virus Disease 2019 (Covid-19) epidemic has raged around the world, with more than 500 million people diagnosed. Relevant medical research and analysis results on Covid-19 indicate that wearing masks is an effective method to prevent and restrain virus transmission. Mask detection stations have been set up in hospitals, railway stations, schools, where there is large crowd flow, but results are not as good as expected. In order to ameliorate pandemic preventing and control measures, a mask wearing detection algorithm YOLOv3-M3 was designed and proposed in this paper. The algorithm can effectively detect people without mask, while consequently reminding them. Firstly, we substituted the feature extraction network of YOLOv3 with MobileNetv3, a lightweight convolutional neural network. Secondly, we utilized K-Means++ to substitute the original ground truth clustering algorithm to improve prediction precision. In addition, the bounding box regression loss function was revised as CIoU loss function. This loss function solves the issues of overlapping between the ground truth and the anchor box, which has increased the training speed. After experiments, the precision of YOLOv3 algorithm on mAP 0.5 and mAP 0.75 is 93.5% and 71.9%, respectively. Elevating 3.1% and 2.6%, respectively, higher than that of YOLOv3 algorithm, and it was superior to SSD, SSD Lite, YOLOv3-Tiny and other one-stage object detection algorithms. The detection speed can reach 13.6 frame/s, which has met the requirements of pandemic prevention and control in most places and can be deployed on terminal devices for object detection. [ FROM AUTHOR]

A Pulmonary Parenchyma Segmentation Algorithm Aiding in COVID-19 Detection

As a rapidly evolving pandemic, COVID-19 has caused severe health and economic impact. In the diagnosis of COVID-19, the extraction of pulmonary parenchyma in chest X-ray images plays an important role. A U-Net-based pulmonary parenchyma segmentation algorithm using the encoding and decoding mode is proposed. The algorithm applies the idea of feature fusion to the construction of an A-Block to fully learn the semantic information of deep features. The attention mechanism is introduced into the deep convolutional neural network by adding a Dense Atrous Convolution (DAC) module and a Residual Multi-kernel Pooling (RMP) module in order to extend the receptive field of the convolution and to extract the contextual feature information. By improving the deformable convolution and the segmentation loss function, the generalization ability and the robustness of the network model are enhanced. Experimental results show that the segmentation accuracy, Dice coefficient, sensitivity and Jaccard index of this algorithm are 98. 16%, 98. 32%, 98. 13% and 98. 54% respectively. The algorithm can effectively implement pulmonary parenchyma segmentation. © 2021, Editorial Office of Computer Engineering. All rights reserved.

Hate Speech Detection in Thai Social Media with Ordinal-Imbalanced Text Classification

Cyberbullying has become a serious problem in Thai social media. For example, some Thai people posted hate speeches on Myanmar workers in Thailand during the COVID-19 pandemic, which might elevate hate crime. It is imperative and urgent to detect cyberbullying on Thai social media. The task is a text classification problem. Moreover, hate speeches contain the order of severity levels, but many pieces of work did not consider this point in the model. Therefore, we developed a Thai hate-speech classification method with various loss functions to detect such hate speeches accurately. We evaluated them on a corpus of ordinal-imbalanced Thai text. The evaluated outcomes indicated that the best-in terms of $F$1 -score-model was the model with a loss function of a hybrid between an Ordinal regression loss function and Pearson correlation coefficients (common in similarity function). It yielded an average F1-score of 78.38 %-0.88 % significantly higher than the score achieved by a conventional loss function-and an average mean squared error of 0.2478-5.49 % relative improvement. Thus, the proposed hybrid loss function improved the efficiency of the model. © 2022 IEEE.

Finding Asymptomatic Spreaders in a COVID-19 Transmission Network by Graph Attention Networks.

In the COVID-19 epidemic the mildly symptomatic and asymptomatic infections generate a substantial portion of virus spread; these undetected individuals make it difficult to assess the effectiveness of preventive measures as most epidemic prevention strategies are based on the detected data. Effectively identifying the undetected infections in local transmission will be of great help in COVID-19 control. In this work, we propose an RNA virus transmission network representation model based on graph attention networks (RVTR); this model is constructed using the principle of natural language processing to learn the information of gene sequence and using a graph attention network to catch the topological character of COVID-19 transmission networks. Since SARS-CoV-2 will mutate when it spreads, our approach makes use of graph context loss function, which can reflect that the genetic sequence of infections with close spreading relation will be more similar than those with a long distance, to train our model. Our approach shows its ability to find asymptomatic spreaders both on simulated and real COVID-19 datasets and performs better when compared with other network representation and feature extraction methods.

Estimates of a ‘Pandemic Taylor rule’ loss function

This article applies a central bank-style loss function to policymaker decision-making during the Covid-19 pandemic. An empirical Taylor rule-style model is used to estimate preference weights associated with lost economic activity and Covid-19 cases and deaths for the four largest US states. Results demonstrate that there are preference differences across states with Republican-led states placing greater weight on economic loss than do those with Democrat governors. Moreover, all states in the sample responded to changes in Covid over the sample period. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

A Discrete Analog of the Inverted Kumaraswamy Distribution: Properties and Estimation with Application to COVID-19 Data

In recent years, researchers focused on introducing discrete type distributions which satisfy the necessary demand to model the complex performance of the real data sets. In this paper, a discrete inverted Kumaraswamy distribution, which is a discrete version of the continuous inverted Kumaraswamy distribution, is derived using the general approach of discretization of a continuous distribution. The new discrete inverted Kumaraswamy distribution can be applied efficiently in discrete lifetime and count data. Some important distributional and reliability properties of discrete inverted Kumaraswamy distribution such as hazard rate, moments, quantiles, order statistics and some transformations are obtained. Maximum likelihood and Bayesian approaches are applied under Type-II censored samples for estimating the parameters, survival, hazard rate and alternative hazard rate functions. Confidence and credible intervals for the parameters are obtained. A simulation study is carried out to illustrate the theoretical results of the maximum likelihood and Bayesian estimation. Finally, the performance of the new distribution is compared with some distributions using three real data sets to illustrate the suitability and flexibility of the proposed model. © 2022. Pakistan Journal of Statistics and Operation Research. All Rights Reserved.

CovSeg-Unet: End-to-End Method-based Computer-Aided Decision Support System in Lung COVID-19 Detection on CT Images

COVID-19 epidemic continues to threaten public health with the appearance of new, more severe mutations, and given the delay in the vaccination process, the situation becomes more complex. Thus, the implementation of rapid solutions for the early detection of this virus is an immediate priority. To this end, we provide a deep learning method called CovSeg-Unet to diagnose COVID-19 from chest CT images. The CovSeg-Unet method consists in the first time of preprocessing the CT images to eliminate the noise and make all images in the same standard. Then, CovSeg-Unet uses an end-to-end architecture to form the network. Since CT images are not balanced, we propose a loss function to balance the pixel distribution of infected/uninfected regions. CovSeg-Unet achieved high performances in localizing COVID-19 lung infections compared to others methods. We performed qualitative and quantitative assessments on two public datasets (Dataset-1 and Dataset-2) annotated by expert radiologists. The experimental results prove that our method is a real solution that can better help in the COVID-19 diagnosis process © 2022,International Journal of Advanced Computer Science and Applications.All Rights Reserved

A Robust Face Recognition Algorithm Based on an Improved Generative Confrontation Network

Objective: In practical applications, an image of a face is often partially occluded, which decreases the recognition rate and the robustness. Therefore, in response to this situation, an effective face recognition model based on an improved generative adversarial network (GAN) is proposed. Methods: First, we use a generator composed of an autoencoder and the adversarial learning of two discriminators (local discriminator and global discriminator) to fill and repair an occluded face image. On this basis, the Resnet-50 network is used to perform image restoration on the face. In our recognition framework, we introduce a classification loss function that can quantify the distance between classes. The image generated by the generator can only capture the rough shape of the missing facial components or generate the wrong pixels. To obtain a clearer and more realistic image, this paper uses two discriminators (local discriminator and global discriminator, as mentioned above). The images generated by the proposed method are coherent and minimally influence facial expression recognition. Through experiments, facial images with different occlusion conditions are compared before and after the facial expressions are filled, and the recognition rates of different algorithms are compared. Results: The images generated by the method in this paper are truly coherent and have little impact on facial expression recognition. When the occlusion area is less than 50%, the overall recognition rate of the model is above 80%, which is close to the recognition rate pertaining to the non-occluded images. Conclusions: The experimental results show that the method in this paper has a better restoration effect and higher recognition rate for face images of different occlusion types and regions. Furthermore, it can be used for face recognition in a daily occlusion environment, and achieve a better recognition effect.

KL-MOB: automated COVID-19 recognition using a novel approach based on image enhancement and a modified MobileNet CNN.

The emergence of the novel coronavirus pneumonia (COVID-19) pandemic at the end of 2019 led to worldwide chaos. However, the world breathed a sigh of relief when a few countries announced the development of a vaccine and gradually began to distribute it. Nevertheless, the emergence of another wave of this pandemic returned us to the starting point. At present, early detection of infected people is the paramount concern of both specialists and health researchers. This paper proposes a method to detect infected patients through chest x-ray images by using the large dataset available online for COVID-19 (COVIDx), which consists of 2128 X-ray images of COVID-19 cases, 8,066 normal cases, and 5,575 cases of pneumonia. A hybrid algorithm is applied to improve image quality before undertaking neural network training. This algorithm combines two different noise-reduction filters in the image, followed by a contrast enhancement algorithm. To detect COVID-19, we propose a novel convolution neural network (CNN) architecture called KL-MOB (COVID-19 detection network based on the MobileNet structure). The performance of KL-MOB is boosted by adding the Kullback-Leibler (KL) divergence loss function when trained from scratch. The KL divergence loss function is adopted for content-based image retrieval and fine-grained classification to improve the quality of image representation. The results are impressive: the overall benchmark accuracy, sensitivity, specificity, and precision are 98.7%, 98.32%, 98.82% and 98.37%, respectively. These promising results should help other researchers develop innovative methods to aid specialists. The tremendous potential of the method proposed herein can also be used to detect COVID-19 quickly and safely in patients throughout the world.

DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images.

(1) Background: COVID-19 has been global epidemic. This work aims to extract 3D infection from COVID-19 CT images; (2) Methods: Firstly, COVID-19 CT images are processed with lung region extraction and data enhancement. In this strategy, gradient changes of voxels in different directions respond to geometric characteristics. Due to the complexity of tubular tissues in lung region, they are clustered to the lung parenchyma center based on their filtered possibility. Thus, infection is improved after data enhancement. Then, deep weighted UNet is established to refining 3D infection texture, and weighted loss function is introduced. It changes cost calculation of different samples, causing target samples to dominate convergence direction. Finally, the trained network effectively extracts 3D infection from CT images by adjusting driving strategy of different samples. (3) Results: Using Accuracy, Precision, Recall and Coincidence rate, 20 subjects from a private dataset and eight subjects from Kaggle Competition COVID-19 CT dataset tested this method in hold-out validation framework. This work achieved good performance both in the private dataset (99.94-00.02%, 60.42-11.25%, 70.79-09.35% and 63.15-08.35%) and public dataset (99.73-00.12%, 77.02-06.06%, 41.23-08.61% and 52.50-08.18%). We also applied some extra indicators to test data augmentation and different models. The statistical tests have verified the significant difference of different models. (4) Conclusions: This study provides a COVID-19 infection segmentation technology, which provides an important prerequisite for the quantitative analysis of COVID-19 CT images.

pin ¯ -TSVM: A Robust Transductive Support Vector Machine and its Application to the Detection of COVID-19 Infected Patients.

Training a machine learning model on the data sets with missing labels is a challenging task. Not all models can handle the problem of missing labels. However, if these data sets are further corrupted with label noise, it becomes even more challenging to train a machine learning model on such data sets. We propose to use a transductive support vector machine (TSVM) for semi-supervised learning in this situation. We make this model robust to label noise by using a truncated pinball loss function with it. We name our approach, pin ¯ -TSVM. We provide both the primal and the dual formulations of the obtained robust TSVM for linear and non-linear kernels. We also perform experiments on synthetic and real-world data sets to prove the superior robustness of our model as compared to the existing approaches. To this end, we use small as well as large-scale data sets to perform the experiments. We show that the model is capable of training in the presence of label noise and finding the missing labels of the data samples. We use this property of pin ¯ -TSVM to detect the coronavirus patients based on their chest X-ray images.