Novel social distancing detector using local binary pattern in comparison with principal component analysis to improve accuracy

The objective is to detect Novel Social Distancing using Local Binary Pattern (LBP) in comparison with Principal Component Analysis (PCA). Social Distance deduction is performed using Local Binary Pattern(N=20) and Principal Component Analysis(N=20) algorithms. Google AI open Images dataset is used for image detection. Dataset contains more than 10,000 images. Accuracy of Principal Component Analysis is 89.8% and Local Binary Pattern is 93.9%. There exists a statistical significant difference between LBP and PCA with (p<0.05). Local Binary Pattern appears to perform significantly better than Principal Component Analysis for Social Distancing Detection. © 2023 Author(s).

Evaluation of a Video Generation Method Linking Dance and Scenes

Owing to the impact of COVID-19, the venues for dancers to perform have shifted from the stage to the media. In this study, we focus on the creation of dance videos that allow audiences to feel a sense of excitement without disturbing their awareness of the dance subject and propose a video generation method that links the dance and the scene by utilizing a sound detection method and an object detection algorithm. The generated video was evaluated using the Semantic Differential method, and it was confirmed that the proposed method could transform the original video into an uplifting video without any sense of discomfort. © 2023 SPIE.

Social Distance and Face Mask Detection using vision-based sensors and IP cameras

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

Antibody levels of COVID-19 vaccine: A comparison between healthy subjects and psychiatric patients on antidepressants

This study compares the serological antibody level post-COVID-19 vaccine among healthy subjects and psychiatric patients on antidepressant therapy. It also examines the difference in antidepressants' side effects experienced by psychiatric patients following the completion of two vaccine doses. A comparative posttest quasi-experimental study was conducted among healthy subjects and psychiatric patients on antidepressant medication in a teaching hospital in Malaysia. Elecsys Anti-SARS-CoV-2 assay was used to detect the antibody titre between weeks 4 and 12 post vaccination. The antidepressant side-effect checklist (ASEC) was used to monitor the occurrence of antidepressant-related side effects pre-and post-vaccination. 24 psychiatric patients and 26 healthy subjects were included. There was no significant difference in the antibody level between the patients (median = 1509 u/ml) and the healthy subjects (median = 995 u/ml). There was no significant worsening in the antidepressant-related side effects. The antibody level post-COVID-19 vaccine did not differ significantly between patients on antidepressant therapy and healthy subjects. Additionally, there was no change in the antidepressant side effects experienced by the patients following the completion of the vaccine.Copyright © 2022, Research Trends (P) LTD.. All rights reserved.

Path Planning of Follow-up Transport Robot Based on Machine Vision

With the upgrading and innovation of the logistics industry, the requirements for the level of transportation smart technologies continue to increase. The outbreak of the COVID-19 has further promoted the development of unmanned transportation machines. Aimed at the requirements of intelligent following and automatic obstacle avoidance of mobile robots in dynamic and complex environments, this paper uses machine vision to realize the visual perception function, and studies the real-time path planning of robots in complicated environment. And this paper proposes the Dijkstra-ant colony optimization (ACO) fusion algorithm, the environment model is established by the link viewable method, the Dijkstra algorithm plans the initial path. The introduction of immune operators improves the ant colony algorithm to optimize the initial path. Finally, the simulation experiment proves that the fusion algorithm has good reliability in a dynamic environment. © 2022 IEEE.

Masked Student Dataset of Expressions

Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scenario. While there are works on the problem of occlusion in FER, little has been done before on the particular face mask scenario. Moreover, the few works in this area largely use synthetically created masked FER datasets. Motivated by these challenges posed by the pandemic to FER, we present a novel dataset, the Masked Student Dataset of Expressions or MSD-E, consisting of 1,960 real-world non-masked and masked facial expression images collected from 142 individuals. Along with the issue of obfuscated facial features, we illustrate how other subtler issues in masked FER are represented in our dataset. We then provide baseline results using ResNet-18, finding that its performance dips in the non-masked case when trained for FER in the presence of masks. To tackle this, we test two training paradigms: contrastive learning and knowledge distillation, and find that they increase the model's performance in the masked scenario while maintaining its non-masked performance. We further visualise our results using t-SNE plots and Grad-CAM, demonstrating that these paradigms capitalise on the limited features available in the masked scenario. Finally, we benchmark SOTA methods on MSD-E. The dataset is available at https://github.com/SridharSola/MSD-E. © 2022 ACM.

Face Mask Detector for Raspberry Pi Based on Computer Vision and Edge Computing

The pandemic of the coronavirus disease 2019 has shown weakness and threats in various fields of human activity. In turn, the World Health Organization has recommended different preventive measures to decrease the spreading of coronavirus. Nonetheless, the world community ought to be ready for worldwide pandemics in the closest future. One of the most productive approaches to prevent spreading the virus is still using a face mask. This case has required staff who would verify visitors in public areas to wear masks. The aim of this paper was to identify persons remotely who wore masks or not, and also inform the personnel about the status through the message queuing telemetry transport as soon as possible using the edge computing paradigm. To solve this problem, we proposed to use the Raspberry Pi with a camera as an edge device, as well as the TensorFlow framework for pre-processing data at the edge. The offered system is developed as a system that could be introduced into the entrance of public areas. Experimental results have shown that the proposed approach was able to optimize network traffic and detect persons without masks. This study can be applied to various closed and public areas for monitoring situations. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Our journey from the Peripatetic Ultrasound Trainers to the Ultrasound Training Academy - HEE (South East)

In 2016, an innovative project of three ultrasound trainers evolved to support multi-professional trainees in Obstetric and Gynaecology ultrasound across the Wessex region. The aim of the project was to deliver a high-quality ultrasound training programme. One of the project's successes resulted in establishing the Sonographer Training Network Forum which allowed ultrasound leads from seven Trusts to meet and share ideas, which ultimately led to the development of regional ultrasound guidelines. From 2016 to early 2020, we have supported 75 trainees in O&G ultrasound. The vision was to create a dedicated ultrasound training centre to support trainees in a safe environment. Late 2019, with the support and funding from Health Education England South-East, the plans were set in motion to create the first Ultrasound Training Academy in the South- East Region. A briefing paper was submitted to the Trust Investment Group for approval. Approval from TIG acknowledged the Trust's support in the project as well as supporting the sonography workforce. This began the search for space that would accommodate the academy. Frustratingly, COVID stopped all activities, however, with reflection, COVID gave us the time to plan accordingly for the Ultrasound Training Academy. Without the normal pressure of a time frame, it was an opportunity to find an ideal location as well as purchasing the required equipment befitting the academy. The Ultrasound Training Academy - HEE (South-East) is based in the Princess Anne Hospital (University Hospital Southampton NHS FT). The advantages based within a hospital setting allowed the academy to follow the Trust's governance as well as absorbing some of the capacity from the ultrasound department. We have two ultrasound rooms and a dedicated space for simulation training. We have plans to create a third ultrasound room.

Newborn-onset chronic urticaria as the first manifestation of NLRP3 mosaicism

Objectives: A 7-month-old boy presented with generalized urticaria since the first week of life, without any other clinical manifestation. Cow's milk allergy was ruled out. His development was normal for his age. Maternal history was significant for COVID-19 infection in the third trimester of pregnancy with mild symptoms. Family history was significant for dermatographism in a maternal uncle. Hives were migratory with no single lesion persisting more than 24 h. There were no recognizable triggers and only relieved for 1-2 days after each vaccination. Patient was treated with optimal doses of antihistamines without improvement. Method(s): Laboratory tests and further studies were performed Results: Laboratory tests were normal including complete blood testing, circulating autoantibodies and infectious studies. C-reactive protein level and erythrocyte sedimentation rate were elevated. Due to chronic urticaria of newborn onset unresponsive to antihistamines a monogenic autoinflammatory disease was suspected. A targeted gene panel covering causative genes revealed the unreported p.Gly307Ala variant in the NLRP3 gene with a variant allele frequency (VAF) of 3% compatible with gene mosaicism. NLRP3 variant was classified as "likely pathogenic" based on its location, where a different variant has been reported as causing a severe form of cryopyrin-associated periodic syndromes (CAPS), and bioinformatic analyses. As expected, the variant was absent in patient's parents supporting for its de novo nature. Vision and hearing exams were normal. Treatment with canakinumab will start soon. Discussion(s): CAPS are dominantly-inherited autoinflammatory diseases caused by gain-of-function NLRP3 variants. These variants are often germline, but in some reported cases the variants are postzygotic causing gene mosaicism as in the patient here described. We believe that the mild presentation in our patient, despite having a likely pathogenic variant, may be explained by the low VAF. The genetic diagnosis in our patient allowed early initiation of anti-IL-1 treatment, which probably will prevent the development of other CAPS manifestations.

Comprehensive review about automatic classification of medicinal plants

The word Taxonomy is the way of Classification. It is the science of naming and classifying all the living organisms as well as extinct organisms of the world. Swedish Botanist Carlous Linnaeus was the father of taxonomy;Out of 17000 plant species present in India, more than 7600 plants are medicinal plants. Indigenous Indian medicines are formulations of traditional knowledge and medicinal plant extracts. The traditional knowledge is transferred from one generation to other generations which is used as drug for various diseases, instead of relying on what is the ingredients and proportions these drugs are based on traditional knowledge. These drugs involve the use of plant extract. The World Health Organization (WHO), leading agency in health care found that 80 % population in low economic output countries depend on traditional medicine for their essential health care[1]. In the current era of pandemic medicinal plant species like citrus spp, allium sativum, allium cepa found effective in management of COVID 19. As per WHO guidelines, In the field of medicinal research where clinical trials are used for new drug discovery, there is need of continuous supply of authenticated products which are correctly identified, classified, and verified [1]. Traditional identification and classification methods are not quick, efficient and reliable. Automated Classification of medicinal Plants help to conserve knowledge of medicinal plant species, share it from one generation to next generation and help the whole society to improve the knowledge about medicinal plants. The paper presents traditional and recent trends using Computer vision and machine learning for classifying medicinal plant species. The main focus is on Leaf image as input. It presents the challenges as well as opportunities in identifying and classifying medicinal plant species by performing comprehensive review of traditional methodologies. © 2023 Author(s).

Association of Long COVID with Seeing Difficulties Among Adults in the United States

Objectives: Many patients with long COVID experience at least one vision problem. This study determines the association of long COVID with seeing difficulties. Method(s): We conducted a cross-sectional analysis with the Census Household Pulse Survey data (N = 51,288). We excluded adults who reported contracting COVID within the past four weeks, those with missing data on seeing difficulty when infected with COVID, and long COVID. Long COVID was defined as having symptoms lasting three months or longer that the adults did not have prior to having COVID. Adults self-reporting to a question on seeing with "some difficulty," "a lot of difficulty," or "unable to do" were classified as having "seeing difficulties." We conducted Chi-square tests and logistic regressions with replicate weights. Logistic regressions adjusted for long COVID, sex, age, race and ethnicity, marital status, income, education, food sufficiency, health insurance, remote work, vaccine doses, region, depression, and anxiety. Result(s): During the survey period (November 2 - November 14, 2022), 37.3% reported seeing difficulties, and 14.4% reported long COVID. A higher percentage of adults with long COVID reported seeing difficulties than those without long COVID (47.6% vs. 31.9%). In the fully adjusted logistic regression model, compared to adults with no COVID or without long COVID, those with long COVID had greater odds of seeing difficulties (AOR = 1.50, 95%CI = 1.32, 1.70). We did not observe a statistically significant difference between adults without long COVID and no COVID (AOR = 1.01, 95%CI = 0.93, 1.10 p = 0.7888). Conclusion(s): One in eight adults had long COVID. Adults with long COVID had significantly higher odds of seeing difficulties than those without long COVID. Therefore, a follow-up of patients with long COVID needs to include screening for seeing difficulties. More research is needed on the links between long Covid and vision care.Copyright © 2023

A Vision Transformer-based Approach for Automatic COVID-19 Diagnosis on Chest X-ray Images.

The new type of coronavirus disease (COVID-19), which has emerged in recent years, has become a serious disease that threatens health worldwide. COVID-19, which can be transmitted very quickly and with serious increases in death, has paved the way for many concerns. With the spread of the epidemic to a universal dimension, many studies have been carried out for the early diagnosis of this disease. With early diagnosis, both fatal cases are prevented and the planning of the epidemic can be easier. The fact that X-ışını images are much more advantageous than other imaging techniques in terms of time and applicability, and also that they are economical, has led to the focus of early diagnosis-based applications and methods on these images. Deep learning approaches have had a great impact in the diagnosis of COVID-19, as in the diagnosis of many diseases. In this study, we propose a diagnostic system based on the transformer method, which is the most up-to-date and much more popular architecture than previous techniques of deep learning such as CNN-based approaches. This method includes an approach based on vision transformer models and a more effective diagnosis of COVID-19 disease on a new dataset, the COVID-QU-Ex dataset. In experimental studies, it has been observed that vision transformer models are more successful than CNN models. In addition, the ViT-L16 model showed a much higher performance compared to similar studies in the literature, providing test accuracy and F1- score of over 96%. (English) [ FROM AUTHOR] Son yıllarda ortaya çıkan yeni tip Koronavirüs hastalığı (COVID-19), dünya çapında sağlığı tehdit eden ciddi bir hastalık olmuştur. COVID-19 çok hızlı bir şekilde bulaşabilen ve ciddi ölüm artışları ile birçok endişeye zemin hazırlamıştır. Salgının evrensel boyuta taşınmasıyla bu hastalığın erken teşhisine yönelik birçok çalışma yapılmıştır. Erken teşhis ile hem ölümcül vakaların önüne geçilmiş olunmakta hem de salgının planlanması daha kolay olabilmektedir. Xışını görüntülerinin zaman ve uygulanabilirlik açısından diğer görüntüleme tekniklerine nazaran çok daha avantajlı olması ve ayrıca ekonomik olması erken teşhis bazlı uygulama ve yöntemlerin bu görüntülerin üzerine yoğunlaşmasına neden olmuştur. Derin öğrenme yaklaşımları birçok hastalık teşhisinde olduğu gibi COVID-19 teşhisinde de çok büyük bir etki oluşturmuştur. Bu çalışmada, derin öğrenmenin CNN tabanlı yaklaşımları gibi daha önceki tekniklerinden ziyade en güncel ve çok daha popüler bir mimarisi olan transformatör yöntemine dayalı bir teşhis sistemi önerdik. Bu sistem, görü transformatör modelleri temelli bir yaklaşım ve yeni bir veri seti olan COVID-QU-Ex üzerinde COVID-19 hastalığının daha efektif bir teşhisini içermektedir. Deneysel çalışmalarda, görü transformatör modellerinin CNN modellerinden daha başarılı olduğu gözlemlenmiştir. Ayrıca, ViT-L16 modeli %96'nın üzerinde test doğruluğu ve F1-skoru sunarak, literatürde benzer çalışmalara kıyasla çok daha yüksek bir başarım göstermiştir. (Turkish) [ FROM AUTHOR] Copyright of Journal of the Institute of Science & Technology / Fen Bilimleri Estitüsü Dergisi is the property of Igdir University, Institute of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

CovidXNet: An Ensemble of Deep Neural Networks for Non-invasive Binary Identification of COVID-19 through Chest Radiographs

This study compared five different image classification algorithms, namely VGG16, VGG19, AlexNet, DenseNet, and ConVNext, based on their ability to detect and classify COVID-19-related cases given chest X-ray images. Using performance metrics like accuracy, F1 score, precision, recall, and MCC compared these intelligent classification algorithms. Upon testing these algorithms, the accuracy for each model was quite unsatisfactory, ranging from 80.00% to 92.50%, provided it is for medical application. As such, an ensemble learning-based image classification model, made up of AlexNet and VGG19 called CovidXNet, was proposed to detect COVID-19 through chest X-ray images discriminating between health and pneumonic lung images. CovidXNet achieved an accuracy of 97.00%, which was significantly better considering past results. Further studies may be conducted to increase the accuracy, particularly for identifying and classifying chest radiographs for COVID-19-related cases, since the current model may still provide false negatives, which may be detrimental to the prevention of the spread of the virus. © 2022 IEEE.

Current status and hotspot of Lonicera Japonica Flos research based on bibliometrics.

In order to comprehensively understand the research hotspots and development trends of Lonicera Japonica Flos in the past 20 years, and to provide intuitive data reference and objective opinions and suggestions for subsequent related research in this field, this study collected 8 871 Chinese literature and 311 English literature related to Lonicera Japonica Flos research in the core collection databases of Wanfang Data), CNKI and Web of Science (WOS) from 2002 to 2021, and conducted bibliometric and visual analysis using vosviewer. The results showed that the research on the active components of Lonicera Japonica Flos based on phenolic acid components, the research on the mechanism of novel coronavirus pneumonia based on data mining and molecular docking technology, and the pharmacological research on the anti-inflammatory and antiviral properties of Lonicera Japonica Flos are the three hot research directions in the may become the future research direction. In this paper, we analyze the research on Lonicera Japonica Flos from five aspects: active ingredients, research methods, formulation and preparation, pharmacological effects and clinical applications, aiming to reveal the research hotspots, frontiers and development trends in this field and provide predictions and references for future research.Copyright © Drug Evaluation Research 2022.

FaST-Net: Face Style Self-Transfer Network for Masked Face Inpainting

During the COVID-19 coronavirus epidemic, people usually wear masks to prevent the spread of the virus, which has become a major obstacle when we use face-based computer vision techniques such as face recognition and face detection. So masked face inpainting technique is desired. Actually, the distribution of face features is strongly correlated with each other, but existing inpainting methods typically ignore the relationship between face feature distributions. To address this issue, in this paper, we first show that the face image inpainting task can be seen as a distribution alignment between face features in damaged and valid regions, and style transfer is a distribution alignment process. Based on this theory, we propose a novel face inpainting model considering the probability distribution between face features, namely Face Style Self-Transfer Network (FaST-Net). Through the proposed style self-transfer mechanism, FaST-Net can align the style distribution of features in the inpainting region with the style distribution of features in the valid region of a face. Ablation studies have validated the effectiveness of FaST-Net, and experimental results on two popular human face datasets (CelebA and VGGFace) exhibit its superior performance compared with existing state-of-the-art methods. © 2023 SPIE.

Multi-Attentional U-Net for Medical Image Segmentation

Deep learning (DL) approaches for image segmentation have been gaining state-of-the-art performance in recent years. Particularly, in deep learning, U-Net model has been successfully used in the field of image segmentation. However, traditional U-Net methods extract features, aggregate remote information, and reconstruct images by stacking convolution, pooling, and up sampling blocks. The traditional approach is very inefficient due of the stacked local operators. In this paper, we propose the multi-attentional U-Net that is equipped with non-local blocks based self-attention, channel-attention, and spatial-attention for image segmentation. These blocks can be inserted into U-Net to flexibly aggregate information on the plane and spatial scales. We perform and evaluate the multi-attentional U-Net model on three benchmark data sets, which are COVID-19 segmentation, skin cancer segmentation, thyroid nodules segmentation. Results show that our proposed models achieve better performances with faster computation and fewer parameters. The multi-attention U-Net can improve the medical image segmentation results. © 2022 IEEE.

Masked Face Recognition Using Multi-Task Cascaded Convolutional Networks and FaceNet

Face masks have been widely used since the start of the COVID-19 pandemic. Facial detection and recognition technologies, such as the iPhone's Face ID, heavily rely on seeing the facial features that are now obscured due to wearing a face mask. Currently, the only way to utilize Face ID with a mask on is by having an Apple Watch as well. As such, this paper intends to find initial means of a reliable personal facial recognition system while the user is wearing a face mask without having the need for an Apple Watch. This may also be applicable to other security systems or measures. Through the use of Multi-Task Cascaded Convolutional Networks or MTCNN, a type of neural network which identifies faces and facial landmarks, and FaceNet, a deep neural network utilized for deriving features from a picture of a face, the masked face of the user could be identified and more importantly be recognized. Utilizing MTCNN, detecting the masked faces and automatically cropping them from the raw images are done. The learning phase then takes place wherein the exposed facial features are given emphasis while the masks themselves are excluded as a factor in recognition. Data in the form of images are acquired from taking multiple pictures of a certain individual's face as well as from repositories online for other people's faces. Images used are taken in various settings or modes such as different lighting levels, facial angles, head angles, colors and designs of face masks, and the presence or absence of glasses. The goal is to recognize whether it is the certain individual or not in the image. The training accuracy is 99.966% while the test accuracy is 99.921%. © 2022 IEEE.

Bayesian optimization for artificial neural networks: application to Covid-19 image classification

Training a Convolutional Neural Network (CNN) is a difficult task, especially for deep architectures that estimate a large number of parameters. Advanced optimization algorithms should be used. Indeed, it is one of the most important steps to reduce the error between the ground truth and the model prediction. In this sense, many methods have been proposed to solve the optimization problems. In general, regularization, more specifically, non-smooth regularization, can be used in order to build sparse networks, which make the optimization task difficult. The main aim is to develop a novel optimizer based on Bayesian framework. Promising results are obtained when our optimizer is applied on classification of Covid-19 images. By using the proposed approach, an accuracy rate equal to 94% is obtained surpasses all the competing optimizers that do not exceed an accuracy rate of 86%, and 84% for standard Deep Learning optimizers. © 2022 IEEE.

Real-Time and Accurate Monitoring of Social Distance and Contact Tracing during COVID-19 Situation

In recent years, a lot of research works have been done on object detection using various machine learning models. However, not many works have been done on detecting and tracking humans in particular. This study works with the YOLOv4 object detector to detect humans to use the detections for maintaining social distance. For this study, the YOLOv4 model is trained on only one class named 'Person'. This is done to improve the speed of detecting humans in real time scenario with satisfying accuracy of 97% to 99%. These detections are then tracked to build a system for maintaining social distance and alerting the authority if a breach in the social distance is detected. This system can be applied at ticket counters, hospitals, offices, factories etc. It can also be used for maintaining social distance among the students and the teachers in the classroom for their safety. © 2022 IEEE.