Needs, Challenges and Countermeasures of SARS-CoV-2 Surveillance in Cold-Chain Foods and Packaging to Prevent Possible COVID-19 Resurgence: A Perspective from Advanced Detections.

The pandemic caused by SARS-CoV-2 has a huge impact on the global economy. SARS-CoV-2 could possibly and potentially be transmitted to humans through cold-chain foods and packaging (namely good-to-human), although it mainly depends on a human-to-human route. It is imperative to develop countermeasures to cope with the spread of viruses and fulfil effective surveillance of cold-chain foods and packaging. This review outlined SARS-CoV-2-related cold-chain food incidents and current methods for detecting SARS-CoV-2. Then the needs, challenges and practicable countermeasures for SARS-CoV-2 detection, specifically for cold-chain foods and packaging, were underlined. In fact, currently established detection methods for SARS-CoV-2 are mostly used for humans; thus, these may not be ideally applied to cold-chain foods directly. Therefore, it creates a need to develop novel methods and low-cost, automatic, mini-sized devices specifically for cold-chain foods and packaging. The review intended to draw people's attention to the possible spread of SARS-CoV-2 with cold-chain foods and proposed perspectives for futuristic cold-chain foods monitoring during the pandemic.

A New Machine Learning Framework for Detecting COVID-19 From Clinical Data on Lung And Heart Function

Currently, the need for real-time COVID-19 detection methods with minimal tools and cost is an important challenge. The available methods are still difficult to apply, slow, costly, and their accuracy is low. In this work, a novel machine learning-based framework to predict COVID-19 is proposed, which is based on rapid inpatient clinical tests of lung and heart function. Compared with current cognition therapy techniques, the proposed framework can significantly improve the accuracy and time performance of COVID-19 diagnosis without any lab or equipment requirements. In this work, five parameters of clinical testing were adopted;Respiration rate, Heart rate, systolic blood pressure, diastolic blood pressure, and mean arterial blood pressure. After obtaining results for these tests, a pre-trained intelligent model based on Random Forest Tree (RFT) machine learning algorithm is used for detection. This model was trained by about 13,558 records of the COVID19 testing dataset collected from King Faisal Specialist Hospital and Research Centre (KFSH&RC) in Saudi Arabia. Experiments have shown that the proposed framework performs highly in detecting COVID infections by 96.9%. Its results can be output in minutes, which supports clinical staff in screening COVID-19 patients from their inpatient clinical data. © 2022 IEEE.

A Review on the Application of Nanomaterials in Virus Detection

R apid, sensitive and specific detection of viruses is a key issue in the medical field. Since 2020, the global outbreak of COVID-19 requires more sensitive virus detection methods. With the development of new materials, especially nanomaterials, many materials have demonstrated great physical, chemical and mechanical properties, which present potential for virus detection. Nanomaterials can be divided into zero-dimensional materials, one-dimensional materials and two-dimensional materials by structure. In this paper, the classification and the latest progress of nanomaterials are reviewed, highlighting their applications in the field of virus detection. The future prospect of nanomaterials in virus detection is also presented and discussed. © 2023 Cailiao Daobaoshe/ Materials Review. All rights reserved.

SARS-CoV-2 in the environment: Contamination routes, detection methods, persistence and removal in wastewater treatment plants.

The present study reviewed the occurrence of SARS-CoV-2 RNA and the evaluation of virus infectivity in feces and environmental matrices. The detection of SARS-CoV-2 RNA in feces and wastewater samples, reported in several studies, has generated interest and concern regarding the possible fecal-oral route of SARS-CoV-2 transmission. To date, the presence of viable SARS-CoV-2 in feces of COVID-19 infected people is not clearly confirmed although its isolation from feces of six different patients. Further, there is no documented evidence on the infectivity of SARS-CoV-2 in wastewater, sludge and environmental water samples, although the viral genome has been detected in these matrices. Decay data revealed that SARS-CoV-2 RNA persisted longer than infectious particle in all aquatic environment, indicating that genome quantification of SARS-CoV-2 does not imply the presence of infective viral particles. In addition, this review also outlined the fate of SARS-CoV-2 RNA during the different steps in the wastewater treatment plant and focusing on the virus elimination along the sludge treatment line. Studies showed complete removal of SARS-CoV-2 during the tertiary treatment. Moreover, thermophilic sludge treatments present high efficiency in SARS-CoV-2 inactivation. Further studies are required to provide more evidence with respect to the inactivation behavior of infectious SARS-CoV-2 in different environmental matrices and to examine factors affecting SARS-CoV-2 persistence.

A COVID-19 detection method based on self-supervised learning and transfer learning

Since the global outbreak of COVID-19, the epidemic has had a great impact on people's lives and the world economy. Diagnosis of COVID-19 using deep learning has become increasingly important due to the inefficiency of traditional RT-PCR test. However, training deep neural networks requires a large amount of manually labeled data, and collecting a large number of COVID-19 CT images is difficult. To address this issue, we explore the effect of Pretext-Invariant Representation Learning (PIRL) using unlabeled datasets to pre-train the network on classification results. In addition, we also explore the prediction effect of PIRL combined with transfer learning (TF). According to the experimental results, applying the TF-PIRL prediction model constructed in this paper to COVID-19 diagnosis, the accuracy and AUC are 0.7734 and 0.8556 respectively, which outperform the network training from scratch, transfer learning-based network training and PIRL-based network training. © 2022 IEEE.

3D CNN-Based Classification of Severity in COVID-19 Using CT Images

With the pandemic worldwide due to COVID-19, several detections and diagnostic methods have been in place. One of the standard modes of detection is computed tomography imaging. With the availability of computing resources and powerful GPUs, the analyses of extensive image data have been possible. Our proposed work initially deals with the classification of CT images as normal and infected images, and later, from the infected data, the images are classified based on their severity. The proposed work uses a 3D convolution neural network model to extract all the relevant features from the CT scan images. The results are also compared with the existing state-of-the-art algorithms. The proposed work is evaluated in accuracy, precision, recall, kappa value, and Intersection over Union. The model achieved an overall accuracy of 94.234% and a kappa value of 0.894. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

An Application of Information Granules to Detect Anomalies in COVID-19 Reports

The COVID-19 pandemic has affected almost every aspect of life. The patterns of interpersonal contacts, the ways of doing business and the methods of school education have changed. A significant part of worldwide business has migrated to the virtual world, and the global supply chains have been disrupted. On the other hand, this new situation created opportunities for a much faster development of some areas of business and science. For example, the observation and analysis of pandemic data has contributed to the development of new techniques for effective mathematical forecasting. It is worth noting that during a pandemic most political and economic decisions are based on official data on the number of new infections at the country level. Therefore, the quality of this data is very important for making difficult decisions, such as implementing new restrictions. In this study, we will focus on the problem of pandemic data quality and present a novel anomaly detection method based on information granules. In numerical experiments, data from several European countries were compared. The selection of data for analysis was based on the following information: the movement of people between countries, similar quality of medical care and the sanitary standards. An appropriate adaptation of the author's anomaly detection method based on information granules allowed to identify potential anomalies in daily COVID reports. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Initial Stage COVID-19 Detection System Based on Patients' Symptoms and Chest X-Ray Images

The accurate diagnosis of the initial stage COVID-19 is necessary for minimizing its spreading rate. The physicians most often recommend RT-PCR tests;this is invasive, time-consuming, and ineffective in reducing the spread rate of COVID-19. However, this can be minimized by using noninvasive and fast machine learning methods trained either on labeled patients' symptoms or medical images. The machine learning methods trained on labeled patients' symptoms cannot differentiate between different types of pneumonias like COVID-19, viral pneumonia, and bacterial pneumonia because of similar symptoms, i.e., cough, fever, headache, sore throat, and shortness of breath. The machine learning methods trained on labeled patients' medical images have the potential to overcome the limitation of the symptom-based method;however, these methods are incapable of detecting COVID-19 in the initial stage because the infection of COVID-19 takes 3 to 12 days to appear. This research proposes a COVID-19 detection system with the potential to detect COVID-19 in the initial stage by employing deep learning models over patients' symptoms and chest X-Ray images. The proposed system obtained average accuracy 78.88%, specificity 94%, and sensitivity 77% on a testing dataset containing 800 patients' X-Ray images and 800 patients' symptoms, better than existing COVID-19 detection methods. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

A study of Real-Time Non-invasive Detection Techniques to Identify COVID-19 contagious person

The whole world has witnessed the global pandemic situation caused and hampered very badly due to COVID-19. We had seen the adverse effect globally, in terms of health, economy, social lifestyle. So, it's an urgent need to find a rapid detection technique/test to avoid the spread of the virus. The most effective and world-wide accepted detection method of COVID-19 is the RT-PCR. But due to its slow detection time and False-negative rates, researchers and scientists are trying different detection methods such as use of GC-MS, E-nose, Electrochemical method, use of nanomaterial-based sensor arrays. But all these have limitations in terms of real time sensing, detection time, sample preparation, etc. In order to overcome said drawbacks and to get real-time analysis, we are proposing a concept for COVID-19 detection based on the reported literature. As per recent advancement researchers have evident the presence of VOCs in COVID-19 infected person's breath by GC-MS method. A real time system is very much necessary to detect the VOCs in the Exhaled breath of the COVID-19 infected person to minimize the burden of healthcare system. In this article we will discuss and propose the probable detection techniques for real time sensing of the VOCs presence in the Exhaled breath of the COVID-19 infected person. © Published under licence by IOP Publishing Ltd.

Survey on Plagiarism Challenges

This paper describes the current state of plagiarism detection and the challenges that arise in modern society and are caused by plagiarism. There are several significant aspects that were highlighted: technological aspects caused by recent developments of modern NLP tools, social aspects caused by the ongoing COVID-19 pandemic, development of new content similarity detection methods, etc. All of them add new aspects to plagiarism challenges. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Comparative analysis of different scene for face mask detection based on YOLOv4

The 2019-nCoV can be transmitted through respiratory droplets and other methods, which greatly endangers public health security. Wearing masks correctly has been proven to be one of the effective means to prevent virus infection, but limited by the complexity of practical application scenarios, the wearing of masks still relies heavily on manual supervision. Therefore, a fast and accurate face mask wearing detection method is urgently needed. In this paper, a mask detection algorithm based on improved YOLO-v4 is proposed as a solution to the problems of low accuracy, poor real-time performance, and poor robustness caused by complicated environments. In addition, a number of different training approaches, such as mosaic data augment, CIOU, label smoothing, cosine annealing, etc., are introduced. These techniques help to increase the training speed of the model as well as the accuracy of its detection. With a fast-training model, the model will be able to detect and compare the results of samples from different scenarios. The experiment will compare front and side faces, different colored masks, scenes of varying complexity and other perspectives in a systematic way. The experiment's result was able to reach 99.38 % accuracy after the model was trained using data from a variety of face masks being worn. Experiment results, both quantitative and qualitative, indicate that the method can be adapted to most scenarios and offers effective ideas for improvement. © 2023 SPIE.

Efficient Face Mask Detection Method Using YOLOX: An Approach to Reduce Coronavirus Spread

Many lives have been put in danger since the start of the COVID-19 outbreak. According to WHO (World Health Organization)'s statements, breathing without a mask is highly dangerous in public and crowded places. Wearing a mask is essential as long as the vaccination is not widely used and does not fully protect everyone. Wearing masks does minimize the chances of becoming infected and the transmission of Coronavirus. Due to this, many public service providers may mandate customers to wear masks in order to get service.However, manually monitoring whether people are wearing a mask or not in public is inefficient and difficult. This paper proposes replacing manual inspection with a deep learning-based method using YOLOX, the most powerful objection detection algorithm. The results of the experiments show that the algorithm described in this work can efficiently detect face masks and enable more effective personnel monitoring than other YOLO series models. In addition, as compared to other models, our technique has significant benefits in terms of speed and accuracy in small and crowded areas. © 2022 IEEE.

Consume Machine Learning Integration Algorithms to Predict COVID-19 Infection

In the context of the emerging coronavirus pneumonia epidemic becoming a global epidemic, nucleic acid testing as a as a precise prevention and control method has been universally recognized, but because the scope of the test is too big and the production process is complicated, the kits produced by biological companies are difficult to use widely, for this reason I develop some machine learning integrated algorithms which can forecast whether a man is infected with COVID-19 based on three highly accessible features. This method can predict whether a person has been infected with COVID-19 based only on three indicators: heart rate, blood oxygen level, and body surface temperature, and we use several tree integration. We used several tree integration algorithms such as Random Forest, XGBoost, and GBM, and its accuracy, recall, and F1 score obtained 100% accuracy on the test set, which has been better than the current nucleic acid detection methods, proving that this method can be theoretically used as an accurate, convenient, and efficient self-detection method. © 2022 IEEE.

Detector Face Mask using UAV-based CNN Transfer Learning of YOLOv5

Detection of the use of masks on someone is helpful in health protocols during the COVID-19 pandemic. All public services or places require people to wear masks during the pandemic. There are about three types of masks commonly used by the public today: surgical/medical masks, cloth masks, and scuba masks. This research aims to detect masks by monitoring a user using a mask through a camera. also detects the type of mask used by the community. So that it can provide convenience in implementing discipline in carrying out the COVID-19 health protocol using masks. In addition, this research proposes the detection of masks on the face by monitoring using a drone. The detection method used in this research is Transfer Learning CNN. This algorithm is a deep learning method that can classify and detect in digital image processing. The initial step of the research is to collect the types of masks on the market in the form of digital images, followed by the application before being modeled into mathematical calculations, which will later be processed using the Convolutional Neural Network method. This research compares two architectural transfer learning methods in deep learning, namely mobile net V2 with YOLOv5. The system testing process will be carried out by analyzing the recall value, precision, and accuracy. The testing process on drone camera-based devices uses the python programming language. Based on the results of the transfer learning method using YOLOv5, the results of the data training accuracy are 97% in detecting masks. © 2022 IEEE.

SARS-CoV-2 detection methods: A comprehensive review.

The ongoing novel COVID-19 has remained the center of attention, since its declaration as a pandemic in March 2020, due to its rapid and uncontrollable worldwide spread. Diagnostic tests are the first line of defense against the transmission of this infectious disease among individuals, with reverse-transcription quantitative polymerase chain reaction (RT-qPCR) being the approved gold standard for showing high sensitivity and specificity in detecting SARS-CoV-2. However, alternative tests are being invested due to the global demand for facilities, reagents, and healthcare workers needed for rapid population-based testing. Also, the rapid evolution of the viral genome and the emergence of new variants necessitates updating the existing methods. Scientists are aiming to improve tests to be affordable, simple, fast, and at the same time accurate, and efficient, as well as friendly user testing. The current diagnostic methods are either molecular-based that detect nucleic acids abundance, like RT-qPCR and reverse-transcription loop-mediated isothermal amplification (RT-LAMP); or immunologically based that detect the presence of antigens or antibodies in patients' specimens, like enzyme-linked immunosorbent assay (ELISA), lateral flow assay (LFA), chemiluminescent immunoassay (CLIA), and neutralization assay. In addition to these strategies, sensor-based or CRISPR applications are promising tools for the rapid detection of SARS-CoV-2. This review summarizes the most recent updates on the SARS-CoV-2 detection methods with their limitations. It will guide researchers, epidemiologists, and clinicians in identifying a more rapid, reliable, and sensitive method of diagnosing SARS-CoV-2 including the most recent variant of concern Omicron.

The Effect of Source Location on the Effectiveness of Source Detection in Finite Networks

The topic of source identification has attracted wide attention from researchers. In practice, the source identification method aims to locate the sources of rumors, computer viruses, and epidemics, such as COVID-19. However, there are two main problems with existing propagation source detection methods. First, most source detection methods are based on infinite networks, not in line with reality. Second, sources are often randomly selected in simulations, but different sources often cause significantly different detection results in real-world applications. To this end, we study how does the source location impact the effectiveness of source detection in finite networks. This paper first proposes a diameter-based node division method to classify the nodes based on their structural location. We further offer different evaluation indicators to measure the effectiveness of source detection methods. Then, we conduct systematic experiments on three synthetic networks and two real-world networks. Our experiments demonstrate that the location of the source directly effects detection effectiveness in finite networks for all source detection methods. Specifically, sources closer to the network boundary will lead to worse detection performance. It means that attackers can choose sources close to the network boundary to reduce the probability of detection to achieve a larger spreading scale. © 2022 IEEE.

COVID-19 Classification with CT Scan and Advanced Deep Learning Technologies

The COVID-19 epidemic is still very serious, because the United States and other countries have relaxed prevention and control, and the vaccine is ineffective against the mutant virus, resulting in a large number of new cases. The existing epidemic detection methods are still insufficient, and some detection methods are relatively expensive and complicated, resulting in the supply not keeping up with the demand for detection. The purpose of this study is to use relatively convenient, fast and low-cost computer vision technology for epidemic detection. We tried the VGG, ResNet and DenseNet models on an open Kaggle dataset, and found that DenseNet achieved the best results, achieving 95% accuracy, and there is hope for further applications in the future. © 2022 IEEE.

MDN: Meta-transfer Learning Method for Fake News Detection

The rapid development of social media has brought convenience to people’s lives, but at the same time, it has also led to the widespread and rapid dissemination of false information among the population, which has had a bad impact on society. Therefore, effective detection of fake news is of great significance. Traditional fake news detection methods require a large amount of labeled data for model training. For emerging events (such as COVID-19), it is often hard to collect high-quality labeled data required for training models in a short period of time. To solve the above problems, this paper proposes a fake news detection method MDN (Meta Detection Network) based on meta-transfer learning. This method can extract the text and image features of tweets to improve accuracy. On this basis, a meta-training method is proposed based on the model-agnostic meta-learning algorithm, so that the model can use the knowledge of different kinds of events, and can realize rapid detection on new events. Finally, it was trained on a multi-modal real data set. The experimental results show that the detection accuracy has reached 76.7%, the accuracy rate has reached 77.8%, and the recall rate has reached 85.3%, which is at a better level among the baseline methods. © 2022, Springer Nature Singapore Pte Ltd.

COVID-19 Detection Method Based on Attentional Mechanism and LSTM

Since 2020, the Novel Coronavirus virus, which can cause upper respiratory and lung infections and even kill in severe cases, has been ravaging the globe. Rapid diagnostic tests have become one of the main challenges due to the severe shortage of test kits. This article proposes a model combining Long short-term Memory (LSTM) and Convolutional Block Attention Module for detection of COVID-19 from chest X-ray images. In this article, chest X-ray images from the COVID-19 radiology standard data set in the Kaggle repository were used to extract features by MobileNet, VGG19, VGG16 and ResNet50. CBAM and LSTM were used for classifcation detection. The simulation results showed that the experimental results showed that VGG16–CBAM–LSTM combination was the best combination to detect and classify COVID-19 from chest X-ray images. The classification accuracy of VGG-16-CBAM-LSTM combination was 95.80% for COVID-19, pneumonia and normal. The sensitivity and specificity of the combination were 96.54% and 98.21%. The F1 score was 94.11%. The CNN model proposed in this article contributes to automated screening of COVID-19 patients and reduces the burden on the healthcare delivery framework. © 2022, Springer Nature Switzerland AG.

Deepfake Detection for Fake Images with Facemasks

Hyper-realistic face image generation and manipulation have given rise to numerous unethical social issues, e.g., invasion of privacy, threat of security, and malicious political maneuvering, which resulted in the development of recent deepfake detection methods with the rising demands of deepfake forensics. Proposed deepfake detection methods to date have shown remarkable detection performance and robustness. However, none of the suggested deepfake detection methods assessed the performance of deepfakes with the facemask during the pandemic crisis after the outbreak of the COVID-19. In this paper, we thoroughly evaluate the performance of state-of-The-Art deepfake detection models on the deepfakes with the facemask. Our result shows that fake facial images with facemask can deceive well-known deepfake detection models, thereby evading the real-world security systems. © 2022 ACM.