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The COVID-19 (corona virus disease 2019) has caused serious impacts worldwide. Many scholars have done a lot of research on the prevention and control of the epidemic. The diagnosis of COVID-19 by cough is non-contact, low-cost, and easy-access, however, such research is still relatively scarce in China. Mel frequency cepstral coefficients (MFCC) feature can only represent the static sound feature, while the first-order differential MFCC feature can also reflect the dynamic feature of sound. In order to better prevent and treat COVID-19, the paper proposes a dynamic-static dual input deep neural network algorithm for diagnosing COVID-19 by cough. Based on Coswara dataset, cough audio is clipped, MFCC and first-order differential MFCC features are extracted, and a dynamic and static feature dual-input neural network model is trained. The model adopts a statistic pooling layer so that different length of MFCC features can be input. The experiment results show the proposed algorithm can significantly improve the recognition accuracy, recall rate, specificity, and F1-score compared with the existing models. © 2023 Chinese Institute of Electronics. All rights reserved.
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Background: The coronavirus disease (COVID-19) pandemic has affected the medical education throughout the world. A study was done to assess the effect of education and psychological behavior on medical students. Aims and Objectives: The objective of the study is to evaluate the effect of COVID-19 on medical graduates in various aspects such as education, effect on clinical rotations, impact on the technology used for online classes, effect on quality of life, loneliness, sleep, and depressive symptoms. Material(s) and Method(s): A set of questions were distributed to Government Medical college, Suryapet students during November 2021-January 2022. Questionnaire aimed to study students' viewpoint of COVID-19's impact on their education, mental health, and willingness to participate clinically. Result(s): One hundred medical students from Government Medical College, Suryapet participated in this study. Most students (88%) agreed that pandemic had disrupted their medical education. About 64% agreed to attend clinical rotations and 68% of students accepting the risk of contracting COVID-19 in clinical rotations. COVID-19 had an impact on technology tools used for medical education. Students reported that COVID-19 had moderate impact on quality of life, sleep quality, anxiety, and depressive symptoms. Conclusion(s): The COVID-19 had an overall significant negative impact on undergraduate medical education. It is recommended that measures need to be taken to relieve students' stress.Copyright © 2023, Mr Bhawani Singh. All rights reserved.
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COVID-19, an infectious disease, has become a leading cause of death in many people. The rapid emergence of the pandemic prompted the development of a vaccine to mitigate the disease's harmful consequences. Vaccination is the only effective way to prevent infection from spreading and build immunity to the virus. However, developing adverse effects has become a major problem for vaccine reluctance. Accordingly, the interest has been shifted towards identifying the adverse effects developed following immunization. The current study objective is to assess and compare the intensity of adverse effects following 1st and 2nd dose of COVID-19 vaccination and the medication administered to relieve the symptoms associated with vaccination. A cross-sectional study was performed in a community over six months. A total of 836 participants were involved in the study. All the data regarding the vaccination were collected through a specially designed questionnaire form and analyzed in all the participants within the study group. According to the study, at least 1 AEFI was developed in about 90% of the study population. The most common systemic and local effect developed in the study population was fever (59.42%) and pain at the injection site (69.82%), respectively. With both vaccines (ChAdOx1 nCoV-19 and BBV152), the incidence and severity of AEFIs were lower after the second dose than after the first dose, and most of the symptoms associated with vaccination were alleviated by taking home remedies and symptomatic treatment. The adverse effects reported after receiving the ChAdOx1 nCoV-19 and BBV152 vaccines are typical of most vaccines, and the majority of them were tolerated, and most subsided in less than 24 hours.
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[] Atherosclerosis (As) is the pathological basis of coronary heart disease, and vascular endothelial injury is the initiating factor of coronary atherosclerosis. Vascular endothelial cells are a single layer of cells located in the inner layer of blood vessels and regulates exchanges between the blood stream and the surrounding tissues, and their integrity is very important. Many active monomers and the derivatives in natural products of traditional Chinese medicine modulate the function of endothelial cells by intervening oxidative stress, regulating the release of vasoactive substances, reducing inflammation, and equilibrating coagulation and anticoagulant system. They have the advantages of multi-pathway, multi-link and multi-target regulation in protecting from endothelial injury and attenuating atherogenesis. They have also been used to protect against corona virus disease 2019 (COVID-19) induced endothelial injury and atheroslerosis. This article reviews the research progress of the above issues in this field. © 2023, Editorial Office of Chinese Journal of Arteriosclerosis. All rights reserved.
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Introduction: Mosquito-borne diseases have historically affected communities, especially in tropical areas where mosquitoes and illnesses are endemic. Globalization, climate change, and increased travel have created ideal conditions for outbreaks of mosquito-borne diseases that could threaten the American health system and place a burden on the national economy, especially in southern states. Methods: The study adopts a quantitative cross-sectional design with a retrospective survey carried out using the Pollfish platform in June 2022. The data were analyzed using descriptive statistics and hierarchical multiple regression to assess the three hypotheses: (H1) Chikungunya awareness is related to sociodemographic factors;(H2) Wearing long sleeves and pants is related to (a) Chikungunya awareness and (b) information-seeking behaviors, when controlling for sociodemographic variables;(H3) Use of insect repellents is related to (a) Chikungunya awareness and (b) information-seeking behaviors when controlling for sociodemographic variables. Results: The results highlight the relationships between chikungunya's awareness, information-seeking behavior, and willingness to engage in protective behaviors. 45.91% of the participants mentioned not having heard about chikungunya, and 67.07% of respondents had sought information about mosquito-borne illnesses in the past, 55.9% have looked at the U.S. State Department's website for mosquito-borne diseases, 38.32% have visited the U.S. CDC website for information specifically about chikungunya. Conclusions: The results of this study show that most American travelers are unaware of chikungunya and its mode of transmission. Travel could likely introduce the chikungunya virus to the United States. Despite increased health information-seeking behavior among U.S. residents after the Covid19 pandemic, Chikungunya awareness is low.
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Ebola virus disease (EVD) is a rare but highly contagious and lethal disease that occurs predominantly in African countries, with a case-fatality rate of 30–90%. The causative viral pathogens of EVD are within the genus Ebolavirus in the family Filoviridae. The primary route of human-to-human transmission is through direct contact with blood, bodily fluids and secretions from infected individuals. Direct contact with virally contaminated objects and sexual transmission have also been reported. Management of EVD is aggressive supportive care with possibly new therapeutic options. On 20 September 2022, an EVD outbreak was declared in Uganda, caused by Sudan ebolavirus. As of 7 November 2022, a total of 136 confirmed cases, 53 confirmed deaths have been reported, including 18 cases with seven deaths among healthcare workers. In the Democratic Republic of Congo (DRC), an EVD outbreak was also declared on 22 August 2022 (which ended on 27 September 2022);with only one case, a middle-aged woman. At the time when most countries in the world have been occupied with the coronavirus disease 2019 (COVID-19) pandemic and the recent human monkeypox outbreak, these two outbreaks of EVD have the potential to significantly add to the burden on global health. Authorities need to augment their multi-faceted response, including stringent contact tracing and border control, to avoid the catastrophe of the 2014–2016 EVD epidemic. © 2022, School of Medicine, Universitas Syiah Kuala. All rights reserved.
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Recently, virus diseases, such as SARS-CoV, MERS-CoV, and COVID-19, continue to emerge and pose a severe public health problem. These diseases threaten the lives of many people and cause serious social and economic losses. Recent developments in information technology (IT) and connectivity have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries. These industries, where IoT and AI together are making significant impacts, are the healthcare and the diagnosis department. In addition, by actively communicating with smart devices and various biometric sensors, it is expanding its application fields to telemedicine, healthcare, and disease prevention. Even though existing IoT and AI technologies can enhance disease detection, monitoring, and quarantine, their impact is very limited because they are not integrated or applied rapidly to the emergence of a sudden epidemic. Especially in the situation where infectious diseases are rapidly spreading, the conventional methods fail to prevent large-scale infections and block global spreads through prediction, resulting in great loss of lives. Therefore, in this paper, various sources of infection information with local limitations are collected through virus disease information collector, and AI analysis and severity matching are performed through AI broker. Finally, through the Integrated Disease Control Center, risk alerts are issued, proliferation block letters are sent, and post-response services are provided quickly. Suppose we further develop the proposed integrated virus disease control model. In that case, it will be possible to proactively detect and warn of risk factors in response to infectious diseases that are rapidly spreading worldwide and strengthen measures to prevent spreading of infection in no time.
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Populations affected by humanitarian crises and emerging infectious disease outbreaks may have unique concerns and experiences that influence their perceptions toward vaccines. In March 2021, we conducted a survey to examine the perceptions toward COVID-19 vaccines and identify the factors associated with vaccine intention among 631 community members (CMs) and 438 healthcare workers (HCWs) affected by the 2018-2020 Ebola Virus Disease outbreak in North Kivu, Democratic Republic of the Congo. A multivariable logistic regression was used to identify correlates of vaccine intention. Most HCWs (81.7%) and 53.6% of CMs felt at risk of contracting COVID-19; however, vaccine intention was low (27.6% CMs; 39.7% HCWs). In both groups, the perceived risk of contracting COVID-19, general vaccine confidence, and male sex were associated with the intention to get vaccinated, with security concerns preventing vaccine access being negatively associated. Among CMs, getting the Ebola vaccine was associated with the intention to get vaccinated (RR 1.43, 95% CI 1.05-1.94). Among HCWs, concerns about new vaccines' safety and side effects (OR 0.72, 95% CI 0.57-0.91), religion's influence on health decisions (OR 0.45, 95% CI 0.34-0.61), security concerns (OR 0.52, 95% CI 0.37-0.74), and governmental distrust (OR 0.50, 95% CI 0.35-0.70) were negatively associated with vaccine perceptions. Enhanced community engagement and communication that address this population's concerns could help improve vaccine perceptions and vaccination decisions. These findings could facilitate the success of vaccine campaigns in North Kivu and similar settings.
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[Purpose] Behavioral restrictions during the corona virus disease 2019 (COVID-19) pandemic may have affected the physical activity levels of college students. We aimed to characterize the body composition and physical activity of college students during these behavioral restrictions. [Participants and Methods] The body composition (height, weight, body mass index, body fat mass, body fat percentage, total body muscle mass, free-fat muscle index [FFMI], and fat mass index [FMI]), physical activity, amount the of walking, amount of daily activity, and the number of steps were measured in 52 university students. [Results] For both male and females, the number of steps taken was lower than the average steps reported by the Ministry of Health, Labour and Welfare. In males, FFMI had a strong positive correlation with physical activity, amount of walking, and the number of steps taken. In females, FFMI had a strong positive correlation with physical activity and the amount of walking, as well as a moderate positive correlation with the amount of daily activity. [Conclusion] Since physical activity and walking of university students during COVID-19 affect FFMI, it is necessary to develop an exercise program that considers behavioral patterns.
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Respiratory tract viruses are the second leading cause of olfactory dysfunction. Between 2019 to 2022, the world has been plagued by the problem of olfaction caused by the COVID-19. As we learn more about the impact of severe acute respiratory syndrome coronavirus 2ï¼SARS-CoV-2ï¼, with the recognition that olfactory dysfunction is a key symptom of this disease process, there is a greater need than ever for evidence-based management of postinfectious olfactory dysfunctionï¼PIODï¼. The Clinical Olfactory Working Group has proposed theconsensus on the roles of PIOD. This paper is the detailed interpretation of the consensus.
Subject(s)
Asthma , COVID-19 , Hypersensitivity , Olfaction Disorders , Humans , United States , Smell , COVID-19/complications , SARS-CoV-2 , Olfaction Disorders/etiology , Olfaction Disorders/therapy , Consensus , Hypersensitivity/complications , Asthma/complicationsABSTRACT
Background: The influx of people across the national borders of Ghana has been of interest and concern in the public health and national security community in recent times due to the low capacity for the prevention and management of epidemics and other public health risks. Although the international health regulations (IHR) stipulate core public health capacities for designated border facilities such as international airports, seaports, and ground crossings, contextual factors that influence the attainment of effective public health measures and response capabilities remain understudied. Objective: This study aims to assess the relationship between contextual factors and COVID-19 procurement to help strengthen infrastructure resources for points of entry (PoE) public health surveillance functions, thereby eliminating gaps in the design, implementation, monitoring, and evaluation of pandemic-related interventions in Ghana. Methods: This study employed a mixed-methods design, where quantitative variables were examined for relationships and effect size interactions using multiple linear regression techniques and the wild bootstrap technique. Country-level data was sourced from multiple publicly available sources using the social-ecological framework, logic model, and IHR capacity monitoring framework. The qualitative portion included triangulation with an expert panel to determine areas of convergence and divergence. Results: The most general findings were that laboratory capacity and Kotoka International Airport testing center positively predicted COVID-19 procurement, and public health response and airline boarding rule negatively predicted COVID-19 procurement. Conclusion: Contextual understanding of the COVID-19 pandemic and Ebola epidemic is vital for strengthening PoE mitigation measures and preventing disease importation.
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BACKGROUND: Pneumonia from COVID-19 that results in ARDS may require invasive mechanical ventilation. This retrospective study assessed the characteristics and outcomes of subjects with COVID-19-associated ARDS versus ARDS (non-COVID) during the first 6 months of the COVID-19 pandemic in 2020. The primary objective was to determine whether mechanical ventilation duration differed between these cohorts and identify other potential contributory factors. METHODS: We retrospectively identified 73 subjects admitted between March 1 and August 12, 2020, with either COVID-19-associated ARDS (37) or ARDS (36) who were managed with the lung protective ventilator protocol and required > 48 h of mechanical ventilation. Exclusion criteria were the following: <18 years old or the patient required tracheostomy or interfacility transfer. Demographic and baseline clinical data were collected at ARDS onset (ARDS day 0), with subsequent data collected on ARDS days 1-3, 5, 7, 10, 14, and 21. Comparisons were made by using the Wilcoxon rank-sum test (continuous variables) and chi-square test (categorical variables) stratified by COVID-19 status. A Cox proportional hazards model assessed the cause-specific hazard ratio for extubation. RESULTS: The median (interquartile range) mechanical ventilation duration among the subjects who survived to extubation was longer in those with COVID-19-ARDS versus the subjects with non-COVID ARDS: 10 (6-20) d versus 4 (2-8) d; P < .001. Hospital mortality was not different between the two groups (22% vs 39%; P = .11). The competing risks Cox proportional hazard analysis (fit among the total sample, including non-survivors) revealed that improved compliance of the respiratory system and oxygenation were associated with the probability of extubation. Oxygenation improved at a lower rate in the subjects with COVID-19-associated ARDS than in the subjects with non-COVID ARDS. CONCLUSIONS: Mechanical ventilation duration was longer in subjects with COVID-19-associated ARDS compared with the subjects with non-COVID ARDS, which may be explained by a lower rate of improvement in oxygenation status.
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The Corona Virus Disease 2019 (COVID-19) outbreak took a heavy toll on the global tourism industry in 2020, and affected the value realization of coastal recreational ecosystem service. From the micro perspective, this paper combines travel cost method with contingent behaviour method to obtain residents' actual behaviour and contingent behaviour data, and discusses the impact of the outbreak of COVID-19 on the value realization of coastal recreational resources from the perspective of the change in residents' recreational behaviour in Qingdao, China. Residents are observed to significantly reduce their outdoor activities in response to the COVID-19. The number of visits to the beach decreases by 25.2% when there is an outbreak, and decreases by 0.064% for every 1% increase in the number of confirmed cases, which is used to represent the severity of the epidemic. The asymmetries effects of epidemic situation on residents' recreational behaviour show that the improvements lead to larger and more significant impacts than the deteriorations. The disappearance of the pandemic crisis will provide considerable welfare for the citizens in Qingdao, which reaches to 1.9323 billion CNY/year. If the number of confirmed cases deteriorates to 900, the environmental welfare loss will be 0.3366 billion CNY/year. Additionally, we test the effects of residents' cognitive variables, and find that risk perception can strengthen the negative impacts of COVID-19 cases. Furthermore, the deteriorations in the environmental attributes are found to have stronger impacts on the number of visits than the improvements. This paper provides empirical-support results about the change of coastal recreational value through the evaluation of recreational behaviours in the post-epidemic period, which will give important implications for government's marine ecosystem restoration and coastal management work.
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The global pandemic Corona Virus Disease 2019 (COVID-19) has become one of the deadliest epidemics in human history, bringing enormous harm to human society. To help health policymakers respond to the threat of COVID-19, prediction of outbreaks is needed. Research on COVID-19 prediction usually uses data-driven models and mechanism models. However, in the early stages of the epidemic, there were not enough data to establish a data-driven model. The inadequate understanding of the virus that causes COVID-19, SARS-COV-2, has also led to the inaccuracies of the mechanism model. This has left the government with the toughest Non-pharmaceutical interventions (NPIs) to curb the spread of the virus, such as the lockdown of Wuhan in 2020. Yet man is a social animal, and social relations and interactions are necessary for his existence. The novel coronavirus and containment measures have challenged human and community interactions, affecting the lives of individuals and collective societies. To help governments take appropriate and necessary actions in the early stages of an epidemic, and to mitigate its impact on people's psychology and lives, we used the COVID-19 pandemic as an example to develop a model that uses surveillance data from one epidemic to predict the development trend of another. Based on the fact that both influenza and COVID-19 are transmitted through infectious respiratory droplets, we hypothesized that they may have the same underlying contact structure, and we proposed the influenza data-based COVID-19 prediction (ICP) model. In this model, the underlying contact pattern is firstly inferred by using a singular value decomposition method from influenza surveillance data. Then the contact matrix was used to simulate the influenza virus transmission through close contact of people, and the influenza virus transmission model was established. In order to be able to simulate the spread of COVID-19 virus using influenza transmission models, we used influenza contact matrix and COVID-19 infection data to estimate the risk of a population contracting COVID-19, i.e. force of infection of COVID-19. Finally, we used force of infection and influenza virus transmission model to simulate and predict the spread of COVID-19 in the population. We obtained age-disaggregated influenza and COVID-19 infection data for the United States in 2020, as well as data for Europe, which was not disaggregated by age. We use correlation coefficients as an evaluation indicator, and the final results prove that the predicted value and the actual value are positively correlated. So, the development trend of COVID-19 can be predicted using influenza surveillance data. © 2022 IEEE.
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BACKGROUND: The high prevalence of diabetes mellitus (DM) in the population causes DM to become one of the most common comorbidities of coronavirus disease 2019 (COVID-19). Patients with diabetes have a higher risk of experiencing serious complications from COVID-19 and even death. AIM: This study was aimed to determine the difference in survival probability of COVID-19 patients, based on their DM status and to determine the association between type 2 DM and COVID-19 mortality at Al Ihsan Hospital, West Java Province, Indonesia. METHOD(S): The population of this retrospective cohort study were COVID-19 patients, aged >=18 years and were treated at Al Ihsan Hospital, from March 2020 to December 31, 2021. Differences in survival probability were obtained from survival analysis with Kaplan-Meier. Cox Proportional Hazard regression was used to determine the association between type 2 DM and COVID-19 mortality. RESULT(S): Totally, 308 confirmed positive COVID-19 patients were recruited in this study. During the 21 days of observation, survival probability of COVID-19 patients with type 2 DM was significantly lower than those without type 2 DM (71.24% vs. 84.13% respectively, with p = 0.0056). There was a statistically significant association between type 2 DM and COVID-19 mortality after controlling for age, cough symptoms, acute respiratory distress syndrome, vaccination, chronic kidney disease, ventilator use, antiviral therapy, and the percentage of bed occupation rate COVID-19 isolation at admission. The adjusted hazard ratio showing association between type 2 DM and COVID-19 mortality in the final model of multivariate analysis was 2.68 (95% CI 1.24-5.73). CONCLUSION(S): The survival probability of COVID-19 patients with type 2 DM was significantly lower than those without type 2 DM. COVID-19 patients with DM in Al Ihsan Hospital were almost 3 times more likely to be fatal as compared COVID-19 patients without DM.Copyright © 2023 Oka Septiriani, Mondastri Korib Sudaryo, Syahrizal Syarif, Citra Citra.
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Both diabetes and Corona Virus Disease 2019 (COVID-19) are seriously harmful to human health, and they are closely related. It is of great significance to find drugs that can simultaneously treat diabetes and COVID-19. Based on the theory of traditional Chinese medicine for treating COVID-19, this study first sorted out the compounds of Guizhou Miao medicine with "return to the lung channel" and "clear heat and detoxify" effects in China. The active components against COVID-19 were screened by molecular docking with SARS-CoV-2 PLpro and angiotensin-converting enzyme II as targets. Furthermore, the common target dipeptidyl peptidase 4 (DPP4) of diabetes and COVID-19 was used as a screening protein, and molecular docking was used to obtain potential components for the treatment of diabetes and COVID-19. Finally, the mechanism of potential ingredients in the treatment of diabetes and COVID-19 was explored with bioinformatics. More than 80 kinds of Miao medicine were obtained, and 584 compounds were obtained. Further, 110 compounds against COVID-19 were screened, and top 6 potential ingredients for the treatment of diabetes and COVID-19 were screened, including 3-O-ß-D-Xylopyranosyl-(1-6)-ß-D-glucopyranosyl-(1-6)-ß-D-glucopyranosyl oleanolic acid 28-O-ß-D-glucopyranosyl ester, Glycyrrhizic acid, Sequoiaflavone, 2-O-Caffeoyl maslinic acid, Pholidotin, and Ambewelamide A. Bioinformatics analysis found that their mechanism of action in treating diabetes and COVID-19 may be related to regulating the expression of DPP4, angiotensin II type 1 receptor, vitamin D receptor, plasminogen, chemokine C-C-motif receptor 6, and interleukin 2. We believe that Guizhou Miao medicine is rich in potential ingredients for the treatment of diabetes and COVID-19.
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Objective: To explore the guidance value of "treatment of disease in accordance with three conditions" theory in the prevention and treatment of corona virus disease 2019 (COVID-19) based on the differences of syndromes and traditional Chinese medicine (TCM) treatments in COVID-19 patients from Xingtai Hospital of Chinese Medicine of Hebei province and Ruili Hospital of Chinese Medicine and Dai Medicine of Yunnan province and discuss its significance in the prevention and treatment of the unexpected acute infectious diseases. Method(s): Demographics data and clinical characteristics of COVID-19 patients from the two hospitals were collected retrospectively and analyzed by SPSS 18.0. The information on formulas was obtained from the hospital information system (HIS) of the two hospitals and analyzed by the big data intelligent processing and knowledge service system of Guangdong Hospital of Chinese Medicine for frequency statistics and association rules analysis. Heat map-hierarchical clustering analysis was used to explore the correlation between clinical characteristics and formulas. Result(s): A total of 175 patients with COVID-19 were included in this study. The 70 patients in Xingtai, dominated by young and middle-aged males, had clinical symptoms of fever, abnormal sweating, and fatigue. The main pathogenesis is stagnant cold-dampness in the exterior and impaired yin by depressed heat, with manifest cold, dampness, and deficiency syndromes. The therapeutic methods highlight relieving exterior syndrome and resolving dampness, accompanied by draining depressed heat. The core Chinese medicines used are Poria, Armeniacae Semen Amarum, Gypsum Fibrosum, Citri Reticulatae Pericarpium, and Pogostemonis Herba. By contrast, the 105 patients in Ruili, dominated by young females, had atypical clinical symptoms, and most of them were asymptomatic patients or mild cases. The main pathogenesis is dampness obstructing the lung and the stomach, with obvious dampness and heat syndromes. The therapeutic methods are mainly invigorating the spleen, resolving dampness, and dispersing Qi with light drugs. The core Chinese medicines used are Poria, Atractylodis Macrocephalae Rhizoma, Glycyrrhizae Radix et Rhizoma, Coicis Semen, Platycodonis Radix, Lonicerae Japonicae Flos, and Pogostemonis Herba. Conclusion(s): The differences in clinical characteristics, TCM syndromes, and medication of COVID-19 patients from the two places may result from different regions, population characteristics, and the time point of the COVID-19 outbreak. The "treatment of disease in accordance with three conditions" theory can help to understand the internal correlation and guide the treatments.Copyright © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.
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COVID-19 Corona virus disease is a rapidly spreading contagious disease that is causing a global public health crisis. In December 2019, the coronavirus was identified in Wuhan, China. COVID-19 is causing severe disease issues and many people are losing their lives daily. SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2) is a severe infectious disease that is spreading very fast and is currently inflicting a healthcare crisis across the globe. The lethal coronavirus was founded in Wuhan, China in December 2019. The symptoms of this disease are fever, cough, fatigue, no taste or smell, stinging throat, headache, and difficulty in breathing. This deadly disease, COVID-19, is difficult to identify and spread. The vaccination process is still going on around the world. There are some existing strategies to minimize the spread of the COVID-19 virus by monitoring the temperature rise using sensors, wearing masks, and sanitizing their hands frequently. The proposed system comprises of an RFID reader, an IR sensor, a temperature sensor, a buzzer, a laptop or a personal computer with a web cam. A person on entry gets detected for their body temperature, wearing a face mask and then sanitizing their hands. If the temperature of the person is below 37.6 degrees, i.e., below the acceptance limit, then mask detection takes place by using MATLAB followed by spraying the sanitizer. Now the door will open automatically. Otherwise, the door will not open and the buzzer will sound. With these precautionary steps, people can survive this pandemic situation. © 2023 IEEE.
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The lives and health of individuals are significantly threatened by the extremely infectious and dangerous Corona Virus Disease 2019 (COVID-19). For the containment of the epidemic, quick and precise COVID-19 detection and diagnosis are essential. Currently, artificial diagnosis based on medical imaging and nucleic acid detection are the major approaches used for COVID-19 detection and diagnosis. However, nucleic acid detection takes a long time and requires a dedicated test box, while manual diagnosis based on medical images relies too much on professional knowledge, and analysis takes a long time, and it is difficult to find hidden lesions. Thanks to the rapid development of pattern recognition algorithms, building a COVID-19 diagnostic model based on machine learning and clinical symptoms has become a feasible rapid detection solution. In this paper, support vector machines and random forest algorithms are used to build a COVID-19 diagnostic model, respectively. Based on the quantitative comparison of the performance of the two methods, the future development trends in this field are discussed. © 2023 IEEE.
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The Corona Virus Disease 2019 (COVID-19) effect has made telecommuting and remote learning the norm. The growing number of Internet-connected devices provides cyber attackers with more attack vectors. The development of malware by criminals also incorporates a number of sophisticated obfuscation techniques, making it difficult to classify and detect malware using conventional approaches. Therefore, this paper proposes a novel visualization-based malware classification system using transfer and ensemble learning (VMCTE). VMCTE has a strong anti-interference ability. Even if malware uses obfuscation, fuzzing, encryption, and other techniques to evade detection, it can be accurately classified into its corresponding malware family. Unlike traditional dynamic and static analysis techniques, VMCTE does not require either reverse engineering or the aid of domain expert knowledge. The proposed classification system combines three strong deep convolutional neural networks (ResNet50, MobilenetV1, and MobilenetV2) as feature extractors, lessens the dimension of the extracted features using principal component analysis, and employs a support vector machine to establish the classification model. The semantic representations of malware images can be extracted using various convolutional neural network (CNN) architectures, obtaining higher-quality features than traditional methods. Integrating fine-tuned and non-fine-tuned classification models based on transfer learning can greatly enhance the capacity to classify various families of malware. The experimental findings on the Malimg dataset demonstrate that VMCTE can attain 99.64%, 99.64%, 99.66%, and 99.64% accuracy, F1-score, precision, and recall, respectively. © 2023 Tech Science Press. All rights reserved.