Prediction and Categorization Of COVID-19 Related Dermatological Manifestations Using Machine Learning

COVID-19 is global epidemic instigated because of 'severe acute respiratory syndrome corona virus 2 '. Fever, cough, tiredness, dyspnea, and hypogeusia/ hyposmia are all common signs. Dermatological indications have become more common in recent months among the extrapulmonary indicators associated with COVID-19. Our group proposed a taxonomy based on the polymorphic character of COVID-19-related cutaneous symptoms, which includes the following six primary clinical patterns:Urticarial rash, confluent erythematous/maculopapular/ morbilliform rash, papulovesicular exanthem, chilblain-like acral, livedo reticularis / racemosa-like, purpuric 'vasculitic' patterns. To offer an evaluation of possible pathophysiological routes of COVID19- related cutaneous symptoms, this research focuses upon that clinical features ampersand therapeutic treatment of every category. Machine learning algorithms such as SVM, RF, DT, KNN, LR, and NB are used in the analysis. © 2022 IEEE.

Implementation of Data Analytics and Machine Learning in Thailand Education Sector

Since the global epidemic of the coronavirus disease 2019 (COVID-19) over the past few years, Thailand education sector has been affected by the requisites for a digitization system and distance education. This sudden change has affected the quality of learning and statistical evaluations in the long term. Consequently, data analysis and categorization in learning quality assessment are critical for predicting the number of future students and learning performance after the COVID-19 outbreak. However, vast data analytics might be applied to the education sector in many aspects. In addition, machine learning can influence the categorization of students that are useful for analyzing the performance of different educational systems. Therefore, this study reviews the perspective and usability of data analytics and machine learning that influences current situations in Thailand education sector © 2023, International Journal of Emerging Technologies in Learning.All Rights Reserved.

Raspberry-based Low-resolution Thermal image system using a Smoothing Filter-based Kalman

Since the emergence of global epidemics such as SARS-CoV-2, H1N1, SARS and MERS, a wide range of systems for measuring temperature have been developed based on computer vision to reduce and prevent the virus contagious. By implementing a Raspberry-based Low-resolution embedded system based and a FLIR Lepton® sensor human body temperature is measured and improved by four different algorithms implemented. Firstly, three traditional time-series processes solving such as, Simple Mean (SM), Simple Moving Average (SMA), and Multi Lineal Regression (MLR), and secondly, and online filter-based Kalman predictor were implemented to increase the signal to noise ratio of the acquired temperature magnitude. Results of average prediction for different benchmarks demonstrate the best performance of Kalman Filter upon traditional processes. In addition, this algorithm achieves to smooth output temperature with fewer samples (∼10% of total samples) in comparison MLR and SMA. Finally, Raspberry-based Low-resolution Thermal image system is a feasible tool as a high-speed temperature estimator, by implementation of algorithms codified in Python language. © 2022 IEEE.

Detection of Corona Virus from Chest X-Rays Using CNN Algorithm

COVID19's global epidemic has wreaked havoc on our lives in every aspect. Healthcare systems, to be more specific, was pushed beyond their limits. Artificial intelligence developments have paved the way for the creation of complicated applications that can meet a wide range of requirements. Precision in clinical practice is necessary. In this study, machine learning-based deep learning models that were customized and pretrained were used. Convolutional Neural Networks that's utilized from detected COVID-19 respiratory pneumonia complications. Then more number of COVID-19 patients' radiographs pictures were collected locally. In Data was also used from three publicly available datasets. There are four options for evaluating performance. The public dataset was utilized first for training and testing. Second, data from both the local and national levels]. A variety of public sources were used to train and test the models. Because all diagnostic procedures have little retrieved data at the moment, medical conciliation should examine the likelihood of incorporating X-rays into illness diagnosis based on the data, while all research-based X-ray is carried out. It is possible to approach the problem from various angle. © 2022 IEEE.

UV-C Disinfection Smart Device

SARS-CoV-2 started a global epidemic that resulted in COVID-19, a real infectious disease that disrupted regular living all over the world. Sterilizing our hands is crucial since the virus and other diseases are spread by touching contaminated surfaces. In this manuscript, a prototype for low-cost sterilisation is created that uses an IR thermal sensor to measure temperature and UV C light rays to disinfect our hands. Numerous bacteria are affected throughout the sanitization process, which has a number of advantages over chemical-based sanitization techniques. In contrast to relevant, it is also easy to customise. There are proprietary devices that can be purchased commercially. This gadget is an excellent illustration of open-source technology. automatic, quick, and safe hand sanitising device. © 2022 IEEE.

Development a Teleconsultation Platform for Outpatients during the COVID-19 Pandemic based on Cloud Firestore and Realtime Databases

Due to the global epidemic situation of the Coronavirus Disease 2019 (Covid-19), in addition to serving patients with suspected symptoms and sickness from COVID-19, the hospital also provides services to patients outside requiring a lot of treatment causing a large number of queues in patients. It takes a long time to wait to see the doctor. The researcher therefore developed a teleconsultation platform. Hence, that patients can talk or seek advice from a doctor without the need to go to the hospital, allow patients to schedule appointments to see a doctor. Also, the patient can talk to the doctor via video calling developed in the system. Moreover, doctors can dispense medicines to patients by mail. To increase the efficiency of the system more and to support a wide range of applications, any devices, real-Time data updates, appointment notification via chatbot using Cloud Firestore and Realtime Databases, a NoSQL database, and study the performance gained. The results obtained from the test were satisfactory, with an average tracing server response of 107 ms + 0.14%, and an average handling latency in Thailand at 108 ms. © 2022 IEEE.

Masked Face Recognition Using FaceNet

The global epidemic of COVID-19 has seriously affected people's life. To prevent and control the outbreak, people are required to wear masks, which poses a formidable challenge to the existing face recognition system. A masked face recognition method based on FaceNet is proposed to tackle the problems. In this paper, a smaller model based on the Inception-ResN et Vl model is proposed. The main idea is to reduce filter numbers in each inception block while maintaining the whole structure. The reduced version has much fewer parameters to compute and can recognize faces with and without masks. Comprehensive experiments on both masked and unmasked datasets have been conducted. With 99.79% test accuracy in the masked MS-Celeb-1M dataset, the model trained in this paper can be integrated into existing face recognition programs designed to recognize faces for verification purposes. © 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.

Infectious Diseases Institute at Makerere University College of Health Sciences: a case study of a sustainable capacity building model for health care, research and training.

The Infectious Diseases Institute (IDI), established in 2001, was the first autonomous institution of Makerere University set up as an example of what self-governing institutes can do in transforming the academic environment to become a rapidly progressive University addressing the needs of society This paper describes the success factors and lessons learned in development of sustainable centers of excellence to prepare academic institutions to respond appropriately to current and future challenges to global health. Key success factors included a) strong collaboration by local and international experts to combat the HIV pandemic, along with b) seed funding from Pfizer Inc., c) longstanding collaboration with Accordia Global Health Foundation to create and sustain institutional strengthening programs, d) development of a critical mass of multi-disciplinary research leaders and managers of the center, and e) a series of strong directors who built strong governance structures to execute the vision of the institute, with subsequent transition to local leadership. Conclusion: Twenty years of sustained investment in infrastructure, human capital, leadership, and collaborations present Makerere University and the sub-Saharan Africa region with an agile center of excellence with preparedness to meet the current and future challenges to global health.

BIOCOLLABORATIONS IN THE DESIGN PROCESS: A PILOT EXPERIMENT ON THE PRODUCTION OF MASK FROM MICROBIAL CELLULOSE

The growth of cities, the excessive use of natural resources, and the agglomeration of undegradable materials in nature are very common problems in recent years and whose results are seen more and more every day. Latest, with the "Covid-19" pandemic, which was declared as a global epidemic in 2020, petroleum-based medical masks with disposable and non-biodegradable properties became one of the most used products. This situation has led to an increase in the damage to the environment. The aim of this study is to seek for an environmentally friendly alternative to medical masks that are frequently used in daily life by incorporating biocollaborative approaches into the design process. In this direction, the microbial cellulose application obtained by the symbiotic culture of bacteria and yeast was used to create the surface. The use of microbial cellulose in design-related fields is a topic that is only recently becoming widespread and researched. There is not much academic research in this field, especially on a national scale, and the article aims to contribute to the literature in this context. The recipe of the mask consists of easily accessible materials to be easily produced in the home environment and Kombucha culture was used as a source of microbial cellulose. During the experiment process, different experimental environments and coloring experiments were made, and a mask design with origami folding was carried out to take the shape of the human face more easily as a final product, to enrich the potential for folding and its aesthetic appearance. The medical use of the developed mask, the efficiency of use and the physical properties of the surface require detailed analysis and research. As a result, within the scope of this study, an environmentally friendly alternative to conventional methods for medical masks has been presented and the potential of microbial cellulose to replace plastic-based masks has been revealed. Using growing biomaterials and incorporating them into the design field can be a unique opportunity to use materials with a truly sustainable production method. © 2022. Tekstil ve Muhendis.All Rights Reserved

Analysis for the spread of COVID-19 in China based on the impulsive partial differential equation with diffusion

At the beginning of 2020, COVID-19 broke out in Wuhan and quickly swept the world. At present, the global epidemic prevention and control is still facing severe challenges. Scientific and effective measures of the epidemic is crucial to epidemic prevention and control. In this paper, a COVID-19 diffusion prediction model is established based on the impulsive partial differential equation and traditional infectious disease model, which can describe the spatial diffusion of viruses. This is also a lack of other models. The model divides the total population into seven groups: susceptible, quarantine, exposed, asymptomatic, infected, diagnosed and recovered, while considering the influence of time and space on the spread of the virus. In order to test the model, we take Jiangsu Province in China as an example, compare the calculated results with the actual data, and verify the effectiveness of the model through numerical calculation. © COPYRIGHT SPIE.

Could motivation, volition, and engagement be a panacea in online distance learning? [References]

The current pandemic that we are going through once again showed us the value of motivation in education. During the COVID-19 global pandemic process, do you think strategies that increase motivation, student engagement, and the power of volition in online distance learning environments can be a panacea in overcoming the troubled process? This study aims to address the situations that are considered to be the basis for the disruptions in education during the COVID-19 global epidemic process from the perspective of motivation, student engagement, and the power of volition. For this purpose, the chapter plans to present the strategies that can be taken as a basis in overcoming the problems encountered in online distance learning in terms of learner, learning environment, and guide (instructor) with the theoretical background of the types of interaction in online learning that is suggested by Moore. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Analysis of the effect on US manufacture industry during Covid-19 based on Fama-French five-factor model

It is crucial to find the fair value of financial assets for asset pricing model in relevant research and practice. With continuous improvement of the model, researchers are expected to improve the applicability and interpretation ability. Covid-19 is a rare global epidemic disease in human history, causing large-scale negative impact on the financial market. Therefore, it is necessary to study the practicability of asset pricing model, facing major and unpredictable factors. Based on the Fama French five-factor model, this paper compares the model factors of American manufacturing stocks from July 2019 to February 2020, and March 2020 to October 2020. The data mentioned in this paper are all selected from the database of Kenneth R. French's web. French, using the relevant information of American stock market to get various data. Multiple linear regression method and t-test are applied to analyze. The results showed that intercept investment. The asset pricing model changed from non-significant before the epidemic to significant;SMB coefficient is significant both before and after the epidemic, while coefficient increases after the epidemic;RMW is significant before the epidemic and is non-significant after the epidemic;MKT coefficient is significant both before and after the epidemic;CMA is non-significant both before and after the epidemic. Investors are advised to focus more on the explanatory power of MKT, SMB and HML factors on asset pricing when investing on US manufacturing stocks during the epidemic. © 2021 IEEE.

Classification of CT Scan Images for diagnosis of Covid-19 using Deep Learning

Coronavirus diseases is a contagious transmissible infectious malady rooted by the SARS-CoV-2 virus and it mostly affects the lungs thereby causing a global health care problem. Coronavirus triggers respiratory tract infection by infecting upper respiratory tract consisting of sinuses, nose, and throat or lower tract of respiratory system that includes windpipe and lungs. WHO proclaimed the COVID-19 outbreak a global epidemic. To control the spreading of novel Coronavirus, early detection and cure is mandatory. Although RT-PCR test is used to detect the infected humans but owing to colossal demand RT-PCR kits are now limited, and its low reliability made way for implementation of radiographic procedures such as X-Rays and Computed Tomography-Scan for symptomatic purposes. These come with a great specificity for diagnosing and detecting Covid-19 instances. This study suggests adopting a Deep Learning technique to automate the diagnosis of COVID19 infection using CT scans of patients for explicit identification of Covid-19. CNN namely Densenet, Inception and Xception networks or architectures are used in a deep learning architecture to distinguish human beings based on whether confirmed positive or not for COVID-19 infection. These networks are then collated on the ground of their accuracy and the outcomes procured from various CNN models are analysed to obtain a robust system. © 2022 IEEE.

Efficient AI-Enabled Pneumonia Detection in Chest X-ray Images

Recent years have witnessed the rapid development of artificial intelligence (AI) in different fields, including biomedical, in which timely detection of anomalies can play a vital role in patients' health monitoring. COVID-19, a contagious disease caused by the Severe Acute Respiratory Syndrome Corona-Virus 2 (SARS-CoV-2), has become a global epidemic. The key to combating this and other epidemics is detecting and isolating the infected patients in time. Therefore, there is an urgent need for a timely, practical detection approach. This paper proposes an AI-enabled pneumonia detection system, AIRBiS, to detect pneumonia (i.e., COVID-19) efficiently. AIRBiS is based on a high-performance Artificial Neural Network and an interactive user interface for effective operation and monitoring. The evaluation results demonstrate that the proposed system achieved 94.4% detection accuracy of pneumonia (i.e., COVID-19) over the collected test data. © 2022 IEEE.

Screening Covid-19 Infection from Chest CT Images using Deep Learning Models based on Transfer Learning

As the global epidemic of Covid19 progresses, accurate diagnosis of Covid19 patients becomes important. The biggest problem in diagnosing test-positive people is the lack or lack of test kits due to the rapid spread of Covid19 in the community. As an alternative rapid diagnostic method, an automated detection system is needed to prevent Covid 19 from spreading to humans. This article proposes to use a convolutional neural network (CNN) to detect patients infected with coronavirus using computer tomography (CT) images. In addition, the transfer learning of the deep CNN model VGG16 is investigated to detect infections on CT scans. The pretrained VGG16 classifier is used as a classifier, feature extractor, and fine tuner in three different sets of tests. Image augmentation is used to boost the model's generalization capacity, while Bayesian optimization is used to pick optimum values for hyperparameters. In order to fine-tune the models and reduce training time, transfer learning is being researched. Surprisingly, all of the proposed models scored greater than 93% accuracy, which is on par with or better than previous deep learning models. The results show that optimization improved generalization in all models and highlight the efficacy of the proposed strategies. © 2022 IEEE.

Remote Patient Monitoring During the COVID-19 Pandemic in the Framework of Home Device Manufacturing for IoT-Based BPM and SPO2 Measurements

One of the most important issues of the period we are in is the COVID-19 virus. Corona disease has been officially named SARS-CoV-2 by the world health organization. Since March 2002, this disease has been declared as a global epidemic. The COVID-19 virus has killed more than 4 million people. Like many other diseases, early detection of this virus increases the chances of survival. It was observed that the oxygen level in the blood decreased in people who were infected with this virus. The oxygen level in the blood is measured in hospitals using special devices. Measuring the SPO2 value is time consuming and costly. The easiest and cheapest way to solve this problem is to measure the SPO2 value at home. The main purpose of this study is to present a device design that can measure SPO2 and BPM (blood pressure) at home using IoT peripherals in a low-cost way. In this presented design, the algorithm and the devices used are explained in detail. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Initial COVID-19 Vaccine Distribution Policy Optimisation

As COVID-19 becoming a global epidemic, owing to the interventions' operation limited efficacy and virus' super transmission ability, the vaccine is considered the most potent method left to cease the COVID-19 effectively. At the beginning of the vaccine distribution policy design, there were many real concerns: vaccine priority, budget control, vaccine inventory limitation, and expected objectives making the problem complex. The research optimised the vaccine distribution policy (VDP) in an explicit form incorporated in an age-stratified SEIR model based on the proposed policy optimisation methodology. The VDP could explain when and how many vaccines to take for each age group. The designed evaluation system consisted of direct policy cost, indirect healthcare cost, and extra financial budget during the pandemic, combined as a weighted sum equalling one to suit flexible scenarios and decision-makers' requirements. A case study with ground truth data in the U.K was implemented, where the optimised VDP could decrease the comprehensive cost and suppress the virus transmission. Furthermore, the sensitivity analysis demonstrated the effect of some critical parameters for optimised VDP. The vaccination priority and policy objectives' weight combination play a significant role in impacting the VDP optimisation. The research could be a framework for flexible vaccination policy design in different scenarios by changing weights, vaccine limitations, and other initial parameter configurations. © 2021 ACM.

Propose a New Quality Model for M-Learning Application in Light of COVID-19

The coronavirus disease (COVID-19) prevented millions of students around the world from receiving their lessons, because of the closure of thousands of schools. The new COVID-19 global epidemic invaded the barriers of time and space. Using mobile phones in education is a new form of the distance learning system. M-learning is characterized by many characteristics, the most important of which are providing an interactive educational environment, flexibility in space and time, better adaptation to individual needs, acquisition of knowledge, interactive effectiveness, and developing self-learning skills for students. The main aim of this paper is to suggest a quality model for M-learning applications for children which contains the most common characteristics of M-learning, which must be taken into account when designing M-learning applications. Through previous studies related to the quality model for M-learning applications, we proposed two quality characteristics, technical and pedagogical. We proposed 8 subcharacteristics with their features following the structure of the IOS/IEC 912 and DeLone and McLean IS model to find the effect of technical and pedagogical factors on user satisfaction with M-learning applications for children. Results show that the proposed model can be useful and effective to ensure the development of high-quality M-learning applications. © 2022 Ahmad Althunibat et al.

Demystify the Black-box of Deep Learning Models for COVID-19 Detection from Chest CT Radiographs

Covid 19 continues to have a catastrpoic effect on the world, causing terrible spots to appear all over the place. Due to global epidemics and doctor and healthcare personel shortages, developing an AI-based system to detect COVID in a timely and cost-effective method has become a requirement. It is also essential to detect covid from chest X-ray and CT radiographs due to their accuracy in detecting lung infection and as well as to understand the severity. Moreover, though the number of infected people around the globe is enormous, the amount of covid data set to build an AI system is scarce and scattered. In this letter, we presented a Chest CT scan data (HRCT) set for Covid and healthy patients considering a varying range of severity of COVID, which we published on kaggle, that can assist other researchers to contribute to healthcare AI. We also developed three deep learning approaches for detecting covid quickly and cheaply. Our three transfer learning-based approaches, Inception v3, Resnet 50, and VGG16, achieve accuracy of 99.8%, 91.3%, and 99.3%, respectively on unseen data. We delve deeper into the black boxes of those models to demonstrate how our model comes to a certain conclusion, and we found that, despite the low accuracy of the model based on VGG16, it detects the covid spot of images well, which we believe may further assist doctors in visualizing which regions are affected. © 2021 IEEE.