Research on Chest X-Ray Image Diagnosis of Covid-19 Based on Improved ResNet

With the outbreak of covid-19 in 2020, timely and effective diagnosis and treatment of each covid-19 patient is particularly important. This paper combines the advantages of deep learning in image recognition, takes RESNET as the basic network framework, and carries out the experiment of improving the residual structure on this basis. It is tested on the open source new coronal chest radiograph data set, and the accuracy rate is 82.3%. Through a series of experiments, the training model has the advantages of good generalization, high accuracy and fast convergence. This paper proves the feasibility of the improved residual neural network in the diagnosis of covid-19. © 2023 SPIE.

Comparative study of Deep Learning Models for Covid 19 Diagnosis

The Covid 19 pandemic that started a couple of years ago has had a devastating effect on mankind across the globe. The disease had no known treatment. Early detection and prevention was very important to curtail the effects of the Pandemic. In this work two deep learning models the RestNet and the models are proposed for diagnosing Corona from chest X-rays and CT scans. The models were trained with publicly available data sets of covid and non covid images. It has been found that Inception V3 performs better than ResNet for chest x-rays and RestNet performs better for CT Scans. The performance of the RestNet is found to be similar for both the chest x-rays and CT scans datasets. © 2023 IEEE.

Ischemic Heart Disease Prediction Using Machine Learning and Deep Learning Techniques

To have control over heart patient health, we need a capable detector which finds out based onhealth records. The idea is to work on coronary artery disease (CAD), which has been the majorhealth issue at present. We took a data set to train our system (machine learning algorithm) towork on the CAD and identify the user's health stage and provide the required information. Asper previous analysis, we got accuracy of 96% now with a minor modification we are trying to impact the accuracy. CAD has been the major health disease that is leading to death in world at present after COVID19, it is causing 33% of death rate by a survey by WHO. So, it is essentialto overcome the disease with proper analysis and prevention, which is all about our project. We are trying to make healthcare handy such that a person that analyze and know about his/her health condition from anywhere and at any time regardless of working hours. © 2023 IEEE.

Deep Convolutional Neural Network Based Automatic COVID-19 Detection from Chest X-ray Images

To minimize the rate of death from COVID-19 and stop the disease from spreading early detection is vital. The normal RT-PCR tests for COVID-19 detection take a long time to complete. In contrast to this test, Covid-19 can be quickly detected using various machine-learning technologies. Previous studies only had access to smaller datasets, as COVID-19 data was not readily available back then. Since COVID-19 is a dangerous virus, the model needs to be robust and trustworthy, and the model must be trained on a large and diverse dataset. To overcome that problem, this study combines six publicly available Chest X-ray datasets to produce a larger and more diverse balanced dataset with a total of 68,424 images. In this study, we develop a CNN model that primarily entails two steps: (a) feature extraction and (b) classification, which are used to identify COVID-19 positive cases from X-ray images. The accuracy of this proposed model is 97.58%, which is higher than most state-of-the-art models. © 2022 IEEE.

Health worker registries: managing the health care workforce

Registries play an instrumental role in facilitating the transfer, aggregation, and analysis of standardized data in health information exchange (HIE). One such example is a health worker registry (HWR), a central, authoritative registry that maintains the unique identities of health workers according to a defined, minimum data set. Currently, data comprising workers' information—such as education, licensure, and place of employment—are collected through disparate methods and maintained in a variety of information systems. Harmonization of these data via an HWR can support interoperability and comparability of worker information across systems, thereby facilitating efficient workforce enumeration, planning, regulation and deployment, verification of training and education, identification of workforce shortages, and rapid communication and coordination of emergency response. In fact, HWR technologies played a role in coordinating response to both Ebola in West Africa in 2014 and more recently in response to COVID-19, making a HWR integral to nations' infrastructure upgrades postpandemic. This chapter identifies who is considered a "health worker” and why a registry of these individuals is a useful component of an HIE, especially in the wake of the COVID-19 pandemic. It also provides guidance on selection of data elements and standards to include in the development of an HWR. © 2023 Elsevier Inc. All rights reserved.

Preliminary Diagnosis of COVID-19 using Speech Processing Techniques

This project aims to devise an alternative for Coronavirus detection using various audio signals. The aim is to create a machine-learning model assisted by speech processing techniques that can be trained to distinguish symptomatic and asymptomatic Coronavirus cases. Here the features exclusive to the vocal cord of a person is used for covid detection. The procedure is to train the classifier using a data set containing data of people of various ages both infected and disease-free, including patients with comorbidities. We presented a machine learning-based Coronavirus classifier model that can separate Coronavirus positive or negative patients from cough, breathing, and speech recordings. The model was trained and evaluated using several machine learning classifiers such as Random Forest Classifier, Logistic Regression (LR), Decision Tree Classifier, k-nearest Neighbour (KNN), Naive Bayes Classifier, Linear Discriminant Analysis, and a neural network. This project helps track COVID-19 patients at a low cost using a non-contactable procedure and reduces the workload on testing centers. © 2023 IEEE.

Data Visualization for Developing Effective Performance Dashboard with Power BI

Data visualization is a very important step in data analysis as it provides insight into the data in a more effective manner that is interesting, simple, and understandable to every-one without any language barrier. It can also represent a huge amount of data in a small space very easily. In the previous two years, the whole world has suffered from a very terrifying nightmare known as COVID-19. Known to be starting from the country of China, the pandemic affected not only the health and well-being of mankind, but also had serious impacts on the economies of various countries. Hence, a visualization of the data set of the pandemic might provide beneficial insights for finding a possible solution and can help in overcoming the impacts of the pandemic. Microsoft Power BI is a very famous tool for analyzing data. Power BI provides a different way to visualize the data. This paper has been analyzed the covid-19 data by using Power BI to understand the trends and patterns of the Pandemic. With the help of visualizing the data, it can be represented in stacked column charts, tables, and maps. These three ways are easy and simple to understand the patterns of the pandemic. It also helps to understand how covid impact the world. This research with power BI dashboard by using a dashboard feature that connects different pieces of visual graphs. © 2023 IEEE.

Sentiment Analysis and Topic Classification of Twitter-based Data Set on the Face-to-face Classes Resumption in the Philippines during the COVID-19 Pandemic

Due to the Corona Virus Disease 2019 (COVID-19) pandemic, there was a need for shift in pedagogy of education. Several delivery modes for educational materials and activities had to be implemented to adapt in the situation brought about by the pandemic. In the Philippines, there has been a call to fully transition to face-to-face classes expressed on social media. In this study, a data set was built consisting of tweets (Twitter data) regarding the resumption of face-to-face classes in the Philippines. This data set was subjected to training and testing to classify them in terms of topic and sentiment using Recurrent Neural Network Long Short-Term Memory (LSTM) and Multinomial Naïve Bayes. The LSTM sentiment classifier resulted to 78.33% accuracy and LSTM topic classifier produced 61.34% accuracy. The Multinomial Naïve Bayes classifier obtained 77.22% accuracy for classifying sentiment while 58.33% accuracy for topic classification. © 2022 IEEE.

ADAPTATION OF DOMAIN-SPECIFIC TRANSFORMER MODELS WITH TEXT OVERSAMPLING FOR SENTIMENT ANALYSIS OF SOCIAL MEDIA POSTS ON COVID-19 VACCINE

Covid-19 has spread across the world, and several vaccines have been developed to counter its surge. To identify the correct sentiments that are associated with the vaccines from social media posts, we fine-tune various state-of-the-art pretrained transformer models on tweets that are associated with Covid-19 vaccines. Specifically, we use the recently introduced state-of-the-art RoBERTa, XLNet, and BERT pre-trained transformer models, and the domain-specific CT-BERT and BERTweet transformer models that have been pre-trained on Covid-19 tweets. We further explore the option of text augmentation by oversampling using the language model-based oversampling technique (LMOTE) to improve the accuracies of these models – specifically, for small sample data sets where there is an imbalanced class distribution among the positive, negative, and neutral sentiment classes. Our results summarize our findings on the suitability of text oversampling for imbalanced small-sample data sets that are used to fine-tune state-of-the-art pre-trained transformer models as well as the utility of domain-specific transformer models for the classification task. © 2023 Author(s). This is an open access publication, which can be used, distributed and reproduced in any medium according to the Creative Commons CC-BY 4.0 License.

Utilizing Sentiment Analysis to Enhance the Quality of Online Learning

The COVID-19 pandemic spread worldwide in the year 2020 and became a global health emergency. This pandemic has brought awareness that social distancing and quarantine are ideal ways to protect people in the community from infection. Therefore, Saudi Arabia used online learning instead of stopping it completely to continue the education process. This paper proposes to use machine-learning algorithms for Arabic sentiment analysis to find out what students and teaching staff thought about online learning during the COVID-19 outbreak. During the pandemic, a real-world data set was gathered that included about 100,000 Arabic tweets related to online learning. The overall goal is to use sentiment analysis of tweets to find patterns that help improve the quality of online learning. The data set that was collected has three classes: 'Positive,' 'Negative,' and 'Neutral.' Crossvalidation is used to run the experiments ten times. Precision, recall, and F-measure was used to measure how well the algorithms worked. Classifiers, such as Support Vector Machines, K nearest neighbors, and Random Forest, were used to classify the dataset. Moreover, a detailed analysis and comparison of the results are made in this research. Finally, a visual examination of the data is made using the word cloud technique. © 2022 IEEE.

Assessment of a pandemic emergency financing facility

The pandemic bond issued by the World Bank (WB) in 2017 is a financial innovation enabling the transfer of the pandemic risk from the underdeveloped/developing countries to the financial market. It covers perils of various diseases that could overwhelm the global health systems and adversely impact the world economy. If all the triggers are activated, the bond's principal and coupons are used to finance coordinated, swift and resilient medical response to safeguard the well-being of the populace. This product, however, is criticised for its onerous trigger requirements. We examine the WB's pandemic-bond pricing framework, which requires inputs that are only partially available. From a rather unstructured COVID-19 data set, an information database is created and customised for pandemic-bond valuation. A vector auto-regressive moving average model is utilised to jointly describe the triggers dynamics. Our modelling simulations of risk triggers reveal that the bond payout could be made in less than half of the WB's earliest opportunity of 85 days. © 2023 The Authors

Robotization and labour demand in post-pandemic era: Microeconomic evidence from China

We study how robotization, namely the "machine substitution” policy, impacts firms' labour demand in the post pandemic era. Using a unique firm-level data set of online job postings in Dongguan, known as "The World Factory” in China, we find that "machine substitution” policy fosters the funded firms to expand their labour demand. The expansion is mainly driven by the growing demand for manufacturing workers, which offsets the reduced demand for service workers. Also, the expansion can be attributed to an increase in the number of employees listed in job postings rather than an increase in position types. Further analysis suggests that this positive impact is mainly attributable to the productivity effect rather than the restatement effect. Furthermore, there is no evidence of heterogeneity by sector or firm size but the effect of the policy varies by regional epidemic severity. Our results not only reveal the labour demand in the Covid-19 but also provide prominent implications for occupational security and steady economic growth. © 2023 Elsevier Inc.

Changing Post-Adoptive Personal Healthcare IT Use during the COVID-19 Pandemic

We examine post-adoptive IT use of fitness tracking technologies longitudinally using three data sets gathered before, during, and after the COVID-19 lockdowns in the United States. Using adaptive structuration theory (AST) as a meta-theory, we model post-adoptive IT use as having two fundamental types (continued and novel), each having distinct psychological and sociological antecedents. Sociological antecedents are further broken down into those coming from society and those coming from the technology. Findings indicate there are strong correlations between antecedents and the two types of use in all three data sets. Post-hoc analysis indicates continued and novel use vary across time. These variations are not static and appear to be non-linear. Implications and future research directions are also discussed. © 2022 IEEE Computer Society. All rights reserved.

Uncoupling Inequality: Reflections on the Ethics of Benchmarks for Digital Media

Our collaboration seeks to demonstrate shared interrogation by exploring the ethics of machine learning benchmarks from a socio-technical management perspective with insight from public health and ethnic studies. Benchmarks, such as ImageNet, are annotated open data sets for training algorithms. The COVID-19 pandemic reinforced the practical need for ethical information infrastructures to analyze digital and social media, especially related to medicine and race. Social media analysis that obscures Black teen mental health and ignores anti-Asian hate fails as information infrastructure. Despite inadequately handling non-dominant voices, machine learning benchmarks are the basis for analysis in operational systems. Turning to the management literature, we interrogate cross-cutting problems of benchmarks through the lens of coupling, or mutual interdependence between people, technologies, and environments. Uncoupling inequality from machine learning benchmarks may require conceptualizing the social dependencies that build structural barriers to inclusion. © 2022 IEEE Computer Society. All rights reserved.

Statistical properties and estimation of the three-parameter lindley distribution with application to COVID-19 Data

In a recent article by Shanker et al. (2017), the three-parameter Lindley distribution has been studied. The present paper is a continuation of the investigation of the properties of this distribution because of its high flexibility for modeling lifetime data. We studied some statistical properties of this distribution as central tendency measures, dispersion measures, and shape measures. In addition, the mode and the quantile function of the distribution were derived by the authors. The three parameters were estimated by the Maximum Product of Spacing Method (MPS) due to its fame in estimating parameters. A simulation study is carried out to examine the consistency of estimators using mean square error (MSE). The estimators showed that they have the property of consistency because MSEs decrease with increasing the size of the sample. On the practical side, the MPS estimates were used to obtain statistical properties, probability density function (p.d.f), cumulative distribution function (c.d.f), survival function, and hazard function for real data which represents COVID-19 Data in Iraq/Al-Anbar Province. We found the flexibility of the distribution in representing life data and the possibility of getting the patients probability of death and probability of survival for the time. © 2023 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.

Energy Prediction under Changed Demand Conditions: Robust Machine Learning Models and Input Feature Combinations

Deciding on a suitable algorithm for energy demand prediction in a building is non-trivial and depends on the availability of data. In this paper we compare four machine learning models, commonly found in the literature, in terms of their generalization performance and in terms of how using different sets of input features affects accuracy. This is tested on a data set where consumption patterns differ significantly between training and evaluation because of the Covid-19 pandemic. We provide a hands-on guide and supply a Python framework for building operators to adapt and use in their applications. © International Building Performance Simulation Association, 2022

Multi-National Topics Maps for Parliamentary Debate Analysis

In recent years, automated political text processing became an indispensable requirement for providing automatic access to political debate. During the Covid-19 worldwide pandemic, this need became visible not only in social sciences but also in public opinion. We provide a path to operationalize this need in a multi-lingual topic-oriented manner. Using a publicly available data set consisting of parliamentary speeches, we create a novel process pipeline to identify a good reference model and to link national topics to the cross-national topics. We use design science research to create this process pipeline as an artifact. © 2022 IEEE Computer Society. All rights reserved.

Pandemic Outbreak Time: Evaluation of Public Tweet Opinion by Machine Learning

In this work, a Twitter data-set was utilized to do sentiment analysis of people's thoughts on the corona-virus (COVID-19) period, which is a major concern throughout the world these days, impacting a number of nations. To better understand people's feelings about the epidemic, machine learning approaches (mla) and sentiment methodology such as Bert Model (BMO), Naive_Bayes_Bernoulli (nBB), Multi Nominal Naive_Bayes (mnNB), Support_ Vector_Machine (svM), Logistic_Regression (IR), Gradient_Boosting_ Classifier (gbR), Decision Tree Classifiers (dtC), K N eighbors(knN) and Random Forest Classifier (rfC) have been presented in this work. Also, we have classified that which Classifiers provides highest accuracy. Additionally, in this paper, we also analysis from the data set, the most that has been tweeted (hashtag), positive, negative as well as neutral with data visualization in the Covid-19 epidemic time. © 2022 IEEE.

Covid-19 Dataset Analysis: A Systematic Review

Due to their need to be connected to the rest of the world, people started to use social networks extensively to share their feelings and be informed, especially during the Covid-19 pandemic and its lockdown. The tremendous growth of content in social media increased the frequency of researchers' work on natural language understanding, text classification, and information retrieval. Unfortunately, not all languages have benefited equally from this interest. Arabic is an example of such languages. The main reason behind this gap is the limited number of datasets that addressed Covid-19-related topics. To this aim, we performed the first-of-its-kind systematic review that covered, to the best of our knowledge, the most Arabic Covid-19 datasets freely available or access granted upon request. This paper presents these 15 datasets alongside their features and the type of analysis conducted. The general concern of the authors is to direct researchers to reliable and freely available datasets that advance the progress of Arabic Covid-19-related studies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Face Mask Detection using Single Shot Multibox Detector and Mobile Net

During the pandemic time government took many safety measures to protect the public at common gathering places. People are insisted on wearing a face mask to protect themselves from COVID. Even then many people were roaming without a mask in public places. The proposed technique to detect the face mask is to identify the person's face with mask and person's face without mask and reporting to the safety officers about the persons without mask for further action. The proposed Face mask detection is developed using the ML technique which can be used to classify the people wearing masks and not wearing masks with the input given to the model. The proposed face mask detector is a one-stage detector that focuses on detecting the face mask alone. This work is implemented using the Tensor flow and Computer vision libraries. NumPy is used for image processing. The data set used in MAFA dataset. The model is trained using this data set to get the accurate results. To enable multiple detection here the single shot with multi box detector is used. The base model used for this process is Mobile Net V2. The proposed model is simple and it can be integrated with several other technologies to provide high accuracy percentage of output in the minimum possible time. © 2022 IEEE.