A Systematic Literature Review of Research From 2010 to 2020 Addressing User-Generated Online Comments Related to Health Issues and Recommendations for Future Research

This systematic literature review was conducted to provide insights into how online readers' comments have been studied in the context of health over a 10-year span. About 593 studies published between 2010 and 2020;of these, 34 met the research criteria for inclusion. Our findings reveal that 60% of the studies focused on the United States, and a qualitative method was used in 74.3% of these studies. About 23.5% of the studies explored vaccine-related issues. Our results reveal that among the selected studies, 76.5% and 20.6% had female and male first authors, respectively. Textual analysis of s shows that the top five keywords were news, HPV, vaccine, themes, and vaccination. However, 58.8% of the identified studies did not use any theoretical framework. In addition, nine health topics emerged: vaccines;health policies;nutritional and dietary choices;women's health issues;quality of life and wellbeing;smoking;engagement with health-related news content;COVID-19;and suicide and mental health.

Body Posture Correction and Hand Gesture Detection Using Federated Learning and Mediapipe

In today's post-covid culture, where everyone works from home, there is a huge possibility of serious long-term health problems. A lot of people have started taking up exercises at home and if done incorrectly, they can have major negative effects. Another one of the main contributors to these health issues is bad sitting posture, which is only exacerbated when working for hours on end. Hand gesture detection has many useful applications in elderly healthcare, automating actions and gesture-based presentations and games. To help users with these actions, our paper proposes pinpointing the points of the error to the user in real-time and in a lightweight manner for yoga posture correction. The incorrect positions shall be shown in real-time on top of the user's video feed to help them correct it properly. The user shall be told about when they are sitting in a bad position, and the overall bad posture time will also be shown for the session, which will provide the required information to the user. To further help users in a useful manner, our paper looks to augment the hand gesture detection feature with federated learning and personalization to avoid the common pitfall of privacy concerns, while still allowing users to customize their experience. The proposed library for the implementation of these tasks is the MediaPipe library. This library is one of the key components that makes the features lightweight and easy to use. The aforementioned library also looks to implement the features in real time with no lag while keeping the resource requirements as low as possible. © 2023 IEEE.

Smart Device for CO2 Measuring and Supplementing with O2 Using IOT

Globally, atmospheric carbon dioxide (CO2) concentration is rising due to rising carbon-based fuel consumption and ongoing deforestation. As carbon dioxide levels grow due to the warming trend, the atmosphere's temperature is predicted to climb. Increased fatigue, headaches, and tinnitus are just a few health issues that high CO2 concentrations in the atmosphere can cause. The electrical activities of the brain, the heart, and the lungs have all been demonstrated to change significantly after a brief exposure to 0.1 percent CO2. Continuous measurements of the atmospheric CO2 content have recently been shown to help evaluate the ventilation conditions in buildings or rooms. Additionally, it prevents the development of the severe acute respiratory syndrome coronavirus 2 (Severe acute respiratory). The coronavirus, known as a powerful acute respiratory, can make people ill. This has grown to be a significant concern in emergency medicine. © 2022 IEEE.

Students' mental health issues in Europe and other countries

This chapter describes and analyzes how different countries dealt with children and youth with mental health issues before and during the COVID-19 pandemic beginning in March 2020. The pandemic and measures worldwide to control the spread of the virus COVID-19, such as lockdowns, closures of schools and preschools, social distancing rules, restrictions of movement, contact limits, and quarantine, changed the daily life of millions of people, especially children and youth. The countries include: Germany, Greece, Portugal, Tanzania/Vietnam, and the Netherlands. The chapter also analyzes how fear of infection and death, high uncertainty, and the containment measures that were implemented on affected children and youth with mental health issues. Students with disabilities and students from disadvantaged backgrounds were particularly affected by school closures. Mental health systems in the various countries coped in different ways, also depending on how they operated before the pandemic. Developing prevention programs, building resiliency, peer support, online support measures, and raising awareness of mental health all seem to be useful strategies to address mental health problems in children and youth. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Students' well-being and factors impacting it during COVID-19 pandemic–early findings from Delft University of Technology

Even before the COVID-19 pandemic, student well-being was highlighted as an important public health issue. The study aims to gain insights into the exact factors that bachelor and master students from engineering fields at Delft University of Technology are impacted by. Multiple interviews were performed to identify the key areas of impact and then incorporated into a comprehensive survey. The questionnaire was divided into five blocks: course work factors, thesis, communication, study environment, the COVID-19 pandemic and disseminated between June and September of 2021. A convenience sample of 165 responses was collected and the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) test was employed to quantify the well-being of the students. The survey analysis found different well-being scores between the students from the bachelor and master programs and concluded that having a consistent work environment played an important role in students' welfare. The COVID-19-related findings revealed that the recordings of lectures and remote studying were the most appreciated. The thesis-related section showed that the clarity and objectives of the thesis writing are particularly impactful. Although some of the findings are university specific, the recommendations could be considered by other universities as they refer to general indicators and relationships. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Analysis of the Role of Individuals and Governments in Post-COVID-19 Health Issues: The Case of Korea

This study was conducted in order to ascertain what role government and individuals should play in the event of a pandemic such as Coronavirus occurring in Korea in the future, using information deriving from news articles available at the Bigkinds news portal site in Korea. The analysis period ran from 11 March 2020, when the pandemic was declared by the World Health Organization, to 31 January 2023, almost three years later. Text mining analysis was conducted on all the articles, as a result of which six important roles that individuals should play, and ten roles that government should play, in a pandemic situation were suggested. © 2022, European Alliance for Innovation. All rights reserved.

Black Fungus Classification using Adaboost with SVM-based classifier and Compare accuracy with Support Vector Machine

India recognize a severe public health issue in addition to the COVID-19 outbreak and the growing percentage of patients with related mucormycosis from 2021. An uncommon condition known as mucormycosis is brought on by fungus in the family Mucorales. Mucormycosis is a fairly uncommon illness that is caused by common environmental moulds that may be found in soil and decomposing organic materials. Spores develop into hyphae in a susceptible individual, which subsequently infect nearby tissue, including blood vessels, leading to hemorrhagic infarction. Doctors have offered many hypotheses on this. The issue is if black fungus is present in other countries given how uncontrolled it is growing in India. Patients in India with weakened immune systems are more susceptible to illnesses other than corona virus infection. The revised machine learning strategy which will be created in this work is Adaboost with an Support Vector Machine-based classifier (ASVM). Due of the difficulties in learning SVM and the differential in variety as well as efficiency over straightforward SVM classifiers, ASVM classifier is frequently believed to violate the Boosting principle. The Adaboost classifier used in the study gradually replaces SVM as the primary classifier when the weight value of the training sample changes. On testing data, the mean accuracy of the classification was 97.1%, which was much higher than that of SVM classifiers without Adaboost. © 2022 IEEE.

Offerings and challenges of demand response ventilation under covid-19 scenarios

Owning to the outbreak of COVID-19, individuals have to spend more time indoor. It is therefore essential to prepare for a long-term healthy indoor working environment in the transition of post COVID-19 pandemic. However, there is no relevant research so far in investigating such crisis impacts around indoor environmental quality and economic-health issues while home offices are expected becoming common practice soon. Therefore, a case of single-family house in Sweden is specially investigated using IDA ICE. By comparing four predominant ventilation approaches, three operational schedules are proposed, covering different confinement for occupants. Main results show that the demand response ventilation (DRV) generally should sacrifice in remarkable performance in energy saving, and emission reduction to better confront with more challenges in indoor air quality, occupied thermal dissatisfaction fraction and air stagnation under the challenge of COVID-19 pandemic scenario. Altered ventilation strategy should be customized from increased outdoor air supply, various demand-control signal, displacement method towards a heathier homeworking environment. © International Building Performance Simulation Association, 2022

Public Health Importance of Emporiatrics: A Review.

People travel all around the world to explore, trade, sojourn, etc. Millions of individuals cross national and international borders. Travel medicine services are offered by general practitioners, specialized travel clinics, or immunization centers. Epidemiology, illness prevention, and travel-related self-treatment are all included in the interdisciplinary field of travel medicine. The main objective is to keep travelers alive and in good health, by reducing the effects of illness and accidents through preventative measures and self-care. The danger to a traveler's health and well-being must be understood, and the travel medicine practitioner's job is to help their patient or client recognize and manage those risks. The absence of any disease or symptom does not always indicate good health. Chronic illness sufferers, including those with cancer, diabetes, and hypertension, can maintain a reasonable level of health and mobility. Travel medicine is a rapidly developing, extremely dynamic, multidisciplinary field that calls for knowledge of a range of travel-related illnesses as well as current information on the global epidemiology of infectious and non-infectious health risks, immunization laws and requirements around the world, and the shifting trends in drug-resistant infections. Pre-travel consultation aims to reduce the traveler's risk of disease and harm while on the road through preventive counseling, education, recommended drugs, and essential vaccines. Specialized medical guidance can help reduce the potential health risks of travel. Emporiatrics is not only used for traveling advice or things to be done during the period of the journey but it also creates room in implementing the interdisciplinary subject with new methods or development of new policies, technologies, and various programs to reduce unnecessary problems of the travelers, which will boost tourism.

Socioeconomic Impacts of Households' Vulnerability during COVID-19 Pandemic in South Africa: Application of Tobit and Probit Models

Coronavirus is a public health issue with socioeconomic and livelihood dimensions. The World Health Organization declared the current novel coronavirus disease (COVID-19) epidemic a public health emergency of international concern on January 30, 2020, and a global pandemic on March 11, 2020. The South African government has implemented different strategies, ranging from total lockdown in certain locations and provision of palliatives in some provinces across the country. This study, therefore, investigated the correlates of vulnerability and responsiveness to the adverse impacts of COVID-19 in South Africa. The study utilized primary data collected among 477 respondents. Descriptive statistical tools, Tobit and Probit regression models, were used to analyze the data. The study found different levels of vulnerability (low, medium, and high) and responsiveness among households, including stocking up of food items, remote working, reliance on palliatives, and social grant provision, among others. Some of the correlates of responsiveness to the COVID-19 pandemic include being employed, the type of community, and the income of respondents. The study, therefore, recommends increased investments in welfare programmes (safety nets, palliative measures and economic stimulus packages) as well as capacity building of households through education to reduce vulnerability. © Authors retain all copyrights.

Human capital as an essential component of sustainability reporting during COVID-19: A cross-sectional study among employees in sedentary occupation

Introduction: With the ongoing pandemic, sustainability reporting regarding its different components and stakeholders needs modifications accordingly. These modifications are required because of the new challenges and opportunities emerging in the face of Covid-19 with regards to various guiding components of sustainability reporting. One such component that gained much popularity during the pandemic is 'Human Capital'. Maintaining the wellbeing of the employees is one of the essential elements of the human capital of the organization or firm. Objective(s): To study the association of sedentary behavior and physical activity of the respondents to their health. Material(s) and Method(s): A cross-sectional study has been conducted within the region of Delhi-NCR for a sample size of 147 which was calculated. Result(s): No association of health outcomes was found with sedentary behavior and physical activity. Although no association was found between current health outcomes and sedentary behavior, there had been a tremendous increase in the sedentary lifestyle during the Covid-19 pandemic. Conclusion(s): Sustainability reporting should mention a transparent report regarding the physical and mental wellbeing of their employees and shall provide effective solutions for dealing with the long-term impacts of the pandemic on the employees' health.

An effective Approach for Pneumonia Detection using Convolution Vision Transformer

Early detection of pneumonia in patients through effective medical imaging may enable timely remedial measures and reduce the severity of the infection. There is an increase in cases among new-borns, teenagers and also people with health issues in recent years. The COVID-19 pandemic also revealed the major impact pneumonia had on the lungs and the consequences of delayed detection. The presence of the infection in the lungs is examined through images of Chest X-ray, however, for an early diagnosis of the infection, this paper proposes an automated model as a more effective alternative. Convolutional Vision Transformer (CVT) which gives an accuracy of 97.13%, and is a robust combination of Convolution and Vision Transformer (ViT), is suggested in this paper as a potential model to detect pneumonia early in patients. © 2022 IEEE.

Stress Analysis Using Machine Learning

Stress is one of the major health issues of the world and one of the major reasons for committing suicide. Also, it leads to other mental health issues such as depression, anxiety etc., and damage to organs related to respiratory, cardiovascular and nervous systems. In recent years, stress has impacted many individuals due to the pandemic situation. Since the governments across the globe had started to impose lockdowns, the levels of stress significantly raised because of the disturbances led by covid infections, losing loved ones, continuous engagement with laptops and mobiles etc. It is also found that stress has not only disturbed the health condition but also disturbed the relationships and became a self-destruction component. This project is aimed to help those people to understand their stress and consult a psychologist at right time to overcome the situation. Though stress is an active area of research and achieved high performance of models, those were based on signal and speech which were computationally costlier and text-based research work using a state-of-the-art model called the BERT has achieved an f1-score i.e. 80.65%. This project focuses on text-domain and uses open-sourced Stress Analysis on Social Media dataset available on Kaggle which contains 3.6 K samples. In this project, both Machine Learning and Deep Learning Models were trained with 80% of the data and validated with 20% of the data. After, optimization and evaluation of several models, the best model has achieved a benchmark result of 83.74% f1-score on test data using a new network architecture i.e. combination of stacked Transformer Encoder layers with stacked Bi-directional-LSTM. In addition to this, an explainable AI has been implemented for an embedding layer to inspect input attributions in predicting the results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analyzing Wearable Data for Diagnosing COVID-19 Using Machine Learning Model

Health is an indispensable part of human life, but we realize its importance when we face health issues. Technology can play an important role in the healthcare sector. During the COVID-19 pandemic, many countries used technology to control the situation. Internet of Things-based wearable devices can change the whole scenario of diagnosing the disease. The physiological features collected using wearables can be used for pre-symptomatic prediction of disease. In this study, from the cohort of 185 participants, data of 36 participants are analyzed to predict COVID-19 before symptoms begin using the machine learning model. Our findings suggest that heart rate, BPM, SDNN, and steps features can be used to detect the COVID-19 before the symptoms appear. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

How the COVID-19 pandemic reshaped demographic variation in mental health among diverse engineering student populations

Mental health issues have long posed a challenge on university campuses. While no population is immune, research has shown that students from marginalised backgrounds can have higher rates of mental health issues and suffer worse outcomes as a result. These discrepancies have been attributed to everything from different cultural norms to the micro-aggressions and other barriers that students from marginalised populations face on university campuses. With the onset of COVID-19 in the United States, many residential universities switched to a remote learning model, fundamentally changing the relationship between students, campus, and family support. This work uses survey data from students in the United States to explore how COVID-19 affected mental health issues among students from different backgrounds. While the pandemic drastically increased rates of depressive disorder among all respondents, discrepancies between mental health rates for women and Hispanic/Latinx compared to men and White respondents either decreased or disappeared. Additionally, respondents identifying as Asians were less likely to screen positive for several mental health conditions than White, Non-Hispanic respondents. These findings may point to important new insights about the ways in which engineering education undermines some groups' mental health. ©, Engineers Australia.

The relations between resilience and self-efficacy among healthcare practitioners in context of the COVID-19 pandemic - a rapid review

Purpose: The purpose of this rapid review was to present current evidence on relations between resilience and self-efficacy among healthcare practitioners in the context of COVID-19 pandemic. Design/methodology/approach: Literature searches were conducted in February/2022 in the online database MEDLINE EBSCO and not date/time limited. Eligibility criteria were as follows: population - healthcare practitioners, interest - relations between resilience and self-efficacy and context - COVID-19. Finding(s): Six eligible studies from Italy, China, United Kingdom, India, Pakistan and Spain, published between 2020 and 2021 were included in the review. All studies used quantitative methods. The relations between resilience and self-efficacy were identified in contexts of resilience programs, measuring mental health of frontline nurses, measuring nurses' and nursing students' perception of psychological preparedness for pandemic management, perception of COVID-19 severity and mediating roles of self-efficacy and resilience between stress and both physical and mental quality of life. Findings indicated limited research on this topic and a need for more research. Practical implications: Broader understanding of the relations between resilience and self-efficacy may help healthcare organizations' leaders/managers aiming to support resilience of their employers under challenging circumstances such as future pandemic. Originality/value: The latest COVID-19 pandemic presented the opportunity to research relations between resilience and self-efficacy and enrich existed research in a new and extraordinary context.Copyright © 2023, Joanna Barbara Baluszek, Kolbjorn Kallesten Bronnick and Siri Wiig.

The Security Algorithm BR22-01 Used to Secure the COVID'19 Health Data

The current generation data is most valuable in people's life, because data only decided people's health affected in COVID'19 or not, and not only COVID'19 all related to health issues data. To analyze and predict the health issue data by using Machine Learning Algorithm. This prediction issues data has most confidential data and need more security. So, applying the previous method is ChaCha method. This method focusing only performance not fully security. The new method is BR22-01. This method has five stages. The 1st stage is finding the secret key x & y value. The 2nd stage is applying key in Eq. (1). The 3rd stage is merge all values into single row then pair from left and swap the values in the HS matrix. The 4th stage is applying key in Eq. (2). The 5th stage is merge all values into single line then pair from left and swap the values in the HC matrix but reverse. The new method has provide good security as well as performance while compared to ChaCha method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An effective classification of Skin Disease using Deep Learning Techniques

According to WHO, Skin Infection is very common but sometimes very serious and affects a large no population all over the world. Monkeypox, Chickenpox, and Measles are the major infectious disease that causes skin infection all over the world. It has been obverse that the cases of Monkeypox have drastically increased as an effect of Covid 19. This infection has spread easily and exponentially that cause serious health issues in many underdeveloped and developing countries. Some time it has been observed that people are not able to properly classify the type of skin infection well in time, which can be a main reason of serious health issues. So, it became important to propose an effective classification of Skin Disease. In this paper the authors have proposed an effective classification of Skin Disease using Deep Learning Techniques. This approach will help in classification of chicken pox, measles, and monkeypox through skin images. The authors have utilized Monkeypox Skin Images Dataset (MSID) dataset to apply the proposed approach. The Loss, Accuracy, Precision, Recall, AUC, and F1 Score parameters have been used to analyze the performance of proposed approaches. The best algorithms with maximum accuracy and other parameters are Xception, EfficientNetV2L, and EfficientNetV2M, and CNN, VGG16, and VGG19 are the least favored algorithms for this research. © 2023 IEEE.

Blockchain in Big Data for Agriculture Supply Chain

Agricultural food supply chain is a complex system starting from the production of food on a farm to the table of the consumer involving multiple stakeholders and a variety of processes. In recent years, the food supply chain has grown rapidly across nations, with customers demanding fresh, exotic foods all year round. The global shutdown due to the COVID pandemic has further complicated the food supply chain which has become prone to various contaminations and adulterations. Adulterated food is highly toxic to human health leading to several health issues, nutritional deficiencies, kidney disorders, and failure of vital organs. The existing systems used in the food supply chain do not provide enough transparency, traceability, food safety, or consumer trust. With today's Big Data integrated supply chains, such technologies are highly ineffective. In order to ensure food safety and consumer satisfaction, this chapter proposes using blockchain as an efficient technology to provide transparency, traceability, and trust in food supply chains. The chapter discusses the significance of smart agriculture and how blockchain might help agricultural supply chains that have Big Data incorporated overcome their difficulties. A thorough description of exclusive applications of blockchain in Big Data integrated food supply chains is provided. The chapter also describes how blockchain is integrated into each stage of the food supply chain management process and explores the challenges in implementing blockchain in Big Data integrated food supply chain systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Mental Health Tracker Using Machine Learning

One of the most pernicious consequences of COVID-19 on society is how it has affected global mental health, creating new problems and aggravating existing ones. Mental health issues and therapy typically take a backseat when the limited resources are equipped for the pandemic. So, it is necessary to track any psychological problems before they get out of our hands. This paper focuses on building a mental health tracker using a machine learning algorithm which mainly concentrates on cognitive mental disorder. It is critical in ensuring that these are caught early and one of the screening tools used for that is MMSE evaluation;it provides a quantitative assessment of cognitive impairment and to log cognitive changes over time. Using K-means clustering algorithm clusters are formed with the possibility of dementia occurrence. © 2022 IEEE.