COVID-19 and beyond: understanding travel risk perception as a process

The unprecedented occurrence of COVID-19 highlights the susceptibility of the tourism industry to external threats. From flight cancellations to the closure of hospitality establishments, COVID-19 has greatly transformed the industry. Whilst a crisis such as a pandemic is not new in tourism and hospitality, the unique characteristics of COVID-19 have altered the risk perceptions associated with destinations. To date, the tourism risk literature has predominantly focused on typology of risks, at the expense of examining the process of how risk perceptions are formed. Following a social constructivist epistemological position, this conceptual paper proposes an integrative model that unpacks the underlying psychological process of risk perceptions and demonstrates how the framing process influences the safety perceptions and moulds the travel image of a destination in the COVID-19 context. The paper proposes several suggestions for future studies to consider when testing the conceptual model.

Diagnosis of COVID-19 Using Artificial Intelligence Techniques

Artificial intelligence is being used in a variety of ways by those trying to address variants and for data management. AI, on the other hand, not only uses historical data, it makes assumptions about the data without applying a defined set of rules. This allows the software to learn and adapt to information patterns in more real time. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed. Chest CT is an emergency diagnostic tool to identify lung disease. Artificial intelligence (AI) gives big guidance in the rapid analysis of CT scans to differentiate variants of COVID-19 findings. This work focuses on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Diagnosis of COVID-19 Using Artificial Intelligence Techniques

Artificial intelligence is being used in a variety of ways by those trying to address variants and for data management. AI, on the other hand, not only uses historical data, it makes assumptions about the data without applying a defined set of rules. This allows the software to learn and adapt to information patterns in more real time. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed. Chest CT is an emergency diagnostic tool to identify lung disease. Artificial intelligence (AI) gives big guidance in the rapid analysis of CT scans to differentiate variants of COVID-19 findings. This work focuses on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Factors Associated with Healthy Life Expectancy: An Ecological Analysis of Global Data

Healthy life expectancy (HALE) measures the quality of life a person expects to live. This study aims to find out the most associated factors of HALE at birth globally. The data of 212 countries came from the World Health Organization, Worldometer, World Bank, and United Nations. HALE at birth is considered as the dependent variable;and social, economic, and health factors are considered as the predictors. Descriptive statistics, Pearson’s correlation analysis, and multiple linear regression models were used as the statistical tools to reach the objective. The results revealed that HALE is found lower in Central African Republic and higher in Singapore. The highest death rate due to coronavirus disease 2019 (COVID 19), alcohol consumption rate, human immunodeficiency virus (HIV) prevalence rate, and average household size are found in Nicaragua, Moldova Republic, Eswatini, and Senegal, respectively. And the lowest recovery rate from COVID 19, and universal health coverage (UHC) service index are found in Tajikistan, and Montserrat, respectively. The recovery rate from COVID 19, UHC service index, gross domestic product (GDP), current health expenditure, tuberculosis (TB) incidence, tobacco smoking, HIV prevalence rate and average household size were significantly correlated with the HALE at birth. The multiple linear regression models identified that the UHC service index, alcohol consumption rate, HIV prevalence rate and average household size are the most associate factors of HALE at birth globally. Therefore, the necessary steps should be taken to maximize the UHC service index, and to minimize the alcohol consumption rate, HIV prevalence rate and average household size for increasing the HALE at birth in the world.

Data Mining Technique for Prediction System of Heart Disease Using Associative Classifications

In this study, several aspects of the human body have been focused upon. This paper attempts to cast light on pre-and post-pathological conditions, man-machine interactions, human mindset, and ethics of AI. The paper emphasizes the cultural impacts of overeating, profuse drinking, and smoking habits. It uplifts the basic necessity of growing awareness schemes. Patients are seeking treatment in health care centers with the following serious pathological conditions and complications (We exclude the COVID-19 pandemic because it has been adequately publicized by media and press): Heart Attack, Stroke Cancer, Fatty liver & liver cirrhosis. Because of being the leading causes of sudden death prediction of heart attack is very important. Our main focus is to determine the best machine learning method. With optimal parameters, we evaluate the Dataset. Model Accuracy for the heart Attack Machine Learning Model was the highest for the Logistic Regression mode land it was 93.41%. On the contrary, the accuracy for Linear Regression Model was 60.10% which was the least. © 2021 IEEE.

COVID-19 and Beyond: understanding travel risk perception as a process

The unprecedented occurrence of COVID-19 highlights the susceptibility of the tourism industry to external threats. From flight cancellations to the closure of hospitality establishments, COVID-19 has greatly transformed the industry. Whilst a crisis such as a pandemic is not new in tourism and hospitality, the unique characteristics of COVID-19 have altered the risk perceptions associated with destinations. To date, the tourism risk literature has predominantly focused on typology of risks, at the expense of examining the process of how risk perceptions are formed. Following a social constructivist epistemological position, this conceptual paper proposes an integrative model that unpacks the underlying psychological process of risk perceptions and demonstrates how the framing process influences the safety perceptions and moulds the travel image of a destination in the COVID-19 context. The paper proposes several suggestions for future studies to consider when testing the conceptual model. © 2021 Informa UK Limited, trading as Taylor & Francis Group.