An econometric analysis of 'hotel-hospital tie up' for wellness promotion and related tourism in Indian health cities with reference to Kolkata

The scope for wellness promotion and related tourism has increased manifold amidst this global crisis exhibited through COVID 19 pandemic that results in formation and popularity of medical hotels, patient hotels and such other new types through partnerships and alliances in many cities in Asia. Severely affected hospitality industry and their professionals are in search for new avenues and scopes now. This present study is an understanding of perceptions and attitudes of hospitality professionals in two diverse sectors with relatively new expertise and skills. This study includes the finding of determinants from the perspective of hospitality professionals by employing an ordered probit model and expressing its implications for future industry and academia.

Educating poor village children during the pandemic

Kaikala Chetana is a village-based community group which works with around 900 children in rural West Bengal. The community educators work with and engage the children of the rural poor in innovative ways, kindling their interest in learning and building their confidence. Here, Chatterjee shares how they educate the children in Kaikala Chetana during the pandemic.

Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models

The novel SARS-CoV-2 virus, responsible for the dangerous pneumonia-type disease, COVID-19, has undoubtedly changed the world by killing at least 3,900,000 people as of June 2021 and compromising the health of millions across the globe. Though the vaccination process has started, in developing countries such as India, the process has not been fully developed. Thereby, a diagnosis of COVID-19 can restrict its spreading and level the pestilence curve. As the quickest indicative choice, a computerized identification framework ought to be carried out to hinder COVID-19 from spreading more. Meanwhile, Computed Tomography (CT) imaging reveals that the attributes of these images for COVID-19 infected patients vary from healthy patients with or without other respiratory diseases, such as pneumonia. This study aims to establish an effective COVID-19 prediction model through chest CT images using efficient transfer learning (TL) models. Initially, we used three standard deep learning (DL) models, namely, VGG-16, ResNet50, and Xception, for the prediction of COVID-19. After that, we proposed a mechanism to combine the above-mentioned pre-trained models for the overall improvement of the prediction capability of the system. The proposed model provides 98.79% classification accuracy and a high F1-score of 0.99 on the publicly available SARS-CoV-2 CT dataset. The model proposed in this study is effective for the accurate screening of COVID-19 CT scans and, hence, can be a promising supplementary diagnostic tool for the forefront clinical specialists.

A bi-stage feature selection approach for COVID-19 prediction using chest CT images.

The rapid spread of coronavirus disease has become an example of the worst disruptive disasters of the century around the globe. To fight against the spread of this virus, clinical image analysis of chest CT (computed tomography) images can play an important role for an accurate diagnostic. In the present work, a bi-modular hybrid model is proposed to detect COVID-19 from the chest CT images. In the first module, we have used a Convolutional Neural Network (CNN) architecture to extract features from the chest CT images. In the second module, we have used a bi-stage feature selection (FS) approach to find out the most relevant features for the prediction of COVID and non-COVID cases from the chest CT images. At the first stage of FS, we have applied a guided FS methodology by employing two filter methods: Mutual Information (MI) and Relief-F, for the initial screening of the features obtained from the CNN model. In the second stage, Dragonfly algorithm (DA) has been used for the further selection of most relevant features. The final feature set has been used for the classification of the COVID-19 and non-COVID chest CT images using the Support Vector Machine (SVM) classifier. The proposed model has been tested on two open-access datasets: SARS-CoV-2 CT images and COVID-CT datasets and the model shows substantial prediction rates of 98.39% and 90.0% on the said datasets respectively. The proposed model has been compared with a few past works for the prediction of COVID-19 cases. The supporting codes are uploaded in the Github link: https://github.com/Soumyajit-Saha/A-Bi-Stage-Feature-Selection-on-Covid-19-Dataset.