The hotel industry is one of the most booming industry contributing tremendous growth in the global economy. It has never got affected by any kind of recession or economic turmoil, and this happens because of the fact that individuals/ families would need services of hotel industry for various reasons of human activities like business, recreation, pilgrimage educational tour, historical tours, festivals, carnivals, medical assistance trip etc. and so on. The biggest apprehension about this industry is attrition/turnover rate of employees; and to trounce this matter, selection of the right candidate at the right profile for the right post is the way to success. Selection criteria include all the essential and desirable skills, attributes, experience, and education which an organization decides is necessary for a position. Selection criteria help to select the most capable, effective, suited, experienced, qualified, the person for the job. Applicants must demonstrate and prove the ways in which they will be of valued for the job and the organization. Job selection criteria are also known as key selection criteria or KSC. They are designed to help make the most accurate match between the requirements of a position and the skills of an applicant. For selecting the right candidate, perfect for a particular job, selection has to be well planned, tactically accurate and strategically correct, as there is a huge pressure of short listing, filtering and selecting the right candidate, which makes the whole exercise lengthy as well as painstaking.

The significance of social media has increased manifold in the past few decades as it helps people from even the most remote corners of the world stay connected. With the COVID-19 pandemic raging, social media has become more relevant and widely used than ever before, and along with this, there has been a resurgence in the circulation of fake news and tweets that demand immediate attention. In this paper, we describe our Fake News Detection system that automatically identifies whether a tweet related to COVID-19 is "real" or "fake", as a part of CONSTRAINT COVID19 Fake News Detection in English challenge. We have used an ensemble model consisting of pre-trained models that has helped us achieve a joint 8 th position on the leader board. We have achieved an F1-score of 0.9831 against a top score of 0.9869. Post completion of the competition, we have been able to drastically improve our system by incorporating a novel heuristic algorithm based on username handles and link domains in tweets fetching an F1-score of 0.9883 and achieving state-of-the art results on the given dataset.

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The significance of social media has increased manifold in the past few decades as it helps people from even the most remote corners of the world to stay connected. With the advent of technology, digital media has become more relevant and widely used than ever before and along with this, there has been a resurgence in the circulation of fake news and tweets that demand immediate attention. In this paper, we describe a novel Fake News Detection system that automatically identifies whether a news item is "real" or "fake", as an extension of our work in the CONSTRAINT COVID-19 Fake News Detection in English challenge. We have used an ensemble model consisting of pre-trained models followed by a statistical feature fusion network , along with a novel heuristic algorithm by incorporating various attributes present in news items or tweets like source, username handles, URL domains and authors as statistical feature.Our proposed framework have also quantified reliable predictive uncertainty along with proper class output confidence level for the classification task. We have evaluated our results on the COVID-19 Fake News dataset and FakeNewsNet dataset to show the effectiveness of the proposed algorithm on detecting fake news in short news content as well as in news articles. We obtained a best F1-score of 0.9892 on the COVID-19 dataset, and an F1-score of 0.9073 on the FakeNewsNet dataset.