Purpose: The hospitality industry experienced an unanticipated challenge from the COVID-19 pandemic. However, research in this area is scarce. Accordingly, this study aims to unfold a three-angled research agenda to intensify the knowledge advancement in the hospitality sector. It proposes a theoretical framework by extending the protection motivation theory (PMT) to explain the guest’s intent to adopt artificial intelligence (AI) and robotics as a protective measure in reaction to COVID-19.Design/methodology/approach: The research is centered on outlining the pertinent literature on hospitality management practices and the guest’s transformed behavior during the current crisis. This study intends to identify a research agenda based on investigating hospitality service trends in today’s changing times.Findings: The study sets out a research agenda that includes three dimensions as follows: AI and robotics, cleanliness and sanitation and health care and wellness. This study’s findings suggest that AI and robotics may bring out definite research directions at the connection of health crisis and hospitality management, taking into account the COVID-19 crisis.Practical implications: The suggested research areas are anticipated to propel the knowledge base and help the hospitality industry retrieve the COVID-19 crisis through digital transformation. AI and robotics are at the cusp of invaluable advancement that can revive the hotels while re-establish guests’ confidence in safe hotel practices. The proposed research areas are likely to impart pragmatic lessons to the hospitality industry to fight against disruptive situations.Originality/value: This study stands out to be pioneer research that incorporated AI and robotics to expand the PMT and highlights how behavioral choices during emergencies can bring technological revolution.

Cancer research has seen explosive development exploring deep learning (DL) techniques for analysing magnetic resonance imaging (MRI) images for predicting brain tumours. We have observed a substantial gap in explanation, interpretability, and high accuracy for DL models. Consequently, we propose an explanation-driven DL model by utilising a convolutional neural network (CNN), local interpretable model-agnostic explanation (LIME), and Shapley additive explanation (SHAP) for the prediction of discrete subtypes of brain tumours (meningioma, glioma, and pituitary) using an MRI image dataset. Unlike previous models, our model used a dual-input CNN approach to prevail over the classification challenge with images of inferior quality in terms of noise and metal artifacts by adding Gaussian noise. Our CNN training results reveal 94.64% accuracy as compared to other state-of-the-art methods. We used SHAP to ensure consistency and local accuracy for interpretation as Shapley values examine all future predictions applying all possible combinations of inputs. In contrast, LIME constructs sparse linear models around each prediction to illustrate how the model operates in the immediate area. Our emphasis for this study is interpretability and high accuracy, which is critical for realising disparities in predictive performance, helpful in developing trust, and essential in integration into clinical practice. The proposed method has a vast clinical application that could potentially be used for mass screening in resource-constraint countries.

Document/Text Classification has become an important area in the field of Machine Learning. On account of its wide applications in business, ham/spam filtering, health, e-commerce, social media sentiment, product sentiment among customers etc., various approaches have been devised to accurately predict the category or to classify any of the new text/document under consideration. Nowadays, news articles in the newspaper present various kinds of sentiments or inclination of the news article towards a negative or positive sentiment and hence, the content of the news can actively be used to judge the impact on the reader. The paper aims to predict that whether the sentiment of the news article is positive or negative using the two popular approaches of Naïve Bayes Text Categorization i.e. Multivariate Bernoulli Naïve Bayes Classification and Multinomial Naïve Bayes Classification. Also, the research aims to identify that which approach between the given two approaches perform better for the given dataset.

Purpose: This study defines a three-angled research plan to intensify the knowledge and development undergoing in the retail sector. It proposes a theoretical framework of the customer journey to explain the customers' intent to adopt artificial intelligence (AI) and machine learning (ML) as a protective measure for interaction between the customer and the brand.Design/methodology/approach: This study presents a research agenda from three-dimensional online search, ML and AI algorithms. This paper enhances the readers' understanding by reviewing the literature present in utilizing AI in the customer journey and presenting a theoretical framework.Findings: Using AI tools like Chatbots, Recommenders, Virtual Assistance and Interactive Voice Recognition (IVR) helps create improved brand awareness, better customer relationships marketing and personalized product modification.Originality/value: This study intends to identify a research plan based on investigating customer journey trends in today's changing times with AI incorporation. The research provides a novel model framework of the customer journey by directing customers into different stages and providing different touchpoints in each stage, all supported with AI and ML.