PurposeThe new coronavirus is a highly infectious disease with mutating variants leading to pervasive risk around geographies and public health system. The economy has been suffering due to the strategic lockdown adopted by the local administrative bodies, and in most of the countries, it is further leading to a major wave of unemployment with millions of job and business losses affecting the hotels, travel and tourism industry widely. To attain a sustainable business in the post-pandemic situations, the industry now must think of information system approaches to convince tourists to feel safe with the most hygienic hospitality and services to be offered in any property. The key aspect of the study is to provide the impact of new-age AI-driven technology solutions that will dominate the future direction of the modernized hospitality industry promising robust health-safety measures in a hotel, and further help create sustainable business and leisure travel facilities to cope with post-epidemic scenarios.Design/methodology/approachThe study emphasizes to provide a robust technology-oriented framework based on a mixed research method that would help hotels to adopt and implement new-age AI-driven solution within the hotel premise to serve customers with at most hygiene, contactless service and thereafter, aiming for faster recovery of businesses and regaining customer trust to fuel booking intent in the post-epidemic scenario.FindingsThe paper provides a technology-focused solution that would impact hotel industries' post-pandemic scenario. The study contributes to helping boost the tourism industry using information management solutions such as biosensors, robotic room services and contactless hosting. The findings show the adoption of robots/RPA solutions and Biosensors by the industry will be a disruptive paradigm shift.Originality/valueThe study expands the scope of research in information technology and management with a focus on the hospitality industry while contributing to new factors impacting customer buying behavior in the industry.

Background In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice. Methods This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases. Results The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%. Conclusion The results suggest that quantum neural networks outperform in COVID-19 traits’ classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.

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The purpose of the paper is to provide innovative emerging technology framework for community to combat epidemic situations. The paper proposes a unique outbreak response system framework based on artificial intelligence and edge computing for citizen centric services to help track and trace people eluding safety policies like mask detection and social distancing measure in public or workplace setup. The framework further provides implementation guideline in industrial setup as well for governance and contact tracing tasks. The adoption will thus lead in smart city planning and development focusing on citizen health systems contributing to improved quality of life. The conceptual framework presented is validated through quantitative data analysis via secondary data collection from researcher's public websites, GitHub repositories and renowned journals and further benchmarking were conducted for experimental results in Microsoft Azure cloud environment. The study includes selective AI models for benchmark analysis and were assessed on performance and accuracy in edge computing environment for large-scale societal setup. Overall YOLO model outperforms in object detection task and is faster enough for mask detection and HRNetV2 outperform semantic segmentation problem applied to solve social distancing task in AI-Edge inferencing environmental setup. The paper proposes new Edge-AI algorithm for building technology-oriented solutions for detecting mask in human movement and social distance. The paper enriches the technological advancement in artificial intelligence and edge computing applied to problems in society and healthcare systems. The framework further equips government agency, system providers to design and construct technology-oriented models in community setup to increase the quality of life using emerging technologies into smart urban environments. Keywords Deep learning • Edge-computing • Social and industrial safetyThis article is part of the topical collection "Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications" guest edited by Bhanu Prakash K. N. and M. Shivakumar.