PurposeGiven the pace of global environmental degradation, companies and individuals alike are exploring ways and means of protecting the environment. In this context, the attitudes of hoteliers and their employees toward sustainability are key to the successful implementation of these practices. This paper aims to consider the impact of attitude development and COVID-19 on the sustainability performance of hotels. The study also explores contributions made by hotels to environmental sustainability and society more generally.Design/methodology/approachThe study is based on interviews and survey questionnaires completed by employees of five-star hotels in India, and qualitative methods were used to process the data.FindingsThe findings of this study confirm the devastating impact of COVID-19 on both economic and societal sustainability in what otherwise would be a profitable sector of the economy.Practical implicationsThe study has implications for hoteliers, the government, environmental agencies and for employees and could assist with the formulation of recovery packages by government and in the development of new standard operating procedures to enable hotels to step-up on the self-recovery path.Originality/valueThe paper provides an analysis of the direct effects of the pandemic on financial sustainability and its mediating impact on the efforts of hotels to attain sustainable environment.

No abstract

Background. Even in today’s environment, when there is a plethora of information accessible, it may be difficult to make appropriate choices for one’s well-being. Data mining, machine learning, and computational statistics are among the most popular arenas of training today, and they are all aimed at secondary empowered person in making good decisions that will maximize the outcome of whatever working area they are involved with. Because the degree of rise in the number of patient roles is directly related to the rate of people growth and lifestyle variations, the healthcare sector has a significant need for data processing services. When it comes to cancer, the prognosis is an expression that relates to the possibility of the patient surviving in general, but it may also be used to describe the severity of the sickness as it will present itself in the patient's future timeline. Methodology. The proposed technique consists of three stages: input data acquisition, preprocessing, and classification. Data acquisition consists of input raw data which is followed by preprocessing to eliminate the missed data and the classification is carried out using ensemble classifier to analyze the stages of cancer. This study explored the combined influence of the prominent labels in conjunction with one another utilizing the multilabel classifier approach, which is successful. Finally, an ensemble classifier model has been constructed and experimentally validated to increase the accuracy of the classifier model, which has been previously shown. The entire performance of the recommended and tested models demonstrates a steady development of 2% to 6% over the baseline presentation on the baseline performance. Results. Providing a good contribution to the general health welfare of noncommercial potential workers in the healthcare sector is an opportunity provided by this recommended job outcome. It is anticipated that alternative solutions to these constraints, as well as automation of the whole process flow of all five phases, will be the key focus of the work to be carried out shortly. Predicting health status of employee in industry or information trends is made easier by these data patterns. The proposed classifier achieves the accuracy rate of 93.265%.

No abstract

In this research work, an attempt was made to machine the titanium (Ti6Al4V) alloy utilizing electric discharge machining technique. The distinct process parameters and its impact on the machining performance were identified using the cause-and-effect diagram (CED). The key process parameters identified by CED diagram were current, pulse on time (Ton), aluminium oxide (Al2O3) powder concentration, and gap distance; experiments were conducted by varying the process parameters, experimental runs were designed using the Taguchi mixed orthogonal array. The experimental results revealed that improvement in material removal rate (MRR) was due to the bridging effect; reduction in tool wear rate (TWR) owing to the expansion of spark gap and enhancement in the surface roughness (Ra) was due to the complete flushing of machined debris. The interaction impact was analysed using the contour plot and with the aid of mathematical modelling experimental fits that were identified and the results were validated utilizing the sensitivity analysis. The obtained results were optimized using the technique for order of preference by similarity to ideal solution (TOPSIS) optimization technique.