Purpose
Stress is the most common emotional or mental state that employees experience during their work. The employees in academics and industry are facing increased levels of stress as they progress through their work. The study aims to investigate the relationship between academic and industry employees’ stress personalities. West Coast psychological consultants Mary Dempcy and Rene Tihista distinguish between the seven various types of stress and offer suggestions to deal with it.
Design/methodology/approach
In this study, the authors have built a survey questionnaire using a sample of 195 respondents from the industry and academic of North India and analysed their responses to find their stress personalities at work. The Independent sample t-test approach has been applied to analyse the employee stress personality.
Findings
The study finds out that stress is a sophisticated defence mechanism that is unique to each individual and varies depending on the environment. Using employee response of academic and industry, the study covers the essence of seven types of stress for individuals, and that lent good support to the framed hypothesis.
Research limitations/implications
These seven types of stress have importance and different levels to knowing their appropriateness to the individuals and suggest to take necessary action of plan. It shows the individuals feel about stress, how the bodies react to it and how to cope with it are all indicators of the personalities, attitudes and adaptability.
Originality/value
The novelty of this study is to apply Mary Dempcy and Rene Tihista’s stress personalities compared with the two respective categories.
Organizations can use weather forecasting to help with decision-making when it comes to preventing disasters. Forecasting rain is challenging since weather conditions are always unpredictable in general. The prediction of rainfall uses a variety of methodologies, including statistical, hybrid, and physical approaches. In this research, we have implemented various machine learning models such as Logistic Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP) to predict the density of rainfall. This study has used Taiwan Ruiyan rainfall hourly dataset from 1998 to 2018 which contains five features like Air Pressure, Humidity, Temperature, Windspeed, and Wind Direction to predict the rainfall density such as low, medium, and heavy rainfall. The results data in this study are compared using statistical metrics like AUC, accuracy, recall, precision, and F1-score. The Random Forest, and Multi-Layer Perceptron models, had the highest accuracy scores of 0.71, accurately predicting the results. This study offers a comprehensive overview of several methods and their rainfall density predictions. By comparing these models, we can decide which one is best for predicting rainfall. The suggested work is extensively used in a variety of agriculture and civil applications, including hazard prediction, prevention, operational planning, and many more.
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