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The widespread use of airborne vehicles and its growing applications in various domains can be attributed to their ability to operate in inaccessible areas, thus decreasing the human loss in major accidents, and making access easy to dangerous conditions. These pilotless airborne vehicles with some remote sensing techniques involved in it, are emerging as a new technology. The airborne vehicles are enduring, cost effective and also provide a reliable platform for surveying. Consequently, small airborne vehicles, here Quad copter, equipped with sophisticated components can capture and transmit the image or video to the Data Base. They are sent for processing and made accessible to the Geographical Information System (GIS), where further analysis is done for the proceedings of the rescue operation. This application finds a great utility predominantly in industries and military.

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With the ongoing COVID-19 pandemic, businesses and organizations have acclimated to unconventional and different working ways and patterns, like working from home, working with limited employees at office premises. With the new normal here to stay for the recent future, employees have also adapted to different working environments and customs, which has also resulted in psychological stress and lethargy for many, as they adapt to the new normal and adjust their personal and professional lives. In this work, data visualization techniques and machine learning algorithms have been used to predict employees stress levels. Based on data, we can develop a model that will assist to predict if an employee is likely to be under stress or not. Here, the XGB classifier is used for the prediction process and the results are presented showing that the method facilitates getting a more reliable model performance. After performing interpretation utilizing XGB classifier it is determined that working hours, workload, age, and, role ambiguity have a significant and negative influence on employee performance. The additional factors do not hold much significance when associated to the above discussed. Therefore, It is concluded that concluded that increasing working hours, role ambiguity, the workload would diminish employee representation in all perspectives.

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