Health and energy are two important elements of life that must be constantly monitored. In the present pre-endemic or post-pandemic age, it is critical to monitor individuals to ensure that crucial standard operating procedures, such as wearing face masks, are followed. Furthermore, any measures to reduce energy use, particularly in public buildings that are now being utilised again in the postpandemic context, are critical. As a result, this study presents a live dashboard method for tracking both energy and face mask wear. The detection system is built on a Jetson Nano device and is then interfaced to a live dashboard to monitor people who wear masks, including those who wear them incorrectly. The observed findings for the confusion matrix and F1 score are also shown. At the same time, the suggested visual detection system is also capable of monitoring the energy use in the building where the device is mounted.
<span>Traffic flow prediction is an integral part of the intelligent transportation system (ITS) that helps in making well-informed decisions. Traffic flow prediction helps in alleviating traffic congestion as well as in some connected vehicles applications such as resources allocation. However, most of the existing models do not consider external factors such as weather data. Traffic flow in road networks is affected by weather conditions which affects the periodicity of traffic. These effects introduce some irregularity to the traffic pattern, making traffic flow prediction a challenging issue. In this paper, we present a detailed investigation on the impact of weather data on different traffic flow prediction models. The investigation presented in this paper demonstrates how adding weather data could improve the models’ prediction accuracy and efficiency.</span>
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