No abstract

Improvement of public safety and reducing accidents are the intelligent system's critical goals for detecting drivers' fatigue and distracted behavior during the driving project. The essential factors in accidents are driver fatigue and monotony, especially on rural roads. Such distracted behavior of the driver reduces their thinking ability for that particular instant. Because of this loss in decision-making ability, they lose control of their vehicle. Studies tell that usually the driver gets tired after an hour of driving. Driver fatigue and drowsiness happens much more in the afternoon, early hours, after eating lunch, and at midnight. These losses of consciousness could also be because of drinking alcohol, drug addiction, etc. The distracted driver detection system proposed in this chapter takes a multi-faceted approach by monitoring driver actions and fatigue levels. The proposed activity monitor achieves an accuracy of 86.3%. The fatigue monitor has been developed and tuned to work well in real-life scenarios.

The ongoing pandemic of COVID-19 has shown the limitations of our current medical institutions. There is a need for research in automated diagnosis for speeding up the process while maintaining accuracy and reducing computational requirements. In this work, an IoT and edge computing based framework is proposed to automatically diagnose COVID-19 from CT scans of the patients using Deep Learning techniques. The proposed method requires less computational power and uses ensemble learning to increase the models' overall predictive performance. In the simulation, it was found that each model performs better in some areas than the other. The proposed scheme uses ensemble learning to take advantage of such an occurrence and achieved an accuracy of 86.2% and an AUC score of 89.8% on the COVID-CT-Dataset. This accuracy is achieved keeping the hardware accessibility in mind by training the models using a labeled dataset of CT-scans of the patients. Unlike other works, we were able to train models on a single enterprise-level GPU. It can easily be provided on the edge of the network, which reduces communication overhead and latency. This work aims to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance combined with medical equipment and help ease the examination procedure.