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Electroencephalography (EEG) is a reliable method for identifying the onset of sleepiness behind the wheel. Using EEG technology for driving fatigue detection still presents challenges in extracting informative elements from noisy EEG signals. Due to their extensive computational parallelism, which is similar to how the brain processes information, neural networks have been explored as potential solutions for extracting relevant information from EEG data. The existing machine learning frameworks suffer from high computing costs and slow convergence, both of which contribute to low classification accuracy and efficiency due to the large number of hyper parameters that need to be improved. It is necessary to automate this micronap detection process before it can be used in real-time scenarios. To distinguish between micronap and non-micronap states, a deep neural network (DNN) framework is developed in this research using different EEG representations as input. Additional EEG representations utilized in this investigation include cleaned EEG as a time series, log-power spectrum, 2D-spatial map of log-power spectrum, and raw EEG. Finally, traditional machine learning algorithms are evaluated for their effectiveness in detecting micronaps from these EEG inputs. The findings suggest that micronap detection can be greatly improved by combining cleaned EEG with DNN.

Robots are becoming more sociable nowadays. Recently, socio robots are used to mimic the human-like intelligence by taking human emotions, posture and so on into consideration. The designed R-assistant is the real-time robotic assistant which accompanies humans to eliminate their stress outburst and loneliness. In this scenario, R-Assistant with its underlying Machine Learning architecture with data analytics model perceives human emotions with a highly precise technique called Image processing, which can appropriately sense the facial expression of humans and delivers potential insights to predict human emotions.

In this modern world, we are depending on devices for doing our work in day-to-day life, which is interconnected through networks of networks. In this paper, we mainly focused on the safety measures for both driver and vehicle by using three types of sensors: Heartbeat sensor, Traffic light sensor and Level sensor. Heartbeat sensor is used to monitor heartbeat rate of the driver constantly and prevents from the accidents by controlling through IOT. IOT conveys the emergency message to the Owner, Ambulance and the Police. Traffic light sensor is used to follow the traffic rules and regulations by the driver. If the Red light is in the ON state, then the vehicle automatically stops before it reaches the white line. Fuel level sensor is used to measure vehicle's fuel level and calculate whether the available fuel is enough to reach the destination or not, if it is not enough then map will suggest the driver to reach the nearby petrol bunks.

Road accidents are an absolute woe to the people in India. It destroys thousands of happy families every year. Many of these are an aftermath of either non-stringent Traffic Rules or Inefficient Emergency Help. In addition to this, fire accidents, general rescue and patrol assistance comes under emergency situations. Our goal is to provide a mobile application that helps people get access to emergency services in smart and easy fashion. The hypothesis is that the operators of emergency services can be able to assess the situation better by depending less on oral communication and relying more on real time data from smart phone application. The identified problems with existing system of situation assessment by operators and help arrival to the victim are discussed. A smart phone application for users (CAEAS) and a smart web application for the operators (EAS) are our presented proposed solution. As an add-on, a solution for women harassment is also included in the application. Most of the available smart phone resources such as GPS, Camera, Mobile network, Internet, Camera, Gestures such as shake are utilized to provide maximum performance of application.