According to the World Health Organization, the Coronavirus (COVID-19) pandemic is causing a worldwide emergency, and one safe way to cover oneself is to wear masks. This pandemic constrained governments everywhere in the world to force lock-downs to avoid the transmission of infection. Reports show that wearing masks at work diminishes the danger of infection. We assemble our model by utilizing the concept of deep neural learning and AI. The dataset comprises pictures with masked faces and non-masked faces. Several computer algorithms are there for face detection. But this analysis centers around two of the most widely recognized procedures: The Viola-Jones algorithm and the Convolution Neural Networks. We will check whether the individual in the image/video wears a mask or not with a CV and Deep neural learning. Not only finding out about face mask detection, but this project also introduced the chance to delve into the field of computer algorithms.

SummaryElectric vehicles (EVs) are drastically growing, and their emergence is more popular. Moreover, with the constraint driving range, the batteries of these vehicles are rechargeable throughout the routes in various situations. Here, a new problem is formulated with the inclusion of both battery swapping and partial recharging decisions. The energy level and the routing directions are more important, and the investigators intend to handle these issues effectually. However, the major constraint is routing and time window. Here, a heuristic model (LTS‐RSS) is designed to find the best matching solution. A novel Random Sub‐Space (RSS) and local Tabu search (LTS) are modeled to handle these issues. The probabilistic model of RSS is anticipated by integrating the consequences of time windows and distances. The experimentation is done with an online database and used for performance validation. The outcomes show that the newly modeled (LTS‐RSS) approach enhances the significance of the model. The outcomes of all the instances with diverse strategies enhance the model's robustness and stability for resolving these issues. The empirical analysis is done with MATLAB 2020b simulator, and metrics like optimization of routing solution, the best vehicle, best distance, and numbers of vehicles are evaluated, and the outcomes are compared with various other approaches.

Water pollution is one of the problems in the world. Water is used for industrial purpose. So, it is compulsory for an each officer to visit the ponds at a designated time and perform manually testing to measure the purity level of the water. The industrial visitors are not come directly to the pond and the information about the water to send the Short Message Service (SMS). A pond is a body of standing water, either natural or artificial. The sensor is fixed to the pond water, it senses the water and the data will be collected through these phase pH meters, humidity and temperature sensor is sending the signal to Arduino microcontroller. pH meter is used to measure the hydrogen ion in the water, temperature sensor will sense the temperature is one of the most frequently calculated variables and sensing can be made either through straight contact with the heating basis without straight contact with the basis using radiated energy in its place. Humidity is used to measure the amount of water present in the air. The GSM module sends the message to industrial visitor. The pH is normal or abnormal and humidity value, temperature value. The motor is fixed in the pond and if industries need water then they can switch on (or) off the motor. The motor can be switched on (or) off using microcontroller.

The advancement in robotic technology has improved drastically in the past decade, from load carrying to assembling of huge aircrafts has made it easier and has reduced human labour. Furthermore, with technologies like Artificial Intelligence and Machine Learning becoming pervasive robots are tending to grow smarter. Human following robots employing RFID and vision techniques like camera and laser are current trend in the market. But they all are mainly based on line of sight which implies that it cannot reacquire the target when lost. In this paper, a novel method is employed to obtain the human following feature using Inertial Measurement Unit (IMU). Unlike the former techniques, using IMU would not pose any shortcomings of losing the target. The most important feature needed is the ability for the robot to follow that particular person. The application called the Hyper IMU is utilised to convert a regular Smartphone into a powerful IMU device. The availability of the sensors which are already embedded in the Smartphone these days is highly advantageous for the implementation of human following robot. The robot tracks the movement of the human and heads in the same direction as guided by the sensors. The implementation of ultrasonic sensor is to obtain obstacle detection which can avoid damages to the robot in case of its absence.

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