The number of people infected with the corona virus is steadily rising. Even after being treated and returned to normality, many who were impacted are still suffering from a variety of health problems. We suggest a new, more effective approach to dealing with this issue, as well as putting in place preventative measures to prevent the spread of disease. The modified convolutional neural networks (M-CNN) architecture is modified deepCNN architecture. Using existingcorona virus disease 2019(COVID-19) computerizedtomographyscan (CT scan) images, this suggested approach intends to develop a deep model for screening and forecasting the risk of disease propagation. The suggested model was trained using 1000 scan pictures from various sources, yielding a prediction accuracy of 93 percent, which is much greater than previous methods.

With the high rise in population and a huge level of unwanted materials, conventional methods of waste disposal are becoming outdated as it involves more manual scavenging work and unwanted human potential. In order to overcome the above manual issues, there is a need to design a machine that can clear the litter and leftover wastes without much involvement of human indulgence. To overcome the above issues, the smart garbage collector was designed and implemented. It consists of a vacuum machine that sucks out leftover trash on the ground. Then, it is lifted to a certain height by servo motors to drop it into the shredding area. The shredder then cuts the waste into very tiny pieces. Then, the waste pieces are transferred to the storage box. This box is placed at an inclined angle to support the disposal of waste without human intervention. The box opening and closing actions are controlled by a servo motor that is placed outside the box and the slide opens vertically to avoid any unwanted residual waste in the storage box. The storage box consists of an ultrasonic sensor to notify the level of waste accumulated inside the box, and when the maximum threshold value is attained, the proposed machine has to dispose of the crushed waste. All the actions of the controller are monitored by a private server hosted on the Blynk platform that can be accessed only by the user. The server can be controlled through a mobile interface that acts as a remote control for the proposed machine. The Local Server is set up using Raspberrypi which enables ease of access to the Blynk server hosted in our home router IP.