A System Design With Deep Learning and IoT to Ensure Education Continuity for Post-COVID

Aim:

          Ensure education continuity for post-covid it used to make a attendance for a student with face detection and ensure student were a mask.

Abstract:

        Deep learning-based comprehensive study to reduce the effects of COVID-19 on the education system is presented. The proposed system consists of an edge device, IoT nodes, and a neural network that runs on a server. The purpose of the proposed system is to protect students and staff against infectious diseases and increase the students’ performance during classes by monitoring the environ- mental conditions via an IoT-based sensor network, during the current pandemic to ensure the use of masks in closed areas by training a customized deep learning model, and to monitor the student attendance data by deep learning and IoT-based solution. Furthermore, effective heating and cooling can be done to save energy by transmitting the environmental conditions of the indoor environment to the relevant destinations.

Existing System:

           The system consists of several IoT nodes, an edge device, and a server. The IoT nodes are used to monitor the relative humidity, temperature, and air quality of the target classroom. By using the edge device, fast and immediate data communication can be achieved within a local network. The edge device, which consisted of Raspberry Pi, works as a Wi-Fi Hotspot to create the local network with the sensor nodes. An MQTT (Message Queuing Telemetry Transport) Broker that is run on the edge device establishes he data transmission between sensor nodes which are publishers, and boiler control unit, building ventilation system, and server which are subscribers. Other tasks of the edge device are taking students’ photos and measuring their body temperature as inputs, after that guiding them according to the data received from the server. If a student is not registered to the system, it is also responsible for sending the registration information to the server by taking their photos and reading their IDs for the first time. An ACS (Advanced Card Systems) ACR1281U-C1 model card reader integrated into the edge device was used to register the unknown students to the system. The server performs actions of face recognition, mask detection, checking COVID-19 and contacts status, and processing attendance data.

Problem Definition:

        The main problem Expense in this paper based on ensure education continuity for covid pandemic. Paper says increase the room temperature, ventilation system and boiler then so we do give information to the user.

Proposed System:

            If a mask is on the face, it is requested to be removed for a short time to proceed with the face recognition procedure. Face recognition and body temperature monitoring steps proceed if the student has previously enrolled in the system. If an unregistered face is detected, the student is asked to read his/her student card to the card reader, so the necessary information can be retrieved from the database, and then 50 images of the student are taken and stored in the database. Face mask detection using CNN model with high accuracy and then check allowed a classroom.