Deep Dual Attention Network for Precise Diagnosis of COVID-19 From Chest CT Images

Automatic diagnosis of COVID-19 using chest CT images is of great significance for preventing its spread. However, it is difficult to precisely identify COVID-19 due to the following problems: 1) the location and size of lesions can vary greatly in CT images;2) its unique characteristics are often imperceptible in imaging findings. To solve these problems, a Deep Dual Attention Network (<inline-formula><tex-math notation="LaTeX">$\textrm {D}

The Prototype of Healthy Smart Building Based on Google Assistant

Coronaviruses are a group of viruses from the subfamily Orthocronavirinae in the Coronaviridae family and the order Nidovirales. In general, the transmission of this virus occurs through droplets or body fluids splashed on someone or objects around them within 1-2 meters' distance away through coughing and sneezing. Office buildings, cafes, and shopping areas are one of the clusters for spreading the coronavirus because the following places lack awareness of health protocols. This research will create a healthy and Smart Building system where this system consists of two devices from the following problems. The first device is a health procedures checker system where using an ultrasonic sensor as an automatic switch can provide a hand sanitizer as the first step to kill viruses. The GY609 sensor is used, which can measure the temperature of visitors without touching it. The second system is the Smart Switch;the doors and lights can control using voice using google assistant to minimize the doorknob's touch or the light switch. The healthy and Smart building test results are for the intelligent, healthy procedures system, 3-7 cm is the optimal distance from the hand to the sensor, 5cm is the distance from the hand to the temperature sensor, and the motor working time is 1 second. For the Smart switches, the recommended network system is 3G, 4G, or Wi-Fi to avoid excessive delays. © 2022 IEEE.