Prediction of COVID-19 Victims with Chest X-Ray using COVIDNet and Deep Convolutional Neural Network

The economy of numerous nations has been influenced a great deal because of a viral sickness Corona infection. The virus has spread all over the world and many people have lost their sustenance. Many researches are carried out to find the vaccine for the virus but still there is a problem in prediction of COVID affected people. Most victims don't have any symptoms and they adopt their usual lifestyle, which in turn affects the surroundings by the spread of virus from the victim. The work focuses on foreseeing the COVID influenced casualty from the chest X beam picture. The profound Convolutional Neural Network calculation is utilized to foresee something similar. The accuracy of the algorithm clearly highlights the efficiency of prediction of the disease. © 2021 IEEE.

Traffic Congession Detection through Modified Resnet50 and Prediction of Traffic using Clustering

Today metro cities in India are facing more problems due the traffic congestion in the roads that connects different neighboring cities. Due to overwilling of population and covid - 19 pandemic that increased the usage private vehicles by the mankind. Many of the existing methods use the sensor to detect the number of vehicles in that location and implied on to the google maps. The model has been proposed to overcome the issue by considering the existing video cameras been installed in different location in the signals and crowed places. The proposed model incorporated in the edge computing through Nvidia Jetson nano for the object detection using Common Objects in Context (COCO) to detect the boundary boxes for the ten categories of the object. The detection Score is obtained by considering probability of the detection boxes identified in the particular location with respect to time. The accuracy level of each object in the frame is detected and filtered based on the threshold value such that the false positive and true negative can be avoided in the Contributory matrix. Based on the results obtained from the model the location wise table that classify the images with respect low medium and High. The proposed model proves that accuracy of predicting the traffic based on three categories as Low, Medium and High are estimated correctly. The results are apprised based on the accuracy of the particular object identified in the image or frame with respect to number of images tested for the accuracy. The mean square errors are assessed based on the number of objects identified on each category in a particular image. © 2021 IEEE.

COVID-19: AI-enabled social distancing detector using CNN

The vulnerable coronavirus (COVID-19) changed our lifestyle drastically and is considered as the highest threat to humanity. However, it proves there is an immense possibility for improvement in lifestyle of people. Social control is one of the main aspects to cease the spread of COVID-19. Manifold researches have been published in recent times for effective monitoring and management of social distancing in public/private places. Motivated by this notion, integrating embedded hardware kit like Jetson Nano with deep learning algorithms for automating the entire process is indispensable. Further, to improve the performance of deep learning-based object identifiers, YOLO (You Only Look Once) object detection algorithm utilizes a single-step locator methodology by computing pairwise distance to detect the objects. Using bounding boxes, YOLO algorithm identifies the presence of multiple objects, and thereby multiple bounding boxes are formed and update the shade of the jumping box to red. An alarm message is sent to the respective authority via WhatsApp. A real-time case study is detailed in this chapter for social control, thereby preventing the spread of COVID-19 with the aid of YOLO algorithm. In addition, specifications of Jetson Nano board and the environment setup are elaborated. It is observed from the experimental framework that effective monitoring and maintaining social control is possible to break the chain of spread of this contagious disease through YOLO object detector. © Springer Nature Switzerland AG 2021.

Classification and Forecasting Model for Covid -19 Disease Severities based on Medical Diagnosis using Weighted Average Dynamic Time Warping Technique

Due to Covid-19 pandemic, mankind was affected with respect to mental and physical stress. The causes of the disease have to be analysed based on the correlation factors such as Medication, Age, Gender, physical fitness and habits. In this paper we have proposed a classification model based on weighted average dynamic time warping approach to detect the disease severity as High, Medium, and Low by considering the multi-variant dependent variables that affect the prediction of Covid-19 positive cases. We also proposed the forecasting model based on the time series exponential moving average to identify the growth of disease with respect to Age, Gender and Medical history of the covid-19 positive patient. The results are obtained by defining the correlation function to measure the disease severity in the range of High, Medium and Low. The time series analysis is done with respect to mean average disease severity and also number of positive cases. The forecasting is performed based on the age, gender and existing disorders in health. The results are analysed with other time series classification models such as weighted time wrapping to make the model fits maximum to the available input.