Machine Learning and Image Processing Techniques for Covid-19 Detection: A Review

Covid-19 is a respiratory disease which spreads from person to person. It affects lungs, attacks blood vessels, and causes heart issues so a rapid diagnosis of covid-19 is required. Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) is a method used for covid-19 detection, but it is time consuming and labor intensive also it puts the person collecting the sample in danger, hence CT scan and Xray images are used by doctors. Manual classification of covid-19 is possible but AI speeds up the process. Image processing, machine learning and deep learning are few techniques used in AI. To diagnose covid-19 an AI model is required and to train that model a dataset is required, dataset consists of the information from which model is used to train. This paper consists of the review of different image processing and machine learning papers proposed by different researchers. The goal of this paper is to provide information for future researchers to work with. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Forecasting of COVID-19 Cases in INDIA Using ARIMA and AR Time-Series Algorithm

The COVID-19 pandemic has been spreading and affecting worldwide. On 30th January 2020 India reported its first coronavirus confirmed case. The main aim of the proposed work is to devise an algorithm for prediction of Covid-19 cases in India. In this paper, we propose to use time-series algorithms, Autoregressive Integrated Moving Average (ARIMA) and Autoregressive (AR). We have simulated the designed algorithm with the dataset of COVID-19 till 20th February, 2021 for the wave1 and from 01st March, 2021 till 25th September, 2021 we collected data for wave2 and generated 6-days forecasts of confirmed, recovered and death cases. During the 1st wave we observed that there might be another wave 2, after analyzing the wave1 dataset, of coronavirus as result shows that Covid-19 confirmed cases are rising rapidly. Proposed research observations show that the death rate is decreasing, and recovery rate is increasing, one of the possible reasons is herd immunity and vaccination. We are comparing actual cases with forecasting coronavirus cases. ARIMA based models are showing promising results over AR based models. The most difficult part doing this work is to identify parameters due to sudden increase-decrease trend in coronavirus cases. The proposed work reports quality scoring metrics of forecasting for both the models. This will help future researchers to find the best outcome among Auto Regressive Integrated Moving Average (ARIMA) and Auto Regressive (AR) based models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.