This document summarizes a study that used machine learning and deep learning algorithms like support vector regression, polynomial regression, deep neural networks, and recurrent neural networks with long short-term memory to analyze the COVID-19 epidemic. The models were trained on real-time data from the Johns Hopkins dashboard to predict confirmed, recovered, and death cases worldwide and analyze the daily transmission behavior of the virus. The polynomial regression model yielded the lowest error in forecasting COVID-19 transmission compared to other approaches.

1. An assignment on Artificial Intelligence
CSE-3201(A2)
Computer Science & Engineering Discipline
Khulna University,Khulna,Bangladesh
Submitted To:
Jabed Al Faysal
Lecturer
Computer science & Engineering
Discipline.
Khulna University, Khulna,
Bangladesh.
Submitted by :
Md. Azizul Haque
ID:180235
3rd
year 2nd
term
Computer science &
Engineering Discipline.
Khulna University,Khulna
Date of submission: 31 January,2021

2. Summary(P4): Machine learning based approaches for detecting COVID-19
using clinical text data
The authors in this study weighed the machine learning based applications for
detecting covid-19 through its decision making by experimenting the image and
clinical data. COVID-19 has infected almost all countries in the world within a very
short amount of time. Due to lacking the medical resources, insufficient number of
testing kits, inadequate knowledge about this epidemic disease people’s life has
become afflicted all over the world to its impacts in future. It is obligate to build a
control system that will detect the coronavirus with the help of several AI tools
which is cost effective than standard test for covid-19 and assured high accuracy.
ML can be used to diagnose COVID-19 and predict the mortality risk of a infected
person which needs a lot of research effort. The researchers categorized the textual
clinical reports into four classes of viruses by dint of classical and ensemble machine
learning algorithms. Feature engineering was performed using techniques like Term
frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report
length. The researchers propounded the methodology consist of 5 steps ((1) data
collection (2) the refining of data (3) an overview of preprocessing (4) a mechanism
for feature extraction (5) an overview of ensemble machine learning algorithms.
After performing four classification the result was revealed that logistic regression
and multinomial Naïve Bayesian classifier gives excellent results by having 94%
precision, 96% recall, 95% f1 score and accuracy 96.2%. Various other machine
learning algorithms that showed better results were random forest, stochastic
gradient boosting, decision trees and boosting. In future recurrent neural network
and more feature engineering can be used for better accuracy.

3. Summary(P5): Deep learning based detection and analysis of COVID-19 on
chest X-ray images
Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that infects not only
humans, but animals are also infected because of disease in globally. COVID-19 is
an epidemic disease that threatens the human health, education, daily life of human
beings and the economy of a country at a worldwide level and turned into a
pandemic. A statistics of COVID-19 infected patients has shown that most of the
patients are mostly infected from a lung infection after coming in close contact with
another affected person. Medical imaging is also a method of analyzing and
predicting the effects of covid-19 on the human body. Chest x-ray and chest
CT(computed tomography) are a more effective medical imaging technique for
diagnosing lunge related problems. And a substantial chest x-ray is a lower cost
process than chest CT. Deep learning is the most successful technique of machine
learning, which provides useful analysis to study a large amount of chest x-ray
images that can critically impact on screening of Covid-19.The author collected
uploaded data of X-ray images of healthy and covid-19 infected patients from
different sources and applied deep learning-based CNN models (InceptionV3,
Xception, and ResNeXt) and compared their performance.. To analyze the model
performance, 6432 chest x-ray scans samples have been collected from the Kaggle
repository, out of which 5467 were used for training and 965 for validation. The
author concluded that the Xception model gives the highest accuracy (i.e., 97.97%)
for detecting Chest X-rays images as compared to other models. The researcher
focuses on possible methods of classifying covid-19 infected patients and advised to
consult medical professionals for any practical use case of this project.

4. Summary(P6): COVID-19 Epidemic Analysis using Machine Learning and
Deep Learning Algorithms
Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract
that emerged in late 2019 in China and then progressed to different countries around
the world and caused considerable morbidity and mortality. The whole world is
putting incredible efforts to fight against the spread of this deadly disease in terms
of infrastructure, finance, data sources, protective gears, life-risk treatments and
several other resources. To analyze the transmission growth at the earliest and
forecast the forthcoming possibilities of the transmission, state-of-the-art
mathematical models are adopted based on machine learning such as support vector
regression (SVR) and polynomial regression (PR) and deep learning regression
models such as a standard deep neural network (DNN) and recurrent neural networks
(RNN) using long short-term memory (LSTM) cells. Machine learning and deep
learning approaches are implemented to predict the total number of confirmed,
recovered, and death cases worldwide. The AI researchers are focusing their
expertise knowledge to develop mathematical models for analyzing this epidemic
situation using nationwide shared data. To contribute towards the well-being of
living society, this article proposes to utilize the machine learning and deep learning
models with the aim for understanding its everyday exponential behaviour along
with the prediction of future reachability of the COVID-2019 across the nations by
utilizing the real-time information from the Johns Hopkins dashboard. The author
show that polynomial regression (PR) yielded a minimum root mean square error
(RMSE) score over other approaches in forecasting the COVID-19 transmission.