Emerging Technology against COVID-19 Publications

Emerging Technology
against COVID-19
Publications
Professor Aboul Ella Hassanien
10/9/2021

COVID-19 based Book
Publications

Big Data Analytics and Artificial Intelligence Against COVID-19:
Innovation Vision and Approach by Hassanien, Aboul-Ella, Dey,
Nilanjan, Elghamrawy, Sally M.
https://www.springer.com/gp/book/9783030552572
This book includes research articles and expository papers on
artificial intelligence applications and big data analytics to battle the
Pandemic. In the context of COVID-19, this book focuses on how big
data analytic and artificial intelligence help fight COVID-19. The book
is divided into four parts. The first part discusses the forecasting and
visualization of the COVID-19 data. The second part describes
applications of artificial intelligence in the COVID-19 diagnosis of
chest X-Ray imaging. The third part discusses artificial intelligence
insights to stop the spread of COVID-19, while the last part presents
deep learning and big data analytics that help fight the COVID-19.
Aboul Ella Hassanien, and Ashraf Darwish, Digital
Transformation and Emerging Technologies for Fighting
COVID-19 Pandemic: Innovative Approaches, Studies in
Systems, Decision, and Control, Springer 2020.
This book is one of the first books that deal with the COVID-19
Pandemic. COVID-19 Pandemic has affected countries worldwide
and has significantly impacted daily life and healthcare facilities and
treatment systems. The book covers the main recent emerging
technologies that are related to the COVID-19 crisis. The
technologies that are included in this book play a significant role in
tackling COVID-19 in the future. This book's scope is to cover all
advanced emerging technologies and artificial intelligence
techniques to fight against the COVID-19 Pandemic.

Muhammad Alshurideh, Aboul-Ella Hassanien, Ra'ed Masa'deh,
The effect of Coronavirus Disease (COVID-19) on Business
Intelligent Systems, Studies in Systems, Decision and Control
Springer series, 2020
This book includes recent research on how business worldwide is
affected by the time of the COVID-19 Pandemic. Recent
technological developments have had a tremendous impact on how
we manage disasters. These developments have changed how
countries and governments collect information. The COVID-19
Pandemic has forced online service companies to maintain and build
relationships with consumers when their world turns. Businesses are
now facing tension between generating sales during a period of
severe economic hardship and respect for threats to life and
livelihoods that have changed consumer preferences.
Aboul Ella Hassanien, Ashraf Darwish, Benjamin A. Gyampoh,
Alaa tharwat, Ahmed M. Anter, The Global Environmental
Effects during and beyond COVID-19: Intelligent Computing
Solutions, Studies in Systems, Decision and Control Springer
series, 2021 https://www.springer.com/gp/book/9783030729325
This book aims, through 11 chapters discussing the problems and
challenges and some future research points from the recent
technologies perspective, such as artificial intelligence and the
Internet of things (IoT) that can help the environment and healthcare
sectors reduce COVID-19.
Sally Elghamrawy, Ivan Zilank, and Aboul Ella Hassanien,
Advances in Data Science and Intelligent Data Communication
Technologies for COVID-19 Pandemic" Studies in Systems,
Decision, and Control, 2021
This book presents the emerging developments in intelligent
computing, machine learning, and data mining. It also provides

insights on communications, network technologies, and the Internet
of things. It offers various insights on the role of the Internet of things
against COVID-19 and its potential applications. It provides the latest
cloud computing improvements and advanced computing and
addresses data security and privacy to secure COVID-19 data.
Ahmed Taher, and Aboul Ella Hassanien, Modeling, Control and
Drug Development for COVID-19 Outbreak Prevention, Studies
in Systems, Decision, and Control, 2021
This book is a well-structured book that consists of 31 full chapters.
The book chapters' deal with the recent research problems in
modeling, control and drug development, and it presents various
COVID-19 outbreak prevention modeling techniques. The book also
concentrates on computational simulations that may help speed up
the development of drugs to counter the novel Coronavirus
responsible for COVID-19.

Dalia Ezzat,
Aboul Ella
Hassanien,
Hassan
Aboul Ella
"An
optimized
deep
learning
architecture for the diagnosis of
COVID-19 disease based on
gravitational search optimization,"
Applied Soft Computing, Volume
98, January 2021, 106742
Impact Factor =
5.472
Scopus & Web of
Science
In this paper, a novel approach called GSA-DenseNet121-COVID-19
based on a hybrid convolutional neural network (CNN) architecture
is proposed using an optimization algorithm. The CNN architecture
used is called DenseNet121, and the optimization algorithm used is
called the gravitational search algorithm (GSA). The GSA is used to
determine the best values for the hyperparameters of the
DenseNet121 architecture. To help this architecture to achieve a
high level of accuracy in diagnosing COVID-19 through chest x-ray
images. The obtained results showed that the proposed approach
could classify 98.38% of the test set correctly. To test the efficacy
of the GSA in setting the optimum values for the hyperparameters
of DenseNet121. The GSA was compared to another approach
called SSD-DenseNet121, which depends on the DenseNet121 and
the optimization algorithm called social ski driver (SSD). The
comparison results demonstrated the efficacy of the proposed
GSA-DenseNet121-COVID-19. It was able to diagnose COVID-19
better than SSD-DenseNet121 as the second was able to diagnose
only 94% of the test set. The proposed approach was also
compared to another method based on a CNN architecture called
Inception-v3 and a manual search to quantify hyperparameter
values. The comparison results showed that the GSA-DenseNet121-
COVID-19 beat the comparison method, as the second was able to
classify only 95% of the test set samples.
https://www.sciencedirect.com/science/article/pii/S156849462030
6803
Highlights
1. This paper aims to suggest an approach that can be used to diagnose
the COVID-19.
2. The proposed approach is called GSA-DenseNet121-COVID-19 and
is a hybrid between a CNN architecture called DenseNet121 and an
optimization algorithm called GSA.
3. The GSA was used to set the optimal values for the hyperparameters
of the DenseNet121 architecture that helped the proposed approach
achieve a 98% accuracy on the test set.
4. To prove GSA's effectiveness, it was compared with the SSD
algorithm, the results of the comparison showed the effectiveness of
GSA.
5. The GSA-DenseNet121-COVID-19 performance was compared to the
performance of a CNN architecture called Inception-v3 based on
manual search method, and the comparison results showed the
effectiveness of the proposed approach.

Zohair
Malki,
El-
Sayed
Atlam,
Aboul-
Ella
Hassanie
n, Guesh
Dagnew, Mostafa A.Elhosseini and
Ibrahim Gad "Association between
weather data and COVID-19
pandemic predicting mortality rate:
Machine learning approaches"
Chaos, Solitons & Fractals, Vol
138, September 2020, 110137,
Impact Factor =
3.764
Q1
Scopus and WoS
Association between weather data and COVID-19 pandemic
predicting mortality rate: Machine learning approaches
Nowadays, a significant number of infectious diseases such as human coronavirus
disease (COVID-19) are threatening the world by spreading at an alarming rate.
Some of the literature pointed out that the Pandemic is exhibiting seasonal
patterns in its spread, incidence, and nature of the distribution. In connection to
the spread and distribution of the infection, scientific analysis that answers
whether the next summer can save people from COVID-19 is required. Many
researchers have been exclusively asked whether high temperature during
summer can slow down the spread of the COVID-19 as it has with other seasonal
flues. Since many unanswered questions exist and many mysteries about COVID-
19 are still unknown, in-depth study and analysis of associated weather features
are required.
Moreover, understanding the nature of COVID-19 and forecasting the spread of
COVID-19 request more investigation of the real effect of weather variables on
the transmission of the COVID-19 among people. In this work, various regressor
machine learning models are proposed to extract the relationship between
different factors and the spreading rate of COVID-19. The machine learning
algorithms employed in this work estimate the impact of weather variables such
as temperature and humidity on the transmission of COVID-19 by extracting the
relationship between the number of confirmed cases and the weather variables on
certain regions. We have collected the required datasets related to weather and
census features and necessary prepossessing to validate the proposed method. The
experimental results show that the weather variables are more relevant in
predicting the mortality rate than the other census variables such as population,
age, and urbanization. Thus, we can conclude that temperature and humidity are
essential features for predicting the COVID-19 mortality rate. Moreover, it is
indicated that the higher the value of weather, the lower number of infection cases
5336.
Highlights:
 Find the best predictive model for daily confirmed cases in countries with the
highest COVID-19 instances globally.
 Predict the number of confirmed cases to have more healthcare systems
readiness and make forecasts using advanced machine learning algorithms.
 Includes more weather and climatic condition features that can influence the
spread of the COVID-19 virus.

Arpaci, Ibrahim; Alshehabi,
Shadi; Al-
mran,Most
afa; Khasa
wneh,
Mahmoud;
Mahariq,
Ibrahim; A
bdeljawad,
Thabet; Hassanien, Aboul Ella.,
Analysis of Twitter data using
evolutionary clustering during the
COVID-19 pandemic" Computers,
Materials & Continua, vol.65, no.1,
pp.193-204, 2020,
Impact Factor =
4.89
Scopus & Web of
Science
Analysis of Twitter data using evolutionary
clustering during the COVID-19 Pandemic
People started posting textual tweets on Twitter as soon as the novel coronavirus (COVID-
19) emerged. Analyzing these tweets can assist institutions in better decision-making and
prioritizing their tasks. Therefore, this study aimed to analyze 43 million tweets collected
between March 22 and March 30, 2020 and describe the trend of public attention on topics
related to the COVID-19 epidemic using evolutionary clustering analysis. The results
indicated that unigram terms were trended more frequently than bigram and trigram terms.
A large number of tweets about the COVID-19 were disseminated and received
widespread public attention during the epidemic. The high-frequency words such as
"death", "test", "spread", and "lockdown" suggest that people fear of being infected, and
those who got infection are afraid of death. The results also showed that people agreed to
stay at home due to the fear of the spread, and they were calling for social distancing since
they became aware of the COVID-19. It can be suggested that social media posts may
affect human psychology and behavior. These results may help governments and health
organizations better understand the psychology of the public, thereby better
communicating with them to prevent and manage panic.
https://www.techscience.com/cmc/v65n1/39561
Evolution of clusters for trigram terms
Highlights:
 the effective use of social media can shorten admission times by
establishing factual communication channels
 a large number of tweets about the COVID-19 were disseminated
and received widespread public attention during the epidemic
Sum of term frequencies in the clusters

Gitanjali R
Shinde,
Asmita B
Kalamkar,
Parikshit N
Mahalle,
Nilanjan Dey,
Jyotismita Chaki, Aboul Ella
Hassanien
Shinde, G.R., Kalamkar, A.B.,
Mahalle, P.N. et al. Forecasting
Models for Coronavirus Disease
(COVID-19): A Survey of the State-
of-the-Art. SN COMPUT.
SCI. 1, 197, (2020).
Forecasting Models for Coronavirus Disease (COVID-
19): A Survey of the State-of-the-Art
COVID-19 is a pandemic that has affected over 170 countries around the world. The
number of infected and deceased patients has been increasing at an alarming rate in almost
all the affected nations. Forecasting techniques can be taught, thereby assisting in
designing better strategies and in making productive decisions. These techniques assess
past situations, thereby enabling better predictions about the case to occur in the future.
These predictions might help to prepare against possible threats and consequences.
Forecasting techniques play a significant role in yielding accurate predictions. This study
categorizes forecasting techniques into stochastic theory mathematical models and data
science/machine learning techniques. Data collected from various platforms also play a
vital role in forecasting. In this study, two categories of datasets have been discussed, i.e.,
big data accessed from the World Health Organization/National databases and data from
social media communication. Forecasting a pandemic can be done based on various
parameters such as the impact of environmental factors, incubation period, quarantine, age,
gender, and many more. These techniques and parameters used for forecasting are
extensively studied in this work. However, forecasting techniques come with their own set
of challenges (technical and generic). This study discusses these challenges and provides
recommendations for the current fighting the global COVID-19 Pandemic.
https://link.springer.com/article/10.1007/s42979-020-00209-9

Ashraf Ewis,
Guesh
Dagnew,
Ahmad
Reda, Ghada
Elmarhomy,
Mostafa A
Elhosseini,
Aboul Ella Hassanien, Ibrahim Gad,
"ARIMA Models for Predicting the
End of COVID-19 Pandemic and the
Risk of a Second Rebound" Neural
computing and Application, Neural
Comput & Applic (2020)
Impact Factor =
4.774
Scopus & Web of Science
ARIMA Models for Predicting the End of COVID-19 Pandemic
and the Risk of a Second Rebound
Globally, many research works are going on to study the infectious nature of COVID-19
and every day. We learn something new by flooding the huge data accumulated hourly
rather than daily, which instantly opens hot research topics for artificial intelligence
researchers. However, the public's concern is to find answers to two questions; 1) when
this COVID-19 pandemic will be over? and 2) After coming to its end, will COVID-19
return again in what is known as a second rebound of the Pandemic?. This research
developed a predictive model that can estimate the expected period for the virus to stop
and the risk of the second rebound of the COVID-19 Pandemic. Therefore, this study
considered the SARIMA model to predict the virus's spread in several selected countries
and is used for pandemic life cycle and end date predictions. The study can predict the
same for other countries as the virus's nature is the same everywhere. This study's
advantages are that it helps the governments make decisions and plan for the future,
reduces anxiety, and prepares people's mentality for the next phases of the Pandemic. The
most striking finding to emerge from this experimental and simulation study is that the
proposed algorithm shows that the expected COVID-19 infections for the top countries
with the highest number of confirmed case will slow down in October 2020. Moreover, our
study forecasts that there may be a second rebound of the Pandemic in a year if the current
precautions taken are eased completely. We have to consider the uncertain nature of the
current COVID-19 Pandemic, and the growing inter-connected and complex world; what
are ultimately required are the flexibility, robustness, and resilience to cope with the
unexpected future events and scenarios.
 Finding the best prediction models for daily confirmed cases in countries
with the highest number of COVID-19 cases to have more healthcare
systems ready to forecast the confirmed cases.
 Analysis of the risk of the second rebound of COVID-19 Pandemic
 Estimating the pandemic life cycle and selecting the optimal parameter
of the model using the grid search method. The proposed method
outcomes matched the updated daily data.
 Significant results are achieved when compared with the state-of-the-art
models. Hence, the proposed SARIMA model can be extended and used
to predict other countries as it gives an acceptable performance when
observed.
 The mathematical model presents the statistical estimation of the
Pandemic's slowdown period, which is extracted based on a normal
distribution.

Sujath, R.,
Chatterjee, J.M.
& Hassanien,
A.E. A machine
learning
forecasting model
for COVID-19 Pandemic in
India. Stoch Environ Res Risk
Assess 34, 959–972 (2020).
https://doi.org/10.1007/s00477-
020-01827-8
Impact
Factor=2.351
Scopus & Web of
Science
Machine-learning forecasting model for COVID-19
Pandemic in India
Coronavirus disease (COVID-19) is an inflammation disease from a
new virus. The disease causes respiratory ailments (like influenza)
with manifestations, such as cold, cough, and fever, and in
progressively serious cases, breathing problems. COVID-2019 has
been perceived as a worldwide pandemic, and a few examinations
are being led utilizing different numerical models to anticipate the
likely advancement of this pestilence. These numerical models are
dependent on various factors, and investigations are dependent upon
potential inclination. Here, we presented a model that could be
useful to predict the spread of COVID-2019. We have performed
linear regression. Multilayer perceptron, and Vector autoregression
method for desire on the COVID-19 Kaggle data to anticipate the
epidemiological example of the ailment and pace of COVID-2019
cases in India and anticipated the potential patterns of COVID-19
effects in India dependent on data gathered from Kaggle. The
common data about confirmed, death, and recovered cases across
India over time help anticipate and estimate the not-so-distant
future. For extra assessment or future perspective, case definition
and data combination must be kept up persistently.

Koyel
Chakrabortya,
Surbhi
Bhatia,
Siddhartha
Bhattacharyy
a, Jan Platos,
Rajib Bag,
Aboul Ella
Hassaniene "Sentiment Analysis of
COVID-19 tweets by Deep Learning
Classifiers - a study to show how
popularity is affecting accuracy in
social media
Applied Soft Computing Volume
97, Part A, December 2020, 106754
Impact Factor =
5.472
Scopus & Web of
Science
Sentiment Analysis of COVID-19 tweets by Deep
Learning Classifiers - a study to show how
popularity is affecting accuracy in social media
COVID-19, known initially as Coronavirus, was declared as a
pandemic by the World Health Organization on March 11, 2020.
The unprecedented pressures have arrived in each country to make
compelling requisites for controlling the population by assessing the
cases and properly utilizing available resources. The rapid number
of exponential cases globally has become the apprehension of panic,
fear, and anxiety. Mental and physical health is directly proportional
to this pandemic disease. The current situation has reported more
than two million people tested positive. Therefore, it's necessary to
implement different measures to prevent the country by
demystifying the pertinent facts and information. This paper aims to
discover that tweets containing all Covid-19 and WHO handles
have been unsuccessful in guiding people around this Pandemic,
outbreaking appositely. This study analyses around twenty-three
thousand retweeted tweets within the period from1st Jan 2019 to
March 23 2020. Observation says that the maximum of the tweets
portrays neutral or negative sentiments. The research demonstrates
that no useful words can be found in WordCloud or computations
using word frequency in tweets. The claims have been validated
through a proposed model using deep learning classifiers with an
admissible 73% accuracy.
https://www.sciencedirect.com/science/article/pii/S156849462030692X

Rana Saeed
Al-Maroof,
Said A.
Salloum,
Aboul Ella
Hassanien,
and Khaled
Shaalan, Fear from COVID-19 and
Technology Adoption: The Impact of
Google Meet during Coronavirus
Pandemic, Interactive Learning
Environments, 2020.
Impact Factor =
1.938
Scopus & Web of
Science
Fear from COVID-19 and Technology Adoption:
The Impact of Google Meet during Coronavirus
Pandemic,
This study explores the effect of fear emotion on students' and
teachers' technology adoption during the COVID-19 Pandemic. The
study has used Google Meet© as an educational, social platform in
private higher education institutes. The data obtained from the study
were analyzed by using the partial least squares-structural equation
modeling (PLS-SEM) and machine learning algorithms. The main
hypotheses of this study are related to the effect of COVID-19 on the
adoption of Google Meet as COVID-19 rises various types of fear.
During the Coronavirus pandemic, fear of family lockdown, fear of
education failure, and fear of losing social relationships are the most
common types of threats that may face students and teachers. These
types of fears are connected with two important factors within TAM
theory, which are: perceived Ease of use (PEOU) and perceived
usefulness (PU), and with another external factor of TAM, which is
the subjective norm (SN). The results revealed that both techniques
have successfully provided support to all the research model's
hypothesized relationships. More interesting, the J48 classifier has
performed better than the other classifiers in predicting the
dependent variable in most cases. Our study indicated that using
Google Meet technology for educational purposes during the
Coronavirus pandemic provides a promising outcome for teaching
and learning; however, the emotion of fear of losing friends, stressful
family situation, and fear of future school results may hinder this
effect; hence, students should be evaluated properly in the time of
the Pandemic to cope with this situation emotionally.
https://www.tandfonline.com/doi/full/10.1080/10494820.2020.1830
121

E. El-
shafeiy, A.
E.
Hassanien,
K. M.
Sallam
and A. A.
Abohany,
"Approach for training a quantum
neural network to predict severity of
covid-19 in patients," Computers,
Materials & Continua, vol. 66, no.2,
pp. 1745–1755, 2021.
Impact Factor =
4.89
Scopus & Web of
Science
Approach for training a quantum neural network to
predict severity of covid-19 in patients
Currently, COVID-19 is spreading all over the world and profoundly
impacting people's lives and economic activities. In this paper, a
novel approach called the COVID-19 Quantum Neural Network
(CQNN) for predicting the severity of COVID-19 in patients is
proposed. It consists of two phases: In the first, the most distinct
subset of features in a dataset is identified using a Quick Reduct
Feature Selection (QRFS) method to improve its classification
performance; and, in the second, machine learning is used to train
the quantum neural network to classify the risk. It is found that
patients' serial blood counts (their numbers of lymphocytes from
days 1 to 15 after hospital admission) are associated with relapse
rates and evaluations of COVID-19 infections. Accordingly, the
severity of COVID-19 is classified into two categories, serious and
non-serious. The experimental results indicate that the proposed
CQNN's prediction approach outperforms those of other
classification algorithms, and its high accuracy confirms its
effectiveness. https://www.techscience.com/cmc/v66n2/40661

Ismail Elansary, Walid Hamdy,
Ashraf Darwish and Aboul Ella
Hassanien, "Bat-inspired
Optimizer for Prediction of Anti-
Viral Cure Drug of SARS-CoV-2
based on Recurrent Neural
Network, Journal of System and
Management Sciences Vol. 10
(2020) No. 3, pp. 20-34
Scopus
Bat-inspired Optimizer for Prediction of Anti-Viral Cure
Drug of SARS-CoV-2 based on Recurrent Neural
Network,
COVID-19 is a large family of viruses that causes diseases ranging from
the common cold to severe SARS-CoV infections. There are currently
several attempts to create an anti-viral drug to combat the virus. The
antiviral medicines could be promising treatment choices for COVID-19.
Therefore, a fast strategy for drug application that can be utilized to the
patient immediately is necessary. In this context, deep learning-based
architectures can be considered for predicting drug-target interactions
accurately. This is due to much detailed knowledge, such as hydrophobic
interactions, ionic interactions, and hydrogen bonding. This paper uses
the Recurrent Neural Network (RNN) to build a drug-target interaction
prediction model to predict drug-target interactions. Bat Algorithm (BA)
is used in this paper to optimize RNN (RNN-BA) model parameters and
then use the Coronavirus as a target. The drug with the best binding
affinity will be a potential cure for the virus. The proposed model consists
of four phases; a data preparation phase, hyper-parameters optimizing
phase, learning phase, and fine-tuning for specific ligand subsets. This
paper's used dataset to train and evaluate the proposed model is
selected from a total of 677,044 SMILES. The experimental results of the
proposed model showed a high level of performance compared to the
related approaches.
http://www.aasmr.org/jsms/Vol10/Vol.10.3.2.pdf

Sally M.
Elghamra
wy , Aboul
Ella
Hassniena
nd Vaclav
Snasel
An
Optimized
Deep Learning-Inspired Model for
Diagnosis and Prediction of
COVID-19" CMC-Computers,
Materials & Continua
Impact Factor =
4.89
Scopus & Web of
Science
An Optimized Deep Learning-Inspired Model for
Diagnosis and Prediction of COVID-19
Abstract: This study aimed to develop a COVID-19 diagnosis and
prediction (AIMDP) model to identify patients with COVID-19 and
distinguish it from other viral pneumonia signs detected in chest
computed tomography (CT) scans. The proposed system uses
convolutional neural networks (CNNs) as a deep learning technology to
process hundreds of CT images and speeds up COVID-19 case prediction
to facilitate its containment. We employed the whale optimization
algorithm (WOA) to select the most relevant patient signs. A set of
experiments validated AIMDP performance. It demonstrated the
superiority of AIMDP in terms of the area under the curve - receiver
operating characteristic (AUC - ROC) curve, positive predictive value
(PPV), negative predictive rate (NPR), and negative predictive value
(NPV). AIMDP was applied to a dataset of hundreds of real data and CT
images, and it was found to achieve 96% AUC for diagnosing COVID-19
and 98% for overall accuracy. The results showed the promising
performance of AIMDP for diagnosing COVID-19 compared to other
recent diagnosing and predicting models.
Pre- Processing Phase
Noise/Missing data
handling
Data
Sorter
raw +/-
COVID
Images
Dataset
Segmentation Phase based on
CNNs
Inputs
Max
Pool
Convolutio
n
Pooling
Dense
Output
Initial
generation
(
feature/Patie
nt list creator
Calculate
Fitness Fun
)
Evaluate
No
Replace
)
Remain Yes
Update
solutions
Arrange
) )
Recalculate
Parameters
Calculate
Minimum
DRT(X,Y)
Proposed
(BNAM)
technique
Shrinking
Encircling
Mechanism
Spiral
Mechanism
Check best
)
Best Solution
Selection
Calculate
) )
New
Populati
on
reposito
ry
Iter
>=
Limit
Update Best
solution
Yes
No
Termination
module
Updated
features
with the
highest Fit
Feature
Selection
Phase based
on GWOA
Feature Selection
Phase
Dataset with
Relevant
Features
Populatio
n
initializati
on
module
Fitness
Function
module
Encircle Prey
module
Bubble-Net
Attacking Method
Applier
Classification
Phase
Classifier
Selector
Model
Trainer
Model
Validator
Diagnosis
Recommendatio
n
Phase
Recommende
d Diagnosis
Treatment
Decision
Evaluation
Phase

O. M.
Elzeki,
Mahmo
ud. Y.
Shams,
Shahend
a
Sarhan,
Moham
ed Abd
Elfattah, Aboul
Ella Hassanien, COVID-19: A New
deep learning computer-aided
model for classification, PeerJ
Computer Science, (Accepted)
Impact factor =
3.091
Scopus
COVID-19: A New deep learning computer-aided
model for classification
This paper proposes a model for analyzing and evaluating grayscale
Chest X-Ray images called Chest X-Ray COVID Network (CXRVN)
based on three different COVID-19 X-Ray datasets. The proposed
CXRVN model is a lightweight architecture that depends on a single
fully connected layer representing the essential features and thus
reducing the total memory usage and processing time verse pre-
trained models and others. The CXRVN adopts two optimizers, mini-
batch gradient descent, and Adam optimizer, which are applied, and
the model has almost the same performance. CXRVN accepts CXR
images in grayscale, which perfectly represents CXR and consumes
less memory storage and processing time. Hence, CXRVN can analyze
the CXR image with high accuracy in a few milliseconds. The learning
process's consequences focus on decision-making using a scoring
function called SoftMax, leading to a high rate of true-positive
classification. The CXRVN model is trained using two different
datasets compared to the pre-trained models: GoogleNet, ResNet,
and AlexNet using the fine-tuning and transfer learning technologies
for the evaluation process. The evaluation results based on
sensitivity, precision, recall, accuracy, and F1 score demonstrated
that, after GAN augmentation, the accuracy reached 96.7% in
experiment 2 (dataset-2) for two classes and 93.07% in experiment-3
(dataset-3) for three classes. While the average accuracy of the
proposed CXRVN model is 94.5%.

Mohame
d A. El-
dosuk,
Mona
Soliman,
and
Aboul
Ella
Hassanie
n, Deep
neural network with
Cockroach hyperparameter
optimization for COVID-19
Viral Gene Sequences
Classi_cation between
COVID-19 and Influenza
Viruses. International
Journal of Imaging Systems
and Technology (accepted).
Impact factor
=1.925
Scopus & Web of
Science
Deep neural network with Cockroach
hyperparameter optimization for COVID-19 Viral
Gene Sequences Classi_cation between COVID-19
and Influenza Viruses
It is also evident that distantly related viral proteins could interact
with a conserved cellular protein target and thus increase their
pathogenic potential. As with many other viruses, receptor
interactions are an important determinant of species specificity,
virulence, and pathogenesis among coronaviruses. The pathogenesis
of the COVID-19 depends on the virus's ability to attach to and enter
into a suitable human host cell. This paper presents a deep learning
approach based on viral genome virus sequencing to signi_cantly
detect and di_erentiates between COVID-19 and influenza types (A,
B, and C). A cockroach optimization algorithm inspires the deep
network architecture to optimize the deep neural network
hyperparasite. COVID-19 sequences are obtained from repository
2019 Novel Coronavirus Resource, and influenza A, B, and C sub-
datasets are obtained from other repositories. Five hundred ninety-
four unique sequences are used in the training and testing process
with 99% overall accuracy for the classification model.
https://onlinelibrary.wiley.com/doi/10.1002/ima.22562

Mohamed
Torky,
Essam
Goda,
Vaclav
Snasel,
Aboul Ella
Hassanein
Blockchain
Mobil Application System
for Detecting and Tracking
the Infected Cases of
COVID-19 Pandemic in
Egypt, Scientific Report,
Nature. 2021
Impact factor =
3.998
Scopus & Web of
Science
Blockchain Mobil Application System for Detecting
and Tracking the Infected Cases of COVID-19
Pandemic in Egypt,
The fight against the COVID-19 Pandemic still witnesses a lot of
struggles and challenges. The greatest challenge that most
governments are currently suffering from is the lack of a precise,
accurate, and automated mechanism for detecting and tracking the
new infected COVID-19 coronavirus cases. In response to this
challenge, this study proposes the first blockchain-based COVID-19
Contact Tracing System (CCTS) to verify, track, and detect the newly
infected cases of COVID-19 Coronavirus. The proposed system
consists of four coherent components: The infection verifier
subsystem, Mass Surveillance System, P2P mobile application, and a
blockchain platform for managing all transactions between the three
subsystem models. The proposed system has been simulated and
tested against a created dataset consisting of 300 confirmed cases
and 2539 contact persons. The evaluation results proved that the
proposed blockchain-based system achieved 75.79% accuracy in
recognizing the contact persons for COVID-19 patients. The
simulation results also clarified the proposed system's success in self-
estimating infection probability and sending/receiving infection alerts
in P2P communications within crowds of people. The new system is
forecasted to support the governments, health authorities, and
citizens in Egypt to take critical decisions regarding infection
detection, infection prediction, infection tracking, and infection
avoidance regarding COVID-19 outbreak or other coming pandemics
.
The proposed COVID-19 contact tracing system model

Doaa Mohey El-Din, Aboul
Ella Hassanein, Ehab E.
Hassanien and Walaa M.E.
Hussei, E-Quarantine: A
Smart Health System for
Monitoring Coronavirus
Patients for Remote
Quarantine. Journal of
System and Management
Sciences, Vol. 10 (2020)
No. 4, pp. 102-124
Scopus
E-Quarantine: A Smart Health System for
Monitoring Coronavirus Patients for Remote
Quarantine.
Coronavirus has become a global pandemic officially due to the
speed of spreading off in various countries. An increasing number of
infected with this disease causes the inability to fully care in hospitals
and afflict many doctors and nurses inside the hospitals. This paper
proposes a smart health system that monitors the patients holding
the Coronavirus remotely. It observes the people with this disease
based on putting many sensors to record their patients' many
features every second. These parameters include measuring the
patient's temperature, respiratory rate, pulse rate, blood pressure,
and time. The proposed system saves lives and improves decision-
making in difficult cases. It proposes using artificial intelligence and
Internet-of-things to quarantine and develop decisions in various
situations remotely. It provides monitoring patients remotely and
guarantees giving patients medicines and getting complete health
care without anyone getting sick with this disease. It targets two
people's slides, the most serious medical conditions and infection,
and the lowest serious medical conditions in their houses. They
observe hospitals for the most serious medical cases that cause
infection in thousands of healthcare members, so it is necessary to
use it. Other less serious patients slide, this system enables
physicians to monitor patients and get the healthcare from patient's
houses to save places for the critical cases in hospitals.
http://www.aasmr.org/jsms/Vol10/Vol.10.4.7.pdf

O. M.
Elzeki,
Mahmou
d. Y.
Shams,
Mohame
d Abd
Elfattah,
Hanaa
Salem,
Aboul Ella Hassanien, A
novel Perceptual Two
Layer Image Fusion using
Deep Learning for
Imbalanced COVID-19
Dataset, PeerJ Computer
Science, 2021
Impact factor =
3.091
Scopus
A novel Perceptual Two Layer Image Fusion using
Deep Learning for Imbalanced COVID-19 Dataset,
This paper proposes a novel perceptual two-layer image fusion using DL to
obtain more informative CXR images for the COVID-19 dataset. The pre-
trained proposed framework uses a dataset to assess the proposed
algorithm performance; the dataset used for this work includes 87 CXR
images acquired from 25 cases, all of which were confirmed with COVID-19.
The dataset preprocessing is needed to facilitate the role of (CNN). Thus,
hybrid decomposition and fusion of Nonsubsampled Contourlet Transform
(NSCT) and CNN-VGG19 as feature extractors were used. Results: Our
experimental results show that imbalanced COVID-19 datasets can be
reliably generated by the algorithm established here. Compared to the
COVID-19 dataset used, the fused images have more features and
characteristics. In evaluation performance measures, six metrics are applied,
such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to evaluate various medical
image fusions (MIF). In the QMI, PSNR, SSIM, the pre-trained proposed
algorithm NSCT+CNN-VGG19 achieves the greatest, and the features
characteristics found in the fused image are the largest. We can deduce that
the proposed fusion algorithm is efficient enough to generate CXR COVID-19
images that are more useful for the examiner to explore patient status.
Conclusions: A novel image fusion algorithm using DL for an imbalanced
COVID-19 dataset is the crucial contribution of this work. Extensive results
of the experiment display that the proposed algorithm NSCT+CNN-VGG19
outperforms competitive image fusion algorithms.
https://peerj.com/articles/cs-364/

Afify, H. M., Darwish, A.,
Mohammed, K. K., &
Hassanien, Aboul ella .
E. (2020). An automated CAD
system of CT chest images for
COVID-19 based on genetic
algorithm and K-nearest
neighbor classifier. Ingenierie
des Systemes
d'Information, 25(5).
Scopus
An automated CAD system of CT chest images for
COVID-19 based on genetic algorithm and K-
nearest neighbor classifier
The detection of COVID-19 from computed tomography (CT) scans
suffered from inaccuracies due to its difficulty in data acquisition and
radiologist errors. Therefore, a fully automated computer-aided
detection (CAD) system is proposed to detect Coronavirus versus
non-coronavirus images. In this paper, a total of 200 images for
Coronavirus and non-coronavirus are employed based on 90% for
training images and 10% for testing images. The proposed system
comprised five stages for organizing the virus prevalence. In the first
stage, the images are preprocessed by thresholding-based lung
segmentation. Afterward, the feature extraction technique was
performed on segmented images, while the genetic algorithm was
performed on sixty-four extracted features to adopt the superior
features. The K-nearest neighbor (KNN) and decision tree are applied
for COVID-19 classification in the final stage. This paper's findings
confirmed that the KNN classifier with K=3 is accomplished for
COVID-19 detection with high accuracy of 100% on CT images.
However, the decision tree for COVID-19 classification is achieved
95% accuracy. This system is used to facilitate the radiologist's role in
the prediction of COVID-19 images. This system will prove to be
valuable to the research community working on automation of
COVID-19 images prediction.
https://doi.org/10.18280/ISI.250505

Mohamed Torky, M. Sh
Torky, Azza Ahmed, Aboul
Ella Hassanein, and Wael
Said, "Investigating Epidemic
Growth of COVID-19 in Saudi
Arabia based on Time Series
Models" International Journal
of Advanced Computer
Science and
Applications(IJACSA), 11(12),
2020. http://dx.doi.org/10.14
569/IJACSA.2020.0111256
Scopus and Web
of Science
Investigating Epidemic Growth of COVID-19 in Saudi
Arabia based on Time Series Models
Abstract: Predictive mathematical models for simulating the spread
of the COVID-19 Pandemic are an exciting and fundamental approach
to understanding the epidemic's infection growth curve and plan
effective control strategies. Time series predictive models are among
the essential mathematical models that can be utilized to study the
pandemic growth curve. In this study, three-time series models
(Susceptible-Infected-Recovered-Death (SIRD) model, Susceptible-
Exposed-Infected-Recovered-Death (SEIRD) model, and Susceptible-
Exposed-Infected-Quarantine-Recovered-Death-Insusceptible,
SEIQRDP) model) have been investigated and simulated on a real
dataset for investigating Covid-19 outbreak spread in Saudi Arabia.
The simulation results and evaluation metrics proved that SIRD and
SEIQRDP models provided a minimum difference error between
reported and fitted data. So using SIRD, and SEIQRDP models are
used for predicting the pandemic end in Saudi Arabia. The prediction
results showed that the Covid-19 growth curve would be stable with
detected zero active cases on February 2 2021, according to the
prediction computations of the SEIQRDP model. The prediction
results based on the SIRD model showed that the outbreak would be
stable with active cases after July 2021.

Basha, S.H., Anter, A.M.,
Hassanien, A.E. et al. Hybrid
intelligent model for
classifying chest X-ray
images of COVID-19
patients using genetic
algorithm and neutrosophic
logic. Soft Comput (2021).
https://doi.org/10.1007/s005
00-021-06103-7
IF= 3.643
Scopus and WoS
Hybrid intelligent model for classifying chest X-ray
images of COVID-19 patients using genetic algorithm and
neutrosophic logic
The highly spreading virus, COVID-19, created a huge need for an
accurate and speedy diagnosis method. The famous RT-PCR test is costly
and not available for many suspected cases. This article proposes a
neurotrophic model to diagnose COVID-19 patients based on their chest
X-ray images. The proposed model has five main phases. First, the
speeded-up robust features (SURF) method is applied to each X-ray
image to extract robust invariant features. Second, three sampling
algorithms are applied to treat imbalanced datasets. Third, the
neutrosophic rule-based classification system is proposed to generate a
set of rules based on the three neutrosophic values < T; I; F>, the degrees
of truth, indeterminacy falsity. Fourth, a genetic algorithm is applied to
select the optimal neutrosophic rules to improve the classification
performance. Fifth, in this phase, the classification-based neutrosophic
logic is proposed. The testing rule matrix is constructed with no class
label, and the goal of this phase is to determine the class label for each
testing rule using an intersection percentage between testing and
training rules. The proposed model is referred to as GNRCS. It is
compared with six state-of-the-art classifiers such as multilayer
perceptron (MLP), support vector machines (SVM), linear discriminant
analysis (LDA), decision tree (DT), naive Bayes (NB), and random forest
classifiers (RFC) with quality measures of accuracy, precision, sensitivity,
specificity, and F1-score. The results show that the proposed model is
powerful for COVID-19 recognition with high specificity and high
sensitivity, and less computational complexity. Therefore, the proposed
GNRCS model could be used for real-time automatic early recognition of
COVID-19.

Mahmoud Y. Shams, Omar M.
Elzeki, Lobna M. Abouelmagd,
Aboul Ella Hassanien,
Mohamed Abd Elfattah,
Hanaa Salem,
HANA: A Healthy Artificial
Nutrition Analysis model
during COVID-19 Pandemic,
Computers in Biology and
Medicine,
Volume 135,
2021,
IF= 4.589
Q1
Scopus and
WoS
HANA: A Healthy Artificial Nutrition Analysis model
during COVID-19 Pandemic
The impact of diet on COVID-19 patients has been a global concern since
the Pandemic began. Choosing different types of food affects peoples'
mental and physical health and, with persistent consumption of certain
types of food and frequent eating, there may be an increased likelihood
of death. In this paper, a regression system is employed to evaluate the
prediction of death status based on food categories. A Healthy Artificial
Nutrition Analysis (HANA) model is proposed. The proposed model
generates a food recommendation system and tracks individual habits
during the COVID-19 Pandemic to recommend healthy foods. To collect
information about the different types of foods that most of the world's
population eat, the COVID-19 Healthy Diet Dataset was used. This
dataset includes different types of foods from 170 countries worldwide
and obesity, undernutrition, death, and COVID-19 data as percentages of
the total population. The dataset was used to predict the status of death
using different machine learning regression models, i.e., linear
regression (ridge regression, simple linear regularization, and elastic net
regression), and AdaBoost models. The death status was highly
predicted, and the food categories related to death were identified with
promising accuracy. The Mean Square Error (MSE), Root Mean Square
Error (RMSE), Mean Absolute Error (MAE), and R2 metrics and 20-fold
cross-validation were used to evaluate the accuracy of the prediction
models for the COVID-19 Healthy Diet Dataset. The evaluations
demonstrated that elastic net regression was the most efficient
prediction model. Based on an in-depth analysis of
recent nutrition recommendations by WHO, we confirm the same advice
already introduced in the WHO report1. Overall, the outcomes also
indicate that the remedying effects of COVID-19 patients are most
important to people who eat more vegetal products, oil crops grains,
beverages, and cereals - excluding beer. Moreover, people consuming
more animal products, animal fats, meat, milk, sugar and sweetened
foods, sugar crops were associated with more deaths and fewer patient
recoveries. The outcome of sugar consumption was important, and the
rates of death and recovery were influenced by obesity.

Aboul Ella Hassanien,
Athanasios V. Vasilakos,
Genetic-based adaptive
momentum estimation for
predicting mortality risk
factors for COVID-19 patients
using deep learning Sally M.
Elghamrawy,
Impact factor
=1.925
Scopus & Web
of Science
Genetic-based adaptive momentum estimation for
predicting mortality risk factors for COVID-19 patients
using deep learning
The mortality risk factors for coronavirus disease (COVID-19) must be
early predicted, especially for severe cases, to provide intensive care
before they develop to critically ill immediately. This paper aims to
develop an optimized convolution neural network (CNN) for predicting
mortality risk factors for COVID-19 patients. The proposed model
supports two types of input data clinical variables and computed
tomography (CT) scans. The features are extracted from the optimized
CNN phase and then applied to the classification phase. The CNN
model's hyperparameters were optimized using a proposed genetic-
based adaptive momentum estimation (GB-ADAM) algorithm. The GB-
ADAM algorithm employs the genetic algorithm (GA) to optimize the
Adam optimizer's configuration parameters, consequently improving
the classification accuracy. The model is validated using three recent
cohorts from New York, Mexico, and Wuhan, consisting of 3055,
7497,504 patients, respectively. The results indicated that the most
significant mortality risk factors are: CD T Lymphocyte (Count), D-
dimer greater than 1 Ug/ml, high values of lactate dehydrogenase
(LDH), C-reactive protein (CRP), hypertension, and diabetes. Early
identification of these factors would help the clinicians in providing
immediate care. The results also show that the most frequent COVID-
19 signs in CT scans included ground-glass opacity (GGO), followed by
a crazy-paving pattern, consolidations, and the number of lobes.
Moreover, the experimental results show encouraging performance
for the proposed model compared with different predicting models.
https://onlinelibrary.wiley.com/doi/full/10.1002/ima.22644

Book Chapters and Conferences
Publications

Shams M.Y., Elzeki O.M., Abd Elfattah
M., Abouelmagd L.M., Darwish A.,
Hassanien A.E. (2021) Impact of
COVID-19 Pandemic on Diet Prediction
and Patient Health Based on Support
Vector Machine. In: Hassanien AE.,
Chang KC., Mincong T. (eds) Advanced
Machine Learning Technologies and
Applications. AMLTA 2021. Advances in
Intelligent Systems and Computing, vol
1339. Springer, Cham.
https://doi.org/10.1007/978-3-030-
69717-4_7
Impact of COVID-19 Pandemic on Diet Prediction
and Patient Health Based on Support Vector Machine
Recently, the COVID-19 Pandemic has had an efficient impact on all things
around the world. Food estimation or diet has grown great attention in the recent
Pandemic. This paper utilizes the Support Vector Machine (SVM) to predict the
effect of the COVID-19 Pandemic on a diet and further forecast the number of
persons subject to death due to this Pandemic. This work is based on the
available dataset containing fat quantity, energy intake (kcal), food supply
quantity (kg), and protein for different food categories. Furthermore, we are
concerned the animal products, cereals excluding beer, obesity, including vegetal
products that affect humans' general health during the Pandemic.
Furthermore, the dataset includes confirmed deaths, recovered, and active cases in
the percentage of each country's current population. The results depend on Root
Mean Square Error (RMSE), which indicates that SVM's use with the Radial
Basis Function (RBF) kernel produces0.27. Further, SVM with linear Kernel
achieves 0.18 RMSE, a deep regression model achieves 0.29 RMSE.
Elsersy M., Sherif A., Darwsih A.,
Hassanien A.E. (2021) Digital
Transformation and Emerging
Technologies for Tackling COVID-19
Pandemic. In: Hassanien A.E., Darwish
A. (eds) Digital Transformation and
Emerging Technologies for Fighting
COVID-19 Pandemic: Innovative
Approaches. Studies in Systems, Decision
and Control, vol 322. Springer, Cham.
https://doi.org/10.1007/978-3-
030-63307-3_1
.
Digital Transformation and Emerging Technologies
for Tackling COVID-19 Pandemic
Several emerging technologies were introduced to tackle the unprecedented crisis of the
new COVID-19. Remarkable emerging technologies are outlined, such as machine and
deep learning, Internet of things, cloud and fog computing, and blockchain technology.
Those emerging technologies have been explored to support the solution proposed to
ensure the integration of these technologies to fight the Pandemic. Also, numerous
emerging technologies used for the COVID-19 fight have been highlighted. Finally, the
impact of COVID-19 is discussed, and applications showing how to mitigate this impact
using the emerging technologies are outlined.
Atrab A. Abd El-Aziz, Nour Eldeen
M. Khalifa, Ashraf Darwish, and
Aboul Ella Hassanien, The Role of
Emerging Technologies for
Combating COVID-19 Pandemic,
Digital Transformation and Emerging
Technologies for Fighting COVID-19
Pandemic: Innovative Approaches,
Studies in Systems, Decision, and
Control, Springer 2020.
The Role of Emerging Technologies for Combating
COVID-19 Pandemic
The new coronavirus disease (COVID-19) outbreak in 2019 resulted in more than 100,000
infections and thousands of deaths. The number of deaths and infections continues to rise
rapidly since the virus date of its appearance. COVID-19 threatens human health and many
aspects of life such as manufacturing, social performance, and international relations.
Emerging technologies can help in the fight against COVID-19. Emerging technologies
include blockchain, the Internet of Things (IoT), artificial intelligence (AI), and big data
technologies, and they proved its efficiency in practical fields. These fields include the fast
aggregation of multi-source big data, fast epidemic information visualization, diagnosis,
remote treatment, and spatial tracking of confirmed cases. Every country in the world is

still seeking realistic and cost-effective solutions to stand against COVID-19 under current
epidemiological conditions. This chapter discusses the concepts of emerging technologies,
applications, and contributions to combating COVID-19.
Moreover, the challenges and future research directions are reviewed in detail. Also, a list
of publicly available open-source COVID-19 datasets will be presented. Finally, this
chapter concludes that cooperation among government, medical institutions, and the
scientific community is significant and critical. Also, there is an urgent demand for
improvement in the analytical algorithms and electronic devices to combat the COVID-19
Pandemic.
Nour Eldeen M. Khalifa, Mohamed
Hamed N. Taha, Aboul Ella
Hassanien, Sarah Hamed N. Taha
"The Detection of COVID-19 in CT
Medical Images: A Deep Learning
Approach" Big Data Analytics and
Artificial Intelligence Against COVID-
19: Innovation Vision and Approach,
Springer, Big Data series, 2020
The Detection of COVID-19 in CT Medical Images: A
Deep Learning Approach
The COVID-19 Coronavirus is one of the latest viruses that hit the earth in the new
century. It was declared as a pandemic by the World Health Organization in 2020. This
chapter will present a model for detecting the COVID-19 virus from CT chest medical
images. The proposed model is based on Generative Adversarial Networks (GAN), and a
fine-tuned deep transfer learning model. GAN is used to generate more images from the
available dataset. At the same time, deep transfer models are used to classify the COVID-
19 virus from the normal class. The original dataset consists of 746 images. It is divided
into 90% for the training and validation phase while 10% for the testing phase. The 90%
then is divided into 80% percent for the training and 20% percent for the validation after
using GAN as an image augmenter. The proposed GAN architecture raises the number of
images in the training and validation phase to be 10 times larger than the original dataset.
The deep transfer models which are selected for experimental trials are Resnet50,
Shufflenet, and Mobilenet. They were selected because they include many layers on their
architectures compared with large deep transfer models such as DenseNet and Inception-
ResNet. This will reflect on the proposed model's performance in reducing training time,
memory and CPU usage. The experimental trials show that Shufflenet is the optimal deep
transfer learning in the proposed model as it achieves the highest possible for testing
accuracy and performance metrics. Shufflenet achieves an overall testing accuracy with
84.9% and 85.33% in all performance metrics, including recall, precision, and F1 score.
https://link.springer.com/chapter/10.1007/978-3-030-55258-9_5
M. Y. Shams, O. M. Elzeki,
Mohamed Abd Elfattah, T. Medhat,
and Aboul Ella Hassanien"
Why are Generative Adversarial
Networks Vital for Deep Neural
Networks? A Case Study on COVID-
19 Chest X-Ray Image"
Big Data Analytics and Artificial
Intelligence Against COVID-19:
Innovation Vision and Approach,
Springer, Big Data series, 2020.
Why are Generative Adversarial Networks Vital for
Deep Neural Networks? A Case Study on COVID-19
Chest X-Ray Image
Abstract. The need to generate large-scale datasets from a limited number of determined
data is highly required. Deep neural networks (DNN) are among the most important and
effective tools in machine learning (ML) that require large-scale datasets. Recently,
generative adversarial networks (GAN) is considered the most powerful and effective
method for data augmentation. This chapter investigated GAN's importance as a
preprocessing stage to apply DNN for image data augmentation.
Moreover, we present a case study of using GAN networks for limited COVID-19 X-Ray
Chest images. The results indicate that the proposed system based on GAN-DNN is
powerful with minimum loss function to detect COVID-19 X-Ray Chest images.

Stochastic gradient descent (SGD) and Improved Adam (IAdam) optimizers are used
during the training process of the COVID-19 X-Ray images, and the evaluation results
depend on loss function are determined to ensure the reliability of the proposed GAN
architecture
Ahmed A. Hammam, Haytham H.
Elmousalami, Aboul Ella Hassanien
Stacking Deep Learning for Early
COVID-19 Vision Diagnosis, Big
Data Analytics and Artificial
Intelligence Against COVID-19:
Innovation Vision and Approach,
Springer , Big Data series, 2020.
Stacking Deep Learning for Early COVID-19 Vision
Diagnosis, Big Data Analytics and Artificial
Intelligence Against COVID-19: Innovation Vision
and Approach,
Abstract— early and accurate COVID-19 diagnosis prediction plays a crucial role in
helping radiologists, and health care workers take reliable corrective actions to classify
patients and detect the COVID 19 confirmed cases. Prediction and classification accuracy
are critical for COVID-19 diagnosis application. Current practices for COVID-19 images
classification are mostly built upon convolutional neural networks (CNNs) where CNN is a
single algorithm. On the other hand, ensemble machine learning models produce higher
accuracy than a single machine learning. Therefore, this study conducts stacking deep
learning methodology to produce the highest results of COVID-19 classification. The
stacked ensemble deep learning model accuracy has produced 98.6% test accuracy.
Accordingly, the stacked ensemble deep learning model produced superior performance
than any single model. Accordingly, ensemble machine learning evolves as a future trend
due to its high scalability, stability, and prediction accuracy.
Doaa Mohey El-Din, Aboul Ella
Hassanein, and Ehab E. Hassanien
The effect Coronavirus Pendamic on
Education into Electronic Multi-
Modal Smart Education, Big Data
Analytics and Artificial Intelligence
Against COVID-19: Innovation
Vision and Approach, Springer, Big
Data series, 2020.
The effect Coronavirus Pendamic on Education into
Electronic Multi-Modal Smart Education
Abstract. This paper presents how Coronavirus drives education to smart education in
interpreting multi-modals. It is used to improve electronic learning in multiple data types.
This paper is a survey paper about the importance of smart education and the effect of
Coronavirus on drives education into smart online education. It also presents many
changes in the education vision around the world to utilize multi-modal for enhancing E-
learning. The combination of artificial intelligence and data fusion plays a vital role in
improving decision-making and monitoring students remotely. It also presents benefits and
open research challenges of a multi-modal smart education. The main objective of this
paper is to highlight the deepening digital inequality in smart education in emergencies due
to Coronavirus, the concept of digital equality has been defined as equal opportunities in
accessing technology as hardware and software as well as equal opportunities in obtaining
equal digital education through Ease of access to high-quality and interactive digital
content based on the interaction
Walid Hamdy, Ismail Elansary,
Ashraf Darwish and Aboul Ella
Hassanien" An Optimized
Classification Model for COVID-19
Pandemic based on Convolutional
Neural Networks and Particle Swarm
An Optimized Classification Model for COVID-19
Pandemic based on Convolutional Neural Networks
and Particle Swarm Optimization Algorithm."
With the daily rapid growth in the number of newly confirmed and suspected COVID-19
cases, COVID-19 extremely threatens public health, countries' economic, social life, and

Optimization Algorithm", Digital
Pandemic: Innovative Approaches
Studies in Systems, Decision and
Control, Springer 2020
international relations worldwide. There are different medical methods to detect and
diagnose this disease, such as viral nucleic acid screening, using the lower respiratory
tract's specimens. However, sufficient laboratory screening in the infested counties
represents a critical challenge, especially with the fast-spreading of COVID-19. Therefore,
alternative diagnostic procedures that depend on Artificial Intelligence (AI) techniques are
required in the meantime to ﬁght against this epidemic. This paper focuses on using chest
CT to diagnose COVID-19 as an alternative or assistive method to the reverse-
transcription polymerase chain reaction (RT-PCR) tests. Motivated by this, this paper
introduces a new model based on deep learning for detecting patients infected with
COVID-19 using chest CT. In this paper, a new proposed model for diagnosing COVID-19
based on using Convolutional Neural Networks (CNN) and Particle Swarm Optimization
(PSO) algorithm to classify the CT chest images of patients into infected or not infected. In
this paper, CNN's network hyper-parameters are optimized by using the PSO algorithm to
eliminate the requirement of manual search and enhance network performance. This
paper's used chest radiography dataset is described, which leveraged to train COVID-Net
and includes more than 16,500 chest radiography images across more than 13,500 patient
cases from two open access data repositories. This work's experimental results exhibited
that the suggested system accuracy ratio of 98.04% is competitive to the other models.
Kamel. K. Mohammed, Heba M.
Afify, Ashraf Darwish, Aboul Ella
Hassanien"Automatic Scoring and
Grading of COVID-19 Lung Infection
Approach" Studies in Systems,
Decision and Control, Springer
2020.
Automatic Scoring and Grading of COVID-19 Lung
Infection Approach
Abstract: Although the successful detection of COVID-19 from lung computed
tomography (CT) image mainly depends on radiologists' experience, specialists
occasionally disagree with their judgments. The performance of COVID-19 detection
models needs to be improved. According to COVID-19 symptoms and the human immune
response, there are four types of contagion: asymptomatic, mild, severe, and recovered. In
this chapter, automatic scoring of the COVID-19 lung infection grading approach is
presented. The proposed approach is based on a combination of image segmentation
techniques and the Particle Swarm Optimization (PSO) algorithm to access accurate
evaluation for infection rate. Fuzzy c-means, K-means, and thresholding-based
segmentation algorithms isolate the chest lung from the CT images. Then, PSO is used
with the three segmentation algorithms to cluster the region of interest (ROI) of COVID-19
infected regions in lung CT. Then, scoring the infection rate for each case. Finally, four
infection classes related to the obtained infection COVID-19 are determined and classified.
Walid Hamdy, Ashraf Darwish and
Aboul Ella Hassanien "Artificial
Intelligence Strategy in the Age of
Covid-19: Opportunities and
Challenges" Studies in Systems,
Decision, and Control, Springer
2020.
https://link.springer.com/chapter/10.
1007/978-3-030-63307-3_5
Artificial Intelligence Strategy in the Age of Covid-
19: Opportunities and Challenges
With the frequent speedily rise in the number of recently reported and suspected cases of
COVID-19, COVID-19 is a significant threat to public health, cultural, social, and foreign
relations worldwide. Accurate diagnosis has to turn into a critical issue affecting the
containment of this disease, especially in countries with the virus. In the fight against
COVID-19, Artificial Intelligence (AI) techniques have played a significant role in many
aspects. This chapter introduces a systematics review of the recent work related to COVID-
19 containment using AI and big data techniques, showing their main findings and

limitations to make it easy for researchers to investigate new techniques that will help the
healthcare sector worker and reduce the spread of COVID-19 Pandemic. The chapter also
presents the problems and challenges and present to the researchers and academics some
future research points from the AI point of view that can help healthcare sectors and
curbing the COVID-19 spread.
Jaideep Singh Sachdev, Arti Kamath,
Nitu Bhatnagar, Roheet Bhatnagar,
Arpana Rawal, Ashraf Darwish,
Aboul Ella Hassenian "SAKHA: An
Artificial Intelligence Enabled
VisualBOT for Health and Mental
Wellbeing during COVID'19
Pandemic" Studies in Systems,
Decision and Control, Springer
2020.
An Artificial Intelligence Enabled VisualBOT for
Health and Mental Wellbeing during COVID'19
Pandemic"
Abstract: COVID19 Pandemic is playing havoc all around the world. Though the world is
fighting this invisible enemy, it has succumbed to the devastating potential of the
Coronavirus. The largest of world economies and developed nations have been exposed,
and their health infrastructure has collapsed during this testing time. It is assessed and
predicted that the novel Coronavirus, responsible for the COVID19 Pandemic, may turn
into an endemic (just like HIV) and will never disappear. It will become part and parcel of
our life and humans have to learn to live with it even if the vaccine is developed. The
government's world over is concerned with containment & eradication of this virus at the
earliest and massive efforts are on at all fronts to contain it's spread. As of now (3rd week
of May 2020), more than 4.4 million cases of the disease have been recorded worldwide
and more than 300,000 have died. The world has also seen technological innovation during
this time and mechanisms to tackle COVID19 patients. Innovations in quick testing using
Rapid testing kits, Artificial Intelligence (AI) powered thermal scanning for temperature
monitoring in the crowd, AI-enabled contact tracing, Mobile Apps, low-cost ventilators,
and many other similar solutions. All these pertain to checking for COVID19 symptoms
and taking actions after that, but what about the stress, pain, and shock of a person who has
been put under quarantine in a facility meant for the purpose or the person who is Corona
positive? In this chapter, the authors have discussed the Pandemic briefly and tried to
provide a solution for the mental well-being of such people who are under quarantine and
are isolated but heavily stressed or showing stress symptoms, by creating a VisualBOT
which could understand the facial expression of the person and judge his mood, for
providing appropriate counseling and help.
Hassan Amin, Ashraf Darwish and
Aboul Ella Hassanien "Classification
of COVID19 x-ray images based on
Transfer Learning InceptionV3 Deep
Learning Model" Studies in Systems,
Decision and Control, Springer 2020
Classification of COVID19 x-ray images based on
Transfer Learning InceptionV3 Deep Learning Model
The World Health Organization (WHO) has recently announced the novel Coronavirus
2019 as a pandemic. Many preventative plans and non-pharmaceutical efforts have
emerged and been used to manage and control the disease's spread, including infection
control, proper isolation of patients, and social distancing. The main test used to confirm a
COVID-19 case is the RT-PCR test. However, this approach needs analysis time and
specimen collection. Therefore, the importance of medical imaging is increased to screen
COVID-19 cases. Hence radiology has a pivotal role in managing COVID-19 infection
using CT scans and chest x-ray (CXR) throughout the disease's screening, diagnosis, and
prognostication processes. This paper presents a new model using the transfer learning
method and InceptionV3 algorithm to classify the x-ray images into COVID-19, Normal,
and Pneumonia classes. The experimental results show that the proposed model achieved

98% Accuracy on the test set for classifying the images from the 3 different classes.
Aya Salama, Ashraf Darwish, and
Aboul Ella Hassanien "Artificial
Intelligence Approach to Predict the
COVID-19 Patient's Recovery"
Studies in Systems, Decision, and
Control, Springer 2020.
Artificial Intelligence Approach to Predict the COVID-
19 Patient's Recovery"
Abstract: Coronavirus is the new Pandemic hitting all over the world. Patients all over the
world are facing different symptoms. Most of the patients with severe symptoms die,
especially the elderly. In this chapter, three machine learning techniques have been chosen
and tested to predict the patient's recovery of Coronavirus disease. The support vector
machine has been tested on the given data with a mean absolute error of 0.2155. The
Epidemiological data set is prepared by researchers from many health reports of real-time
cases to represent the different attributes that contribute as the main factors for recovery
prediction. Deep analysis with other machine learning algorithms including artificial neural
networks and regression models has been tested and compared with the SVM results. The
experimental results show that most of the patients who could not recover had a fever,
cough, general fatigue, and most probably malaise.
Mona Soliman, Ashraf Darwish,
Aboul Ella Hassanien" Deep Learning
Technology for Tackling COVID-19
Pandemic" Studies in Systems,
Decision, and Control, Springer
2020.
Deep Learning Technology for Tackling COVID-19
Pandemic
Abstract. Although the COVID-19 Pandemic continues to expand, researchers worldwide
are working to understand, diminish, and curtail its spread. The primary _elds of research
include investigating the transmission of COVID-19, promoting its identi_cation,
designing potential vaccines and therapies, and recognizing the Pandemic's socioeconomic
impacts. Deep Learning (DL), which uses either deep learning architectures or hierarchical
approaches to learning, was developed a machine learning class in 2006. The exponential
growth and availability f data and groundbreaking developments in hardware technology
have led to the rise of new distributed and learning studies. Throughout this chapter, we
discuss how deep learning can contribute to these goals by stepping up ongoing research
activities, improving the e_ciency and speed of existing methods, and proposing original
lines of research
Kumar A., Elsersy M., Darwsih A.,
Hassanien A.E. (2021) Drones Combat
COVID-19 Epidemic: Innovating and
Monitoring Approach. In: Hassanien A.E.,
Darwish A. (eds) Digital Transformation
and Emerging Technologies for Fighting
Approaches. Studies in Systems,
Decision, and Control, vol 322. Springer,
Cham.
https://doi.org/10.1007/978-3-
030-63307-3_11
Drones combat COVID-19 Epidemic: Innovating and
Monitoring Approach
With the daily rapid growth in the number of newly confirmed and suspected Coronavirus
cases, Coronavirus extremely threatens public health, countries' economic, social life, and
international relations worldwide. In the fight against Coronavirus, Unmanned Aerial
Vehicles (UAV) or drones can play a significant role in many aspects to limit the spread of
this Pandemic. Also, the strategic planning of many governments, such as in China, for
controlling this crisis is supported by drones for the Coronavirus outbreak. This chapter
explores the possibilities and opportunities of UAVs, also called drones, in fighting
Coronavirus. Drones are introduced, showing their main findings to make it easy for
researchers to investigate new techniques that will help the healthcare sector worker and
reduce the spread of the Coronavirus pandemic. The chapter also presents some problems

and challenges that can help healthcare sectors and curbing the Coronavirus spread.
Mourad R Mouhamed, Ashraf
Darwish, Aboul Ella Hassanien" 3D
Printing Supports COVID-19
Pandemic Control" Studies in
Systems, Decision, and Control,
Springer 2020.
3D Printing Supports COVID-19 Pandemic
At the end of December last year, a new type of Coronavirus appeared in Wuhan, China,
with new properties the researchers named COVID-19. In February, the world health
organization considered it a world pandemic; it had spread in most world countries. This
virus attacks the respiratory system, which makes failure in the system's function. This
crisis affected all the fielfieldslife, where all countries applied quarantine and roadblock
that makes a real shortage in most of the ple needs. BesiBesides biological scientists'
efforts, computer scientists proposed many ideas to fight this epidemic using emergent
technologies. This chapter covers 3D printing principles the latest efforts against COVID-
19 as one of the emergent technologies. 3D printing technology helps to flatten the curve
of the virus outbreak by reducing the effect of shortage in the supply chain of medical parts
and all personal protective equipment (PPE) (i.e. face masks and goggles), providing
extensive customization capability.
Mahdy L.N., Ezzat K.A., Darwish A.,
Hassanien A.E. (2021) The Role of Social
Robotics to Combat COVID-19
Pandemic. In: Hassanien A.E., Darwish
Cham.
https://doi.org/10.1007/978-3-
030-63307-3_13
The Role of Social Robotics to combat COVID-19
Pandemic
As the COVID-19 Pandemic grows, the shortening of clinical hardware is expanding. A
key bit of hardware getting out of sight has been ventilators. The contrast among the
organic market is significant to be dealt with ordinary creation strategies, particularly
under social removing measures set up. The examination investigates the method of
reasoning of human-robot groups to increase creation utilizing preferences of both the
simplicity of coordination and keeping up social removing. This chapter highlights the role
of social robotic in fighting COVID-19. Also, it presents the requirements of social
robotics.
Elmousalami H.H., Darwish A.,
Hassanien A.E. (2021) The Truth About
5G and COVID-19: Basics, Analysis, and
Opportunities. In: Hassanien A.E.,
Cham.
https://doi.org/10.1007/978-3-
030-63307-3_16
"The Truth about 5G and COVID-19: Basics,
analysis, and opportunities
5G is a paradigm shift for data transfer and wireless communication technology, where 5G
involves massive bandwidths based on high carrier frequencies. Unlike 4G, 5G is highly
integrative to produce a seamless user experience and universal high-rate coverage. The
key role of 5G is increasing data capacity, improving data rate transfer, providing better
service quality, and decreasing latency. Recently, COVID-19 has been declared an
international epidemic. More than 4.5 million confirmed cases and + 308000 death cases
were recorded in more than 209 countries on May 16, 2020. There are several insane
theories about 5G technology and human health. Therefore, people are burning valuable
5G infrastructure down out of fear for their health. People think that 5G towers are
weakening the immune system and causing the global COVID-19 Pandemic. This chapter
reviews the data transmission revolution from 1G to 5G technology and discusses the
impact of 5G technology on human health, Pandemic, and business perspectives.

Torky M., Darwish A., Hassanien
A.E. (2021) Blockchain Use Cases
for COVID-19: Management,
Surveillance, Tracking and Security.
In: Hassanien A.E., Darwish A.
(eds) Digital Transformation and
Decision, and Control, vol 322.
Springer, Cham.
https://doi.org/10.1007/978-3-030-
63307-3_17
Blockchain Use Cases for COVID-19: Management,
Surveillance, Tracking and Security
Blockchain has become a key technology in building and managing healthcare systems.
The distinguished attributes of the blockchain (e.g., security, decentralization, time
stamping, and transparency) make it the best technology for real-time managing the
COVID-19 Pandemic. This chapter investigates five blockchain use cases for fighting
against the COVID-19 virus spread. Finally, this chapter discusses the recent blockchain
platforms that can manage epidemic diseases, HashLog, and XMED Chain.
Nagy M., Abbad H.M., Darwish A.,
Hassanien A.E. (2021) The 4th
Industrial Revolution in Coronavirus
Pandemic Era. In: Hassanien A.E.,
Darwish A. (eds) Digital
Pandemic: Innovative Approaches.
Studies in Systems, Decision and
Control, vol 322. Springer, Cham.
https://doi.org/10.1007/978-3-030-
63307-3_14
The 4th Industrial Revolution in Coronavirus
Pandemic Era
The global prevalence of coronavirus disease 2019 (COVID-19) requires a remarkable
avenue to endure and restrain it; Although the world's most advanced and sophisticated
healthcare systems could not stand against this Pandemic, the synthesis of the fourth
industrial revolution manifests its potential to eradicate this virus. This chapter discusses
how multiple advanced technologies involve diverse perspectives of fighting the
catastrophe, starting from reduction of the spreading of the virus, automated surveillance
for infected cases, contribution to retaining the communication as well as social safety
during the lockdown, and evolving healthcare medical equipment to the process of
developing a vaccine. It also has a vital role in keeping most nations' institutions run
remotely, such as education systems, besides the declination of the expected economic
losses by running businesses online and introducing the essential role of these technologies
to monitor the propagation of COVID-19 globally that permits taking precautionary
measures earlier and evaluating the current situation of each country individually.
Eventually, the inuence of these privileges of this revolution has convinced other nations
of the importance of accelerating and boosting those advanced technologies to defeat the
current situation by considering China as a realistic illustration of the efficiency.
Gabriel A.J., Darwsih A., Hassanien A.E.
(2021) Cyber Security in the Age of
COVID-19. In: Hassanien A.E., Darwish
https://doi.org/10.1007/978-3-
030-63307-3_18
Cyber Security in the Age of COVID-19
As a containment strategy for the dreaded Corona Virus Disease 19 (COVID 19) which is
spreading rapidly and causing severe damage to life and economy of nations, places of
public gathering like schools, places of religious worship, open physical markets, offices as
well as venues for social meetings (such as clubs) are closed down, to promote social
distancing in most nations across the globe. Therefore, most public/private organizations
and even individuals have resorted to using diverse Information Technologies (IT) to
connect themselves and other life essentials. Educational, agricultural, religious and even
health institutions now deliver their services to users/clients and receive payments via

online platforms. Students study from home. Even employees of most organizations now
work remotely (maybe from their homes).
Moreover, there is a sharp growth in demand for food deliveries and online groceries. The
massive adoption of IT by almost all aspects of human life, especially during this
epidemic, has also increased cyber security concerns. Cybercriminals and other individuals
with malicious intent now take COVID-19 as an opportunity to perpetrate cybercrimes,
especially for monetary gains. Domestic violence seems to be on the rise, perhaps due to
the lockdown. Contact tracing approaches are being developed and used, healthcare
systems are being attacked with ransomware, and resources such as patient records
confidentiality and integrity are being compromised. Individuals are falling victim to
phishing attacks through COVID-19 related content. This paper presents an extensive
study of major cybersecurity concerns that could take place during the COVID 19
pandemic and strategies for mitigating them.
Ahmed K., Abdelghafar S., Salama A.,
Khalifa N.E.M., Darwish A., Hassanien
A.E. (2021) Tracking of COVID-19
Geographical Infections on Real-Time
Tweets. In: Hassanien A.E., Darwish A.
(eds) Digital Transformation and
Cham.
https://doi.org/10.1007/978-3-
030-63307-3_19
Tracking of COVID-19 Geographical Infections on
Real-Time Tweets
Abstract. Coronavirus COVID-19 is a global pandemic stated by the World Health
Organization (WHO) in 2020. The COVID-19 devasting impact affected human life and
many aspects of it, such as social interaction, transportation options, personal savings and
expenses, and more. The power of social media data in such world pandemic outbreaks
provides an efficient source of tracking, raising awareness, and alerts with potentials
infection locations. Social networks can fight the Pandemic by sharing helpful content and
statistics based on demographics features of users around the world. There is an urgent
need for such frameworks for tracking helpful content, detecting misleading content,
ranking the trusted user content, presenting accurate demographics statistics of the
outbreak. In this paper, the real-time tweets of Coronavirus pandemic (COVID-19)
analysis will be presented. The proposed framework will track the geographical infections,
trends of the content, and the user's categorization. The framework will include analysis,
demographics features, statistical charts, and classifying tweets related to its usefulness.
The proposed framework's performance is evaluated based on different measures such as
classification accuracy, sensitivity, and specificity. Finally, a set of recommendations will
be presented to benefit from the proposed framework with its full potentials as a tool to
stand against the COVID-19 spreading.
Elansary I., Darwish A., Hassanien A.E.
(2021) The Future Scope of Internet of
Things for Monitoring and Prediction of
COVID-19 Patients. In: Hassanien A.E.,
https://doi.org/10.1007/978-3-
030-63307-3_15
The Future Scope of Internet of Things for Monitoring
and Prediction of COVID-19 Patients"
The new outbreak of pneumonia triggered by a novel coronavirus (COVID-19) poses a
major threat and has been declared a global public health emergency. This outbreak was
first discovered in December 2019 in Wuhan, China, and has spread worldwide. Emerging
technology such as the Internet of Things (IoT) and sensor networks (SN) have been
utilized widely in our everyday lives in various ways. IoT has also played an instrumental
role in fighting against the COVID-19 Pandemic currently outbreaking globally. It plays a
significant role in tracking COVID-19 patients and infected people in hospitals and
hotspots. This paper exhibited a survey of IoT technologies used in the fight against the

Elghamrawy S.M., Darwish A.,
Hassanien A.E. (2021) Monitoring
COVID-19 Disease Using Big Data and
Artificial Intelligence-Driven Tools. In:
Hassanien A.E., Darwish A. (eds) Digital
Pandemic: Innovative Approaches.
Studies in Systems, Decision and Control,
vol 322. Springer, Cham.
https://doi.org/10.1007/978-3-
030-63307-3_10
deadly COVID-19 outbreak in different applications and discussed the key roles of IoT
science in this unparalleled war. Research directions on discovering IoT's potentials,
improving its capabilities and power in the battle, and IoT's issues and problems in
healthcare systems are explored in detail. This study intends to provide an overview of the
current status of IoT applications to IoT researchers and the broader community and
inspire researchers to leverage IoT potentials in the battle against COVID-19.
Monitoring COVID-19 Disease Using Big Data and
Artificial Intelligence-Driven Tools
With the huge daily growth in the number of confirmed COVID-19 cases, COVID-19
extremely threatens public health, countries’ economic, social life, and international
relations worldwide. The accurate diagnosis based on a large amount of data has become a
serious issue that affects disease control, especially in widespread countries. To monitor
COVID-19, big data analytics tools and Artificial Intelligence (AI) techniques play a
significant role in many aspects. The integration between both technologies will help
healthcare workers early and accurately diagnose COVID-19 cases. In addition, the
strategic planning for crisis management is supported by big data aggregation to be used in
the epidemiologic directions. Moreover, AI and big data-driven tools present visualization
for COVID-19 outbreak information that helps detect risk allocation and regional
transmissions. In this chapter, a review of recent works related to COVID-19 containment
using AI and big data techniques is introduced, showing their main findings and limitations
to make it easy for researchers to investigate new techniques that will help in the COVID-
19 Pandemic.
Pre-prints publications
Nour Eldeen Mahmoud
Khalifa, Mohamed Hamed N.
Taha, Aboul Ella Hassanien, Sally M.
Elghamrawy: Detection of
Coronavirus (COVID-19) Associated
Pneumonia based on Generative
Adversarial Networks and a Fine-
Tuned Deep Transfer Learning
Model using Chest X-ray
dataset. CoRR abs/2004.01184 (20
20)
Detection of Coronavirus (COVID-19) Associated
Pneumonia based on Generative Adversarial
Networks and a Fine-Tuned Deep Transfer Learning
Model using Chest X-ray dataset
The COVID-19 Coronavirus is one of the devastating viruses, according to the world
health organization. This novel virus leads to pneumonia, an infection that inflames the
lungs' air sacs of a human. One of the methods to detect those inflames is by using x-rays
for the chest. This paper will present a limited pneumonia chest x-ray detection dataset
based on generative adversarial networks (GAN) with fine-tuned deep transfer learning.
GAN's use positively affects the proposed model robustness, immune to the overfitting
problem, and helps generate more images from the dataset. The dataset used in this
research consists of 5863 X-ray images with two categories: Normal and Pneumonia. This

research uses only 10% of the dataset for training data and generates 90% of images using
GAN to prove the efficiency of the proposed model. Through the paper, AlexNet,
GoogLeNet, Squeeznet, and Resnet18 are selected as deep transfer learning models to
detect pneumonia from chest x-rays. Those models are selected based on their small
number of layers on their architectures, which will reduce the complexity of the models
and the consumed memory and time. Using a combination of GAN and deep transfer
models proved it is efficient according to testing accuracy measurement. The research
concludes that the Resnet18 is the most appropriate deep transfer model according to
testing accuracy measurement and achieved 99% with the other performance metrics such
as precision, recall, and F1 score while using GAN as an image augmenter. Finally, a
comparison result was carried out at the end of the research with related work which used
the same dataset except that this research used only 10% of the original dataset. The
presented work achieved a superior result than the related work in terms of testing
accuracy. https://arxiv.org/abs/2004.01184
V. Rajinikanth, Nilanjan Dey, Alex
Noel Joseph Raj, Aboul Ella
Hassanien, K. C.
Santosh, Nadaradjane Sri Madhava
Raja:
Harmony-Search and Otsu based
System for Coronavirus Disease
(COVID-19) Detection using Lung
CT Scan
Images. CoRR abs/2004.03431 (20
20)
Harmony-Search and Otsu-based System for
Coronavirus Disease (COVID-19) Detection using
Lung CT Scan Images
The COVID-19 Coronavirus is one of the devastating viruses, according to the world
health organization. This novel virus leads to pneumonia, which is an infection that
inflames the lungs' air sacs of a human. One of the methods to detect those inflames is by
using x-rays for the chest. This paper will present a limited pneumonia chest x-ray
detection dataset based on generative adversarial networks (GAN) with a fine-tuned deep
transfer learning. The use of GAN positively affects the proposed model robustness and
immune to the overfitting problem and helps generate more images from the dataset. The
dataset used in this research consists of 5863 X-ray images with two categories: Normal
and Pneumonia. This research uses only 10% of the dataset for training data and generates
90% of images using GAN to prove the efficiency of the proposed model. Through the
paper, AlexNet, GoogLeNet, Squeeznet, and Resnet18 are selected as deep transfer
learning models to detect the pneumonia from chest x-rays. Those models are selected
based on their small number of layers on their architectures, which will reduce the
complexity of the models and the consumed memory and time. Using a combination of
GAN and deep transfer models proved it is efficiency according to testing accuracy
measurement. The research concludes that the Resnet18 is the most appropriate deep
transfer model according to testing accuracy measurement and achieved 99% with the
other performance metrics such as precision, recall, and F1 score while using GAN as an
image augmenter. Finally, a comparison result was carried out at the end of the research
with related work which used the same dataset except that this research used only 10% of
original dataset. The presented work achieved a superior result than the related work in
terms of testing accuracy. https://arxiv.org/abs/2004.01184
Dalia Ezzat, Aboul Ella
Hassanien, Hassan Aboul Ella:
GSA-DenseNet121-COVID-19: a
Hybrid Deep Learning Architecture
for the Diagnosis of COVID-19
Disease based on Gravitational
Search Optimization
Algorithm. CoRR abs/2004.05084 (2
GSA-DenseNet121-COVID-19: a Hybrid Deep
Learning Architecture for the Diagnosis of COVID-19
Disease based on Gravitational Search Optimization
Algorithm.
In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid
convolutional neural network (CNN) architecture is proposed using an optimization
algorithm. The CNN architecture used is called DenseNet121, and the optimization
algorithm used is called the gravitational search algorithm (GSA). The GSA is adapted to

020) determine the best values for the hyperparameters of the DenseNet121 architecture and
achieve a high level of accuracy in diagnosing COVID-19 disease through chest x-ray
image analysis. The obtained results showed that the proposed approach could correctly
classify 98% of the test set. To test the efficacy of the GSA in setting the optimum values
for the hyperparameters of DenseNet121, it was compared to another optimization
algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy
of the proposed GSA-DenseNet121-COVID-19 and its ability to diagnose COVID-19 than
the SSD-DenseNet121 better as the second was able to diagnose only 94% of the test set.
The proposed approach was also compared to an approach based on a CNN architecture
called Inception-v3 and the manual search method for determining the values of the
hyperparameters. The comparison results showed that the GSA-DenseNet121 was able to
beat the other approach, as the second was able to classify only 95% of the test set
samples. https://arxiv.org/abs/2004.05084
Rizk M. Rizk-Allah, Aboul Ella
Hassanien: COVID-19 forecasting is
based on an improved interior search
algorithm and multilayer feed-
forward neural
network. CoRR abs/2004.05960 (20
20)
COVID-19 forecasting is based on an improved
interior search algorithm and multilayer feed-forward
neural network.
COVID-19 is a novel coronavirus that emerged in December 2019 within Wuhan, China.
As the crisis of its serious increasing dynamic outbreak in all parts of the globe, the
forecast maps and analysis of confirmed cases (CS) becomes a vital great changeling task.
This study presents a new forecasting model to analyze and forecast the CS of COVID-19
for the coming days based on the reported data since January 22, 2020. The proposed
forecasting model, named ISACL-MFNN, integrates an improved interior search algorithm
(ISA) based on chaotic learning (CL) strategy into a multilayer feed-forward neural
network (MFNN). The ISACL incorporates the CL strategy to enhance the performance of
ISA and avoid trapping in the local optima. This methodology intends to train the neural
network by tuning its parameters to optimal values and thus achieving high-accuracy level
regarding forecasted results. The ISACL-MFNN model is investigated on the official data
of the COVID-19 reported by the World Health Organization (WHO) to analyze the
confirmed cases for the upcoming days. The performance regarding the proposed
forecasting model is validated and assessed by introducing some indices including the
mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage
error (MAPE) and the comparisons with other optimization algorithms are presented. The
proposed model is investigated in the most affected countries (i.e., USA, Italy, and Spain).
The experimental simulations illustrate that the proposed ISACL-MFNN provides
promising performance than the other algorithms while forecasting the candidate countries'
task. https://arxiv.org/abs/2004.05960
Mohamed Torky, Aboul Ella
Hassanien: COVID-19 Blockchain
Framework: Innovative
Approach. CoRR abs/2004.06081 (
2020)
COVID-19 Blockchain Framework: Innovative
Approach
The world is currently witnessing dangerous shifts in the epidemic of emerging SARS-
CoV-2, the causative agent of (COVID-19) Coronavirus. The infection and death numbers
reported by the World Health Organization (WHO) about this epidemic forecast an
increasing threat to people's lives and the economics of countries. The greatest challenge
that most governments are currently suffering from is the lack of a precise mechanism to
detect unknown infected cases and predict the infection risk of the COVID-19 virus. To
mitigate this challenge, this study proposes a novel, innovative approach for mitigating big
challenges of (COVID-19) coronavirus propagation and contagion. This study proposes a
blockchain-based framework that investigates the possibility of utilizing blockchain's peer-
to-peer, time stamping, and decentralized storage advantages to building a new system for
verifying and detecting unknown infected cases COVID-19 virus.Moreover, the proposed

framework will enable the citizens to predict the infection risk of the COVID-19 virus
within conglomerates of people or public places through a novel design of P2P-Mobile
Application. The proposed approach is forecasted to produce an effective system that can
support governments, health authorities, and citizens in making critical infection detection,
prediction, and avoidance decisions. The framework is currently being developed and
implemented as a new system consisting of four components, Infection Verifier
Subsystem, a Blockchain platform, P2P-Mobile Application, and Mass-Surveillance
System. These four components work together to detect the unknown infected cases and
predict and estimate the infection Risk of Corona Virus (COVID-19).
https://arxiv.org/abs/2004.06081
Aboul Ella Hassanien, Aya Salama,
Ashraf Darwsih, Artificial Intelligence
Approach to Predict the COVID-19
Patient's Recovery, No. 3223.
EasyChair, 2020
Artificial Intelligence Approach to Predict the COVID-
19 Patient's Recovery
Coronavirus is the new Pandemic hitting all over the world. Patients all over the world are
facing different symptoms. Most of the patients with severe symptoms die especially the
elderly. In this paper, we test three machine learning techniques to predict the patient's
recovery. Support vector machine was tested on the given data with mean absolute error
of 0.2155. The Epidemiological data set was prepared by researchers from many health
reports of real-time cases to represent the different attributes that contribute as the main
factors for recovery prediction. A deep analysis with other machine learning algorithms
including artificial neural networks and regression model were test and compared with the
SVM results. We conclude that most of the patients who couldn't recover had fever,
cough, general fatigue, and most probably malaise. Besides, most of the patients who died
live in Wuhan in china or visited Wuhan, France, Italy or Iran.
https://easychair.org/publications/preprint/4bf1
Day Level Forecasting for
Coronavirus Disease (COVID-19)
Spread: Analysis, Modeling, and
Recommendations
Haytham H. Elmousalami, Aboul Ella
Hassanien
arXiv:2003.07778
Day Level Forecasting for Coronavirus Disease
(COVID-19) Spread: Analysis, Modeling, and
Recommendations
In mid-March 2020, Coronaviruses such as COVID-19 are declared as an international
epidemic. More than 125000 confirmed cases and 4,607 death cases have been recorded
around more than 118 countries. Unfortunately, a coronavirus vaccine is expected to take
at least 18 months if it works at all. Moreover, COVID -19 epidemics can mutate into a
more aggressive form. Day-level information about the COVID -19 spread is crucial to
measure the behavior of this new virus globally. Therefore, this study compares day-level
forecasting models on COVID-19 cases using time series models and mathematical
formulation. The forecasting models and data strongly suggest that the number of
coronavirus cases grows exponentially in countries that do not mandate quarantines,
restrictions on travel and public gatherings, and closing of schools, universities, and
workplaces (Social Distancing).
https://arxiv.org/abs/2003.07778

Publications Impact
Publications published on
the World Health
Organization
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2019
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coronavirus
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novel
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on
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re
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