أكثر من خمسون بحث فى التكنولوجيات الناشئة لمكافحة كوفيد-19 هذا عمل مدرسة علمية تضم افضل الباحثين على مستوى الجامعات والمراكز البحثية

1. Emerging Technology
against COVID-19 SRGE
Publications
Professor Aboul Ella Hassanien
9/28/2020
fci

2. Books Publications
Book(1) published 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
Book (2) in production Aboul Ella Hassanien, and Ashraf Darwsih, Digital
Transformation and Emerging Technologies for Fighting COVID-
19 Pandemic: Innovative Approaches, Studies in Systems,
Decision and Control, Springer 2020.
Book (3) in production 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
Book(4) Running 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, 2020
Book(5) Running 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
Book(6) Running Ahmed Taher, and Aboul Ella Hassanien, Modeling, Control and
Drug Development for COVID-19 Outbreak Prevention, Studies
in Systems, Decision and Control, 2021
Journal Publication
Paper Abstracts
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,"
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 that was used is called DenseNet121, and the
optimization algorithm that was 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

3. Applied Soft Computing,
CiteScore =
Impact Factor = 5.472
https://www.sciencedirect.com/science
/article/pii/S1568494620306803
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. As 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
manual search to quantify hyperparameter values. The comparison results showed that
the GSA-DenseNet121-COVID-19 was able to beat the comparison method, as the
second was able to classify only 95% of the test set samples. The proposed GSA-
DenseNet121-COVID-19 was also compared with some related work. The comparison
results showed that GSA-DenseNet121-COVID-19 is very competitive.
Zohair Malki, El-Sayed Atlam, Aboul-
Ella Hassanien, 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, CiteScore = 5.9 and IF=
3.764
https://www.sciencedirect.com/s
cience/article/pii/S09600779203
05336
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 literatures 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 the questions 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 there are a lot of questions that are unanswered
right now, and many mysteries aspects about the COVID-19 that is still unknown to us,
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. To validate the proposed method, we have collected the
required datasets related to weather and census features and necessary prepossessing is
carried out. From the experimental results, it is shown that the weather variables are
more relevant in predicting the mortality rate when compared to the other census
variables such as population, age, and urbanization. Thus, from this result, we can
conclude that temperature and humidity are important features for predicting COVID-19
mortality rate. Moreover, it is indicated that the higher the value of temperature the lower
number of infection cases.
Arpaci, Ibrahim; Alshehabi, Shadi; Al-
Emran, Mostafa; Khasawneh,
Mahmoud; Mahariq,
Ibrahim; Abdeljawad,
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, IF= 4.89
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 given to the 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 become aware of the COVID-19. It can be
suggested that social media posts may affect human psychology and behavior. These

4. results may help governments and health organizations to better understand the
psychology of the public, and thereby, better communicate with them to prevent and
manage the panic.
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).
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 inculcated thereby
assisting in designing better strategies and in taking productive decisions. These
techniques assess the situations of the past thereby enabling better predictions about the
situation to occur in the future. These predictions might help to prepare against possible
threats and consequences. Forecasting techniques play a very important role in yielding
accurate predictions. This study categorizes forecasting techniques into two types,
namely, 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
World Health Organization/National databases and data from a social media
communication. Forecasting of a pandemic can be done based on various parameters
such as the impact of environmental factors, incubation period, the impact of 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 also
provides a set of recommendations for the people who are currently 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, 2020
Globally, many research works are going on to study the infectious nature of COVID-19
and every day we learn something new about it through the flooding of the huge data that
are accumulating hourly rather than daily which instantly opens hot research topics for
artificial intelligence researchers. However, the public’s concern by now is to find
answers for 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 of time that the virus can possibly stopped and the risk of a second
rebound of COVID-19 pandemic. Therefore, this study considered SARIMA model to
predict the spread of the virus on several selected countries and is used for pandemic life
cycle and end date predictions. The study can be applied to predict the same for other
countries as the nature of the virus is the same everywhere. The advantages of this study
are that it helps the governments in making decisions and planning now for the future,
reduces anxiety and prepares the mentality of people for the next phases of the pandemic.
The most striking finding to emerge from this experimental and simulation study is that
the proposed algorithm show that the expected COVID-19 infections for the top
countries of highest number of confirmed case will slowdown in October, 2020.
Moreover, our study forecasts that there may be a second rebound of the pandemic in a
year time, if the current taken precautions 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 up the unexpected future events and scenarios.
Sujath, R., Chatterjee, J.M. &
Hassanien, A.E. A machine learning
forecasting model for COVID-19
pandemic in India. Stoch Environ Res
Coronavirus disease (COVID-19) is an inflammation disease from a new virus. The
disease causes respiratory ailment (like influenza) with manifestations, for example,
cold, cough and fever, and in progressively serious cases, the problem in breathing.
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 dependent on different factors and investigations

5. Risk Assess 34, 959–972 (2020).
https://doi.org/10.1007/s00477-020-
01827-8
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. Anticipated the potential patterns of COVID-19 effects in
India dependent on data gathered from Kaggle. With the common data about confirmed,
death and recovered cases across India for over the time length helps in anticipating and
estimating the not so distant future. For extra assessment or future perspective, case
definition and data combination must be kept up persistently.
https://link.springer.com/article/10.1007/s00477-020-01827-8
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 2020
(accepted)
COVID-19 originally known as Coronavirus has been declared as pandemic by World
Health Organization on 11th March 2020. The unprecedented pressures have arrived on
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 have become the apprehension of panic, fear and anxiety among people.
The 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 the need of the situation to implement different measures to prevent the country by
demystifying the pertinent facts and information. This paper aims to bring out the fact
that tweets containing all handles related to Covid-19 and WHO have been unsuccessful
in guiding people around this pandemic outbreaking appositely. This study analyses
around twenty three thousand most retweeted tweets within the time span from1st Jan
2019 to 23rd March 2020 and observation says that the maximum of the tweets portray
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 admissible
accuracy of 73%.
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.
IF=1.938
This study seeks to explore the effect of fear emotion on students’ and teachers’
technology adoption during COVID-19 pandemic. The study has made use of 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 due to
family lockdown situation, fear of education failure and fear of losing social
relationships are the most common types of threat 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 subjective norm (SN). The results revealed that both
techniques have successfully provided support to all the hypothesized relationships of the
research model. 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 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 in order
to cope with this situation emotionally.
Chapters Publications

6. Mohamed Elsersy , Ahmed Sherif ,
Ashraf Darwsih3, Aboul Ella
Hassanien, Digital Transformation and
Emerging Technologies for Tackling
COVID-19 Pandemic, Studies in
Systems, Decision and Control,
Springer 2020.
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 Darwsih , 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 outbreak of the new coronavirus disease (COVID-19) 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 appearance. COVID-19 threatens not
only human health but also 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, 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
visualization of epidemic information, diagnosing, 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.
Big Data Analytics and Artificial
Intelligence Against COVID-19:
Abstract. 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.
In this chapter, a model for the detection of COVID-19 virus from CT chest medical
images will be presented. 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. While deep transfer models are used to
classify the COVID-19 virus from the normal class. The original dataset consists of 746
images. The is divided into two parts; 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 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 as they

7. Innovation Vision and Approach,
Springer , Big Data series, 2020.
include a medium number of layers on their architectures if they are com-pared with
large deep transfer models such as DenseNet, and Inception-ResNet. This will reflect on
the performance of the proposed model in terms of reducing training time, memory and
CPU usage. The experimental trials show that Shufflenet is selected to be 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 which include recall,
precision, and F1 score.
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.
Abstract. The need to generate large scale datasets from a limited number of determined
data is highly required. Deep neural networks (DNN) is one of the most important and
effective tools in machine learning (ML) that required large scale datasets. Recently,
generative adversarial networks (GAN) is considered as the most potent and effective
method for data augmentation. In this chapter, we investigated the importance of using
GAN as a preprocessing stage to applied DNN for image data augmentation. Moreover,
we present a case study of using GAN networks for a limited COVID-19 X-Ray Chest
images. The results indicate that the proposed system based on using GAN-DNN is
powerful with minimum loss function for detecting 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.
Abstract— early and accurate COVID-19 diagnosis prediction plays a crucial role for
helping radiologists and health care workers to take reliable corrective actions for
classify patients and detecting 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
network (CNNs) where CNN is a single algorithm. On the other hand, ensemble
machine learning models produce higher accuracy than a single machine leaning.
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
Abstract. this paper presents how coronavirus drives education to smart education in
interpreting multi-modals. It uses to improve the 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

8. Approach, Springer , Big Data series,
2020.
presents benefits and open research challenges of a multi-modal smart education. This
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 Optimization Algorithm"
Studies in Systems, Decision and
Control, Springer 2020.
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
international relations around the world. There are different medical methods to detect
and diagnose this disease such as viral nucleic acid screening by using specimens of the
lower respiratory tract. However, the availability of 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 for diagnosis of 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 diagnosis of 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, the network hyper-
parameters in the CNN are optimized by using the PSO algorithm to eliminate the
requirement of manual search and enhance the network performance. The used chest
radiography dataset in this paper is described which leveraged to train COVID-Net and
includes include more 16,500 chest radiography images across more 13,500 patient cases
from two open access data repositories. The experimental results of this work 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.
Abstract: Although the successful detection of COVID-19 from lung computed
tomography (CT) image mainly depends on radiologist's experience, specialists
occasionally disagree with their judgments. The performance of COVID-19 detection
models needs to be improved. According to COVID-19 symptoms and human immune
approach response, there are four types of its contagion such as asymptomatic, mild,
severe, and recovered. In this chapter, an automatic scoring of 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 are used for isolating the chest lung from the CT
images. Then, PSO is used with the three segmentation algorithms for clustering the
region of interest (ROI) that consists 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 is determined and classified.

9. 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.
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 around the world. Accurate diagnosis has to turn into a critical issue affecting
the containment of this disease, especially at the countries which outbreak the virus. In
the fight against COVID-19, Artificial Intelligence (AI) techniques have played a
significant role in many aspects. In this chapter, a systematics review of the recent work
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 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.
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. 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 which is responsible for COVID19 pandemic, may
turn into an endemic (just like HIV) and will never go away. 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 front 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 carrying out 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 such similar
solutions. All these pertain to checking for COVID19 symptoms and taking actions
thereafter, 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 briefly the pandemic and tried to provide a
solution for the mental wellbeing 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 suitable 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.
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 in use to manage and control the spread of the disease which includes
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

10. infection using CT scans and chest x-ray (CXR) throughout the screening, diagnosis, and
prognostication processes of the disease. In this paper, a new model using the transfer
learning method and InceptionV3 algorithm has been presented 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 Darwsih, and
Aboul Ella Hassanien "Artificial
Intelligence Approach to Predict the
COVID-19 Patient's Recovery" Studies
in Systems, Decision and Control,
Springer 2020.
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, Asahraf Daerwish,
Aboul Ella Hassanien" Deep Learning
Technology for Tackling COVID-19
Pandemic" Studies in Systems,
Decision and Control, Springer 2020.
Abstract. Although the COVID-19 pandemic continues to expand, researchers
around the world are working to understand, diminish, and
curtail its spread. The primary _elds of research include investigating
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, is developed a machine
learning class since 2006. The exponential growth and availability
of 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
Adarsh Kumar, Mohamed Elsersy,
Ashraf Darwsih, Aboul Ella
Hassanien"Drones combat COVID-19
Epidemic: Innovating and Monitoring
Approach" Studies in Systems,
Decision and Control, Springer 2020.
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 around the world. 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 the use of drones
for the Coronavirus outbreak. This chapter explores the possibilities and opportunities of
UAV, 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 Coronavirus pandemic. The
chapter also presents some problems and challenges that can help healthcare sectors and
curbing the Coronavirus spread.

11. Mourad R Mouhamed, Ashraf Darwish,
Aboul Ella Hassanien" 3D Printing
Supports COVID-19 Pandemic
Control" Studies in Systems, Decision
and Control, Springer 2020.
At the end of December last year a new type of coronavirus has appeared in Wuhan,
China, with new properties the researchers named it COVID-19. In February, the world
health organization considers 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. The effect of this crisis touched 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, the computer scientists proposed many ideas
to fight this epidemic using emergent technologies. This chapter is covering 3D printing
principals the latest efforts against COVID-19 as one of the emergent technologies. 3D
printing technology helps to flatten the curve of the outbreak of the virus 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), where it provides the extensive
customization capability.
Lamia Nabil Mahdy, Kadry Ali Ezzat,
Ashraf Darwish and Aboul Ella
Hassanien "The Role of Social
Robotics to combat COVID-19
Pandemic" Studies in Systems,
Decision and Control, Springer 2020.
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.
Haytham H. Elmousalami, Ashraf
Darwish and Aboul Ella Hassanien
"The Truth about 5G and COVID-19:
Basics, analysis, and opportunities"
Studies in Systems, Decision and
Control, Springer 2020.
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 is declared
as an international epidemic. More than 4.5 million confirmed cases and + 308000 death
cases have been recorded around more than 209 countries on 16th May 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.
Mohamed Torky, Ashraf Darwish and
Aboul Ella Hassanien "Blockchain Use
Cases for COVID-19: Management,
Surveillance, Tracking and Security"
Studies in Systems, Decision and
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 managing COVID-19
pandemic in real-time. This chapter investigates five blockchain use cases for fighting
against the COVID-19 virus spread. Finally, this chapter is ended with discussing the

12. Control, Springer 2020. recent blockchain platforms that can be utilized for Managing epidemic diseases,
HashLog, and XMED Chain.
Mohamed Nagy, Hagar M. Abbad,,
Ashraf Darwish, Aboul Ella Hassanien
"The 4th Industrial Revolution in
Coronavirus Pandemic Era" Studies in
Systems, Decision and Control,
Springer 2020.
The global prevalence of coronavirus disease 2019 (COVID-19) requires a remarkable
avenue to endure and restrain it; Although the most advanced and sophisticated
healthcare systems around the world 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. Moreover, 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 and how it has convinced other nations the importance of accelerating and
boosting those advanced technologies to defeat the current situation by considering
China as a realistic illustration of the efficiency.
Arome J. Gabriel1, Ashraf
Darwsih and Aboul Ella
Hassanien"Cyber Security in the Age
of COVID-19" Studies in Systems,
Decision and Control, Springer 2020.
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 been closed down, to promote
social distancing in most nations across the globe. Therefore, most public/private
organizations, and even individuals have resorted to the use of diverse Information
Technologies (IT) for connecting 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 grocery. The
massive adoption of IT by almost all aspects of human life especially during this
epidemic has also led to 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 massively been developed
and used, healthcare systems are being attacked with ransom ware and resources such as
patient records confidentiality, and integrity is 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 are and could take place during
the COVID 19 pandemic as well as strategies for mitigating them.
Khaled Ahmed, Sara Abdelghafar, Aya
Salama, Nour Eldeen M.Khalifa,
Abstract. Coronavirus COVID-19 is a global pandemic stated by the World Health
Organization (WHO) in 2020. The COVID-19 devasting impact was not only affect

13. Ashraf Darwish, and Aboul Ella
Hassanien "Tracking of COVID-19
Geographical Infections on Real-Time
Tweets" Studies in Systems, Decision
and Control, Springer 2020.
human life but also many aspects of it such as social interaction, transportation options,
personal saving 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 location. 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 be used
to track the geographical infections, trends of the content, and the user's categorization.
The framework will include analysis, demographics features, statistical charts,
classifying the content of tweets related to its usefulness. The performance of the
proposed framework 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.
Ismail Elansary, Ashraf Darwish and
Aboul Ella Hassanien "The Future
Scope of Internet of Things for
Monitoring and Prediction of COVID-
19 Patients" Studies in Systems,
Decision and Control, Springer 2020.
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 had
first been discovered in December 2019 in Wuhan, China and until now has spread to the
world. Emerging technology such as the Internet of Things (IoT) and sensor networks
(SN) have been utilized widely in our everyday lives in a diversity of ways. IoT has also
been an instrumental role in fighting against the COVID-19 pandemic currently out
breaking across the globe, where 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 deadly COVID-19 outbreak in different
applications and discusses 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 is intended to provide an overview of the current status of IoT applications to IoT
researchers and the broader community and to inspire researchers to leverage IoT
potentials in the battle against COVID-19.
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 (2020
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. In this paper, a pneumonia chest x-ray detection based on
generative adversarial networks (GAN) with a fine-tuned deep transfer learning for a
limited dataset will be presented. The use of GAN positively affects the proposed model
robustness and made it immune to the overfitting problem and helps in generating 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,

14. ) which will reflect in reducing 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
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 (2020
)
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. In this paper, a pneumonia chest x-ray detection based on
generative adversarial networks (GAN) with a fine-tuned deep transfer learning for a
limited dataset will be presented. The use of GAN positively affects the proposed model
robustness and made it immune to the overfitting problem and helps in generating 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 reflect in reducing 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 (20
20)
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 that was used is called DenseNet121 and the
optimization algorithm that was used is called the gravitational search algorithm (GSA).
The GSA is adapted to determine the best values for the hyperparameters of the
DenseNet121 architecture, and to 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 was able to 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 better diagnose COVID-19 disease than the
SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. As well
as, the proposed approach was 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 results of the comparison showed that the GSA-DenseNet121 was
able to beat the other approach, as the second was able to classify only 95% of the test

15. set samples.
https://arxiv.org/abs/2004.05084
Rizk M. Rizk-Allah, Aboul Ella
Hassanien:
COVID-19 forecasting based on an
improved interior search algorithm and
multi-layer feed forward neural
network. CoRR abs/2004.05960 (202
0)
COVID-19 is a novel coronavirus that was 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. In this study, a new forecasting model is presented to analyze and forecast the CS
of COVID-19 for the coming days based on the reported data since 22 Jan 2020. The
proposed forecasting model, named ISACL-MFNN, integrates an improved interior
search algorithm (ISA) based on chaotic learning (CL) strategy into a multi-layer feed-
forward neural network (MFNN). The ISACL incorporates the CL strategy to enhance
the performance of ISA and avoid the trapping in the local optima. By this methodology,
it is intended 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 rather than
the other algorithms while forecasting task for the candidate countries.
https://arxiv.org/abs/2004.05960
Mohamed Torky, Aboul Ella
Hassanien:
COVID-19 Blockchain Framework:
Innovative
Approach. CoRR abs/2004.06081 (20
20)
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 World Health Organization (WHO) about this epidemic forecasts
an increasing threats to the lives of people 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 COVID-19
virus. In response to mitigate this challenge, this study proposes a novel innovative
approach for mitigating big challenges of (COVID-19) coronavirus propagation and
contagion. This study propose a blockchain-based framework which investigate the
possibility of utilizing peer-to peer, time stamping, and decentralized storage advantages
of blockchain to build a new system for verifying and detecting the unknown infected
cases of COVID-19 virus. Moreover, the proposed framework will enable the citizens to
predict the infection risk of COVID-19 virus within conglomerates of people or within
public places through a novel design of P2P-Mobile Application. The proposed approach
is forecasted to produce an effective system able to support governments, health
authorities, and citizens to take critical decision regarding the infection detection,
infection prediction, and infection avoidance. The framework is currently being
developed and implemented as a new system consists of four components, Infection
Verifier Subsystem, Blockchain platform, P2P-Mobile Application, and Mass-
Surveillance System. This four components work together for detecting the unknown
infected cases and predicting and estimating the infection Risk of Corona Virus
(COVID-19).
https://arxiv.org/abs/2004.06081
Aboul Ella Hassanien, Aya Salama,
Ashraf Darwsih
Coronaviruse 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 specially
the elderly. In this paper, we test three machine learning techniques to predict the

16. Artificial Intelligence Approach to
Predict the COVID-19 Patient's
Recovery, No. 3223. EasyChair,
2020
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
In mid of March 2020, Coronaviruses such as COVID-19 is 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 presents a
comparison of day level forecasting models on COVID-19 affected 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
Papers in Reviews
Applied Soft Computing Manash Sarkar, Aboul Ella Hassanien, Saptarshi Gupta, Bhavya Gaur, "
Exploring an IoT Enabled Smart Monitoring System to Combat with COVID-19
pandemic empowered with Hybrid Intelligence Techniques"
Journal of Digital imaging Nour Eldeen M. Khalifa, Mohamed Hamed N. Taha, Aboul Ella Hassanien,
Sally Elghamrawy Detection of SARS-CoV-2 Associated Pneumonia based on
Generative Adversarial Networks
World neural network Lamia Nabil Mahdy, Kadry Ali Ezzat, Haytham H. Elmousalami, Hassan Aboul
Ella, Aboul Ella Hassanien Automatic X-ray COVID-19 Lung Image
Classification System based on Support Vector Machine
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
Annals of Global health Aboul Ella Hassanien, Reham Gharbia , Atrab A. Abd El-Aziz,The Mutual
Influence between COVID-19 Pandemic and Nitrogen Dioxide Air Pollution
with Python
Health Policy and technology Aboul Ella Hassanein, Doaa Mohey El-Din, Ehab E. Hassanien, and Walaa
M.E. Hussein, Remotely Quarantine Smart Health System for
Monitoring Coronavirus Patients

17. 7
8
9
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