This document presents a proposed solution for using Internet of Things (IoT) technology and machine learning to help address challenges posed by the COVID-19 pandemic. The document outlines an agenda including problem definition, introduction, related work, and proposed solution. Under proposed solution, the presenter proposes optimizing the K-Nearest Neighbor algorithm using salp swarm optimization to predict early COVID-19 based on patient history and current symptoms. A second proposal involves using a multi-objective marine predators algorithm for feature selection on COVID-19 x-ray data. The document discusses how IoT can provide an integrated network for healthcare during the pandemic by connecting medical devices to automatically alert staff during critical situations.

1. Presented By
Mohammed Mostafa Ahmed
Assistant Lecturer – Faculty of computers and
artificial Intelligence – University of Sadat City
Anti-COVID 19 pandemic Using
Internet of things (IoT) Technology
With Machine Learning

2. Agenda
• Problem Definition
• Introduction
• Related Work
• Proposed Solution

3. Problem Definition
• COVID-19 faces serious challenges such as
identifying the origin of the epidemic (or
the patient zero),
• Reducing the spread of the virus, and
• Having enough medical resources to treat
all the patients with severe symptoms.

4. Internet of Things (IOT)
• IoT provides an extensive integrated network for
healthcare to fight with COVID-19 pandemic. Al
medical devices are connected to the internet, and
during any critical situation, it automatically
conveys a message to the medical staff
The Internet of things (IoT) is a system of interrelated computing devices,
mechanical and digital machines provided with unique identifiers (UIDs) and the
ability to transfer data over a network without requiring human-to-human or human-
to-computer interaction.
The Internet of Things, or IoT, is a scalable and automated
solution that has seen explosive growth in other industries
such as automated manufacturing, wearable consumer
electronics, and asset management.

5. Internet of Things (IOT) Applications in HealthCare
• Remote patient monitoring or RTM. There are quite a lot of people who suffer from chronic disease
and have to visit doctors regularly. RTM is a system that is profitable for people that have problems
with the heart or diabetics. So, the patient just needs to carry the RTM device that automatically
alerts the doctor about patient deterioration.
• Wearables. Nowadays, there are a lot of devices that patients can wear every day, e.g. fitness bands,
blood pressure and heart rate monitoring cuffs, etc. These popular gadgets monitor not only the
user’s daily activity but also collect data about taken steps, burnt calories, etc. These devices change
the patients’ lives, especially elderly people because they allow constantly tracking their health
conditions. Wearables can send notifications to the family members about changes in the routine
activities or any other condition variation of the user.
• Better drug management. In order to produce and manage medicines, people spend a lot of money.
IoT devices can provide an opportunity to follow all safety standards of the pharmaceutical market.
One of the best examples is the smart vaccine fridge. It is able to prevent vaccines from spoiling and
monitor their conditions 24/7.
• Hospital management. Actually, there are a lot of ways to use IoT for optimizing everyday hospital
activities and reduce costs. Lost and stolen equipment leads to spending a lot of money. The problem
can be solved by integrating sensors to equipment, e.g. RFID or Bluetooth. This solution allows
tracking the locations at any time.

6. Advantages of Internet of Things (IOT)

7. DisAdvantages of Internet of Things (IOT)

8. Machine Learning in
COVID-19
• We want to shine a light on their work
and show how machine learning is helping
us to:
• Identify who is most at risk,
• Diagnose patients,
• Develop drugs faster,
• Finding existing drugs that can help
• Predict the spread of the disease,
• Understand viruses better,
• Map where viruses come from, and
• Predict the next pandemic.First

9. Related Work
• Elaziz, Mohamed Abd, et al. "New machine learning method for image-based diagnosis of COVID-19." PloS one 15.6 (2020): e0235187.
• Lalmuanawma, Samuel, Jamal Hussain, and Lalrinfela Chhakchhuak. "Applications of machine learning and artificial intelligence for Covid-19
(SARS-CoV-2) pandemic: A review." Chaos, Solitons & Fractals (2020): 110059.
• Tuli, Shreshth, et al. "Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing." Internet of Things
(2020): 100222.
• Abdel-Basset, Mohamed, et al. "A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based
Diversity Reduction Strategy." IEEE Access 8 (2020): 79521-79540.
• Martínez-Álvarez, Francisco, et al. "Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model."
arXiv preprint arXiv:2003.13633 (2020).
• Sujath, R., Jyotir Moy Chatterjee, and Aboul Ella Hassanien. "A machine learning forecasting model for COVID-19 pandemic in India." Stochastic
Environmental Research and Risk Assessment (2020): .

10. Proposed Solution
• Predicting of COVID-19 using data that retrieved from monitoring
patients or persons via classification of data to early predict
COVID-19
• First Proposed : Optimizing K-Nearest-Neighbor using salp swarm optimization
to predict early COVID-19 according to Patient history and current Symptoms .
The proposed method evaluated using Numerical dataset.
• Second Proposed : Feature selection, we introduced the Multi-Objective
MARINE PREDATORS ALGORITHM (MOMPA) based on Differential Evolution
(DE) as a feature selection. The proposed method evaluated using COVID-19 x-
ray

11. Proposed Solution
First Proposed : Optimizing K-Nearest-Neighbor using salp swarm optimization to predict
early COVID-19

12. Thanks