IEEE Technology Policy and Ethics: Special COVID Issue 2

September 2021

Detecting False rRT-PCR COVID-19 Test Reports Using Deep Learning Algorithms

By Muhammad Naveed Younis, Department of Computer Science, The University of Lahore, Lahore, Pakistan, Ali Raza, Department of Computer Science, University of Engineering and Technology, Taxilla, Pakistan, Syed Hashim Raza Bukhari, SMIEEE, Department of Electrical and Computer Engineering, CUI, Attock Campus, Pakistan

The novel coronavirus (COVID-19) is a disease that has shattered the entire world [1]. Catastrophic impacts are being observed upon family and social life, education, global supply chain, health care facilities, and the economy [2]. Most importantly, it has infected millions of people and wasted many precious lives, and these numbers are increasing exponentially.

To reduce the spread and save lives, people need an accurate and speedy method to diagnose the disease. The World Health Organization (WHO) recommends a real-time Reverse Transcription-Polymerase Chain Reaction (rRT-PCR) test [3]. In addition to the capabilities to produce rapid results for detecting the coronavirus, rRT-PCR tests can generate false reports. To overcome this flaw, we proposed a Deep Learning (DL)-based False Report Detection Model. The proposed model takes the input of a suspect’s symptoms, as well as the rRT-PCR test results, and then classifies the test report either as false or accurate.

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CARD Predictive Model: COVID-19 in India

By Sougata Mazumder, Debjit Majumder, Prasun Ghosal, Indian Institute of Engineering Science and Technology, Shibpur

Predictive models play a vital role in tackling COVID-19, and the usage varies across industries. The dynamic nature of models allows for analysis of the current situation, and in turn, the development of forecasts and predictions for the near future. These models are essential tools for healthcare experts and government stakeholders making decisions based on data. The importance of predictive models increases exponentially for developing, low-middle income countries, such as India, that have a high population density and a high percentage of citizens living below the poverty line. For socio-economic stability, it is essential for COVID-19 management to leverage predictive models such as the Confirmed-Active-Recovered-Death (CARD) model. In this article, we will discuss models holistically, with a special focus on the CARD Model, which presents time-varying equations to help predict the ongoing situation, regarding coronavirus, using tools like regression, curve fitting, etc.

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Digital Transformation and Cybersecurity in the Context of COVID-19 Proliferation

By Yassine Maleh, IEEE Senior member, University Sultan Moulay Slimane, Beni Mellal, Morocco

The coronavirus pandemic has given a powerful impetus to the mass adoption of digital technologies, which will bring unprecedented changes in the social and economic fabric of our society. More than ever, the digitalization of companies is accelerating. This is reflected in the massive dematerialization of information systems towards the cloud, the explosion of the Internet of Things, and the accumulation of data from users in Big Data. The ongoing measures of social exclusion in most countries of the world have forced a large part of the world’s trade, in terms of goods and services, to go online. Soon, the world is likely to see further explosive growth in the capitalization of online service providers as commodity companies decline [1]. At the same time, these openings are opportunities for companies to deploy innovative and more efficient services.

Nevertheless, this transformation must be secured by more rigorous personal data protection to install digital trust among users [2], especially as cyber-attacks related to digital transformation are multiplying. It may be possible to focus only on technological elements following the still all-too-common belief that adding a new “trendy” security tool is enough to solve IT security problems. However, in reality, cybersecurity involves an intangible triptych: technology, process, and human factor [2].

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The New Normal using Top-Notch Technologies: Artificial Intelligence & Quantum

By Ankita, Cherry Mangla, Shalli Rani (IEEE Senior Member), Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura-140401, Punjab, India

A variety of technologies such as Artificial Intelligence (AI), Data Science, Machine Learning (ML), Deep Learning (DL), Quantum Process (QP), Quantum Dots (QD) have been leveraged to help humans better understand the ongoing Coronavirus pandemic. This virus, which was first reported in 2019 in Wuhan, China, has affected many countries. The increasingly rapid spread of this virus has had a huge impact on people physically, emotionally, and financially. Countries across the globe have faced huge problems resulting from COVID-19. Specifically, countries that have dense populations, such as India and China, have faced even more challenges resulting and worsened from the virus, such as hunger and unemployment, which has further threatened people’s lives. However, many countries are gradually progressing to enhance their healthcare industry by implementing all of the latest technologies in order to reduce the spread of the disease, and in hopes of developing a cure [1]. In this battle of life and death, science plays a major role.

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IEEE Technology Policy and Ethics Editorial Board

Dr. Ali Kashif Bashir

Dr. Syed Hassan Ahmed

Dr. Onur Alparslan

Dr. Adriana Bankston

Dr. Muhammad Bilal

Dr. Syed Ahmad Chan Bukhari

Dr. Syed Hashim Raza Bukhari

Dr. Ankur Chattopadhyay

Dr. Junaid Chaudhry

Dr. Kapal Dev

Dr. Yasir Faheem

Dr. Prasun Ghosal

Dr. Tahir Hameed

Dr. Sinan Hanay

Dr. Shagufta Henna

Dr. Fatima Hussain

Dr. Steve Jones

Dr. Mohammad Saud Khan

Dr. Mohammad Khosravi

Olga Kiconco

Dr. Jerry Chun-Wei Lin

Matteo B. Lodi

Dr. Gunasekaran Manogaran

Dr. Varun G Menon

Dr. Shakil Muhammad

Dr. Zeeshan Pervez

Dr. Shalli Rani

Dr. Mubashir Husain Rehmani
Dr. Kashif Saleem
Dr. Manik Sharma

Dr. Amit Kumar Singh