Deep Learning Methods For COVID-19 Mitigation: Applications, Challenges and Future Implications

The breakout of the COVID-19 infection caused a worldwide pandemic in recent years. Traditional healthcare measures and infrastructure are unable to properly manage the detection, prevention and treatment of the infection. Since the onset of the pandemic, researchers have tried to implement various deep learning approaches to counter COVID-19 with much success. Novel architectures and new ideas are being developed to this day. Motivated by this, we have reviewed the different ways deep learning can be applied in real-world COVID-19 problems. We present the challenges these implementations face. Finally, we discuss the future directions that can be taken to improve upon these DL methods to control the COVID-19 pandemic as well as future pandemics, which will result in a healthier and safer environment. © 2022 IEEE.

The Confluence of AI and Blockchain in Tackling the COVID-19 Pandemic

COVID-19 came with a sudden surge, harshly affecting the day-to-day lives of the entire world. Social and economic problems affected almost every nation, wreaking havoc on people's health, society, and economy everywhere. Although the pandemic is currently in control, the emergence of another pandemic is not unlikely. As technological breakthroughs accelerate, the possibility of controlling virological dangers becomes highly plausible. Better virus containment is attainable with the confluence of technologies such as Blockchain and AI. The newly growing fields and application cases of futuristic technologies for tackling upcoming pandemics are emerging. Several researchers are contributing to COVID-19 management with current and futuristic technologies, and such tools have room for additional improvement. This paper extensively highlights the work done in tackling COVID-19 using Blockchain and AI, illustrating the role of this collaborative approach in dealing with biological threats. We also discuss the prospects and obstacles in combining these technologies to tackle COVID-19-like situations. © 2022 IEEE.

COVID-19 Diagnosis Using Machine Learning Techniques

There have been significant revolutions in various fields like medical and education on account of improved technological advancements. Furthermore, there have been numerous cases where Machine Learning has been of great help to healthcare by analyzing data and in decision making. Early diagnosis of Covid will help reduce the transmission rate and prevent an outbreak or slow down its spread. COVID-19 is a pandemic which is spreading really fast, affecting and killing millions around the globe and this needs to be addressed soon. Big data has been growing rapidly and there are many public datasets available related to COVID-19. ML could aid in the detection of the disease to bring the current chaotic situation under control. Various machine learning algorithms have been applied in this paper to build the most accurate model that can analyses symptoms of a person and predict if they are covid positive or not using a dataset from Kaggle. The performance of each model was analyzed according to different scoring metrics like accuracy measures, R squared, Precision, ROC curve and on how long the model took to be trained. It can be inferred from this paper that Decision Tree Classifier surpasses all the other algorithms by 98.29% accuracy. © 2022 IEEE.

Applicability of ML-IoT in Smart Healthcare Systems: Challenges, Solutions Future Direction

In today's world, as population is at high peak and due to changing life style of people, individuals are suffering from various chronic disease. With shift towards modern methodology, involvement of human efforts has decreased, as know a day's people need to finish particular amount of task within few hours and with less effort. No doubt technology has made less intervention of human but it has certain limitations too. Due to less physical involvement, humans are more prone to diseases. Internet of things (IoT) plays a very crucial role in health care sector. Using various sensors, it become possible to trace the medical health condition of the human, and a message can be forwarded to nearby hospitals which helps the patients with ease. In this paper, three different diseases like heart disease, diabetes and novel COVID-19 are discussed where different machine learning algorithms are reviewed with involvement of IoT sensors. © 2022 IEEE.