Artificial Intelligence and Machine Learning for COVID-19

As the world navigates through the turbulent waters herald by the coronavirus pandemic, digital technologies and the Internet of Things (IoT) have crucial roles to play in bringing lasting solutions. From dissemination of information about COVID-19, protection from the virus, detection of the infected ones, and to the treatment, digital technology has been the best to help and support. This chapter reviews the use of smart technologies in the fight against COVID-19 and suggests the approaches to be adopted in this battle. It examines various digital technologies that have been applied in the fight so far and recommends adjusted ways for better results. This chapter discusses the benefits of the application of the digital technology in health sectors and highlights the challenges which have been encountered.

Artificial intelligence (AI) technology has been one savior in fighting the novel coronavirus pandemic. Moreover, it is helping to accelerate the reduction in costs associated with COVID-19 and speeding up efforts to overcome it. AI fields of applications can be classified according to the measures that have been taken against the pandemic: identification, detection, prevention, prediction, and therapeutic. Dataset collection is one of the most critical issues facing researchers who apply AI models, who tend to use augmentation data to make up for the lack of an actual dataset. According to our study, in this survey we find that the majority of COVID-19 solutions focus on how to apply AI techniques, how to collect real data, and how to further develop existing AI methods used in attacking this pandemic.

COVID-19 disease pandemic is affecting the lives of millions of people in one or another manner. To handle the COVID-19 pandemic situation, technological aspects play a vital role in parallel to medical and healthcare facilities. With the use of existing infrastructure, technologies such as artificial intelligence, neural network, blockchain technology, cloud computing, drone-based monitoring, etc. have given the important observations and awareness to many. It is observed that with the combined efforts of technology and healthcare system, recognition of the outbreak is much faster compared to earlier infections. However, many are working continuously to collect and analyze the available COVID-19-related data and introspect the future. The whole of this work is performed to maximize the use of technology and reduce the risk of a continuous outbreak. This work has discussed the recent work done over the use of technologies in handling the COVID-19 scenario. Here, a comparative analysis of various parameters in each technological aspect is discussed to have an understanding of the preferred approaches in different places. Further, brief surveys are conducted in each technological aspect for a better understanding of technological advantage in handling pandemic.

COVID-19 is a highly communicative disease that is spread throughout the world. It originated in the city of Wuhan, in China’s Hubei Province, in December of 2019. At this writing, COVID-19 has spread throughout 213 countries, and more than ten million people have been infected globally. India has ranked topmost among countries affected by the COVID-19 pandemic, and it is positioned at fourth place in all of the world. The first case of Covid-19 there was detected on 30 January 2020 in Kerala; by 3 July 2020 the number of confirmed cases of coronavirus had increased to around 625,544. It was first, detected in India on 30-January-2020 in Kerala and confirmed cases have increased as 6,25,544 till third-July-2020 across India. As the cases increases and India tas world ranking goes up, it is urgent an analysis of India’s COVID-19 epidemic be conducted. This analytical study presents the effects and trends of the COVID-19 outbreak in India using machine learning and data science techniques along with helpful visual graphs. State-wise, gender-wise and age-wise analyses are presented based on machine learning. Four ensembles, Gradient-Boosting Regressor, Ada-Boost Regressor, Extra-Trees Regressor and Random-Forest Regressor, are applied to the latest confirmed, recovered and death records in India, for predicting COVID-19’s effects and trends. The R² score has been used to measure the effectiveness of regression models. The Gradient-Boosting Regressor scores 99.37%, Extra-Trees Regressor scores 99.86%, Ada-Boost Regressor scores 92.88% and Random-Forest Regressor scores 99.43%. It has revealed that Extra-Trees Regressor outperforms for predicting the confirmed, cured and deaths cases of COVID-19 pandemic in India.

The increase of human population and the number of diseases each day causes the need for a fast and accurate diagnosis. With the development of machine learning applications, it started to be widely used in healthcare to increase the performance of the applications in the area. Autonomous diagnosis is one of the popular research areas, which reduces the workload of the health workers and gives them the opportunity to spend more time on taking care of their patients while using the help of these systems for a fast and accurate diagnosis. But no matter if the diagnosis is done manually or by an autonomous system, image enhancement is very important since it directly affects the result of the diagnosis in both cases. This chapter includes recent studies which are related to image enhancement in healthcare applications.

Coronavirus (COVID-19) is a disease which is spreading rapidly, and nearly 1,436,000 people have been infected in about 200 countries all over the world as of April 2020. It is essential to detect COVID-19 at the earliest stage to care for the infected patients and, moreover, to prevent spreading and protect uninfected people. Deep learning approach, namely, convolutional neural networks (CNNs), requires extensive training data. Due to the recent epidemic, collecting enormous radiographic images in a very short duration is a challenging task. The major issues toward the success of CNN approach is the smaller dataset. Training dataset is scaled, and the results of detecting COVID-19 are boosted by using the proposed 3D-ImpCNN approach. This paper introduces 3D_ImpCNN classification model to categorize the patient affected by COVID. The COVID-19 classification outcomes of the method introduced is analyzed which produced better results when compared against existing methods. Accuracy of 3D-ImpCNN classification method was 96.5%, and moreover this method assists in detecting COVID-19 in a rapid manner.

In recent times, the COVID-19 pandemic has affected billions of people worldwide and has resulted in the slowing down of the economy, industry shutdown, job losses, etc. Every country has taken appropriate measures to fight against pandemic by keeping in mind that health is the primary concern for human beings. This work introduces the COVID-19 pandemic and discusses its types, influence over mankind, prevention methods, and latest observations. Further, this study has designed drone-based case studies for pandemic monitoring, social distance measurements, the necessity of the control room, etc. The simulation is designed to have a single-layer drone movement strategy with a fixed distance. The simulation experimentation is derived from real-time drone movement and area coverage for sanitization. The drone movement and collision avoidance strategy are pre-emptive in nature, i.e., drones are derived to move to a fixed location and execute its functionality. At the ground level, service is designed for which people make queues and maintain social distance before being served. This case study shows its successful execution and can be mapped to a real-time environment. Further, a case study is extended to observe the real-time ambulance monitoring for patient pickup and drop at the hospital. Results show its successful working and continuous operation.

The infectious novel coronavirus (COVID-19) is said to have originated from China. The COVID-19 pandemic has spread over a hundred nations and regions on the planet and has fundamentally influenced each part of our day-by-day lives. As of present, the quantities of COVID-19 cases and deaths despite everything increment fundamentally and do not indicate a very much controlled circumstance; over a thousand cases have been accounted for around the world. Artificial intelligence (AI) goal is to adapt to human conceptual cutoff points. It is getting an outlook on human organizations, filled by the developing accessibility of helpful clinical information and the snappy movement of keen systems. Inspired by ongoing progress and uses of the artificial intelligence (AI) and Big Data in different territories, in this survey we target their underlying significance in reacting to the coronavirus flare-up and forestalling the extreme effect of the epidemic. In this survey, we initially summarize the current territory of AI applications in clinical organizations while combating COVID-19. Besides, we feature the use of Big Data while cubing this infection. We additionally review the feature, difficulties, and issues related to discovering solutions. An overview was made in ordering AI and Big Data, at that point distinguishing their applications in battling against COVID-19. Likewise, an accentuation has been made on districts that use cloud computing in battling different comparable infections to COVID-19 and COVID-19 itself. The explored strategies put forth propel clinical data investigation with a precision of up to 90%. We further end up with a point-by-point conversation about how AI usage can be in a favorable position in fighting different comparative infections. This paper gives specialists and researchers new bits of knowledge into the manners in which AI and Big Data can be used in improving the COVID-19 circumstance and drive further examinations in halting the outbreak of the virus.

Coronavirus disease 2019 (COVID-19) outbreak has affected people worldwide and radically changed routine functions to mankind. We have to use our gifted intelligence as a species on this earth to encounter this novel coronavirus. Using technology, proper governance, healthcare, and coordinated public behavior can help to mitigate the risk. The technological support in dealing with this situation is irreplaceable. In this paper, we are discussing the use of IoT, AI, and 5G technologies to manage the outbreak. We touch a number of areas where IoT, AI, and 5G play an essential role in mitigating the COVID-19’s effect. This survey paper also focuses on different ways in which IoT, AI, and 5G technologies can be used to improve people’s health while assuring the accuracy in drug and medicine delivery process. We have overviewed several examples of the technologies that are being used and will be used for more advanced and safer healthcare in the future.

The sudden outbreak of COVID-19 has challenged the normal day-to-day function of human kind. With the absence of availability of man power to carry out even the regular day-to-day requirements, the availability of basic necessities to common man is becoming a huge challenge. Various industries related to areas like production, logistics management, and supply chain management due to the unavailability of human labor have become handicapped during the phase of lockdown. Even with the resume of operation in various sectors while maintaining a proper social distancing among workers, the options with respect to man power management are very limited. The chapter aims at addressing these issues and solving them using various Artificial Intelligence algorithms and techniques. Artificial Intelligence (AI) refers to the technology which can be used in tasks requiring certain amount of intelligence in order to achieve a goal. The AI technology achieves better accuracy and stability level when incorporating algorithms bridging the quality data along with their respective services. The various AI techniques that can be employed for resource management are virtual network embedding, dynamic resource algorithm, reinforcement learning (RL), etc.