Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.

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Applications of artificial intelligence in battling against covid-19: A literature review.

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12759

Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.

Word Health Organization declared coronavirus disease 2019 (COVID-19) as pandemic on 11 March 2020. After that on 14 March 2020 the Ministry of Home Affairs, India decided to treat COVID-19 as a "notified disaster" due to the spurt in the cases related to coronavirus in the country, leading to a complete shut down from 24 March 2020. This has affected all sectors of the country including the education sector. The near-total closure of schools, colleges, and universities has disrupted academic activities at various levels. The objective of this online survey study is to understand the day to day living, activities, learning styles, and mental health of young students of India during this unprecedented crisis and assess how they are adapting to the new e-learning styles and how they are managing their social lives.

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Effects of the Disastrous Pandemic COVID 19 on Learning Styles, Activities and Mental Health of Young Indian Students - A Machine Learning Approach

Over the last decade, remote sensing technology has advanced dramatically, resulting in significant improvements on image quality, data volume, and application usage. These images have essential applications since they can help with quick and easy interpretation. Many standard detection algorithms fail to accurately categorize a scene from a remote sensing image recorded from the earth. A method that uses bilinear convolution neural networks to produce a lessweighted set of models those results in better visual recognition in remote sensing images using fine-grained techniques. This proposed hybrid method is utilized to extract scene feature information in two times from remote sensing images for improved recognition. In layman's terms, these features are defined as raw, and only have a single defined frame, so they will allow basic recognition from remote sensing images. This research work has proposed a double feature extraction hybrid deep learning approach to classify remotely sensed image scenes based on feature abstraction techniques. Also, the proposed algorithm is applied to feature values in order to convert them to feature vectors that have pure black and white values after many product operations. The next stage is pooling and normalization, which occurs after the CNN feature extraction process has changed. This research work has developed a novel hybrid framework method that has a better level of accuracy and recognition rate than any prior model.

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Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach

The nation has already had to contend with such a major public health issue when this COVID-19 disease was propagated in March 2020. The corona's propagation does have an effect on all areas of people's lives. The hostile effect on education systems throughout all locations is indeed a diverse expansion impact. A complete shutdown keeps adding fresh problems for students to learn and also for educators to effectively manage the class, likely to result in the conversion of such an offline education system into a virtual class. This study investigated and described various online learning platforms, as well as teaching resource distribution technology and modern technologies used to ensure that students can study. Overall, this relates to channels that can be used to create a confidential environment in order to prevent copying in online tests. This also outlines so many difficulties in managing online teaching methods, like learners' perceptions of e-learning being more traumatic and having a negative impact on general wellbeing and social relationships. So, a technique has been proposed to provide learners with a superior online classroom teaching experience, allowing the online classroom to be as good as, if not better than, a single online classroom. This study focused on daily teaching methods that employ online learning supported by a machine teaching approach to provide an individual with a relevant stress-free solution.

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Impact of SARS-CoV-2 in Online Education, Predicting and Contrasting Mental Stress of Young Students: A Machine Learning Approach

Catastrophic disasters are striking more often in recent years forcing researchers to put a lot of emphasis on managing disaster information and establishing the state-of-art technologies to minimize the losses During any natural or man-made disaster, microblogging platforms are increasingly being used to share time-sensitive information In recent years, these platforms have become an important source to gather news about the current situation, supply of food and medicines and checking on family and friends Artificial Intelligence (AI) based information retrieval systems serve as a novel way of gaining situation awareness and planning for relief operations during disasters This paper summarizes various AI techniques that are being applied to process the data posted on popular microblogging platforms at the time of disaster Case studies of few disasters in the last decade are presented which clearly emphasize that the advances in social networking platforms and information and communication technologies have created enormous opportunities for circulation and consumption of time critical information during emergencies This study clearly illustrates the impact of the application of new and emerging AI technologies in managing disasters

Emerging Role of Artificial Intelligence for Disaster Management Based on Microblogged Communication

In the last decade, emergency responders, government organizations and disaster response agencies have certainly acknowledged that microblogging platforms such as Twitter and Instagram can be an important source of actionable information at the time of disaster. However, researchers feel that analyzing social media data for disasters using supervised machine learning algorithms is still a challenge. During the first few hours of any disaster when labeled data is not available, the learning process gets much delayed. The first objective of this study is to explore the domain adaptation techniques by making use of labeled data of some previous disaster along with the abundance of unlabeled data that is available for the current disaster. The second objective is to apply these domain adaptation techniques on disaster-related imagery data from the microblogging platforms since imagery data has largely been unexplored as compared to textual content. To achieve these objectives, domain adaptation methods would be applied to classify the images of an ongoing disaster as informative versus non-informative. This study, for the first time, proposes a semi-supervised domain adaptation technique where the classifier is trained on three types of data, labeled data of the previous disaster, unlabeled data of current disaster and a small batch of labeled data of current disaster. Our experiments have been performed on Twitter images corresponding to three disasters. The experimental results show that there is an improvement in the accuracy of the classification model if a small batch of labeled target images is also added along with the unlabeled target images and labeled source images at the time of training. The experiment aims to make the best use of the labeled data of a previous disaster to analyze the current disaster without any delay for better response and recovery.

Deep Domain Adaptation Approach for Classification of Disaster Images

“Generalization of convolutional network to domain adaptation network for classification of disaster images on twitter”

Since December 2019 the world has been facing an unprecedented crisis in handling the outbreak of the biological disaster COVID-19 putting a lot of pressure on the healthcare systems globally. Poor collection of swab samples, delayed testing, or variations that have possibly changed the disease patterns may be the reasons that have led to the increased false-negative results of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests recently which is considered the gold standard to detect Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). At such times the doctors have to depend on X-rays or CT scans of the chest to establish the diagnosis. Artificial Intelligence (AI) based diagnosis through deep learning models for the classification of medical radiological images can play a very important role in the present scenario. However, for new infections like SARS-CoV-2 and its Variants of Concern (VOC) the labeled data may not be readily available for training a deep learning model while at the same time, the labeled images may be available for similar previously existing infections like viral or bacterial pneumonia. This study aims to propose a novel Semi-Supervised Domain Adaptation neural network, CoVSSDA to handle this limitation. CoVSSDA is an end-to-end deep convolutional neural network that is trained on the labeled images of the related previous infection with two classes {Normal, Pneumonia}, unlabeled images of the new infection with three classes {COVID19, Normal, Pneumonia} and a small batch of labeled images of the new infection such that the trained model acquires the knowledge about the novel class COVID19 and adapts to achieve an accuracy of 93.92% when tested for the new infection on the target domain and also outperforms other models based on domain adaptation.

Anuradha Khattar

Papers

Effects of the Disastrous Pandemic COVID 19 on Learning Styles, Activities and Mental Health of Young Indian Students - A Machine Learning Approach

Emerging Role of Artificial Intelligence for Disaster Management Based on Microblogged Communication

Deep Domain Adaptation Approach for Classification of Disaster Images

“Generalization of convolutional network to domain adaptation network for classification of disaster images on twitter”

A Semi-Supervised Domain Adaptation Approach for Diagnosing SARS-CoV-2 and its Variants of Concern (VOC)