key: cord-0980684-npe7rser
authors: Zafar, Nadiya; Ahamed, Jameel
title: Emerging Technologies for the management of COVID19: A Review
date: 2022-05-19
journal: nan
DOI: 10.1016/j.susoc.2022.05.002
sha: 00c927c8083a7eee7071cd355d2d5ffc26d1071e
doc_id: 980684
cord_uid: npe7rser

The outbreak of COVID19 has put a halt on life over the globe. For a while, everything was stopped except the spread of disease and mortality rate. This has become the greatest challenge of decade to deal with it. Globally, scientists and researchers were busy in finding a way to deal with this deadly pandemic. As this pandemic breaks out a huge demand for healthcare equipment, medicinal facilities has been rises and Industry 4.0 seems to be a hope during this pandemic which has potential to satisfy all these needs. In the battle, against this pandemic branches of computer science: Artificial Intelligence(AI), Internet of Things(IoT), Robotics, Machine Learning(ML) and Deep Learning(DL) played very important roles. Without the help of IoT and Robotics it would be impossible for frontline warriors to remain contactless with an infected person.Meanwhile, rapid testing, prediction of disease, sentiment analysis of population and many more would be only possible due to presence ML and DL algorithms. Undoubtedly, if this pandemichappened before the emergence of AI, IoT, ML, DL and Robotics; then the aftermath will surely be something else.This paper will highlight the contribution of these technologies in handling this pandemic from its treatment to management. This paper will give idea about the role of technologies, their affects, solutions provided by them, improvement needed in healthcare facilities, their role in managing sentiments of public during pandemic. The innovative part of this paper is that we are exploring each field of industry 4.0 and observing which plays the most important role.

discovered.They also concentrated on simulation and modelling forbetter understanding of aerosol droplet transmission and its pattern.Engineers are building ways to scale up production to billion of doses as scientist and doctors are doing research and trials to create an affective vaccine [7] . The research is still ongoing around the world, with the primary goal of improving screening accuracy, coverage, and speed [8] .The delay in receiving the test results will cause a delay in tracing the affected person's contacts with another healthy person [9] . AI and computer vision systems are useful in categorising many complex structures observed in medical photos and can be employed in computer-aided diagnosis tools [10] . Training, testing, validation and algorithm deployment are done for an effective responding system towards pandemic [11] . In recent years, medical imaging computer-aided diagnosis has increasingly relied on "machine learning techniques", "deep learning models", and "convolutional neural networks (CNNs)" [12] . In recent years, deep learning, the foundation of new AI, has been linked to drastically improved diagnostic accuracy in medical imaging for the automatic detection of pulmonary illnesses. Most basic labelling for covid-19 is patient level labelling whether positive (infected) or negative (noninfected) [13] . One of the most extensively utilised and effective approaches for identifying COVID-19 from digitised photos is the "convolutional neural network (CNN)" [14] . A fuzzy rule-based strategy based on a priority-based method is offered for providing hospital beds for "COVID-19 infected patients" in the worst situation, where the counts of beds availableare much smaller than the number of pandemic infected patients [15] . Table 1 describes the applications of computer science in mitigating the pandemic. Many people's typical working habits and routine tasks have been thrown off by pandemic, but technology innovation and intervention have resulted in best practises that can be expected long after pandemic is over. The most effective are AI, Machine Learning, Analytics, Cloud-Based Platforms, Gamification, and IoT as depicted in Figure 1 [16] .

The review is categorized into various parts as shown below. I. Collection of literature : Papers, articles, thesis etc are gathered from authentic and trustable resources like IEEExplore, Sciencedirect, Elsevier and Springer etc.

Literature Survey: Research carried out all over the world in the woke of COVID-19 to find out the mitigating role of AI, ML, DL, IoT and Robotics in the battle against this pandemic.

Identifying the role of, branches of computer sciences in the battle against this pandemic: After the literature survey, identification of the contribution of "Internet of Things (IoT)", "Robotics", "Artificial Intelligence (AI)", "Machine Learning (ML)" and "Deep Learning (DL)" in handling this pandemic situation. Identification of most used ML and DL algorithm.

Discussion: Overall discussion of their role, significance, contribution and concerns. Discussion part consist of role identification played by AI, ML, DL, IoT and Robotics in the battle against this pandemic. Further in this paper, Figure 1 gives the idea how the combination of various technologies of Industry 4.0 contributing in this pandemic and Figure 2 is structured view of their application and Figure 2 represnts the percentage of literature of various domain of Industry 4.0 surveyed. In this paper, Table 1 illustrates the major domain of computer science and categorize their remarkable applications, Table 2 depicts the name of some machine learning and deep learning algorithms which are widely used in handling this pandemic, Table 3 

The "Internet of Medical Things (IoMT)," often known as the "healthcare IoT," is a network of healthcare equipment and software applications that provide comprehensive healthcare services and are linked to healthcare information technology systems. IoMT applications include1) remote patient monitoring, 2) medication order tracking, and 3) Wearables are being used to send health information to the relevant health care experts. Because of its ability to rapidly gather, analyse, and transmit health data, IoMT technologies have been recognised as having disruptive potential in the health care sector. Several researchers, medical organisations, and government agencies are working together to battle the ongoing pandemic. A. SMART TEMPERATURE SENSING DEVICES: These kind of thermometers are connected to the smartphone applications, which sends their readings to the company immediately. B. IoT BUTTONS: These buttons were developed for quick deployment in any building, regardless of size, to send immediate notifications to management, alerting them to any sanitation or maintenance issues that could jeopardise public safety. C. TELEMEDICINE: Telemedicine is the practise of remotely monitoring patients utilising IoMT technologies. This practise, also called as "telehealth", allows doctors to "evaluate", "diagnose", and "treat" patients without physically interacting with them. D. SENSORS: The "Covid19 mobile defender" is a sensing device which catches virus safety and violation measures to assist authorities in managing the coronavirus's propagation and allocating scarce available means [17] . E. Digital SCREENING TOOLS [18] .

Robots are the finest option for this risky profession since they are innately resistant to virus infection. Autonomous robots, such as the floor-cleaning bots that are already accessible as consumer products, can do more than just clean in hospitals [19] . Similar to "drone technology," other autonomous technologies such as "robots and autonomous vehicles (AVs)" have made important advancements in thebattle against the pandemic [20] .In some countries robots are used for taking care of patients as well as for cleaning and sanitising purpose, in case of severly infected patient [21] 3.3 Artificial Intelligence: Among engineering technologies AI is one of the technology which can help in managing this deadlyvirus in many ways like identification of high risk patients, providing aid in infection management and examining the transmission of virus. It can also help in predicting mortality risk by examining patient's medical records. AI is assisting in many ways in the fight against this virus like; patient screening,, healthcare facilities and assistance, infection cotrol suggestions etc. [22] . Artificial intelligence is a potential and significant method for diagnosing early coronavirus infections and monitoring infected patients' health. It is possible to dramatically increase treatment uniformity and decision making by designing effective algorithms. It is utilised in patient's treatment as well as in their monitoring. It can monitor the COVID-19 outbreak at multiple scales, including medical, molecular, and epidemiological applications. It is also helping in research work by analysing the accessible raw data.AI can help with the formulation of effective treatment regimens, preventative initiatives, and the development of drugs and vaccines [20] .Furthermore, AI has been widely applied in all major healthcare disciplines, either through automating processes or augmenting decisionmaking. "Artificial Intelligence" is a way of integrating human intelligence with machine for developing a system capable of decision making. The main function of AI would be providing necessary and exact analysis for tracking people (infected) or at risk of infection [23] . AI and ML are increasingly being employed as a tool to increase patient diagnosis rate, crowd management and surveillance, prevention of infection [24] . Several imaging-based COVID-19 diagnosis techniques supported by AI and machine learning have been presented in the last year, along with their correlation with RT-PCR. AI techniques are used to process CT and CXR images in order to detect pneumonia-like imaging features. COVID-19 is diagnosed using "CXR images" and "deepconvolutional neural networks (CNNs)." Recently, researchers sought to deliver AI-solutions based on "deep learning techniques" to distinguish between covid infected patients from healthy and other pneumonic patients [24] . There are already various AI based system for several diseases but higher growth velocity is giving tough challenges [25] . As a result, researchers have been attempting to harness the power of ML or DL in order to assist medical personnel in accurately detecting this disease [26] . ML and AI techniques has been utilised for better understanding of patient's category which helps in clinical decision making [27] . Computed tomographic scans are one of the vitally used diagnosis tool in the battle of this pandemic and a huge appalaud to AI techniques for providing rapid decision making system [28] . The combination of AI and open-source data sets results in a practical COVID-19 diagnosis solution that can be implemented in hospitals worldwide [29] . The rising number of COVID-19 hospitalizations resulted in the creation of a large medical and population structural database, which is currently accessible. These datasets enables a very significant computational methods in finding the needle in the haystack which will aid in medical decision-making for 'COVID-19' identification and prediction. Hence, data mining approaches can be carried out using a "supervised machine learning algorithm" to predict future values through classification and/or regression, or by unsupervised learning to cluster data [19] . With the help of AI assisted healthcare devices, only contactless treatment is possible. Furthermore, one of the primary advantages of an AI-based system is remote location self-treatment [30] . Inspite of having advance technology, drug invention and production is a big challenge, with more failure rate and less efficiency. As a result, computer-aided drug design is increasingly utilised to identify medications, reducing costs and chance of failure. CADD refers to computer-aided design tools for keeping, maintaining, analysing, and modelling molecular compounds. As a result, it contains tools for designing compounds, evaluating potential lead candidates, and researching compounds' chemical interactions and physicochemical properties [27] [31] . Below are the domains in which AI is assisting in dealing with this deadly virus: 

Because ML and DL have capability of recognising and predicting patterns in large, complex datasets, they have been highlighted as a viable strategy for developing COVID-19 diagnosis solutions. In comparison to other issue domains, the number of tests on COVID-19 using ML has increased rapidly in just two months [32] . Machine and deep learning (DL) techniques to automatic image analysis have recently shown promise for tissue reconstruction, classification, regression, and segmentation utilising ultrasound data [26] . When analysing vast amounts of data for disease diagnosis, MLandDLapproaches have shown impressive performance. Several methods in illness diagnostics that leverage ML and DL approaches rather than standard computer-aided systems have been developed. When there is a huge medical dataset, deep learning models are often used. To construct a prediction and detection model, it is important to automatically extract characteristics from images. The comprehension of DL approaches has been considerably diminished [25] .

ML, is a subgroup of artificial Intelligence, previously proved its efficacy in the drug discovery procedures during prior health catastrophes [20] . In general, ML is used to improve the structure of data that humans deal with. Input data is trained, and output data is statistically analysed using machine learning methods. The detection of infected people as well as the monitoring of a person's temperature are examples of ML applications [33] .A number of studies have indicated that employing convolutional neural networks or other deep learning algorithms on CXR images for COVID-19 identification produced satisfactory diagnosis accuracy [34] . "Machine learning algorithms," in instances, are used to link a patient's data parameters to the administration of a certain drug using AI technology. This type of correlation can be used to predict how a medicine will affect a certain group of patients. Doctors and medical suppliers can be better prepared for the consequences if they are aware of these factors ahead of time. Various "supervised machine learning techniques" use labelled data and features to build an automatic detection model [20] . From a set of characteristics that included Gross Domestic Product, sex, socio-political group, medical facilities, homeless, type of lockdown, population density, airport activity, and age groups, supervised machine learning (ML) algorithms were utilised to determine the major determinants driving COVID-19 infection and death numbers [35] . "Population density, testing numbers, and airport traffic" are the most prominent characteristics, followed by older age groups (over 40, notably 60+) [36] . In his studies, Dr.JayavrindaVrindavanam et al.use cough audio samples to differentiate between normal cough and covid-19 cough. The samples are then classified using a machine learning algorithm. This method is used to avoid making contact with the patient [19] . However, this procedure does not yield accurate results. Using proper "stacking algorithms,"differentiation between covid-19 patients and normal patients are possible not only this but we can also determine that a patient needed to be admit in ICU or in general ward [37] .The machine learning time series models are built using what we've learned from the spatial distribution of infections over time [38] .

Deep learning (DL) is a subset of AI, influenced by the human brain's structure [39] .DL has been a popular method for creating networks that can mimic higher-order systems and perform like humans [40] . DL is a multilayer artificial neural network that was created to increase the performance of neural networks. The greater the number of layers, the greater the accuracy [33] . It enables artificial intelligence to be trained to predict outcomes based on a set of data. To train artificial intelligence, both supervised and unsupervised learning can be used. The term deep learning refers to artificial neural networks. The human brain inspired artificial neural networks. It is made up of neurons, just like the human brain. The difference between them is the amount and speed with which they learn. To put it another way, in order to train, artificial neural networks require both a data source and computing power. The proper features are chosen to determine the quality of machine learning algorithms. Pre-processing, size reduction, feature selection, and other transactions are performed [41] .

Deep learning, also known as Convolutional Neural Networks, has received a lot of attention and praise for its effectiveness (CNN). CNN is a novel type of "neural network"which mimics the basic structure of the brain, including "neurons" and their connections across "intermediate layers, learnable weights associated with each link, activation function, and bias". A "neuron"gets information from previous links, multiplies it by its "weights", applies an "activation function" to the "weighted values," and reacts with a "new value," which is then passed on to the next layer of "neurons". Neurons for input layer are similar to the pixels for CT scanner. Numerous intermediate layers of neurons, known as "feature maps," exist behind the input layer of neurons. Because they use filters to convolve the outputs of the input layers, feature maps are also known as convolutional layers. Convolution is a feature extraction approach that passes through multiple layers of feature maps, filtering out the uninteresting and leaving the relevant features in each layer. The "feature maps" are collectively referred to as "kernels," and the designer determines their size at random. The selected output has become concise after a feature map has been 'convoluted and sub-sampled' [19] . Each neural network design learns specific patterns from other neural networks since deep CNNs are stochastic. The ensemble technique increases feature extraction as well as improving accuracy [42] . For medical diagnosis, massive DL models incorporating "Convolutional Neural Networks (CNN)" were applied. Deep Methods such as "Stacked Auto-Encoder (SAE)", "Deep Belief Network (DBN)," and "Deep Boltzmann Machine (DBM)"along with vector inputs are responsible for this [43] .The COVID19 disease damages the lungs of humans, which can be seen on a lung X-ray [44] .

To forecast the Pneumonia case from chest X-rays, an effective CNN strategy was applied employing a convolutional neural network (CNN) [45] .

Transfer learning is a deep learning technique that employs a deep convolutional neural network that has been trained to perform one task to perform another. The original model's parameters are fine-tuned for the second task [46] .Transfer learning (TL) has simplified the process of rapidly retraining neural networks on selected datasets with high accuracy [39] . DTLs such as "VGG, ResNet, and DenseNets" are now becoming an essential process in image/video detection and diagnosis for the time being. DL is used in the diagnosis of medical x-rays and computed tomography. DL improves the medical diagnosis system (MDS) to achieve excellent outcomes, and implements a relevant realtime medical diagnosis system [47] . Ensemble learning incorporates several transfer learning models, including "EfficientNet, GoogLeNet, and XceptionNet." Some models can classify patients as having "COVID-19 (+), pneumonia (+), tuberculosis (+), or being healthy" [48] . Because it does not require a huge annotated dataset for training, the transfer learning method is quicker and easier to deploy [41] .

The lockdown resulted in the closure of many businesses, economic downturns, and suicides among the common civilians. Additionally, persons who rely on multimedia devices to pass the time during a lockdown may experience severe psychological repercussions such as loneliness and sadness. As a result, there is a need to examine the psychology of the human mind in such a situation [49] .The public sentiment gleaned from numerous reflexions (hashtags, comments, tweets, and Twitter postings) is accurately measured, ensuring that various policy decisions and communications are taken into account [50] . The application exhibits premonition in the improvement of terror sentiment finally as panemicreaches its peak globally by utilising extensive textual analysis with the assistance of essential text data visualisation [51] .Text mining is defined as "the process of extracting useful information from unstructured textual data by identifying and exploring interesting patterns." Text mining is not only more useful than data mining, but also significantly more sophisticated, as it uses software that combines components of database systems, artificial intelligence, machine learning, and quantitative statistics to filter huge amounts of unstructured data. Following the filtering of this data, interesting and important patterns emerge, which can be examined and used [52] . Because a great deal of misinformation about pandemic is disseminated to the public via various technological platforms, it is necessary to identify misinformation and mis-informants and then provide accurate information [5] .Based on text features, the problem of "misinformation detection" is classed as a supervised learning problem. Shallow or deep learning-based methods for detecting deceptive posts can be used, with at least two training and testing steps. These methods aim to build a binary classification that uses a variety of characteristics and auxiliary qualities during the training process to evaluate whether a post is deceptive or not [53] . To categorise social media information, several natural language processing approaches were applied. We use supervised learning methods like "support vector machines (SVM), naive Bayes (NB), and random forest (RF)" to learn the types of unlabelled data based on labelled data [54] . Sentiment analysis can be performed using hate speeches, polarity and ranking coefficients, as well as spammy and non-spammy nodes [55] .

The following are the most notable DL and MLalgorithms used for COVID-19 detection and prediction: "Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network" [14] "Asmaa Abbas, Mohammed M. Abdelsamea, and Mohamed Medhat Gaber" [14] "Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases" [56] "Ioannis D. Apostolopoulos, Sokratis I. Aznaouridis,and Mpesiana A. Tzani" [56] "Evolving deep convolutional neutral network by hybrid sine-cosine and extreme learning machine for realtime COVID19 diagnosis from X-ray images" [57] "Chao Wu, MohammadKhishe, Mokhtar Mohammadi,Sarkhel H. Taher Karim, Tarik A. Rashid" [54] "XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection

Using Convolutional Neural Networks" [58] "VishuMadaan, Aditya Roy, Charu Gupta, Prateek Agrawal, Anand Sharma, Cristian Bologa and Radu Prodan" [2] "Convolutional neural networks for the diagnosis and prognosis of the coronavirus disease pandemic" [59] "Sneha Kugunavar and C. J. Prabhakar" [55] Deep Neural Network (DNN) "COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images" [60] "Alaa S. Al-Waisy, Shumoos Al-Fahdawi, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Salama A. Mostafa, Mashael S. Maashi, Muhammad Arif, and Begonya Garcia-Zapirain" [56] "Evolving deep convolutional neutral network by hybrid sine-cosine and extreme learning machine for realtime COVID19 diagnosis from X-ray images" [ "A new emergency response of spherical intelligent fuzzy decision process to diagnose of COVID19" [62] "Shahzaib Ashraf, Saleem Abdullah, and Alaa O. Almagrabi" [62] "A fuzzy rule-based efficient hospital bed management approach for coronavirus disease-19 infected patients" [15] "Kalyan Kumar Jena, Sourav Kumar Bhoi, Mukesh Prasad and Deepak Puthal" [15] TransferLearning Algorithm "Control The COVID-19 Pandemic: Face Mask Detection Using Transfer Learning" [63] "AbdellahOumina, Noureddine El Makhfi and Noureddine El Makhfi" [63] "Computer Vision and Radiology for COVID-19 Detection" [64] "RavneetPunia, Lucky Kumar, Mohd. Mujahid and Rajesh Rohilla" [64] "COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking" [24] "R. Elakkiya, Pandi Vijayakumar and Marimuthu Karuppiah" [24] "Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking" [46] "Ebenezer Jangam, Aaron Antonio Dias Barreto and Chandra Sekhara Rao Annavarapu" [44] "Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images" [42] "Neha Gianchandani, Aayush Jaiswal, Dilbag Singh, Vijay Kumar and Manjit Kaur" [41] "A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset" [47] "Nour Eldeen M. Khalifa, FlorentinSmarandache, Gunasekaran Manogaran and Mohamed Loey" [45] "Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images" [48] "N. Kumar, M. Gupta, D. Gupta and S. Tiwari" [46] "Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning" [65] "Mangena VenuMadhavan, Aditya Khamparia, Deepak Gupta, Sagar Pande, Prayag Tiwari and M. Shamim Hossain" [62] "Transfer learning-based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data" [66] "Mukul Sing, Shrey "Random-Forest-BaggingBroad Learning System with Applications for COVID-19 Pandemic" [69] Choujun Zhan, Yufan Zheng, Haijun ZhangandQuansiWen [61] 3.9 Mobile Applications: Based on responses from the mobile app, AI is utilised to substitute human expert judgement in calculating risk level. The software scans the user's information for suspected coronavirus infection, like some indications are (feeling feverish, headache, dry cough, breathing problem, and exhaustion), length and extremity of symptoms, travelling data, employment and home details, and population stats [19] . The "Covid19 mobile defender" is an example of a sensing device which keeps watch on virusdefensive measures and violation of rules done by people during lockdown to help authorities in managing the coronavirus' propagation and allocating scarce resources [45] . Mobile applications, on the other hand, are always a threat to the user's privacy. Malicious actors are quick to exploit the pandemic to launch cyber-attacks [70] .

It is a supervised machine learning technique that classifies new observation into some classes on the basis of trained data.

This technique is basically used to distinghuish between covid infected and non-infected person.

A machine learning technique that estimate or predict from observation.

This is used to predict severity of covid, its peak in any geographical area, its outbreak and how much will affect.

Applications of computer science used for the management of pandemic.

ML and DL used for sentiment analysis, drone cameras are used for keeping eye on lockdown, mask detection technique in crowd etc.

As covid-19 is an infectious disease, we need technologies which can handle it without contact.

Robotics and various IoT devices plays very important role in tackling it without contact; like robots taking care of highly infected patient, unmanned vehicles used for sanitisation purpose. 

Early prediction is crucial in the case of an epidemic in order to control the outbreak [79] . Based on the projection, government agencies and public health organisations can plan accordingly [80] .If an infected individual is not promptly detected and treated, he or she may become a carrier of the virus, unwittingly transmitting it to healthy persons. As a result, early detection of infected individuals would aid in quarantining them, which would aid in limiting the spread of pandemic [73] . Therefore, it would be very necessary to have some method to detect it with higher pace and accuracy. During this period of pandemic, Internet of Things (IoT), Robotics, Artificial Intelligence, Machine Learning and Deep Learning plays a vital role. Especially subset of AI; Machine Learning (ML) and Deep Learning (DL) proved to be a boon in handling this pandemic situation. Whether, they are used for classification of COVID-19 infected person, prediction of outbreak, prediction of its peak, severity of this disease, prediction of the intensive care unit (ICU) requirement. These algorithms are also used for sentiment analysis. Gathering data from social media platforms like Twitter or Facebook, and classifying them as positive, negative and neutral responses. It also helps in busting fake news and rumours. Through image classification, mask detection is also applied (recognising whether a person has wearing mask or not). All researchers use these algorithms for classification and prediction purpose. Most of them follows the approach of neural network concept, like: Convolution Neural Network (CNN), Deep Neural Network (DNN)or Transfer Learning. Internet of Things (IoT) are used in handling situation through using devices like; smart wearables for tracing isolated patient, keeping record of patient (where they go and to whom they meet), oximeter for checking oxygen level of patient and many more. In the same way Robotics technology are used for contactless treatment of patient, drones are used to keep watch on lockdown imposition, robots are also used for sanitisation and cleaning purpose, at some places unmanned vehicles are used for sanitisation. With the combination of these fields various mobile application are developed to handle pandemic. These apps have functionalities like vaccination slot booking, isolated patient tracking and many more. However, they always not happened to be a boon many a time mobile apps but the privacy of user at stake and not trustable by all users.After relief in pandemic many people experienced data breaches through many apps which they have downloaded for pandemic management purpose.

In countries with huge populations like India; rapid testing, proper imposition of lockdown, proper isolation could be only possible with the help of these (Internet of Things (IoT), Robotics, Artificial Intelligence, Machine Learning and Deep Learning). It is also observed that people with healty diet have survived this virus, more than people with improper diet [67] . Indeed, three well-known AI flaws can lead to failure: a lack of strong AI, the inability to execute without domain knowledge, and the requirement for adequate features and propagation. In addition, ML and DL methodologies should be scrutinised for identifying not only most recent elucidation but also for future improvements and research opportunities, while neglecting moral considerations (like"trust and privacy") which now limit Artificial Intelligence implementation for theh humankind [81] . COVID-19 testing labs and kits are still in short supply around the world [82] .

After the literature survey, we come to the result that, emergence of computer engineering into the battle againstthis pandemic proved to be a master-stroke. Whether it would be about treatment, advancement in rapid testing or management, it plays a vital role in all fields. Especially, a tribute to Machine learning and Deep learning that makes many predictions and classification possible. However; we can't ignore its limitations and threats. Like, through machine learning and deep learning only image tomography is possible. There are lot of things remains unsolved. Another major issue is the privacy concern. Installation of mobile apps for tracking infected patients put the privacyat stake and didn't warm welcomed by everyone. Since, the outbreak of pandemicresearch work over its prediction, classification, peak and severity using machine learning and deep learning algorithms, are still going on.Rapid testing, manufacturing PPE kits and vaccines in bulks becomes easier using AI backed techniques.Various types of sensors and wearables invented for contactless treatment. Still research is going on for detecting disease with 100% accuracy.Still research is going on vaccines for higher efficiency. However, all these technologies united are not able to defeat this pandemic properly. As this deadly virus is changing its variant rapidly and mutating itself more and more exponentially; giving a tough time to these all technologies.Here, we can see one more thing that after the disastorous wave of covid-19 world became more dependent on technology and and people became more techno-friendly. As, the situation of pandemic compelled world to go on online mode. People, now keen to do their work from home. The online delivery at their doorstep becomes more popular after this pandemic. People became familiar with new devices like rapid testing kit, automatic temperature detecting device, oximeteter etc. day by day. So, conclusively we can say that this pandemic made people more dependent on technologies. However, we can't deny this fact that these technologies created a major class difference. Privilged class and those who can afford these technologies; enjoys life even during pandemic and marganilised class suffers a lot. People suffered from malnutrition and those who have unhealthy food habits died more than people who do healthy diets.However, when this pandemic will be completely over then only real and most accurate aftermath can be calculated. When this deadly virus stop mutatingmore researches can be done over that which mutant will be most effectively handled or whether they all are handled similarly.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact

A novel machine learning based screening method for high-risk covid-19 patients based on simple blood exams

COVID-19 Candidate Treatments, a Data Analytics Approach

Medical image-based detection of COVID-19 using Deep Convolution Neural Networks

Mohamadou-2020-A review of mathematical modeli.pdf

Deep Learning for Classification and Localization of COVID-19 Markers in Pointof-Care Lung Ultrasound

Engineering Solutions to COVID-19 and Racial and Ethnic Health Disparities

AI-Enabled Technologies that Fight the Coronavirus Outbreak

Comparative Analysis of COVID-19 X-ray Images Classification Using Convolutional Neural Network, Transfer Learning, and Machine Learning Classifiers Using Deep Features

A novel framework for rapid diagnosis of COVID-19 on computed tomography scans

Machine learning to assist clinical decision-making during the COVID-19 pandemic

An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis

Supervised framework for COVID-19 classification and lesion localization from chest CT

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

A fuzzy rule-based efficient hospital bed management approach for coronavirus disease-19 infected patients

Hr during covid-19 era: Study on recent HR transformations through technological tools and trends

Distributed and scalable platform architecture for smart cities complex events data collection: Covid19 pandemic use case

Digital Screening Tool to detect Covid-19 infected People

Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network

Real-time estimation of COVID-19 cases using machine learning and mathematical models-The case of India

The CLAIRE COVID-19 initiative: approach, experiences and recommendations

Artificial Intelligence (AI) applications for COVID-19 pandemic

Is There a Place for Responsible Artificial Intelligence in Pandemics? A Tale of Two Countries

COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking

How artificial intelligence and machine learning can help healthcare systems respond to COVID-19

A bi-stage feature selection approach for COVID-19 prediction using chest CT images

Mathematical Multidimensional Modelling and Structural Artificial Intelligence Pipelines Provide Insights for the Designing of Highly Specific AntiSARS-CoV2 Agents

Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research

COVID-19 open source data sets: A comprehensive survey

Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic

Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art

Intelligent system for COVID-19 prognosis : a state-of-the-art survey

Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review

Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

Machine learning approach for clustering of countries to identify the best strategies to combat Covid-19

Application of embedded computer and improved genetic algorithm in the strategy of community of human destiny: the development of artificial intelligence in the context of Covid-19

Machine learning to predict COVID-19 and ICU requirement

Time Series Forecasting of COVID-19 Infections in United Arab Emirates using ARIMA

Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19

COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

COVID-19 Detection Using Deep Learning Methods

Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images

IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification

Deep Learning in Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs on CXR and CT Images

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking

A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset

Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images

A study on the sentiments and psychology of twitter users during COVID-19 lockdown period

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

Smart Simon Bot with Public Sentiment Analysis for Novel Covid-19 Tweets Stratification

Mining Text in Online News Reports of COVID-19 Virus: Key Phrase Extractions and Graphic Modeling

Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization

Characterizing the Propagation of Situational Information in Social Media during COVID-19 Epidemic: A Case Study on Weibo

An Intelligent Centrality Measures for Influential Node Detection in COVID-19 Environment

Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases

Evolving deep convolutional neutral network by hybrid sine-cosine and extreme learning machine for realtime COVID19 diagnosis from X-ray images

XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

Convolutional neural networks for the diagnosis and prognosis of the coronavirus disease pandemic

COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images

Evaluation of Factors to Respond to the COVID-19 Pandemic Using DEMATEL and Fuzzy Rule-Based Techniques

A new emergency response of spherical intelligent fuzzy decision process to diagnose of COVID19

Control the COVID-19 Pandemic: Face Mask Detection Using Transfer Learning

Computer vision and radiology for COVID-19 detection

Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning

Transfer learningbased ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data

Predicting the Probability of Covid-19 Recovered in South Asian Countries Based on Healthy Diet Pattern Using a Machine Learning Approach

Preliminary diagnosis of COVID-19 based on cough sounds using machine learning algorithms

Random-Forest-Bagging Broad Learning System with Applications for COVID-19 Pandemic

Beyond the virus: a first look at coronavirus-themed Android malware

Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays

COVID-19 Chest CT image segmentation network by multi-scale fusion and enhancement operations

A new clustering method for the diagnosis of CoVID19 using medical images

Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification

routing for the urgent delivery of face shields during the COVID-19 pandemiVehiclec

A new approach to qlinear Diophantine fuzzy emergency decision support system for COVID19

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Industry 4.0 technologies and their applications in fighting COVID-19 pandemic

Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model

Kalman filter based short term prediction model for COVID-19 spread

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients