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The COVID-19 (Corona virus disease 2019) outbreak induced millions of human beings the loss of life, still spread of the disease was unstoppable and was declared a pandemic with the aid of the WHO (World Health Organization). Therefore, human beings across the world are nevertheless getting contaminated every day. Reverse Transcription Polymerase Chain Reaction (RT-PCR) to identify COVID-19 is not feasible in terms of both cost and time of identification, which might cost the patient's life. Therefore, to make identification economical and feasible, researchers were attempting to use clinical images (x-ray and CT etc.,) to detect COVID-19 with the assist of Machine Learning (ML) and Artificial Intelligence (AI) approaches to aid in automating the identification of the pandemic. This paper helps in understanding some ML and AI approaches for detecting COVID-19 from clinical lung images. The amassed records about accessible research sources and inspected a complete of 30 research papers until august 2021. This paper includes the exploration and analysis of data sets, pre-processing methods, segmentation approaches, feature extraction, classification, and test effects, which can be useful for discovering future lookup instructions in the area of automated analysis of COVID-19 sickness the usage of AI-based frameworks. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the world has witnessed rampant use of digital technology in almost every facet of huma lives. However, these digital techniques like use of mobile apps have certain drawbacks and constrains. These could be addressed with the use of emerging technologies like Internet of Things (IoT). In healthcare, that is Internet of Medical Things (IoMT). A number of ongoing studies show that integrating security measures with technology will lead to the adoption of stable IoMT applications. Emerging IoMT technologies used in conjunction with Blockchain, Artificial Intelligence, and Big Data, provide feasible solutions. The paper focuses on IoMT models, innovations, and security progress made to tackle COVID-19 issues. This paper contains the bibliometric research highlighting the use of disruptive technology for the diagnosis of COVID-19. It reviews the adoption of contactless services in the post-COVID-19 period along with potential opportunities as well as challenging obstacles for healthcare agencies, policymakers, and customers. [ABSTRACT FROM AUTHOR]

The goal to promote human limits is for Artificial Intelligence (AI). It takes a posture on public administrations, represents the increasing availability of regaining clinical data and the rapid creation of intelligent strategies. The need to stress the need to use AI in the fight against the COVID-19 crisis. The paper outlines the main role played by Ai technologies in this unprecedented war and introduces a survey of AI methods used for multiple purposes in the fight against the outbreak of COVID-19. This paper also explains how the body temperature and coughing of the incoming person are assessed and whether the incoming person has not a protective facial mask. Should either of the above tests disqualify the participant, an alarming device invokes the local officials; the entrant may otherwise enter the premises after his/her hand has been sanitized. [ABSTRACT FROM AUTHOR]

The recent outbreak of COVID-19 across the globe has been a challenge not just to the health & well-being of people. However, it has also led to a downturn in the economy throughout the world. The purpose of this study is to thoroughly investigate the possible use of emerging technologies such as robotic process automation (RPA) and artificial intelligence (AI) in order to find ways to tackle corona virus, its impact on the economy, and the people. The other technology involved is RPA which is a type of process that can easily and efficiently make it convenient for any machine to handle a repetitive task. When these two technologies are coupled, they can be used in a wide range of applications. This paper explains how RPA and AI can separately, used in various industries and sectors to tackle and effectively reduce the corona virus pandemic's negative impact. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has prompted an urgent need for reliable and efficient diagnostic tools to combat its spread. Since its origin in 2019, researchers have been trying to find out effective methods for identifying and classifying COVID-19 from other similar disease like pneumonia. This review paper gives a comparable overview of various diagnostic techniques employed by various experts. Artificial Intelligence (AI) based methods, like Machine Learning (ML) and Deep learning (DL) have widely been used for predicting COVID-19. These models usually generate good accuracy rates but still there is a scope of improvement. Hence, attention was shifted towards hybrid approaches which show better detection accuracy results then individual AI models. However, both AI and hybrid models overlook human social connections and migration patterns, which are diverse and not well-mixed in populations. COVID-19 modeling should consider the heterogeneity of individuals, especially as it disproportionately affects the elderly and those with underlying medical conditions like cancer, heart disease, diabetes, and hypertension. Furthermore, people's mobility patterns and behaviors significantly influence virus spread dynamics. Therefore, role of multi-agent systems (MAS) comes into play, wherein each agent represents different data or information. However, not much work has been done in this filed and hence it needs to be explored more in future studies. Furthermore, it was noted that a significant proportion of researchers favored the utilization of CNN and its variants and SVM classifiers, while a minority opted for RF and regression models for the purpose of classification. Overall, this review provides insights into the strengths and limitations of each approach, emphasizing the need for a comprehensive strategy that harnesses multi-agent systems for COVID-19 detection. [ABSTRACT FROM AUTHOR]

Since the emerging coronavirus pandemic spreads worldwide, countries continue to find new ways to combat and control the spread of "Covid-19." As part of the virus-fighting efforts, information on diagnostic procedures, infection and symptoms, and the most recent therapy and vaccine research have been updated. The main objective of this paper is to reduce the enormous load on the healthcare system by providing the best way to diagnose patients and predict the infection of COV-19 effectively. As a result of the scientific development in computers and their applications, this science has treated many medical problems. However, clinical trials and human skills are still required despite the undeniable contributions of artificial intelligence (AI) and data research responsible for fighting the pandemic. They are a global and open-source tool capable of assisting in this health emergency. However, due to the severity of the threat of this virus to global health and its rapid development, these solutions remain insufficient to combat it. This research paper uses data mining based on algorithms of AI and machine learning (ML) to detect and diagnose COVID-19 infection based on clinical diagnostic tests prepared previously in the Iraqi Ministry of Health. The model was provided with a dataset of the COVID-19 virus using the Python programming language. To create the model where the model predicts whether this person is infected or not infected with the virus, and if it is proven that he is in the danger zone (reaching death), can he bypass the virus and be cured. The current study results showed that the model was developed using the Random Forest Classifier algorithm more efficiently to predict infection with the Coronavirus. This represents the best model developed among other models that used various algorithms, such as Gaussian Naive Bayes, k-nearest neighbors, Support vector machine, Logistic Regression, Random Forest, Gradient boosting, Multi-layer Perceptron. [ABSTRACT FROM AUTHOR]

Due to the huge fast spread of Covid-19 around the world, which resulted in the loss of many lives, the maximum level of emergency was triggered all over the world. The best way to reduce COVID-19 infection is to prediction it early based on artificial intelligence (AI). To determine whether the patient has COVID-19 infection or not. An accurate and effective diagnosis system for Covid-19 was proposed in this paper. The diagnostic parameters for right and left lungs, D-dimer, and physiological parameters such as SpO2, temperature, and heart rate were collected from CT scans (three RGB colors) for right and left lungs, D-dimer, and physiological parameters such as SpO2, temperature, and heart rate. The data was collected from 300 patients, with each patient receiving 10 samples; 114 of them were infected with Covid-19, while the remaining 186 were uninfected. For training, testing, and verifying the gathered data, an artificial neural network (ANN) on one hidden layer at 20 nodes-based Backpropagation method was used. For all diagnostic parameters, a total of 30,000 samples were obtained (300 patient x 10 parameters x 10 samples per patient). The 3,000 data samples (300 individuals, 10 samples each) were divided into three datasets: 70% for training ANN (2,100 out of 3,000 samples), 15% for testing ANN (450 out of 3,000 samples), and 15% for validation ANN (450 out of 3,000 samples) (450 out of 3,000 samples). In terms of ANN performance, correlation coefficient, error, mean absolute error (MAE), and histogram, the results of COVID-19 diagnosis based on ANN were studied. The MAE for training, validation, and testing at 20 nodes, respectively, was 0.0012, 0.012, and 0.013, indicating that the ANN achieves good diagnostic accuracy. In training, validation, and testing at 20 nodes, the correlation coefficient (R2) between the actual and estimated value was 0.9999, 0.9996, and 0.9998, respectively. In terms of correlation coefficient and MAE, the suggested technique beat the current state-of-the-art. [ABSTRACT FROM AUTHOR]

In 2020, the Covid-19 virus has become a global pandemic, which started in the Chinese province of Wuhan and spread around the world, The aim of the present study is to discuss the most important and latest methods of tracking Covid-19 patients and the strategies followed by developed countries in integrating digital technology to control the epidemic, it also covers the most important issues and discusses the difficulties these technologies as well as how people interact with and feel about the use of this type of technology. Among these methods used, are those developed by technology companies in cooperation with telecom companies, such as Covid-19 patient tracking applications that rely on raising case information. These applications have been effective (GPS, Wi-Fi, and Bluetooth), as also the most important prevention measures to be followed and symptoms that are likely to appear on infected or carriers of the virus have been announced by global health agencies, which have been accepted all over the world. In this paper we present. A progressive of digital solutions technologies from the methods used to confront COVID-19 since the appearance of the first case used to control and stop the spread of the pandemic and track infected people, to the contemporary technologies in use, depending on the country used for each of these strategies as well, in addition to the most prominent issues that impede these preventive methods. Then there are the advantage and disadvantages of these technologies, by analyzing the different features of contemporary technology in order to combat the COVID-19 pandemic, we provide a window of ideas for reviewing technological advances used to reduce and control the spread of the epidemic. Despite the emergence of various studies related to modern technology towards COVID-19, there are still limited applications and contributions of technology in this battle. On-going progress in modern technology has contributed to improving people's lives and hence there is a solid conviction that validated research plans including artificial intelligence will be of significant advantage in helping people to fight this infection. [ABSTRACT FROM AUTHOR]

During the evolution of the COVID-19 outbreak, the necessity for companies to re-evaluate and restructure themselves is still not greater. It will make sense for things to change in the business operations. Most companies redesigned current existing ways of running business operations and capacity to make choices to benefit. The present condition sees Artificial Intelligence as a significant facilitator for companies to make their existing situation better (recover from their economic crisis), reconsider (prepare for a long-term change) and reinvent (completely re-engineer) their business model for long-term gain. Automated bots that could identify items and carry out duties that were previously reserved for people would make companies and other infrastructures operational around the clock, through more significant numbers, and at a lower cost. Simulated actual working conditions, including labour forces, would be created by using Artificial intelligence platforms. Businesses would use machine learning and sophisticated business intelligence to use artificial intelligence to explore better market dynamics and provide consumers with "hyper-personalized" goods. Some of the most compelling case studies can have human intelligence and expertise mixed with AI. Many firms should revamp current business processes and capacity to benefit the company in the near future. In this research paper, we have showcased how artificial intelligence would benefit businesses as they adopt with these current developments and during a condition of pandemic without inhibiting their activities. The research is carried in a descriptive way, choosing the diverse sectors in the economy like Banking & Finance, Manufacturing, Education, Retail, Telecommunications, Entertainment and media to make the research more robust and reliable. [ABSTRACT FROM AUTHOR]

Since early months of 2020s, when Corona virus disease-2019 (COVID-19) first appeared, the major challenge for the healthcare sector was to identify, monitor the disease and take timely decision for the treatment and management of the patients. However, the extreme spread of the disease in 2020 brought scarcity of laboratory diagnostic kits. This is how radiology became a forefront method during the outbreak of COVID-19 for diagnosis and treatment of the disease. Several studies have recently been reported using AI-based methods to diagnose COVID-19 from non-COVID pictures after training them on X-ray and CT scan images. However, to overcome the limitations of the CT, the purpose of the present paper is to develop a deep learning model to distinguish and diagnose COVID-19 and non-COVID pneumonia in chest radiographs using ResNet50 model. Only three steps: Resizing, Training and Heatmap generation were conducted on dataset. An overall accuracy of predicting COVID-19 pneumonia on ResNet50 model was found to be 85% with a specificity of 88% and sensitivity of 81% using Confusion matrix. The precision value for COVID and non-COVID pneumonia cases was 90% and 80% respectively. Thus, the current model of ResNet50 would aid radiologists in the deployment of AI-based approaches for the speedy and accurate classificationof COVID-19 cases and further control future behavior of the pandemic. [ABSTRACT FROM AUTHOR]

In this modern era the automation and artificial intelligence plays an important role. Automation not onlyincreases the quality and the productivity but it also improves the efficiency and safety of the labor or workers. By using automation we can reduce the production time. On the other hand artificial intelligence enables us to do the multitasking. By using it we can take our decision faster and smarter. In this paper we had combined the artificial intelligence with automation, in which the robotic ARM will firstly detect the cylinder by using camera. Then the gripper will moves towards the cylinder, then open its hands and pick up the cylinder and place it at required destination. We used a general purpose robot simulator in an integrated develop environment. After simulation we successfully implement it on a robotic ARM with the help of Arduino board using 18 channel servomotors. This robotic arm will firstly detect the vegetable and move towards it. While picking and placing the vegetable it had been take care that the shape of the vegetable was not deformed, so that vegetable can used in cooking vegetable. This process can also play a major role in delivering the fruits in a hospital room and living room to a person who is infected by a coronavirus disease. [ABSTRACT FROM AUTHOR]

The Latest emergency issues are impacting the world as a result of the new 2019 corona virus rise and pandemic. It has spread to the worldwide and afflicted people to Covid-19. The virus transmitting has regarded as being transmitted by individuals who find it a convenient disease explosion. While coughing and sneezing the infection spreads from the infectious droplets. Although in the air, these droplets will still survive and transfer the virus on to humans. In worldwide, robots have been used to alleviate the proliferation of new corona virus infections, COVID-19 with food preservation, food supplies, sanitation tasks, spraying disinfectant, temperature monitoring, hand sanitizers distributing, work on sensitizing, etc. For fast strategizing, that are considered hazardous for human beings. This paper discuss the difficulties and opportunities associated with using humanoid robots to minimize the risk of spread of COVID-19 in.public healthcare. The primary application of humanoid robots is the minimization of individual interaction in public places, and the provision of containment to hygiene, disinfectants and helping. The following discussion aims to underline the value of humanoid robot's purposes in specific and to link their use as the COVID-19 perspectives. Throughout the testing, review and diagnosis of a vulnerability and for subset of events, artificial intelligence plays a crucial role. It may be used during potential for the forecast of events but also to record the number of alternative cases, restored instances and deaths. Technology based on artificial intelligence is being used to provide outstanding services such as the detection and substitution of drugs for the care of employees by robotics for the provision of prescriptions and nutrition in clinics. It also disinfects the substances in response to the spread of Covid-19. [ABSTRACT FROM AUTHOR]

The healthcare industry has evolved to be one of the largest industries in almost every country due to a popular trigger of Covid-19 mechanism. COVID-19 health services and education are highly relevant because they are fundamental public resources that define the form of society in which we live both before and after the pandemic. The COVID-19 pandemic has exacerbated the already-existing informality in the healthcare sector. Because of the information asymmetry, doctors are often required to identify and treat their patients using heuristics. The use of protocols and solutions that surface insights into care unit workflows ahead of time will eliminate the need for surface-level data and disarm latent biases, resulting in better patient outcomes. The AI based applications are used to monitor records that have been reported, recovered, or expired. Through AI we can track the mutation of any deadly virus or infection in a comparatively faster duration of time and help prevent potential individuals from getting the infection. [ABSTRACT FROM AUTHOR]

The fight during pandemic has been very difficult, and the only effective prevention was to maintain Social Distancing. Maintaining social distancing allowed the disease to spread at a slower rate. Social Distancingis basically meant for people to avoid contact and interaction in public. Even in the past Artificial Intelligence proved to give some promising results to avoid common life problems. The desired prototype will be provided with some detailed information on using python, CV, deep learning to keep a check on people and their distancing. In order to maintain social distancing on streets this tool will be subjected, thus it will allow a safe distance between people. Observing People at work environments, processing plants, shops we can coordinate this device to theirsurveillance camera frameworks and can screen whether individuals are maintaining a protected separation fromone another or not. [ABSTRACT FROM AUTHOR]

COVID 19 infects many countries thereby increasing the death rate worldwide. Detection in early stage is a big challenge to government, medical practitioners. Deep learning is an artificial Intelligence method having the ability of learning quickly and can easily apply in the related to clinical traits. The learning capability of DL which are difficult for humans to sense can provide high dimensional mapping in the clinical outcomes It can be used to understand the nature of this virus and further forecast the future issues. This analysis of research is done by penetrating the related effort for COVID-19 using DL for detection and analyzing of COVID-19 deep learning provide an exact and effective structure. Deep learning can fight against this disease by applying information technology. By predicting this transferable disease by using available procedures; we can develop the ability to handle the spreading of COVID-19. [ABSTRACT FROM AUTHOR]

The very rapid development of technology today cannot be separated from the role of artificial intelligence. Artificial intelligence is a thought process or intelligence like humans' that machines can use to do specific jobs. The use of artificial intelligence is very diverse in various fields, including the health sector. Recently the world's media has been shocked by the virus spread in China, namely the coronavirus. Corona can cause severe pneumonia for sufferers. Pneumonia itself is inflammation of the lungs caused by a virus. One of pneumonia sufferers' characteristics is that there is a fluid that looks like white spots on radiology or chest X-rays. However, it will be challenging to diagnose because at a glance, the radiological test results of lungs with pneumonia and normal lungs are similar. So, misdiagnosis is possible. Therefore, this study aims to create a machine learning model to classify lungs with pneumonia and normal lung on chest radiological images using the Convolutional Neural Network (CNN) algorithm. CNN is well known as a method in which the data process both image and video. The classification obtained for validation results in an accuracy value of 94%-99% for the pneumonia category, which suggests that the model can predict the image correctly. Meanwhile, for normal lung images, the accuracy rate is 84% -98%. This shows that the model can predict almost all of the images well; there is only one image that is not predicted correctly. [ABSTRACT FROM AUTHOR]

The rise in the Covid -19 cases throughout the world has put us into a devastating situation which is affecting the well-being and health of every person. The key symptoms of Corona virus disease are illness, cough, and tiredness. Since these types of the symptoms seem to be present in pneumonia patients, so this creates difficulties in detection covid cases particularly during the flu time. Initial research in this area has found irregularities in chest X-ray images of COVID-19 diseased patients. So, chest X-ray image-based disease classification has become another way to provide support in medical diagnosis. Though, manually COVID-19 detection done from set of chest X-ray images having both COVID-19 and pneumonia cases are cumbersome and prone to human error. Thus, the use of artificial intelligence (AI) techniques followed by various machine learning and deep learning algorithms has potential to enhance the current diagnosis process by learning through radiography images to predict the presence of covid-19 disease. The COVID-19 disease has shown to be a deadly illness that has harmed the human body on a global scale and is rapidly progressing. It's tough to tell the difference between COVID-19-infected and healthy people. COVID-19-infected individuals require greater caution and must be conserved using tight procedures to minimize the risk of patients who are not infected with the virus. [ABSTRACT FROM AUTHOR]