Social Economic Impacts for Covid-19 Pandemics Using Machine Learning Based Optimization Algorithm

As the number of COVID-19 patients grows exponentially, not all cases are likely dealt with by doctors and medical professionals. Researchers will add to the fight against COVID-19 by developing smarter strategies to achieve accelerated control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus that causes disease. Proposed method suggests best ways to optimize protection and avoid COVID-19 spread. Big benefit of the hybrid algorithm is that COVID-19 is diagnosed and treated more rapidly. Pandemic diseases possibilities are handling with help of Computational Intelligence, using cases and applications from current COVID-19 pandemic. This work discusses data that can be analyzed based on optimization algorithm which provides betterCOVID-19 detection and diagnosis. This algorithm uses a machine learning model to decide how the hazard function changes concerning characteristics of potential methods to find parameters in optimization of machine learning model, which has in many cases been shown to be accurate for actual clinical datasets. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Computational Intelligence Against Covid-19 for Diagnosis Using Image Processing Techniques in Healthcare Sector

Coronavirus 2019 (COVID-19) medical images detection and classification are used in artificial intelligence (AI) techniques. Few months back, from the observation it is witnessed that there is a rapid increase in using AI techniques for diagnosing COVID-19 with chest computed tomography (CT) images. AI more accurately detects COVID-19;moreover efficiently differentiates this from other lung infection and pneumonia. AI is very useful and has been broadly accepted in medical applications as its accuracy and prediction rates are high. This paper is developed and aims to fight against corona through AI using computational intelligence in detecting and classifying COVID-19 using Densnet-121 architecture on chest CT images from a global diverse multi-institution dataset. Furthermore, data from clinics and images from medical applications improve the performance of the proposed approach and provide better response with practical applications. Classification performance was evaluated by confusion matrices followed by overall accuracy, precision, recall and specificity for precisely classifying COVID-19 against any condition. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Computational Intelligence Using Big Data for Fight Against Covid-19 Pandemic in Healthcare Environment

In world, COVID-19 disease spread over 214 countries and areas which efficiently affects every aspect of our daily lives. In various areas, motivated by recent applications and advances of big data and computational intelligence (CI), this research aims at increasing their significance in COVID-19 response like prevention of severe effects and outbreaks. To improve diagnosis efforts, assess risk factors from blood tests and deliver medical supplies, CI is used during COVID-19. To forecast future COVID-19 cases, CI is used. To check goodness as high accuracy prediction method, the proposed method is checked with real-world data which focus on CI and big data, method which are used in current pandemic. In upcoming days, to enact necessary protection plans, it is very difficult to detect as well as diagnose. For computational methods with help of big data, this research provides prediction and detection of COVID-19. For predicting and detecting cases of COVID-19, performances of proposed models are used as criteria. To improve detection accuracy of COVID-19 cases, proposed method increases combination of big data analytics and CI models with nature-inspired techniques. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Factor Analysis and Reliability of the Pro-Lockdown Compliance Scale

A multilevel lockdown was introduced during the COVID-19 pandemic worldwide. This new experience, however, received mixed responses from the public in different countries including India. A quantitative self-report, the Pro-Lockdown Compliance Scale (Pro-LCS), was developed to help 1) the Government and enforcing agents understand the compliance level of the public and 2) researchers investigate the antecedent factors of the compliance of the lockdown measures. The initial 10 items were administered to 309 male residents in Kerala via an online survey. The responses were randomly divided and submitted to exploratory and confirmatory factor analyses. Both analyses consistently support that the scale is best represented by a 5-item unidimensional model. Moreover, the Pro-LBS also demonstrated adequate internal consistency. The preliminary findings suggest that the scale is a brief and useful tool to examine the compliance level of the lockdown measures.

Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images

Diagnosis of COVID-19 pneumonia using patients' chest x-ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest x-ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and support vector machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm, and it was found to have a high classification accuracy of 95%.

Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19

Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 discusses how the role of recent technologies applied to health settings can help fight virus outbreaks. Moreover, it provides guidelines on how governments and institutions should prepare and quickly respond to drastic situations using technology to support their communities in order to maintain life and functional as efficiently as possible. The book discusses topics such as AI-driven histopathology analysis for COVID-19 diagnosis, bioinformatics for subtype rational drug design, deep learning-based treatment evaluation and outcome prediction, sensor informatics for monitoring infected patients, and machine learning for tracking and prediction models. In addition, the book presents AI solutions for hospital management during an epidemic or pandemic, along with real-world solutions and case studies of successful measures to support different types of communities. This is a valuable source for medical informaticians, bioinformaticians, clinicians and other healthcare workers and researchers who are interested in learning more on how recently developed technologies can help us fight and minimize the effects of global pandemics. © 2022 Elsevier Inc. All rights reserved.

A study on issues and preventive measures taken to control Covid-19

COVID-19 different individuals different ways affects Most of the affected and disease being admitted to hospital. headache, or taste Loss of smell, rash on the skin, fingers or toes discoloration COVID-19 virus Most infected People mild and moderate respiratory illness experience special treatment need without recover Elderly and heart problems, Diabetes, chronic respiratory disease and For those with medical problems such as cancer The chances of getting a serious illness are high. COVID-19 virus, it causes disease it how spreads find out. By washing your hands without touching your face or by frequent use of alcohol-based scrubs protect yourself and others from infection. The COVID-19 virus is transmitted saliva or comes out the affected person when coughing or sneezing Nose, so you need to observe breathing habits as well. Be informed: Protect yourself: Public consultation, Myth Busters, Questions and Answers, situational reports. The Union Health Ministry clarified on Saturday that its procurement price for the vaccines Coaxing and Covishield remains the same at 150 a dose and it will continue to provide them free to States. "It is clarified that the Government's procurement price for both Covid-19 vaccines remains 150 per dose. Kobayashi noted that experts still do not know whether a person who has been vaccinated can spread the virus. U.S. Department of Disease Control and Prevention Centers "keep an eye"on COVID-19 cases in fully vaccinated people. The vaccines can cause tiredness achiness, and fever, side effects, vast majority a only day or two and serious or dangerous Side effects actually working vaccine normal signs They are different from some of the symptoms that people experience when they are vaccinated, such as fatigue, sore throat or joint pain. These types of things are common, they appear soon after vaccination and usually go away after three to five days. © 2022 Author(s).

Clinical governance: Protecting the health-care workers

Clinical governance in protecting the health-care worker (HCW) refers to measures taken by the organization in providing a safe environment for the HCW while maintaining excellence in the quality of care for the patients. In the wake of the SARS-CoV-2 virus pandemic, the key regulatory measures are taken by the infection control authority of the hospital. The Donabedian model suggests that this process is considered as structure, process, and outcome review measures. Structural changes include surveillance, screening measures, creation of outpatient clinics for COVID-suspected patients, and separate isolated bay for collection of the nasopharyngeal swab. Structural processes also include the creation of separate intensive care units (ICUs) and theaters for infected patients, negative pressure gradient in the operating room (OR), and sites where aerosol generation could occur. Creation of operational pathways such as intubation in the ICU and in the OR should be included in this. The process involves training of HCWs at various levels on the use of personal protective equipment (PPE). Provision of adequate numbers of PPE and cleaning solutions and establishing the diagnostic pathways such as the antigen test, reverse transcriptase-polymerase chain reaction, or nucleic acid amplification test are part of the processes set up by any organization. Outcome analysis involves rates of HCW infection from COVID care wards and ICU, patients testing positive at screening, and patients who may test positive after they undergo treatment at the facility. Long-term outcome measure may include mortality and length of hospital stay.

Deep Learning Approach Using 3D-ImpCNN Classification for Coronavirus Disease

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. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.