A comparison between some methods of analysis count data by using R-packages

The Poisson regression model is a simple count data model that combines regression models in which the response variable is in the form of counts rather than fractional numbers in generalized linear models (GLMs). Three models (Poisson regression, quasi-Poisson regression, and negative binomial regression) were compared in r packages and applied to a sample of COVID-19 data in this study. The Poisson regression model was shown to be the best and most efficient of the other models. © 2023 Author(s).

Early-Stage Detection of Covid-19 Patient using ML Model: A Case Study

The Covid-19 pandemic has drastically changed the daily living style of human beings by astonishing the cultural, educational, regional, business, social, and marketing activities within a limited boundary. It also has impacted the healthcare system globally and provided a lot of burden on the healthcare system. The circumstances that arose due to such a pandemic require a vital solution to deal with it. In such a situation, most innovative technologies have grown up to find alternative solutions to track the situation that arises due to Covid-19. Among all innovative technologies, IoT can be counted as the best approach to deal with such a type of pandemic due to its associated features of transmitting data from any remote location without human intervention. Such type of technology has the capability of providing connectivity among various medical devices either in hospitals or other deliberate places to deal with such type of pandemic. First of all, this paper introduces the concept of IoT to deal with the circumstances of the Covid-19 pandemic. Along with that, a framework of a real-time Covid-19 patient monitoring system has been proposed in this paper that can be utilized in the future. The proposed framework helps in monitoring the symptoms of Covid-19 infected patients. On the basis of that model, a case study is done on Covid-19 symptom data by using different ML algorithms. The findings indicate that all algorithms achieved an accuracy of more than 80% and RFT achieved the highest accuracy of 92%. Based on these findings, we believe that these algorithms will produce efficient and precise outcomes when applied to real-time symptom data. © Ismail Saritas. All rights reserved.

Predicting Intensive Care Unit Admission Using Machine and Deep Learning: COVID-19 Case Study

The COVID-19 pandemic has affected the entire world by causing widespread panic and disrupting normal life. Since the outbreak began in December 2019, the virus has killed thousands of people and infected millions more. Hospitals are struggling to keep up with large patient flows. In some situations, hospitals are lacking enough beds and ventilators to accommodate all of their patients or are running low on supplies such as masks and gloves. Predicting intensive care unit (ICU) admission of patients with COVID-19 could help clinicians better allocate scarce ICU resources. In this study, many machine and deep learning algorithms are tested over predicting ICU admission of patients with COVID-19. Most of the algorithms we studied are extremely accurate toward this goal. With the convolutional neural network (CNN), we reach the highest results on our metrics (90.09% accuracy and 93.08% ROC-AUC), which demonstrates the usability of these learning models to identify patients who are likely to require ICU admission and assist hospitals in optimizing their resource management and allocation during the COVID-19 pandemic or others. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Deep Learning for Detection and Prediction of Covid-19 Virus on CT-Scan Image Dataset

As we all know, COVID-19 appears to be having a terrible impact on world health and well-being. Furthermore, at its peak, the COVID-19 cases worldwide reached a huge number i.e., in millions. The objective of the present work is to develop a model that detects COVID-19 utilizing CT-Scan Image Dataset and DL Techniques. As the number of verified cases rises, it becomes more critical to monitor and precisely categorize healthy and infected people. RT-PCR testing is the most used approach for the detection of Covid-19. However, several investigations have found that it has a low sensitivity in the early stages. Computer tomography (CT) is also used to detect image-morphological patterns of COVID-19-related chest lesions. The RT-PCR technique for diagnosing COVID has some drawbacks. For starters, test kits are insufficiently available, necessitating greater testing time and the sensitivity of testing varies. Therefore, employing CT scan pictures to screen COVID-19 is essential. The results showed that CT scan pictures might efficiently identify COVID-19, saving more lives. A Convolutional Neural Network (CNN) is a sort of Artificial Neural Network that is commonly used for image/object detection with class. An Input layer, Hidden layers, and an Output layer are common components of a neural network (NN). CNN is inspired by the brain's architecture. Artificial neurons or nodes in CNNs, like neurons in the brain, take inputs, process them, and deliver the result as output. Illness severity can be detected and calculated for future scopes and research. Another challenge encountered when dealing with severity infection detection and extending the existing work by using frameworks in order to increase accuracy. The proposed ECNN technique outperformed than CNN in terms of accuracy (95.35), execution time, and performance. This study could be extended or improved in the future by directing severity identification on the CT-Scan image dataset. © 2023 IEEE.

Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction.

With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction.

A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19

The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID19 since its outbreak. The Internet of Things (IoT) along with other technologies like Machine Learning can revolutionize the traditional healthcare system. Instead of reactive healthcare systems, IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services. In this study, a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection. The proposed framework is based on edge computing to provide personalized healthcare facilities with minimal latency, short response time, and optimal energy consumption. In this paper, the COVID-19 primary novel dataset has been used for experimental purposes employing various classification-based machine learning models. The proposed models were validated using k cross-validation to ensure the consistency of models. Based on the experimental results, our proposed models have recorded good accuracies with highest of 97.767% by Support Vector Machine. According to the findings of experiments, the proposed conceptual model will aid in the early detection and prediction of COVID-19 suspects, as well as continuous monitoring of the patient in order to provide emergency care in case of medical volatile situation.

COVID-19: machine learning methods applied for twitter sentiment analysis of Indians before, during and after lockdown

This paper emphasises the analysing sentiment of Indian citizens based on Twitter data using machine learning (ML) based approaches. The sentiment of about 1,51,798 tweets extracted from Twitter social networking and analysed based on tweets divided into six different segments, i.e., before lockdown, first lockdown, lockdown 2.0, lockdown 3.0, lockdown 4.0 and after lockdown (Unlock 1.0). Empirical results show that ML-based approach is efficient for sentiment analysis (SA) and producing better results, out of 10 ML-based models developed using N-Gram (N = 1,2,3,1-2,1-3) features for SA, linear regression model with term frequency - inverse term frequency (Tf-Idf) and 1-3 Gram features is outperforming with 81.35% of accuracy. Comparative study of the sentiment of the above six periods indicates that negative sentiment of Indians due to COVID-19 is increasing (About 4%) during first lockdown by 4.0% and then decreasing during lockdown 2.0 (34.10%) and 3.0 (34.12%) by 2% and suddenly increased again by 4% (36%) during 4.0 and finally reached to its highest value of 38.57% during unlock 1.0.

Tactile Based Intelligence Touch Technology in IoT Configured WCN in B5G/6G-A Survey

Touch-enabled sensation and actuation are expected to be the most promising, straightforward, and important uses of the B5G/6G communication networks. In light of the next generation (6G) systems' prerequisite for low latency, the infrastructure should be reconfigurable, intelligent, and interoperable in the real-time existing wireless network. It has a drastic impact on society due to its high precision, accuracy, reliability, and efficiency, combined with the ability to connect a user from remote areas. Hence, the touch-enabled interaction is primarily concerned with the real-time transmission of tactile-based haptic information over the internet, in addition to the usual audio, visual, and data traffic, thus enabling a paradigm shift towards a real-time control and steering communication system. The existing system latency and overhead often have delays and limitations on the application's usability. In light of the aforementioned concerns, the study proposes an intelligent touch-enabled system for B5G/6G and an IoT-based wireless communication network, incorporating AR/VR technologies. The tactile internet and network-slicing serve as the backbone of touch technology and incorporates intelligence from techniques such as artificial intelligence and machine/deep learning. The survey also introduces a layered and interfacing architecture with its E2E solution for the intelligent touch-based wireless communication system. It is anticipated for the upcoming 6G system to provide numerous opportunities for various sectors to utilize AR/VR technology in robotics and healthcare facilities to help in addressing several problems faced by society. Conclusively the article presents a few use cases concerning the deployment of touch infrastructure in automation, robotics, and intelligent healthcare systems, assisting in the diagnosis and treatment of the prevailing Covid-19 cases. The paper concludes with some considerable future research aspects of the proposed system with a few ongoing projects concerning the development and incorporation of the 6G wireless communication system.

The application and development of Smart city technologies in public health during the Covid-19 pandemic

Over the past few years, during the Cov-19 pandemic, a great deal of smart city technologies have been used by the public health sector to fight against the common enemy of humanity. This paper studies the challenges faced by public hospitals and public monitoring. Meanwhile, it also introduces the application and development of smart city technologies, such as AI and IoT, in the field of public health during the pandemic. From a practical standpoint, the author believes that this pandemic has provided numerous opportunities for testing smart city technologies in public health. In the future, public health must be integrated with smart city technologies in a wider range to improve cities' ability to deal with major outbreaks and provide public health care. © 2023 SPIE.

Accurate Machine Learning Algorithm for Monkey Pox Based on Covid-19

The Covid-19 virus is still marching all over the world. Many people are getting infected and a few are fatal to death. This research paper expressed that supervised learning has revealed supreme results than unsupervised learning in machine learning. Within supervised learning, random forest regression outplays all other algorithms like logistic regression (LR), support vector machine (SVM), decision tree (DT), etc. Now monkeypox is escalating in other countries at present. This virus is allied to human orthopox viruses. It can expand from one to one through contact person having rash or body fluids etc. The symptoms of monkeypox are much similar to covid19 virus-like fever, cold, fatigue, and body pains. Herewith we concluded that random forest regression shows possible foremost (97.15%) accuracy. © 2023 IEEE.

A Sustainable Quality Model for Mobile Learning in Post-Pandemic Higher Education: A Structural Equation Modeling-Based Investigation

As an effect of the digital transformation encountered by higher education institutions in the post-pandemic phase, the current study aims to inspect the factors affecting the actual use of mobile learning among higher education students. A novel hybrid model based on the information system success and technology acceptance models was proposed and tested. The study included 400 undergraduate and postgraduate students from four Saudi universities who responded to a questionnaire consisting of two parts and seven dimensions, with a total of 26 items. For the analysis, a quantitative approach was applied using structural equation modeling. The results displayed that information quality had no impact on the actual use of mobile learning among higher education students. In contrast, other quality factors (system quality, service quality, and satisfaction) and perceived factors (perceived usefulness and perceived ease of use) had a positive effect. Accordingly, this study proposed an integrated framework to assist decision makers at higher education institutions in scaffolding students to develop their educational performance by depending on mobile applications comprising high-quality factors that address their real needs. This would also enable higher education institutions to enhance their digital transformation experience, thus contributing to achieving positive learning sustainability after the pandemic.

PiMS: A Pre-ML Labelling Tool

Machine Learning (ML) techniques in clinical decision support systems are scarce due to the limited availability of clinically validated and labelled training data sets. We present a framework to (1) enable quality controls at data submission toward ML appropriate data, (2) provide in-situ algorithm assessments, and (3) prepare dataframes for ML training and robust stochastic analysis. We developed and evaluated PiMS (Pandemic Intervention and Monitoring Systems): a remote monitoring solution for patients that are Covid-positive. The system was trialled at two hospitals in Melbourne, Australia (Alfred Health and Monash Health) involving 109 patients and 15 clinicians.

CHARACTERIZING A "N-CoV-2-IgG PS" DIAGNOSTIC KIT TO QUANTIFY SARS-CoV-2 NUCLEOCAPSID PROTEIN-SPECIFIC HUMAN IgG ANTIBODIES

Confirming detected SARS-CoV-2-specific antibodies is necessary to reveal immune response in COVID-19 convalescent subjects as well as to conduct population studies by screening for specific antibodies to assess rate of COVID-19 prevalence. With this purpose St. Petersburg Pasteur Institute was the first in Russia to develop the ELISA kit for the quantitative determination of human IgG to the SARS-CoV-2 nucleocapsid (N-CoV-2-IgG PS). Arbitrary units (AU/ml) were used to assess the level of antibodies. The data shown in AU/ml were recalculated later to the international units (BAU/ml) in accordance with established the First WHO International Standard for anti-SARS-CoV-2 human Immunoglobulin. Comparing the data of the N-CoV-2-IgG PS calibration curve with those of the First WHO International Standard for anti-SARS- CoV-2 human Immunoglobulin revealed a complete inter-assay association (r = 0.999, R-2 = 0.997) allowing to find that 1BAU/ml = 5.97 AU/ml. The aim of the study was to characterize the "SARS-CoV-2 protein N Human IgG Quantitative ELISA Kit" (N-CoV-2-IgG PS), compare quantitative and qualitative data of ELISA kits, assess a correlation between the binding antibodies to SARS-CoV-2 N proteins and the neutralizing antibodies against SARS-CoV-2. The data of correlation analysis of the 83 COVID-19 convalescent blood plasma samples a significant relationship between the antibodies quantitative values and titers SARS-CoV-2-specific antibody (r = 0.8436, R-2 = 0.7802) as well as a moderate relationship between antibody concentration and positivity index (r = 0.6648, R-2 = 0.3307), assessed by Chaddock scale. Comparing concentration of N-protein binding antibodies with neutralizing antibody titers level uncovered data consistency obtained by quantitative and virus microneutralization assays (r = 0.7310, R-2 = 0.6527) used in parallel to analyze 80 blood plasma samples obtained from COVID-19 patients and convalescents. AUC under the ROC curve comprised 0.701 (P < 0.0001) evidencing about a satisfactory informative value for "N-CoV-2-IgG PS" compared with microneutralization assay. In addition, the efficacy of the "N-CoV-2-IgG PS" was 95%, while the positive and negative prognostic value was 97% and 87%, respectively. The data obtained confirmed a correlation between N-protein binding antibody level and neutralizing antibody titer. Checking inter-assay agreement evidenced about acceptance for informativeness and efficacy of using "N-CoV-2-IgG PS", thereby confirming an opportunity to apply the Kit to screen for SARS-CoV-2 N protein-specific IgG antibody level and assess seroprevalence in diverse population cohorts.

Autonomous UAV with obstacle management using AIoT: a case study on healthcare application

With the simultaneously connected 26.66 billion devices worldwide, the Internet of Things (IoT) is becoming a vast field of research and helping hand to every individual. However, when IoT and Artificial Intelligence (AI) and machine learning (ML) consolidate, it results in smart applications and future revolutions that are known as Artificial Intelligent of Things (AIoT). Similarly, the unmanned aerial vehicle (UAV) domain is also developing daily, helping many unrest people in the healthcare industry. One step towards developing the healthcare industry is the use of UAV devices like drones embedded with AIoT to work autonomously in the healthcare industry. This can help the healthcare industry in many ways. This chapter proposes an algorithm to recast these UAV drones to autonomous UAV drones and use them as intelligent or smart for various healthcare purposes like COVID-19. The proposed autonomous UAV drone uses Raspberry Pi 3, a Hubney, and a bearing formula to automatically determine the direction of the UAV movement, making it work without any controller. Also, the comparative study presented in this chapter highlighted the benefits of this proposed algorithm with others present in the literature.

A Decision Support System Based on Machine Learning to Counteract Covid-Like Pandemic Events

In this paper, the authors aim to design a decision support system (DSS) based on machine learning (ML) to assist institutions in implementing targeted countermeasures to combat and prevent emergencies such as the COVID -19 pandemic. The DSS relies on an ensemble of several ML models that combine heterogeneous data to predict risk levels at the micro and macro levels. Some preliminary analyses have already been conducted showing the correlation between nitrogen dioxide (NO2), mobility-related parameters, and COVID -19 data. However, given the complexity of the virus spread mechanism, which is related to many different factors, these preliminary studies confirmed the need to perform more in-depth analyses on the one hand and to use ML algorithms on the other hand to capture the hidden relationships between the huge amounts of data that need to be processed.

Successful Role of Data Science In Managing Covid-19 Battle

The corona pandemic's wild and unchecked spread over more than a few months around the world is a worldwide problem. To solve this worldwide issue, information technology innovation is employed along with medicine, biotechnology, and medical equipment. The fight against COVID-19 is greatly aided by Machine-Learning (ML), Artificial-Intelligence (AI), and data science (DS). By utilising such technologies, there is a good chance that the pandemic may be stopped, and that life can return to normal, as it did before the pandemic. In this essay, many technologies are analysed in relation to various situations, including social exclusion and prevention, confinement and isolation, corona virus testing and detection, management of the hospital, patient care, and therapy. This study provides transparent planning, technological techniques, digital procedures, together with the most recent smart technology in a number of disciplines, to battle the severity of the coronavirus. © 2023 IEEE.

AI Enabled Chat Bot for COVID'19

The coronavirus outbreak has far-reaching ramifications for civilizations all around the world. People are worried and have a lot of requests. A research department from Covid19 Awareness was our recommendation. We supplemented it with AI-based chatbot models to aid hospitals, patients, medical facilities, and congested areas such as airports. We propose to develop this chatbot to support current scenarios and enable hospitals or governments to achieve more to solve the objective, given the two primary factors that inexpensive and fast production is now necessary. It is an immediate necessity in this epidemic circumstance. We built this bot from the ground up to be open source, so that anybody or any institution can use it to fight Corona, and commercialization is strictly prohibited. This bot isn't for sale;instead, we'd like to devote it to the country to help with current pandemic situations. The design of advanced artificial intelligence is presented in this paper (AI). If patients are exposed to COVID-19, the chatbot assesses the severity of the illness and consults with registered clinicians if the symptoms are severe, evaluating the diagnosis and recommending prompt action. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Machine learning methods and approaches for predicting Covid19

The extensive existence about COVID-19 diseases has led to global initiatives to regulate and manage the virus with the goal of eradicating it. Machine Learning (ML) is one key for analyzing and combating COVID-19 in line with research. This is a hotly debated topic right now. Even though numerous studies are in line with medical literature, there is a requirement to follow maintain with fast-increasing quantity of papers on ML applications connected to COVID-19. Day-to-day information on the COVID-19 virus's transmission is critical for assessing the virus's global behavior. As a result, in the state of COVID-19, this paper examines forecasting methods for COVID-19 affected instances utilizing existing machine learning methods. Most of the ML algorithms used in the early detection and diagnosis of contagion are guided learning methods. The prognosis features reported by ML models are in line with medical literature findings. Many of the relevant studies are still in their early phases. One of the limitations of machine learning approaches is the use of unbalanced datasets exposed to bias in selection. © 2023 Author(s).

Generating Masked Facial Datasets Using Dlib-Machine Learning Library

In 2020, the COVID-19 pandemic spread globally, leading to countries imposing health restrictions on people, including wearing masks, to prevent the spread of the disease. Wearing a mask significantly decreases distinguishing ability due to its concealment of the main facial features. After the outbreak of the pandemic, the existing datasets became unsuitable because they did not contain images of people wearing masks. To address the shortage of large-scale masked faces datasets, a developed method was proposed to generate artificial masks and place them on the faces in the unmasked faces dataset to generate the masked faces dataset. Following the proposed method, masked faces are generated in two steps. First, the face is detected in the unmasked image, and then the detected face image is aligned. The second step is to overlay the mask on the cropped face images using the dlib-ml library. Depending on the proposed method, two datasets of masked faces called masked-dataset-1 and masked-dataset-2 were created. Promising results were obtained when they were evaluated using the Labeled Faces in the Wild (LFW) dataset, and two of the state-of-the-art facial recognition systems for evaluation are FaceNet and ArcFace, where the accuracy of using the two systems was 96.1 and 97, respectively with masked-dataset-1 and 87.6 and 88.9, respectively with masked-dataset-2. © 2022 IEEE.

An Ingenious Method to Detect COVID in X-Ray Images Using Machine Learning Techniques

Covid-19 is a fatal disease caused by the Covid-19 virus. It is very big problem for the whole world. The World Health Organization (WHO) has declared a pandemic. In May 2020, more people throughout the world had a favorable experience. The COVID illness is rapidly growing, and we are unable to stop it. We addressed the COVID-19 data science research initiatives employing a number of approaches, including statics, machine learning (ML), modelling, simulation, data visualization, and artificial intelligence (AI). We all suffering from COVID-19. in this case higher value of case comes from negative and lower false positive rate. The global impact of the COVID-19 outbreak was enormous. To tackle the pandemic, many projects have been launched, including those in the field of deep learning. This paper proposes a deep neural network modification based on the Xception model. The model is used to detect COVID-19 using chest X-ray images. Batch normalization and two stacks of two dense layers each are used in the proposed model. The layer addition is intended to avoid overfitting the proposed model. The proposed as a result, we compare the model's loss, accuracy, and performance speed, and the results show that the quality of the machine learning model has higher prediction accuracy and loss, but it takes longer to execute than traditional machine learning languages. Machine learning algorithms in general, and convolutional neural networks (CNNs) in particular, have shown promise in medical picture analysis and categorization. The architecture of this study has been presented for the diagnosis of COVID-19. © 2022 IEEE.