A proposed collaborative framework by using artificial intelligence-internet of things (AI-IoT) in COVID-19 pandemic situation for healthcare workers

Exploring the Role of Artificial Intelligence in Internet of Things Systems: A Systematic Mapping Study

The use of Artificial Intelligence (AI) in Internet of Things (IoT) systems has gained significant attention due to its potential to improve efficiency, functionality and decision-making. To further advance research and practical implementation, it is crucial to better understand the specific roles of AI in IoT systems and identify the key application domains. In this article we aim to identify the different roles of AI in IoT systems and the application domains where AI is used most significantly. We have conducted a systematic mapping study using multiple databases, i.e., Scopus, ACM Digital Library, IEEE Xplore and Wiley Online. Eighty-one relevant survey articles were selected after applying the selection criteria and then analyzed to extract the key information. As a result, six general tasks of AI in IoT systems were identified: pattern recognition, decision support, decision-making and acting, prediction, data management and human interaction. Moreover, 15 subtasks were identified, as well as 13 application domains, where healthcare was the most frequent. We conclude that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains.

Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs)

In pulmonary inflammation diseases, like COVID-19, lung involvement and inflammation determine the treatment regime. Respiratory inflammation is typically arisen due to the cytokine storm and the leakage of the vessels for immune cells recruitment. Currently, such a situation is detected by the clinical judgment of a specialist or precisely by a chest CT scan. However, the lack of accessibility to the CT machines in many poor medical centers as well as its expensive service, demands more accessible methods for fast and cheap detection of lung inflammation. Here, we have introduced a novel method for tracing the inflammation and lung involvement in patients with pulmonary inflammation, such as COVID-19, by a simple electrolyte detection in their sputum samples. The presence of the electrolyte in the sputum sample results in the fern-like structures after air-drying. These fern patterns are different in the CT positive and negative cases that are detected by an AI application on a smartphone and using a low-cost and portable mini-microscope. Evaluating 160 patient-derived sputum sample images, this method demonstrated an interesting accuracy of 95%, as confirmed by CT-scan results. This finding suggests that the method has the potential to serve as a promising and reliable approach for recognizing lung inflammatory diseases, such as COVID-19.

COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.

Emerging Technology-Driven Hybrid Models for Preventing and Monitoring Infectious Diseases: A Comprehensive Review and Conceptual Framework

The emergence of the infectious diseases, such as the novel coronavirus, as a significant global health threat has emphasized the urgent need for effective treatments and vaccines. As infectious diseases become more common around the world, it is important to have strategies in place to prevent and monitor them. This study reviews hybrid models that incorporate emerging technologies for preventing and monitoring infectious diseases. It also presents a comprehensive review of the hybrid models employed for preventing and monitoring infectious diseases since the outbreak of COVID-19. The review encompasses models that integrate emerging and innovative technologies, such as blockchain, Internet of Things (IoT), big data, and artificial intelligence (AI). By harnessing these technologies, the hybrid system enables secure contact tracing and source isolation. Based on the review, a hybrid conceptual framework model proposes a hybrid model that incorporates emerging technologies. The proposed hybrid model enables effective contact tracing, secure source isolation using blockchain technology, IoT sensors, and big data collection. A hybrid model that incorporates emerging technologies is proposed as a comprehensive approach to preventing and monitoring infectious diseases. With continued research on and the development of the proposed model, the global efforts to effectively combat infectious diseases and safeguard public health will continue.

Implementing a chatbot on Facebook to reach and collect data from thousands of health care providers: PharmindBot as a case

BACKGROUND

The world is moving fast toward digital transformation as we live in the artificial intelligence (AI) era. The COVID-19 pandemic accelerates this movement. Chatbots were used successfully to help researchers collect data for research purposes.

OBJECTIVE

To implement a chatbot on the Facebook platform to establish connections with health care professionals who had subscribed to the chatbot, provide medical and pharmaceutical educational content, and collect data for online pharmacy research projects. Facebook was chosen because it has billions of daily active users, which offers a massive potential audience for research projects.

PRACTICE DESCRIPTION

The chatbot was successfully implemented on the Facebook platform following 3 consecutive steps. Firstly, the ChatPion script was installed on the Pharmind website to establish the chatbot system. Secondly, the PharmindBot application was developed on Facebook. Finally, the PharmindBot app was integrated with the chatbot system.

PRACTICE INNOVATION

The chatbot responds automatically to public comments and sends subscribers private responses using AI. The chatbot collected quantitative and qualitative data with minimal costs.

EVALUATION METHODS

The chatbot's auto-reply function was tested using a post published on a specific page on Facebook. Testers were asked to leave predefined keywords to test its functionality. The chatbot's ability to collect and save data was tested by asking testers to fill out an online survey within Facebook Messenger for quantitative data and answer predefined questions for qualitative data.

RESULTS

The chatbot was tested on 1000 subscribers who interacted with it. Almost all testers (n = 990, 99%) obtained a successful private reply from the chatbot after sending a predefined keyword. Also, the chatbot replied privately to almost all public comments (n = 985, 98.5%) which helped to increase the organic reach and to establish a connection with the chatbot subscribers. No missing data were found when the chatbot was used to collect quantitative and qualitative data.

CONCLUSIONS

The chatbot reached thousands of health care professionals and provided them with automated responses. At a low cost, the chatbot was able to gather both qualitative and quantitative data without relying on Facebook ads to reach the intended audience. The data collection was efficient and effective. Using chatbots by pharmacy and medical researchers will help do more feasible online studies using AI to advance health care research.

Fuzzy-logic-based IoMT framework for COVID19 patient monitoring

Smart healthcare is an integral part of a smart city, which provides real time and intelligent remote monitoring and tracking services to patients and elderly persons. In the era of an extraordinary public health crisis due to the spread of the novel coronavirus (2019-nCoV), which caused the deaths of millions and affected a multitude of people worldwide in different ways, the role of smart healthcare has become indispensable. Any modern method that allows for speedy and efficient monitoring of COVID19-affected patients could be highly beneficial to medical staff. Several smart-healthcare systems based on the Internet of Medical Things (IoMT) have attracted worldwide interest in their growing technical assistance in health services, notably in predicting, identifying and preventing, and their remote surveillance of most infectious diseases. In this paper, a real time health monitoring system for COVID19 patients based on edge computing and fuzzy logic technique is proposed. The proposed model makes use of the IoMT architecture to collect real time biological data (or health information) from the patients to monitor and analyze the health conditions of the infected patients and generates alert messages that are transmitted to the concerned parties such as relatives, medical staff and doctors to provide appropriate treatment in a timely fashion. The health data are collected through sensors attached to the patients and transmitted to the edge devices and cloud storage for further processing. The collected data are analyzed through fuzzy logic in edge devices to efficiently identify the risk status (such as low risk, moderate risk and high risk) of the COVID19 patients in real time. The proposed system is also associated with a mobile app that enables the continuous monitoring of the health status of the patients. Moreover, once alerted by the system about the high risk status of a patient, a doctor can fetch all the health records of the patient for a specified period, which can be utilized for a detailed clinical diagnosis.

Managing healthcare supply chain through artificial intelligence (AI): A study of critical success factors

Healthcare is one of the most critical sectors due to its importance in handling public health. With the outbreak of various diseases, more recently during Covid-19, this sector has gained further attention. The pandemic has exposed vulnerabilities in the healthcare supply chain (HSC). Recent advancements like the adoption of various advanced technologies viz. AI and Industry 4.0 in the healthcare supply chain are turning out to be game-changers. This study focuses on identifying critical success factors (CSFs) for AI adoption in HSC in the emerging economy context. Rough SWARA is used for ranking CSFs of AI adoption in HSC. Results indicate that technological (TEC) factors are the most influential factor that impacts the adoption of AI in HSC in the context of emerging economies, followed by institutional or environmental (INT), human (HUM), and organizational (ORG) dimensions.

Integration of cloud computing with the Internet of things for the treatment and management of the COVID-19 pandemic

The globe is now fighting the COVID-19 epidemic, and a cost-effective method to battle the outbreak is urgently required. In this era, the Internet of Things (IoT) offers healthcare systems or checks and discovers patients in epidemics to prevent the virus from spreading. In order to tackle the current COVID-19 pandemic, intelligent health care based on IoT is becoming increasingly vital in better digital technologies. This technology allows gadgets to link hospitals and other predefined sites to tackle this dilemma. The cognitive Internet of Medical Things (IoMT) is a potential technology for quick analysis, dynamic monitoring and control, improved therapy and management, and prevention of viral transmission. This technology can send a message to the hospital immediately in an emergency. Due to the importance of this subject, the influence of IoT-centered innovations in the management of COVID-19 is examined in this article. The three main areas, including early detection, viral prevention, and patient care, are considered in this paper. The results showed that using IoT, such as drones and robots, has been fruitful in reducing contact with patients and identifying disease symptoms. Also, they showed that the IoT wearable gadgets and the cloud platform to store the patient’s information have helped doctors track the patient’s condition.

Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel on Artificial Intelligence in Interventional Radiology: From Code to Bedside

Artificial intelligence (AI)-based technologies are the most rapidly growing field of innovation in healthcare with the promise to achieve substantial improvements in delivery of patient care across all disciplines of medicine. Recent advances in imaging technology along with marked expansion of readily available advanced health information, data offer a unique opportunity for interventional radiology (IR) to reinvent itself as a data-driven specialty. Additionally, the growth of AI-based applications in diagnostic imaging is expected to have downstream effects on all image-guidance modalities. Therefore, the Society of Interventional Radiology Foundation has called upon 13 key opinion leaders in the field of IR to develop research priorities for clinical applications of AI in IR. The objectives of the assembled research consensus panel were to assess the availability and understand the applicability of AI for IR, estimate current needs and clinical use cases, and assemble a list of research priorities for the development of AI in IR. Individual panel members proposed and all participants voted upon consensus statements to rank them according to their overall impact for IR. The results identified the top priorities for the IR research community and provide organizing principles for innovative academic-industrial research collaborations that will leverage both clinical expertise and cutting-edge technology to benefit patient care in IR.

College Smart Classroom Attendance Management System Based on Internet of Things

Since entering the information age, educational informatization reform has become the inevitable trend of the development of colleges and universities. The traditional education management methods, especially the classroom attendance methods, not only need to rely on a large number of manpower for data collection and analysis but also cannot dynamically monitor students' attendance and low efficiency. The development of Internet of things technology provides technical support for the informatization reform of education management in colleges and universities and makes the classroom attendance management in colleges and universities have a new development direction. In this study, a college smart classroom attendance management system based on RFID technology and face recognition technology is constructed under the architecture of the Internet of things, and the corresponding simulation experiments are carried out. The experimental results show that the smart classroom attendance management system based on RFID technology can accurately identify the absence and substitution of students and has the advantages of fast response and low cost. However, its recognition is easily affected by obstructions, which requires students to place identification cards uniformly. The smart classroom attendance management system based on face recognition technology can accurately record and identify the situation of students entering and leaving the classroom and identify the situations of being late and leaving early, absenteeism, and substitute classes. The experimental results are basically consistent with the sample results, and the error rate is low. However, the system is easily affected by environmental light, students' sitting posture, expression, and other factors, so it cannot be recognized. Generally speaking, both can meet the needs of classroom attendance in colleges and universities and have high accuracy and efficiency.

Hand hygiene monitoring and compliance system using convolution neural networks

Hand hygiene monitoring and compliance systems play a significant role in curbing the spread of healthcare associated infections and the COVID-19 virus. In this paper, a model has been developed using convolution neural networks (CNN) and computer vision to detect an individual's germ level, monitor their hand wash technique and create a database containing all records. The proposed model ensures all individuals entering a public place prevent the spread of healthcare associated infections (HCAI). In our model, the individual's identity is verified using two-factor authentication, followed by checking the hand germ level. Furthermore, if required the model will request sanitizing/ hand wash for completion of the process. During this time, the hand movements are checked to ensure each hand wash step is completed according to World Health Organization (WHO) guidelines. Upon completion of the process, a database with details of the individual's germ level is created. The advantage of our model is that it can be implemented in every public place and it is easily integrable. The performance of each segment of the model has been tested on real-time images an validated. The accuracy of the model is 100% for personal identification, 96.87% for hand detection, 93.33% for germ detection and 85.5% for the compliance system respectively.

An Effective Mechanism for the Early Detection and Containment of Healthcare Worker Infections in the Setting of the COVID-19 Pandemic: A Systematic Review and Meta-Synthesis

The COVID-19 pandemic has exposed healthcare workers (HCWs) to serious infection risks. In this context, the proactive monitoring of HCWs is the first step toward reducing intrahospital transmissions and safeguarding the HCW population, as well as reflecting the preparedness and response of the healthcare system. As such, this study systematically reviewed the literature on evidence-based effective monitoring measures for HCWs during the COVID-19 pandemic. This was followed by a meta-synthesis to compile the key findings, thus, providing a clearer overall understanding of the subject. Effective monitoring measures of syndromic surveillance, testing, contact tracing, and exposure management are distilled and further integrated to create a whole-process monitoring workflow framework. Taken together, a mechanism for the early detection and containment of HCW infections is, thus, constituted, providing a composite set of practical recommendations to healthcare facility leadership and policy makers to reduce nosocomial transmission rates while maintaining adequate staff for medical services. In this regard, our study paves the way for future studies aimed at strengthening surveillance capacities and upgrading public health system resilience, in order to respond more efficiently to future pandemic threats.

Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: artificial intelligence-based solutions

In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence.

Internet of things in the management of chronic diseases during the COVID-19 pandemic: A systematic review

Introduction

The use of new technologies such as the Internet of Things (IoT) in the management of chronic diseases, especially in the COVID pandemics, could be a life-saving appliance for public health practice. The purpose of the current study is to identify the applications and capability of IoT and digital health in the management of the COVID-19 pandemic.

Methods

This systematic review was conducted by searching the online databases of PubMed, Scopus, and Web of Science using selected keywords to retrieve the relevant literature published until December 25th, 2021. The most relevant original English studies were included after initial screening based on the inclusion criteria.

Results

Overall, 18 studies were included. Most of the studies reported benefits and positive responses in the form of patients' and healthcare providers' satisfaction and trust in the online systems. Many services were provided to the patients, including but not limited to training the patients on their conditions; monitoring vital signs and required actions when vital signs were altered; ensuring treatment adherence; monitoring and consulting the patients regarding diet, physical activity, and lifestyle.

Conclusion

IoT is a new technology, which can help us improve health care services during the COVID-19 pandemic. It has a network of various sensors, obtaining data from patients. We have found several applications for this technology. Future studies can be conducted for the capability of other technologies in the management of chronic diseases.

Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data

Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high-quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain-based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559.

Population Access to Hospital Emergency Departments: The Spatial Analysis in Public Health Research

The emergency medical services support the primary health care system. Hospital emergency departments (HEDs), which provide medical assistance to all patients in a state of emergency are of considerable importance to the system. When studying access to HEDs, attention should be focused on spatial relations resulting from the location of HEDs and the places of residence of the potential patients. The aim of the paper is to explain the level of spatial accessibility of HEDs and its changes as a result of organizational and spatial transformations of HEDs' networks in Poland. The research was conducted within two time series, comparing the changes in the distribution of HEDs in 2011 and 2021. GIS techniques were used to measure the distances between emergency departments and places of residence. It was observed that the transformation of the spatial organization of the hospital emergency department network in 2011-2021 resulted in the overall improvement of the spatial accessibility of these facilities, reducing the distance between them and places of residence.

SDN-IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic

The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed "EdgeSDN-I4COVID" architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.

Hybrid Computational Intelligence Algorithm for Autonomous Handling of COVID-19 Pandemic Emergency in Smart Cities

New cities exploit the smartness of the IoT-based architecture to run their vital and organizational processes. The smart response of pandemic emergency response services needs optimizing methodologies of caring and limit infection without direct connection with patients. In this paper, a hybrid Computational Intelligence (CI) algorithm called Moth-Flame Optimization and Marine Predators Algorithms (MOMPA) is proposed for planning the COVID-19 pandemic medical robot's path without collisions. MOMPA is validated on several benchmarks and compared with many CI algorithms. The results of the Friedman Ranked Mean test indicate the proposed algorithm can find the shortest collision-free path in almost all test cases. In addition, the proposed algorithm reaches an almost %100 success ratio for solving all test cases without constraint violation of the regarded problem. After the validation experiment, the proposed algorithm is applied to smart medical emergency handling in Egypt's New Galala mountainous city. Both experimental and statistical results ensure the prosperity of the proposed algorithm. Also, it ensures that MOMPA can efficiently find the shortest path to the emergency location without any collisions.

The implications of Industry 4.0 on supply chains amid the COVID-19 pandemic: a systematic review

Background: COVID-19 has caused significant disruptions in supply chains. It has increased the demand for products and decreased the supply of raw materials. This has interrupted many production processes. The emerging technologies of Industry 4.0 have the potential to streamline supply chains by improving time-sensitive customized solutions during this emergency. Purpose: This research examines the effects of the epidemic on supply chains and how these effects are reduced through Industry 4.0 technology. Design/methodology/approach: An extensive literature review using the "Preferred Reporting Items for Systematic Review and Meta-Analysis" method was carried out on the impact of the COVID-19 pandemic on supply chains and Industry 4.0 technologies. The study was undertaken by selecting keywords validated by experts and a search was conducted in the Scopus, ProQuest, and Google Scholar databases. Publications from the leading journals on these topics were selected. The bibliographical search resulted in 1484 articles followed by multiple layers of filtering. Finally, the most pertinent articles were selected for reviewing, and a total of 53 articles were analysed. Findings: This study discusses the impact of COVID-19 on the supply chain and how the emerging technologies of Industry 4.0 can help manufacturers to ease the impact. These technologies will enhance the production system through the automation and optimization of production flow convergence, enabling efficiencies and improvements among the suppliers, manufacturers, and consumers in the COVID-19 situation. Originality/value: The study summarizes the impact of the COVID-19 on supply chains and shows the potential of Industry 4.0 technologies to lessen the impact on manufacturing supply chains. This is valuable information for policymakers and practitioners so that they can get insights and take necessary actions.

The implications of Industry 4.0 on supply chains amid the COVID-19 pandemic: a systematic review

Background: COVID-19 has caused significant disruptions in supply chains. It has increased the demand for products and decreased the supply of raw materials. This has interrupted many production processes. The emerging technologies of Industry 4.0 have the potential to streamline supply chains by improving time-sensitive customized solutions during this emergency. Purpose: The study identifies the core technologies of Industry 4.0 and the role and impact of these technologies in managing the disruption caused by the COVID-19 outbreak in strengthening the supply chain resilience. Design/methodology/approach: An extensive literature review using the "Preferred Reporting Items for Systematic Review and Meta-Analysis" method was carried out on the impact of the COVID-19 pandemic on supply chains and Industry 4.0 technologies. The study was undertaken by selecting keywords validated by experts, and a search was conducted in the Scopus, ProQuest, and Google Scholar databases. Publications from the leading journals on these topics were selected. The bibliographical search resulted in 1484 articles, followed by multiple layers of filtering. Finally, the most pertinent articles were selected for review, and a total of 42 articles were analyzed. Findings: The findings of the study showed that the majority of the articles emphasized the digitalization of supply chain management, acknowledging the fundamentals, applications, and prospects, revealing the drivers and challenges of Industry 4.0 technologies to manage disruptions. Most of the authors identified IoT, big data, cloud computing, additive manufacturing, and blockchain to maintain the supply chain resilience. Originality/value: Existing literature on epidemics lacks the basics and practices of utilizing Industry 4.0 technologies in the supply chain recovery process. To fill this research gap, the study summarizes the potential of Industry 4.0 technologies to lessen supply chain disruptions caused by COVID-19. The study findings are valuable for policymakers and practitioners and contribute to supply chain management studies.

Assessment of Machine Learning Techniques in IoT-Based Architecture for the Monitoring and Prediction of COVID-19

From the end of 2019, the world has been facing the threat of COVID-19. It is predicted that, before herd immunity is achieved globally via vaccination, people around the world will have to tackle the COVID-19 pandemic using precautionary steps. This paper suggests a COVID-19 identification and control system that operates in real-time. The proposed system utilizes the Internet of Things (IoT) platform to capture users’ time-sensitive symptom information to detect potential cases of coronaviruses early on, to track the clinical measures adopted by survivors, and to gather and examine appropriate data to verify the existence of the virus. There are five key components in the framework: symptom data collection and uploading (via communication technology), a quarantine/isolation center, an information processing core (using artificial intelligent techniques), cloud computing, and visualization to healthcare doctors. This research utilizes eight machine/deep learning techniques—Neural Network, Decision Table, Support Vector Machine (SVM), Naive Bayes, OneR, K-Nearest Neighbor (K-NN), Dense Neural Network (DNN), and the Long Short-Term Memory technique—to detect coronavirus cases from time-sensitive information. A simulation was performed to verify the eight algorithms, after selecting the relevant symptoms, on real-world COVID-19 data values. The results showed that five of these eight algorithms obtained an accuracy of over 90%. Conclusively, it is shown that real-world symptomatic information would enable these three algorithms to identify potential COVID-19 cases effectively with enhanced accuracy. Additionally, the framework presents responses to treatment for COVID-19 patients.

BIM for Smart Hospital Management during COVID-19 Using MCDM

In context of the recent COVID-19 pandemic, smart hospitals’ contributions to pre-medical, remote diagnosis, and social distancing has been further vetted. Smart hospital management evolves with new technology and knowledge management, which needs an evaluation system to prioritize its associated criteria and sub-criteria. The global effect of the COVID-19 pandemic further necessitates a comprehensive research of smart hospital management. This paper will utilize Analytical Hierarchy Process (AHP) within Multiple Criteria Decision Making (MCDM) to establish a smart hospital evaluation system with evaluation criteria and sub-criteria, which were then further prioritized and mapped to BIM-related alternatives to inform asset information management (AIM) practices. This context of this study included the expert opinions of six professionals in the smart hospital field and collected 113 responses from hospital-related personnel. The results indicated that functionalities connected to end users are critical, in particular IoT’s Network Core Functionalities, AI’s Deep Learning and CPS’s Special Network Technologies. Furthermore, BIM’s capability to contribute to the lifecycle management of assets can relate and contribute to the asset-intensive physical criteria of smart hospitals, in particular IoT, service technology innovations and their sub-criteria.

Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic

BACKGROUND/OBJECTIVES

The Internet of Things (IoT) can create disruptive innovation in healthcare. Thus, during COVID-19 Pandemic, there is a need to study different applications of IoT enabled healthcare. For this, a brief study is required for research directions.

METHODS

Research papers on IoT in healthcare and COVID-19 Pandemic are studied to identify this technology's capabilities. This literature-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic.

RESULTS

Briefly studied the significant achievements of IoT with the help of a process chart. Then identifies seven major technologies of IoT that seem helpful for healthcare during COVID-19 Pandemic. Finally, the study identifies sixteen basic IoT applications for the medical field during the COVID-19 Pandemic with a brief description of them.

CONCLUSIONS

In the current scenario, advanced information technologies have opened a new door to innovation in our daily lives. Out of these information technologies, the Internet of Things is an emerging technology that provides enhancement and better solutions in the medical field, like proper medical record-keeping, sampling, integration of devices, and causes of diseases. IoT's sensor-based technology provides an excellent capability to reduce the risk of surgery during complicated cases and helpful for COVID-19 type pandemic. In the medical field, IoT's focus is to help perform the treatment of different COVID-19 cases precisely. It makes the surgeon job easier by minimising risks and increasing the overall performance. By using this technology, doctors can easily detect changes in critical parameters of the COVID-19 patient. This information-based service opens up new healthcare opportunities as it moves towards the best way of an information system to adapt world-class results as it enables improvement of treatment systems in the hospital. Medical students can now be better trained for disease detection and well guided for the future course of action. IoT's proper usage can help correctly resolve different medical challenges like speed, price, and complexity. It can easily be customised to monitor calorific intake and treatment like asthma, diabetes, and arthritis of the COVID-19 patient. This digitally controlled health management system can improve the overall performance of healthcare during COVID-19 pandemic days.

Dentistry 4.0 technologies applications for dentistry during COVID-19 pandemic

The term, Dentistry 4.0, coincides with Industry 4.0, in which the traditional methods of manufacturing and information are made more precise to enhance process efficiency by using automation and advanced computer technologies. The main of this paper is to discuss the major potential of Dentistry 4.0 technologies in the field of dentistry during Coronavirus (COVID-19) Pandemic. Thereon, Dentistry 4.0 is advancing on its way with the use of advanced technologies in dentistry. Dental healthcare makes an essential part of the overall health of the masses. New technological advancements are essential to make the dentist work quicker, patient comfortable, and process reliable. So, we introduced the concept of Dentistry 4.0 to improve efficiency and impart innovation in dentistry during this pandemic. This paper briefs about the Dentistry 4.0 technologies helpful for the COVID-19 pandemic. Further discusses various issues and challenges in implementing Dentistry 4.0 for dentistry during the COVID-19 pandemic. Finally, the paper identifies and discussed fifteen significant applications of Dentistry 4.0 technologies for dentistry during the COVID-19 pandemic. With the onset of the pandemic, globally, the healthcare sector is taking initiatives to strengthen affordable and high-speed data connectivity. This up-gradation and investment will also help dentists to access patients' data from smaller towns or villages using Dentistry 4.0 technologies. Thus, globally there is the onset of the fourth dentistry revolution, and we understand that this will change the trend of dentistry during and post-COVID-19 Pandemic. Dentistry 4.0 technologies are helpful during the COVID-19 pandemic to create teledentistry, virtual clinical practice and connect all dental devices to improve health conditions. This approach is to help progress towards the integrated capabilities, patient-centric remedies with predicted results in an easier way than the traditional way of the health care industry.