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Demirbaga U, Kaur N, Aujla GS. Uncovering hidden and complex relations of pandemic dynamics using an AI driven system. Sci Rep 2024; 14:15433. [PMID: 38965354 PMCID: PMC11224231 DOI: 10.1038/s41598-024-65845-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 06/25/2024] [Indexed: 07/06/2024] Open
Abstract
The COVID-19 pandemic continues to challenge healthcare systems globally, necessitating advanced tools for clinical decision support. Amidst the complexity of COVID-19 symptomatology and disease severity prediction, there is a critical need for robust decision support systems to aid healthcare professionals in timely and informed decision-making. In response to this pressing demand, we introduce BayesCovid, a novel decision support system integrating Bayesian network models and deep learning techniques. BayesCovid automates data preprocessing and leverages advanced computational methods to unravel intricate patterns in COVID-19 symptom dynamics. By combining Bayesian networks and Bayesian deep learning models, BayesCovid offers a comprehensive solution for uncovering hidden relationships between symptoms and predicting disease severity. Experimental validation demonstrates BayesCovid 's high prediction accuracy (83.52-98.97%). Our work represents a significant stride in addressing the urgent need for clinical decision support systems tailored to the complexities of managing COVID-19 cases. By providing healthcare professionals with actionable insights derived from sophisticated computational analysis, BayesCovid aims to enhance clinical decision-making, optimise resource allocation, and improve patient outcomes in the ongoing battle against the COVID-19 pandemic.
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Affiliation(s)
- Umit Demirbaga
- Department of Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- European Bioinformatics Institute (EMBL-EBI), Cambridge, CB10 1SD, UK
- Department of Computer Engineering, Bartin University, 74110, Bartin, Turkey
| | - Navneet Kaur
- Department of Biochemistry,Guru Gobind Singh Medical College and Hospital, Baba Farid University of Health Sciences, Faridkot, Punjab, 151203, India
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2
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Meri A, Hasan MK, Dauwed M, Jarrar M, Aldujaili A, Al-Bsheish M, Shehab S, Kareem HM. Organizational and behavioral attributes' roles in adopting cloud services: An empirical study in the healthcare industry. PLoS One 2023; 18:e0290654. [PMID: 37624836 PMCID: PMC10456173 DOI: 10.1371/journal.pone.0290654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
The need for cloud services has been raised globally to provide a platform for healthcare providers to efficiently manage their citizens' health records and thus provide treatment remotely. In Iraq, the healthcare records of public hospitals are increasing progressively with poor digital management. While recent works indicate cloud computing as a platform for all sectors globally, a lack of empirical evidence demands a comprehensive investigation to identify the significant factors that influence the utilization of cloud health computing. Here we provide a cost-effective, modular, and computationally efficient model of utilizing cloud computing based on the organization theory and the theory of reasoned action perspectives. A total of 105 key informant data were further analyzed. The partial least square structural equation modeling was used for data analysis to explore the effect of organizational structure variables on healthcare information technicians' behaviors to utilize cloud services. Empirical results revealed that Internet networks, software modularity, hardware modularity, and training availability significantly influence information technicians' behavioral control and confirmation. Furthermore, these factors positively impacted their utilization of cloud systems, while behavioral control had no significant effect. The importance-performance map analysis further confirms that these factors exhibit high importance in shaping user utilization. Our findings can provide a comprehensive and unified guide to policymakers in the healthcare industry by focusing on the significant factors in organizational and behavioral contexts to engage health information technicians in the development and implementation phases.
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Affiliation(s)
- Ahmed Meri
- Department of Medical Instrumentation Techniques Engineering, Al-Hussain University College, Karbala, Iraq
| | - Mohammad Khatim Hasan
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Mohammed Dauwed
- Computer Science, College of Science, University of Baghdad, Baghdad, Iraq
| | - Mu’taman Jarrar
- Vice Deanship for Development and Community Partnership, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
- Medical Education Department, King Fahd Hospital of the University, Al-Khobar, Saudi Arabia
| | - Ali Aldujaili
- Department Affairs of Student Accommodation, University of Baghdad, Baghdad, Iraq
- Department of Signal Theory and Communications, Information and Communication Technologies, University of Alcalá, Madrid, Spain
| | - Mohammed Al-Bsheish
- Health Management Department, Batterjee Medical College (PMC), Jeddah, Saudi Arabia
- Al-Nadeem Governmental Hospital, Ministry of Health, Amman, Jordan
| | - Salah Shehab
- College of Graduate Studies, Universiti Tenaga Nasional, Kajang, Selangor, Malaysia
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Yavari A, Korala H, Georgakopoulos D, Kua J, Bagha H. Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing. SENSORS (BASEL, SWITZERLAND) 2023; 23:5211. [PMID: 37299938 PMCID: PMC10255853 DOI: 10.3390/s23115211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 05/15/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance.
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Affiliation(s)
- Ali Yavari
- 6G Research and Innovation Lab, Swinburne University of Technology, Melbourne, VIC 3122, Australia
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia; (D.G.); (H.B.)
| | - Harindu Korala
- Institute of Railway Technology, Monash University, Melbourne, VIC 3800, Australia;
| | - Dimitrios Georgakopoulos
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia; (D.G.); (H.B.)
| | - Jonathan Kua
- School of Information Technology, Deakin University, Geelong, VIC 3220, Australia;
| | - Hamid Bagha
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia; (D.G.); (H.B.)
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Artificial Intelligence Functionalities During the COVID-19 Pandemic. Disaster Med Public Health Prep 2023; 17:e336. [PMID: 36847255 DOI: 10.1017/dmp.2023.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has led us to use virtual solutions and emerging technologies such as artificial intelligence (AI). Recent studies have clearly demonstrated the role of AI in health care and medical practice; however, a comprehensive review can identify potential yet not fulfilled functionalities of such technologies in pandemics. Therefore, this scoping review study aims at assessing AI functionalities in the COVID-19 pandemic in 2022. METHODS A systematic search was carried out in PubMed, Cochran Library, Scopus, Science Direct, ProQuest, and Web of Science from 2019 to May 9, 2022. Researchers selected the articles according to the search keywords. Finally, the articles mentioning the functionalities of AI in the COVID-19 pandemic were evaluated. Two investigators performed this process. RESULTS Initial search resulted in 9123 articles. After reviewing the title, abstract, and full text of these articles, and applying the inclusion and exclusion criteria, 4 articles were selectd for the final analysis. All 4 were cross-sectional studies. Two studies (50%) were performed in the United States, 1 (25%) in Israel, and 1 (25%) in Saudi Arabia. They covered the functionalities of AI in the prediction, detection, and diagnosis of COVID-19. CONCLUSIONS To the extent of the researchers' knowledge, this study is the first scoping review that assesses the AI functionalities in the COVID-19 pandemic. Health-care organizations need decision support technologies and evidence-based apparatuses that can perceive, think, and reason not dissimilar to human beings. Potential functionalities of such technologies can be used to predict mortality, detect, screen, and trace current and former patients, analyze health data, prioritize high-risk patients, and better allocate hospital resources in pandemics, and generally in health-care settings.
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ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India. Neural Comput Appl 2023; 35:7207-7220. [PMID: 34566264 PMCID: PMC8452449 DOI: 10.1007/s00521-021-06412-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 08/17/2021] [Indexed: 02/07/2023]
Abstract
Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40-60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10-3 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic.
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Sarkar S, Bijoy BS, Saba SJ, Feng D, Mahajan Y, Amin MR, Islam SR, (“Santu”) SKK. Ad-Hoc Monitoring of COVID-19 Global Research Trends for Well-Informed Policy Making. ACM T INTEL SYST TEC 2022. [DOI: 10.1145/3576901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The COVID-19 pandemic has affected millions of people worldwide with severe health, economic, social, and political implications. Healthcare Policy Makers (HPM) and medical experts are at the core of responding to this continuously evolving pandemic situation and are working hard to restrain the spread and severity of this relatively unknown virus. Biomedical researchers are continually discovering new information about this virus and communicating the findings through scientific articles. As such, it is crucial for HPM and funding agencies to monitor the COVID-19 research trend globally on a regular basis. However, given the influx of biomedical research articles, monitoring COVID-19 research trends has become more challenging than ever, especially when HPMs want on-demand guided search techniques with a set of topics of interest in their minds. Unfortunately, existing topic trend modeling techniques are unable to serve this purpose as 1) Traditional topic models are unsupervised, and 2) HPMs in different regions may have different topics of interest that they want to track.
To address this problem, we introduce a novel computational task in this paper called
Ad-Hoc Topic Tracking
, which is essentially a combination of
zero-shot
topic categorization and the Spatio-temporal analysis task. We then propose multiple
zero-shot
classification methods to solve this task by extending upon the state-of-the-art language understanding techniques. Next, we picked the best-performing method based on its accuracy on a separate validation data set and then applied it to a corpus of recent biomedical research articles to track Covid-19 research endeavors across the globe using a Spatio-Temporal analysis. A demo website has also been developed for HPMs to create custom Spatio-Temporal visualizations of COVID-19 research trends. The research outcomes demonstrate that the proposed
zero-shot
classification methods can potentially facilitate further research on this important subject matter, and at the same time, the Spatio-temporal visualization tool will greatly assist HPMs and funding agencies in making well-informed policy decisions for advancing scientific research efforts.
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Digital Twin-assisted blockchain-inspired irregular event analysis for eldercare. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Jabbar MA, Shandilya SK, Kumar A, Shandilya S. Applications of cognitive internet of medical things in modern healthcare. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2022; 102:108276. [PMID: 35958351 PMCID: PMC9356718 DOI: 10.1016/j.compeleceng.2022.108276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
The sudden outbreak of the novel coronavirus disease in 2019, known as COVID-19 has impacted the entire globe and has forced governments of various countries to a partial or full lockdown in the fear of the rapid spread of this disease. The major lesson learned from this pandemic is that there is a need to implement a robust system by using non-pharmaceutical interventions for the prevention and control of new contagious viruses. This goal can be achieved using the platform of the Internet of Things (IoT) because of its seamless connectivity and ubiquitous sensing ability. This technology-enabled healthcare sector is helpful to monitor COVID-19 patients properly by adopting an interconnected network. IoT is useful for improving patient satisfaction by reducing the rate of readmission in the hospital. The presented work discusses the applications and technologies of IoT like smart and wearable devices, drones, and robots which are used in healthcare systems to tackle the Coronavirus pandemic This paper focuses on applications of cognitive radio-based IoT for medical applications, which is referred to as "Cognitive Internet of Medical Things" (CIoMT). CIoMT is a disruptive and promising technology for dynamic monitoring, tracking, rapid diagnosis, and control of pandemics and to stop the spread of the virus. This paper explores the role of the CIoMT in the health domain, especially during pandemics, and also discusses the associated challenges and research directions.
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Affiliation(s)
- M A Jabbar
- Department of Computer Science, Vardhaman College of Engineering, Hyderabad, India
| | | | - Ajit Kumar
- Department of Computer Science, Soongsil University, South Korea
| | - Smita Shandilya
- Department of Electrical and Electronics, Sagar Institute of Research and Technology, India
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Saeed A, Zaffar M, Abbas MA, Quraishi KS, Shahrose A, Irfan M, Huneif MA, Abdulwahab A, Alduraibi SK, Alshehri F, Alduraibi AK, Almushayti Z. A Turf-Based Feature Selection Technique for Predicting Factors Affecting Human Health during Pandemic. Life (Basel) 2022; 12:life12091367. [PMID: 36143404 PMCID: PMC9502730 DOI: 10.3390/life12091367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/22/2022] [Accepted: 08/24/2022] [Indexed: 11/30/2022] Open
Abstract
Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays a vital role in indicating the factor affecting the health of students during the lockdown in the COVID-19 pandemic. The research presented in this article investigates COVID-19’s impact on student health using feature selections. The Filter feature selection technique is used in the presented work to statistically analyze all the features in the dataset, and for better accuracy. ReliefF (TuRF) filter feature selection is tuned and utilized in such a way that it helps to identify the factors affecting students’ health from a benchmark dataset of students studying during COVID-19. Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and 2- layer Neural Network (NN), helps in identifying the most critical indicators for rapid intervention. Results of the approach presented in the paper identified that the students who maintained their weight and kept themselves busy in health activities in the pandemic, such student’s remained healthy through this pandemic and study from home in a positive manner. The results suggest that the 2- layer NN machine-learning algorithm showed better accuracy (90%) to predict the factors affecting on health issues of students during COVID-19 lockdown time.
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Affiliation(s)
- Alqahtani Saeed
- Department of Surgery, Faculty of Medicine, Najran University, Najran 61441, Saudi Arabia
| | - Maryam Zaffar
- Faculty of Computer Sciences, IBADAT International University, Islamabad 44000, Pakistan
- Correspondence:
| | - Mohammed Ali Abbas
- Faculty of Computer Sciences, IBADAT International University, Islamabad 44000, Pakistan
| | - Khurrum Shehzad Quraishi
- Department of Chemical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 44000, Pakistan
| | - Abdullah Shahrose
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan
| | - Muhammad Irfan
- Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia
| | - Mohammed Ayed Huneif
- Department of Pediatrics, College of Medicine, Najran University, Najran 61441, Saudi Arabia
| | - Alqahtani Abdulwahab
- Department of Pediatrics, College of Medicine, Najran University, Najran 61441, Saudi Arabia
| | | | - Fahad Alshehri
- Department of Radiology, College of Medicine, Qassim University, Buraidah 52571, Saudi Arabia
| | - Alaa Khalid Alduraibi
- Department of Radiology, College of Medicine, Qassim University, Buraidah 52571, Saudi Arabia
| | - Ziyad Almushayti
- Department of Radiology, College of Medicine, Qassim University, Buraidah 52571, Saudi Arabia
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Anjum N, Alibakhshikenari M, Rashid J, Jabeen F, Asif A, Mohamed EM, Falcone F. IoT-Based COVID-19 Diagnosing and Monitoring Systems: A Survey. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2022; 10:87168-87181. [PMID: 36345377 PMCID: PMC9454266 DOI: 10.1109/access.2022.3197164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 08/03/2022] [Indexed: 05/28/2023]
Abstract
To date, the novel Coronavirus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning (ML) algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent IoT-based COVID-19 diagnosing, and monitoring systems have been proposed to tackle the pandemic. In this article we have analyzed a wide range of IoTs technologies which can be used in diagnosing and monitoring the infected individuals and hotspot areas. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.
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Affiliation(s)
- Nasreen Anjum
- Department of Cyber and Technical Computing, School of Computing and EngineeringUniversity of Gloucestershire, Park Campus, CheltenhamGloucestershireGL50 2RHU.K.
| | - Mohammad Alibakhshikenari
- Department of Signal Theory and CommunicationsUniversidad Carlos III de Madrid, 28911 LeganésMadridSpain
| | - Junaid Rashid
- Department of Computer and EngineeringKongju National UniversityCheonan31080South Korea
| | - Fouzia Jabeen
- Department of Computer ScienceShaheed Benazir Bhutto Women University PeshawarPeshawar00384Pakistan
| | - Amna Asif
- Department of Computer ScienceShaheed Benazir Bhutto Women University PeshawarPeshawar00384Pakistan
| | - Ehab Mahmoud Mohamed
- Department of Electrical EngineeringCollege of Engineering in Wadi AddawasirPrince Sattam Bin Abdulaziz UniversityWadi Addawasir11991Saudi Arabia
- Department of Electrical EngineeringAswan UniversityAswan81542Egypt
| | - Francisco Falcone
- Department of Electric, Electronic and Communication Engineering and the Institute of Smart CitiesPublic University of Navarre31006PamplonaSpain
- Tecnológico de MonterreySchool of Engineering and SciencesMonterrey64849Mexico
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11
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Vehicle Price Classification and Prediction Using Machine Learning in the IoT Smart Manufacturing Era. SUSTAINABILITY 2022. [DOI: 10.3390/su14159147] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In this paper, machine learning (ML) strategies have been utilized in predicting vehicles’ prices and good deals. Vehicle value prediction has been considered one of the most significant research topics with the rise of IoT for sustainability. This is because it requires observable exertion and massive field information. Towards generating a model that anticipates the vehicles’ price, we applied three ML methods (neural network, decision tree, support vector machine, and linear regression). However, the referenced methods have been applied to function together as a group in a hybrid model. The information utilized was gathered from an information and computer science school that houses different datasets. Separate exhibitions of several ML techniques were contrasted to reveal which one is suitable for the accessible information index. Various difficulties and challenges associated with this design have also been discussed. Moreover, the model was experimented, and a 90% precision was achieved. This potential result can help in providing precise vehicle deals in the emerging Internet of Things (IoT) for the sustainability paradigm.
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Subramanian M, Shanmuga Vadivel K, Hatamleh WA, Alnuaim AA, Abdelhady M, V E S. The role of contemporary digital tools and technologies in COVID-19 crisis: An exploratory analysis. EXPERT SYSTEMS 2022; 39:e12834. [PMID: 34898797 PMCID: PMC8646626 DOI: 10.1111/exsy.12834] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/10/2021] [Accepted: 09/09/2021] [Indexed: 05/17/2023]
Abstract
Following the COVID-19 pandemic, there has been an increase in interest in using digital resources to contain pandemics. To avoid, detect, monitor, regulate, track, and manage diseases, predict outbreaks and conduct data analysis and decision-making processes, a variety of digital technologies are used, ranging from artificial intelligence (AI)-powered machine learning (ML) or deep learning (DL) focused applications to blockchain technology and big data analytics enabled by cloud computing and the internet of things (IoT). In this paper, we look at how emerging technologies such as the IoT and sensors, AI, ML, DL, blockchain, augmented reality, virtual reality, cloud computing, big data, robots and drones, intelligent mobile apps, and 5G are advancing health care and paving the way to combat the COVID-19 pandemic. The aim of this research is to look at possible technologies, processes, and tools for addressing COVID-19 issues such as pre-screening, early detection, monitoring infected/quarantined individuals, forecasting future infection rates, and more. We also look at the research possibilities that have arisen as a result of the use of emerging technology to handle the COVID-19 crisis.
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Affiliation(s)
- Malliga Subramanian
- Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India
| | | | - Wesam Atef Hatamleh
- Department of Computer Science, College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia
| | - Abeer Ali Alnuaim
- Department of Computer Science and Engineering, College of Applied Studies and Community Services King Saud University Riyadh Saudi Arabia
| | - Mohamed Abdelhady
- Electrical and Computer Engineering Department Cleveland State University Cleveland Ohio USA
| | - Sathishkumar V E
- Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India
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13
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Zhao S. Impact of COVID 19 Pandemic and Big Data on China's International Trade: Challenges and Countermeasures. Front Public Health 2022; 10:888335. [PMID: 35757600 PMCID: PMC9218592 DOI: 10.3389/fpubh.2022.888335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 05/06/2022] [Indexed: 11/28/2022] Open
Abstract
Early 2020 witnessed the coronavirus disease 2019 (COVID-19) pandemic followed by a nationwide lockdown in the whole history for the first time. In this raising dilemma, multiple countries had a serious impact on their international trade, especially during the lockdown. It is also widely accepted that the lives of individuals had been changing ever since the spread of COVID-19. Several other sectors were badly affected during the pandemic. For the above reasons, service industries had a significant impact before and after the pandemic. Based on the data collected, it was identified that the pandemic affected the service industries, enterprises, and other organizations that contribute to the economic growth of the nation. It was also found that the pandemic has adversely impacted private and public enterprises. In addition, the study examined the impact of COVID-19 on China's international trade using artificial intelligence and blockchain technology. Another objective of the article is to examine the impact of big data on China's international trade. The study suggests upgrading the trading policies of China to deal with the challenges being faced in the trading industry.
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Affiliation(s)
- Shuang Zhao
- Department of Economics, School of Economics, Inner Mongolia Minzu University, Tongliao, China
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14
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Comito C, Pizzuti C. Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review. Artif Intell Med 2022; 128:102286. [PMID: 35534142 PMCID: PMC8958821 DOI: 10.1016/j.artmed.2022.102286] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 03/16/2022] [Accepted: 03/21/2022] [Indexed: 02/05/2023]
Abstract
The outbreak of novel corona virus 2019 (COVID-19) has been treated as a public health crisis of global concern by the World Health Organization (WHO). COVID-19 pandemic hugely affected countries worldwide raising the need to exploit novel, alternative and emerging technologies to respond to the emergency created by the weak health-care systems. In this context, Artificial Intelligence (AI) techniques can give a valid support to public health authorities, complementing traditional approaches with advanced tools. This study provides a comprehensive review of methods, algorithms, applications, and emerging AI technologies that can be utilized for forecasting and diagnosing COVID-19. The main objectives of this review are summarized as follows. (i) Understanding the importance of AI approaches such as machine learning and deep learning for COVID-19 pandemic; (ii) discussing the efficiency and impact of these methods for COVID-19 forecasting and diagnosing; (iii) providing an extensive background description of AI techniques to help non-expert to better catch the underlying concepts; (iv) for each work surveyed, give a detailed analysis of the rationale behind the approach, highlighting the method used, the type and size of data analyzed, the validation method, the target application and the results achieved; (v) focusing on some future challenges in COVID-19 forecasting and diagnosing.
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15
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An Intelligent IoT Monitoring and Prediction System for Health Critical Conditions. MOBILE NETWORKS AND APPLICATIONS 2022. [PMCID: PMC9115747 DOI: 10.1007/s11036-021-01892-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Diabetes is considered among the major critical health conditions (chronic disease) around the world. This is due the fact that Glucose level could change drastically and lead to critical conditions reaching to death in some advance cases. To prevent this issues, diabetes patient are always advised to monitor their glucose level at least three times a day. Fingertip pricking - as the traditional method for glucose level tracking - leads patients to be distress and it might infect the skin. In some cases, tracking the glucose level might be a hard job especially if the patient is a child, senior, or even have several other health issues. In this paper, an optimum solution to this drawback by adopting the Wireless Sensor Network (WSN)-based non-invasive strategies has been proposed. Near-Infrared (NIR) -as an optical method of the non-invasive technique - has been adopted to help diabetic patients in continuously monitoring their blood without pain. The proposed solution will alert the patients’ parents or guardians of their situation when they about to reach critical conditions specially at night by sending alarms and notifications by Short Messages (SMS) along with the patients current location to up to three people. Moreover, a Machine Learning (ML) model is implemented to predict future events where the patient might have serious issues. This model prediction is best practice in this chronic health domain as it has never been implemented to predicted a future forecast of the patient chart. Multivariate Time-Series data set (i.e. AIM ’94) has been used to train the proposed ML model. The collected data shows a high level of accuracy when predicting serious critical conditions in Glucose levels.
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Chakraborty S. Monitoring COVID-19 Cases and Vaccination in Indian States and Union Territories Using Unsupervised Machine Learning Algorithm. ANNALS OF DATA SCIENCE 2022; 10:967-989. [PMID: 38625290 PMCID: PMC9065662 DOI: 10.1007/s40745-022-00404-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 10/20/2021] [Accepted: 10/30/2021] [Indexed: 11/01/2022]
Abstract
The worldwide spread of the novel coronavirus originating from Wuhan, China led to an ongoing pandemic as COVID-19. The disease being a contagion transmitted rapidly in India through the people having travel histories to the affected countries, and their contacts that tested positive. Millions of people across all states and union territories (UT) were affected leading to serious respiratory illness and deaths. In the present study, two unsupervised clustering algorithms namely k-means clustering and hierarchical agglomerative clustering are applied on the COVID-19 dataset in order to group the Indian states/UTs based on the pandemic effect and the vaccination program from the period of March, 2020 to early June, 2021. The aim of the study is to observe the plight of each state and UT of India combating the novel coronavirus infection and to monitor their vaccination status. The research study will be helpful to the government and to the frontline workers coping to restrict the transmission of the virus in India. Also, the results of the study will provide a source of information for future research regarding the COVID-19 pandemic in India.
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Affiliation(s)
- S. Chakraborty
- Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka, Mangalore, 575025 India
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IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7713939. [PMID: 35432824 PMCID: PMC9006083 DOI: 10.1155/2022/7713939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/12/2022] [Accepted: 03/14/2022] [Indexed: 01/08/2023]
Abstract
COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.
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Development of Advanced Artificial Intelligence and IoT Automation in the Crisis of COVID-19 Detection. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1987917. [PMID: 35281536 PMCID: PMC8906945 DOI: 10.1155/2022/1987917] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 11/24/2021] [Accepted: 01/22/2022] [Indexed: 11/18/2022]
Abstract
Internet of Things (IoT) is a successful area for many industries and academia domains, particularly healthcare is one of the application areas that uses IoT sensors and devices for monitoring. IoT transition replaces contemporary health services with scientific and socioeconomic viewpoints. Since the epidemic began, diverse scientific organizations have been making accelerated efforts to use a wide range of tools to tackle this global challenge and the founders of IoT analytics. Artificial intelligence (AI) plays a key role in measuring, assessing, and diagnosing the risk. It could be used to predict the number of alternate incidents, recovered instances, and casualties, also used for forecasting cases. Within the COVID-19 background, IoT technologies are used to minimize COVID-19 exposure to others by prenatal screening, patient monitoring, and postpatient incident response in specified procedures. In this study, the importance of IoT technology and artificial intelligence in COVID-19 is explored, and the 3 important steps discussed such as the evaluation of networks, implementations, and IoT industries to battle COVID-19, including early detection, quarantine times, and postrecovery activities, are reviewed. In this study, how IoT handles the COVID-19 pandemic at a new level of healthcare is investigated. In this research, the long short-term memory (LSTM) with recurrent neural network (RNN) is used for diagnosis purpose and in particular, its important architecture for the analysis of cough and breathing acoustic characteristics. In comparison with both coughing and respiratory samples, our findings indicate poor accuracy of the voice test.
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19
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Dialameh M, Hamzeh A, Rahmani H, Radmard AR, Dialameh S. Proposing a novel deep network for detecting COVID-19 based on chest images. Sci Rep 2022; 12:3116. [PMID: 35210447 PMCID: PMC8873454 DOI: 10.1038/s41598-022-06802-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 01/24/2022] [Indexed: 11/29/2022] Open
Abstract
The rapid outbreak of coronavirus threatens humans’ life all around the world. Due to the insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. To date, researchers have proposed several detection models based on chest imaging analysis, primarily based on deep neural networks; however, none of which could achieve a reliable and highly sensitive performance yet. Therefore, the nature of this study is primary epidemiological research that aims to overcome the limitations mentioned above by proposing a large-scale publicly available dataset of chest computed tomography scan (CT-scan) images consisting of more than 13k samples. Secondly, we propose a more sensitive deep neural networks model for CT-scan images of the lungs, providing a pixel-wise attention layer on top of the high-level features extracted from the network. Moreover, the proposed model is extended through a transfer learning approach for being applicable in the case of chest X-Ray (CXR) images. The proposed model and its extension have been trained and evaluated through several experiments. The inclusion criteria were patients with suspected PE and positive real-time reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2. The exclusion criteria were negative or inconclusive RT-PCR and other chest CT indications. Our model achieves an AUC score of 0.886, significantly better than its closest competitor, whose AUC is 0.843. Moreover, the obtained results on another commonly-used benchmark show an AUC of 0.899, outperforming related models. Additionally, the sensitivity of our model is 0.858, while that of its closest competitor is 0.81, explaining the efficiency of pixel-wise attention strategy in detecting coronavirus. Our promising results and the efficiency of the models imply that the proposed models can be considered reliable tools for assisting doctors in detecting coronavirus.
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Affiliation(s)
- Maryam Dialameh
- Department of Computer Science, Shiraz University, Shiraz, Iran.
| | - Ali Hamzeh
- Department of Computer Science, Shiraz University, Shiraz, Iran
| | - Hossein Rahmani
- School of Computing and Communications, Lancaster University, Lancaster, UK
| | - Amir Reza Radmard
- Department of Radiology, Tehran University of Medical Sciences, Tehran, Iran
| | - Safoura Dialameh
- School of Paramedical Sciences, Bushehr University of Medical Sciences, Bushehr, Iran
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20
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Hassan H, Ren Z, Zhao H, Huang S, Li D, Xiang S, Kang Y, Chen S, Huang B. Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks. Comput Biol Med 2022; 141:105123. [PMID: 34953356 PMCID: PMC8684223 DOI: 10.1016/j.compbiomed.2021.105123] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/03/2021] [Accepted: 12/03/2021] [Indexed: 01/12/2023]
Abstract
This article presents a systematic overview of artificial intelligence (AI) and computer vision strategies for diagnosing the coronavirus disease of 2019 (COVID-19) using computerized tomography (CT) medical images. We analyzed the previous review works and found that all of them ignored classifying and categorizing COVID-19 literature based on computer vision tasks, such as classification, segmentation, and detection. Most of the COVID-19 CT diagnosis methods comprehensively use segmentation and classification tasks. Moreover, most of the review articles are diverse and cover CT as well as X-ray images. Therefore, we focused on the COVID-19 diagnostic methods based on CT images. Well-known search engines and databases such as Google, Google Scholar, Kaggle, Baidu, IEEE Xplore, Web of Science, PubMed, ScienceDirect, and Scopus were utilized to collect relevant studies. After deep analysis, we collected 114 studies and reported highly enriched information for each selected research. According to our analysis, AI and computer vision have substantial potential for rapid COVID-19 diagnosis as they could significantly assist in automating the diagnosis process. Accurate and efficient models will have real-time clinical implications, though further research is still required. Categorization of literature based on computer vision tasks could be helpful for future research; therefore, this review article will provide a good foundation for conducting such research.
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Affiliation(s)
- Haseeb Hassan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China
| | - Zhaoyu Ren
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Huishi Zhao
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Shoujin Huang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Dan Li
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Shaohua Xiang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Yan Kang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China; Medical Device Innovation Research Center, Shenzhen Technology University, Shenzhen, China
| | - Sifan Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China; Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Bingding Huang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
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21
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Xu Z, Su C, Xiao Y, Wang F. Artificial intelligence for COVID-19: battling the pandemic with computational intelligence. INTELLIGENT MEDICINE 2022; 2:13-29. [PMID: 34697578 PMCID: PMC8529224 DOI: 10.1016/j.imed.2021.09.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/15/2021] [Accepted: 09/29/2021] [Indexed: 12/15/2022]
Abstract
The new coronavirus disease 2019 (COVID-19) has become a global pandemic leading to over 180 million confirmed cases and nearly 4 million deaths until June 2021, according to the World Health Organization. Since the initial report in December 2019 , COVID-19 has demonstrated a high transmission rate (with an R0 > 2), a diverse set of clinical characteristics (e.g., high rate of hospital and intensive care unit admission rates, multi-organ dysfunction for critically ill patients due to hyperinflammation, thrombosis, etc.), and a tremendous burden on health care systems around the world. To understand the serious and complex diseases and develop effective control, treatment, and prevention strategies, researchers from different disciplines have been making significant efforts from different aspects including epidemiology and public health, biology and genomic medicine, as well as clinical care and patient management. In recent years, artificial intelligence (AI) has been introduced into the healthcare field to aid clinical decision-making for disease diagnosis and treatment such as detecting cancer based on medical images, and has achieved superior performance in multiple data-rich application scenarios. In the COVID-19 pandemic, AI techniques have also been used as a powerful tool to overcome the complex diseases. In this context, the goal of this study is to review existing studies on applications of AI techniques in combating the COVID-19 pandemic. Specifically, these efforts can be grouped into the fields of epidemiology, therapeutics, clinical research, social and behavioral studies and are summarized. Potential challenges, directions, and open questions are discussed accordingly, which may provide new insights into addressing the COVID-19 pandemic and would be helpful for researchers to explore more related topics in the post-pandemic era.
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Affiliation(s)
- Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York 10065, United States
| | - Chang Su
- Department of Health Service Administration and Policy, Temple University, Philadelphia 19122, United States
| | - Yunyu Xiao
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York 10065, United States
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York 10065, United States,Corresponding author: Fei Wang, Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York 10065, United States of America
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22
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Internet of Things Concept in the Context of the COVID-19 Pandemic: A Multi-Sensor Application Design. SENSORS 2022; 22:s22020503. [PMID: 35062463 PMCID: PMC8778479 DOI: 10.3390/s22020503] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 12/13/2022]
Abstract
In this paper, we present the design, development and implementation of an integrated system for the management of COVID-19 patient, using the LoRaWAN communication infrastructure. Our system offers certain advantages when compared to other similar solutions, allowing remote symptom and health monitoring that can be applied to isolated or quarantined people, without any external interaction with the patient. The IoT wearable device can monitor parameters of health condition like pulse, blood oxygen saturation, and body temperature, as well as the current location. To test the performance of the proposed system, two persons under quarantine were monitored, for a complete 14-day standard quarantine time interval. Based on the data transmitted to the monitoring center, the medical staff decided, after several days of monitoring, when the measured values were outside of the normal parameters, to do an RT-PCR test for one of the two persons, confirming the SARS-CoV2 virus infection. We have to emphasize the high degree of scalability of the proposed solution that can oversee a large number of patients at the same time, thanks to the LoRaWAN communication protocol used. This solution can be successfully implemented by local authorities to increase monitoring capabilities, also saving lives.
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Shah H, Shah S, Tanwar S, Gupta R, Kumar N. Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends. MULTIMEDIA SYSTEMS 2022; 28:1189-1222. [PMID: 34276140 PMCID: PMC8275905 DOI: 10.1007/s00530-021-00818-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 05/29/2021] [Indexed: 05/05/2023]
Abstract
The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.
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Affiliation(s)
- Het Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Saiyam Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Sudeep Tanwar
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Rajesh Gupta
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Neeraj Kumar
- Department of Computer Science Engineering, Thapar Institute of Engineering and Technology, Deemed to be University, Patiala, India
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand India
- King Abdul Aziz University, Jeddah, Saudi Arabia
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24
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Haneya H, AlKaf D, Bajammal F, Brahimi T. A Meta-Analysis of Artificial Intelligence Applications for Tracking COVID-19: The Case of the U.A.E. PROCEDIA COMPUTER SCIENCE 2021; 194:180-189. [PMID: 34876933 PMCID: PMC8641301 DOI: 10.1016/j.procs.2021.10.072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Coronavirus disease (COVID-19) is an infectious respiratory disease that was first found in Wuhan, China, on December 31, 2019. It is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of November 10, 2020, more than fifty million cases have been confirmed, and more than one million deaths have been reported globally. This situation has created a serious challenge for all countries to institute a variety of control measures to track and slow down the spread of the virus and prevent the increasing number of deaths. In recent years, there has been an ongoing interest in using Artificial Intelligence (A.I.) in healthcare to create new treatments and detecting diseases. The objective of this study is to analyze the application and the impact of A.I. on the breakout of COVID-19 and discuss the contribution of A.I. to the fight against the pandemic based on the most recent applications used in the United Arab Emirates, including Dubai Police Movement Restriction Monitoring System, Taxis Preventive Measures Compliance System, Mobile App "Wai-Eye," Smart Helmets, Virtual Doctor, and The Department of Health - Abu Dhabi (DoH) Remote Healthcare App. The method used in this study is based on a meta-analysis of recent COVID-19 studies from various databases such as ScienceDirect, Sage Journals, SpringerLink, ResearchGate, Emerald Open Research, and IEEE Xplore. The COVID-19 data was based on Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE). Results showed that A.I. applications provided the necessary prevention of the spread of COVID-19, assisted in monitoring restrictions and preventive measures violations, and provided remote healthcare, which directly impacted the number of hospital visits amidst the lockdown. The study concluded that A.I. has proven to be effective in supporting governments in fighting the pandemic.
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Affiliation(s)
- Hala Haneya
- Computer Science Department, College of Engineering, Effat University Jeddah, Saudi Arabia
| | - Dhekra AlKaf
- Computer Science Department, College of Engineering, Effat University Jeddah, Saudi Arabia
| | - Faigah Bajammal
- Computer Science Department, College of Engineering, Effat University Jeddah, Saudi Arabia
| | - Tayeb Brahimi
- Energy and Technology Research Center Natural Science, Mathematics and Technology Unit, College of Engineering, Effat University Jeddah, Saudi Arabia
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25
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de Sá AAR, Carvalho JD, Naves ELM. Reflections on epistemological aspects of artificial intelligence during the COVID-19 pandemic. AI & SOCIETY 2021; 38:1-8. [PMID: 34866808 PMCID: PMC8627296 DOI: 10.1007/s00146-021-01315-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 11/05/2021] [Indexed: 12/24/2022]
Abstract
Artificial intelligence plays an important role and has been used by several countries as a health strategy in an attempt to understand, control and find a cure for the disease caused by Coronavirus. These intelligent systems can assist in accelerating the process of developing antivirals for Coronavirus and in predicting new variants of this virus. For this reason, much research on COVID-19 has been developed with the aim of contributing to new discoveries about the Coronavirus. However, there are some epistemological aspects about the use of AI in this pandemic period of Covid-19 that deserve to be discussed and need reflections. In this scenario, this article presents a reflection on the two epistemological aspects faced by the COVID-19 pandemic: (1) The epistemological aspect resulting from the use of patient data to fill the knowledge base of intelligent systems; (2) the epistemological problem arising from the dependence of health professionals on the results/diagnoses issued by intelligent systems. In addition, we present some epistemological challenges to be implemented in a pandemic period.
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Affiliation(s)
- Angela A. R. de Sá
- Assistive Technology Group, Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Brazil
| | - Jairo D. Carvalho
- Technologies Study Group, Faculty of Philosophy, Federal University of Uberlândia, Uberlândia, Brazil
| | - Eduardo L. M. Naves
- Assistive Technology Group, Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Brazil
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Sun R, Rahmadya B, Kong F, Takeda S. Visual management of medical things with an advanced color-change RFID tag. Sci Rep 2021; 11:22990. [PMID: 34837022 PMCID: PMC8626512 DOI: 10.1038/s41598-021-02501-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 11/17/2021] [Indexed: 11/20/2022] Open
Abstract
This paper proposes a visual management scheme of medical things with a color-change radio frequency identification (RFID) tag. The color-change RFID tag employs a specific RFID tag integrated circuit (IC) and a laminated pH-indicating paper. The IC has energy harvesting and switched ground functions, which enable it to generate electricity to the laminated pH-indicating paper. This phenomenon causes electrolysis of NaCl solution absorbed in the laminated pH-indicating paper. Electrolysis generates alkaline matter to change the color of the pH-indicating paper. This paper gives a new and sensitive structure of the laminated pH-indicating paper. The proposed advanced color-change RFID tag with new laminated pH-indicating paper succeeds in changing its color noticeably at a 1 m distance using an RFID reader radiating 1 W radio waves. The color change was observed 3-5 s after starting radio wave irradiation. The results of this experiment also confirm that the changed color can be held for over 24 h. Furthermore, two demonstrations of the visual management system of medical things (patient clothes and sanitizers) are presented.
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Affiliation(s)
- Ran Sun
- College of Engineering, Ibaraki University, Ibaraki, 316-8511, Japan.
| | - Budi Rahmadya
- Department of Computer Engineering, Faculty of Information and Technology, Andalas University, Limau Manis, Padang, Sumatera Barat, 25175, Indonesia
| | - Fangyuan Kong
- College of Engineering, Ibaraki University, Ibaraki, 316-8511, Japan
| | - Shigeki Takeda
- College of Engineering, Ibaraki University, Ibaraki, 316-8511, Japan
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27
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Abstract
Machine learning (ML)-based prediction is considered an important technique for improving decision making during the planning process. Modern ML models are used for prediction, prioritization, and decision making. Multiple ML algorithms are used to improve decision-making at different aspects after forecasting. This study focuses on the future prediction of the effectiveness of the COVID-19 vaccine effectiveness which has been presented as a light in the dark. People bear several reservations, including concerns about the efficacy of the COVID-19 vaccine. Under these presumptions, the COVID-19 vaccine would either lower the risk of developing the malady after injection, or the vaccine would impose side effects, affecting their existing health condition. In this regard, people have publicly expressed their concerns regarding the vaccine. This study intends to estimate what perception the masses will establish about the role of the COVID-19 vaccine in the future. Specifically, this study exhibits people’s predilection toward the COVID-19 vaccine and its results based on the reviews. Five models, e.g., random forest (RF), a support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), and an artificial neural network (ANN), were used for forecasting the overall predilection toward the COVID-19 vaccine. A voting classifier was used at the end of this study to determine the accuracy of all the classifiers. The results prove that the SVM produces the best forecasting results and that artificial neural networks (ANNs) produce the worst prediction toward the individual aptitude to be vaccinated by the COVID-19 vaccine. When using the voting classifier, the proposed system provided an overall accuracy of 89.9% for the random dataset and 45.7% for the date-wise dataset. Thus, the results show that the studied prediction technique is a promising and encouraging procedure for studying the future trends of the COVID-19 vaccine.
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28
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Alzubaidi M, Zubaydi HD, Bin-Salem AA, Abd-Alrazaq AA, Ahmed A, Househ M. Role of deep learning in early detection of COVID-19: Scoping review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE UPDATE 2021; 1:100025. [PMID: 34345877 PMCID: PMC8321699 DOI: 10.1016/j.cmpbup.2021.100025] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/21/2021] [Accepted: 07/21/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Since the onset of the COVID-19 pandemic, the world witnessed disruption on an unprecedented scale affecting our daily lives including but not limited to healthcare, business, education, and transportation. Deep Learning (DL) is a branch of Artificial intelligence (AI) applications, the recent growth of DL includes features that could be helpful in fighting the COVID-19 pandemic. Utilizing such features could support public health efforts. OBJECTIVE Investigate the literature available in the use of DL technology to support dealing with the COVID-19 crisis. We summarize the literature that uses DL features to analyze datasets for the purpose of a quick COVID-19 detection. METHODS This review follows PRISMA Extension for Scoping Reviews (PRISMA-ScR). We have scanned the most two commonly used databases (IEEE, ACM). Search terms were identified based on the target intervention (DL) and the target population (COVID-19). Two authors independently handled study selection and one author assigned for data extraction. A narrative approach is used to synthesize the extracted data. RESULTS We retrieved 53 studies and after passing through PRISMA excluding criteria, only 17 studies are considered in this review. All studies used deep learning for detection of COVID-19 cases in early stage based on different diagnostic modalities. Convolutional Neural Network (CNN) and Transfer Learning (TL) were the most commonly used techniques. CONCLUSION The included studies showed that DL techniques has significant impact on early detection of COVID-19 with high accuracy rate. However, most of the proposed methods are still in development and not tested in a clinical setting. Further investigation and collaboration are required from the research community and healthcare professionals in order to develop and standardize guidelines for use of DL in the healthcare domain.
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Key Words
- AI, Artificial intelligence
- CNN, Convolutional Neural Network
- COVID-19
- COVID-19, Corona Virus 2019
- CT, Computed Tomography
- CXR, Chest X-Ray Radiography
- Coronavirus
- DL, Deep Learning
- Deep learning
- Machine learning
- RNN, Recurrent Neural Network
- SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2
- ULS, Ultrasonography
- WHO, World Health Organization
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Affiliation(s)
- Mahmood Alzubaidi
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Haider Dhia Zubaydi
- National Advanced IPv6 Centre, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
| | | | - Alaa A Abd-Alrazaq
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Arfan Ahmed
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Mowafa Househ
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis. Processes (Basel) 2021. [DOI: 10.3390/pr9081267] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
With the advent of the first pandemic wave of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the question arises as to whether the spread of the virus will be controlled by the application of preventive measures or will follow a different course, regardless of the pattern of spread already recorded. These conditions caused by the unprecedented pandemic have highlighted the importance of reliable data from official sources, their complete recording and analysis, and accurate investigation of epidemiological indicators in almost real time. There is an ongoing research demand for reliable and effective modeling of the disease but also the formulation of substantiated views to make optimal decisions for the design of preventive or repressive measures by those responsible for the implementation of policy in favor of the protection of public health. The main objective of the study is to present an innovative data-analysis system of COVID-19 disease progression in Greece and her border countries by real-time statistics about the epidemiological indicators. This system utilizes visualized data produced by an automated information system developed during the study, which is based on the analysis of large pandemic-related datasets, making extensive use of advanced machine learning methods. Finally, the aim is to support with up-to-date technological means optimal decisions in almost real time as well as the development of medium-term forecast of disease progression, thus assisting the competent bodies in taking appropriate measures for the effective management of the available health resources.
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30
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Bharati S, Podder P, Mondal MRH, Prasath VS. CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images. INTERNATIONAL JOURNAL OF HYBRID INTELLIGENT SYSTEMS 2021. [DOI: 10.3233/his-210008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimized for detecting COVID-19 versus pneumonia with normal healthy lung controls. Different evaluation metrics such as the classification accuracy, F1 score, recall, precision, area under the receiver operating characteristics curve (AUC) are used. Our proposed CO-ResNet obtains consistently best performance in the multi-level data classification problem, including health lung, pneumonia affected lung and COVID-19 affected lung samples. In the experimental evaluation, the detection rate accuracy in discerning COVID-19 is 98.74%, and for healthy normal lungs, pneumonia affected lungs are 92.08% and 91.32% respectively for our CO-ResNet with ResNet101 backbone. Further, our model obtained accuracy values of 83.68% and 82% for healthy normal lungs and pneumonia affected lungs with ResNet152 backbone. Experimental results indicate the potential usage of our new DL driven model for classification of COVID-19 and pneumonia.
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Affiliation(s)
- Subrato Bharati
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Prajoy Podder
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - M. Rubaiyat Hossain Mondal
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - V.B. Surya Prasath
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA
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31
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Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [PMID: 37520766 PMCID: PMC8831917 DOI: 10.1016/j.jksuci.2021.07.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Artificial Intelligence (AI) has played a substantial role in the response to the challenges posed by the current pandemic. The growing interest in using AI to handle Covid-19 issues has accelerated the pace of AI research and resulted in an exponential increase in articles and review studies within a very short period of time. Hence, it is becoming challenging to explore the large corpus of academic publications dedicated to the global health crisis. Even with the presence of systematic review studies, given their number and diversity, identifying trends and research avenues beyond the pandemic should be an arduous task. We conclude therefore that after the one-year mark of the declaration of Covid-19 as a pandemic, the accumulated scientific contribution lacks two fundamental aspects: Knowledge synthesis and Future projections. In contribution to fill this void, this paper is a (i) synthesis study and (ii) foresight exercise. The synthesis study aims to provide the scholars a consolidation of findings and a knowledge synthesis through a systematic review of the reviews (umbrella review) studying AI applications against Covid-19. Following the PRISMA guidelines, we systematically searched PubMed, Scopus, and other preprint sources from 1st December 2019 to 1st June 2021 for eligible reviews. The literature search and screening process resulted in 45 included reviews. Our findings reveal patterns, relationships, and trends in the AI research community response to the pandemic. We found that in the space of few months, the research objectives of the literature have developed rapidly from identifying potential AI applications to evaluating current uses of intelligent systems. Only few reviews have adopted the meta-analysis as a study design. Moreover, a clear dominance of the medical theme and the DNN methods has been observed in the reported AI applications. Based on its constructive systematic umbrella review, this work conducts a foresight exercise that tries to envision the post-Covid-19 research landscape of the AI field. We see seven key themes of research that may be an outcome of the present crisis and which advocate a more sustainable and responsible form of intelligent systems. We set accordingly a post-pandemic research agenda articulated around these seven drivers. The results of this study can be useful for the AI research community to obtain a holistic view of the current literature and to help prioritize research needs as we are heading toward the new normal.
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32
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Nguyen DC, Ding M, Pathirana PN, Seneviratne A. Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:95730-95753. [PMID: 34812398 PMCID: PMC8545197 DOI: 10.1109/access.2021.3093633] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/27/2021] [Indexed: 05/02/2023]
Abstract
The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.
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Affiliation(s)
- Dinh C. Nguyen
- School of EngineeringDeakin UniversityWaurn PondsVIC3216Australia
| | | | | | - Aruna Seneviratne
- School of Electrical Engineering and TelecommunicationsUniversity of New South Wales (UNSW)SydneyNSW2052Australia
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33
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El Akoum M, El Achi M. Reimagining Innovation Amid the COVID-19 Pandemic: Insights From the WISH Innovation Programme. Front Public Health 2021; 9:678768. [PMID: 34164373 PMCID: PMC8215261 DOI: 10.3389/fpubh.2021.678768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 04/20/2021] [Indexed: 11/18/2022] Open
Abstract
The World Innovation Summit for Health (WISH) hosts two innovation competitions as part of its biennial healthcare conference. During the COVID-19 pandemic, WISH received more than 350 applications for both competitions, of which 31 were shortlisted to showcase at the WISH 2020 virtual summit. Of the 31 showcasing innovations, 11 (35.5%) had suggested an alternative use to their innovation as a contribution to the global fight against COVID-19. As such, this article explores the apparent and urgent need for the repurposing of healthcare innovations to reduce the costs and time associated with the conventional approach, in order to best respond to the demands of the global pandemic.
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Affiliation(s)
- Maha El Akoum
- World Innovation Summit for Health, Qatar Foundation, Doha, Qatar
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34
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Rasheed J, Jamil A, Hameed AA, Al-Turjman F, Rasheed A. COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review. Interdiscip Sci 2021; 13:153-175. [PMID: 33886097 PMCID: PMC8060789 DOI: 10.1007/s12539-021-00431-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 04/03/2021] [Accepted: 04/09/2021] [Indexed: 12/23/2022]
Abstract
The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.
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Affiliation(s)
- Jawad Rasheed
- Department of Computer Engineering, Istanbul Aydin University, Istanbul, 34295, Turkey.
| | - Akhtar Jamil
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Alaa Ali Hameed
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
| | - Ahmad Rasheed
- Department of Electrical and Electronics Engineering, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey
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35
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Alhasan M, Hasaneen M. Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic. Comput Med Imaging Graph 2021; 91:101933. [PMID: 34082281 PMCID: PMC8123377 DOI: 10.1016/j.compmedimag.2021.101933] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/15/2021] [Accepted: 04/27/2021] [Indexed: 12/13/2022]
Abstract
The advancement of technology remained an immersive interest for humankind throughout the past decades. Tech enterprises offered a stream of innovation to address the universal healthcare concerns. The novel coronavirus holds a substantial foothold of planet earth which is combatted by digital interventions across afflicted geographical boundaries and territories. This study aims to explore the trends of modern healthcare technologies and Artificial Intelligence (AI) during COVID-19 crisis, define the concepts and clinical role of AI in the mitigation of COVID-19, investigate and correlate the efficacy of AI-enabled technology in medical imaging during COVID-19 and determine advantages, drawbacks, and challenges of artificial intelligence during COVID-19 pandemic. The paper applied systematic review approach using a deliberated research protocol and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart. Digital technologies can coordinate COVID-19 responses in a cascade fashion that extends from the clinical care facility to the exterior of the pending viral epicenter. With cases of healthcare robotics, aerial drones, and the internet of things as evidentiary examples. PCR tests and medical imaging are the frontier diagnostics of COVID-19. Computed tomography helped to correct the accuracy variation of PCR tests at a clinical sensitivity of 98 %. Artificial intelligence can enable autonomous COVID-19 responses using techniques like machine learning. Technology could be an endless system of innovation and opportunities when sourced effectively. Scientists can utilize technology to resolve global concerns challenging the history of tangible possibility. Digital interventions have enhanced the responses to COVID-19, magnified the role of medical imaging amid the COVID-19 crisis and have exposed healthcare professionals to the opportunity of contactless care.
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Affiliation(s)
- Mustafa Alhasan
- Radiography and Medical Imaging Department, Fatima College of Health Sciences, United Arab Emirates; Radiologic Technology Program, Applied Medical Sciences College, Jordan University of Science and Technology, Jordan.
| | - Mohamed Hasaneen
- Radiography and Medical Imaging Department, Fatima College of Health Sciences, United Arab Emirates.
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36
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Singh PD, Kaur R, Singh KD, Dhiman G. A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2021; 23:1385-1401. [PMID: 33935584 PMCID: PMC8068562 DOI: 10.1007/s10796-021-10132-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/30/2021] [Indexed: 05/02/2023]
Abstract
The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier's accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy.
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Affiliation(s)
- Prabh Deep Singh
- Department of Computer Science & Engineering, Punjabi University, Patiala, Punjab India
| | - Rajbir Kaur
- Department of Electronics & Communication Engineering, Punjabi University, Patiala, Punjab India
| | - Kiran Deep Singh
- Department of Computer Science & Engineering, IKG Punjab Technical University, Punjab, India
| | - Gaurav Dhiman
- Department of Computer Science, Government Bikram College of Commerce, Punjabi University, Patiala, Punjab India
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37
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Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak 2021; 21:125. [PMID: 33836752 PMCID: PMC8035061 DOI: 10.1186/s12911-021-01488-9] [Citation(s) in RCA: 108] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 04/01/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND/INTRODUCTION Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. METHODS The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. RESULTS The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. CONCLUSIONS The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.
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Affiliation(s)
| | - Davide Calandra
- Department of Management, University of Turin, Turin, Italy.
| | | | - Vivek Muthurangu
- Institute of Child Health, University College London, London, UK
| | - Paolo Biancone
- Department of Management, University of Turin, Turin, Italy
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38
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Gholamzadeh M, Abtahi H, Safdari R. Suggesting a framework for preparedness against the pandemic outbreak based on medical informatics solutions: a thematic analysis. Int J Health Plann Manage 2021; 36:754-783. [PMID: 33502766 PMCID: PMC8014158 DOI: 10.1002/hpm.3106] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 11/05/2020] [Accepted: 12/14/2020] [Indexed: 11/15/2022] Open
Abstract
Background When an outbreak emerged, each country needs a coherent and preventive plan to deal with epidemics. In the era of technology, adopting informatics‐based solutions is essential. The main objective of this study is to propose a conceptual framework to provide a rapid and responsive surveillance system against pandemics. Methods A three‐step approach was employed in this research to develop a conceptual framework. These three steps comprise (1) literature review, (2) extracting and coding concepts, and determining main themes based on thematic analysis using ATLAS.ti® software, and (3) mapping concepts. Later, all of the results synthesized under expert consultation to design a conceptual framework based on the main themes and identified strategies related to medical informatics. Results In the literature review phase, 65 articles were identified as eligible studies for analysis. Through line by line coding in thematic analysis, more than 46 themes were extracted as potential foremost themes. Based on the key themes and strategies were employed by studies, the proposed framework designed in three main components. The most appropriate strategies that can be used in each section were identified based on the demands of each part and the available solutions. These solutions were employed in the final framework. Conclusion The presented model in this study can be the first step for a better understanding of the potential of medical informatics solutions in promoting epidemic disease management. It can be applied as a reference model for designing intelligent surveillance systems to prepare for probable future pandemics.
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Affiliation(s)
- Marsa Gholamzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamidreza Abtahi
- Pulmonary and Critical care Medicine Department, Thoracic Research Center, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Safdari
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
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39
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Rauf HT, Lali MIU, Khan MA, Kadry S, Alolaiyan H, Razaq A, Irfan R. Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks. PERSONAL AND UBIQUITOUS COMPUTING 2021; 27:733-750. [PMID: 33456433 PMCID: PMC7797027 DOI: 10.1007/s00779-020-01494-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 11/18/2020] [Indexed: 05/22/2023]
Abstract
The novel human coronavirus disease COVID-19 has become the fifth documented pandemic since the 1918 flu pandemic. COVID-19 was first reported in Wuhan, China, and subsequently spread worldwide. Almost all of the countries of the world are facing this natural challenge. We present forecasting models to estimate and predict COVID-19 outbreak in Asia Pacific countries, particularly Pakistan, Afghanistan, India, and Bangladesh. We have utilized the latest deep learning techniques such as Long Short Term Memory networks (LSTM), Recurrent Neural Network (RNN), and Gated Recurrent Units (GRU) to quantify the intensity of pandemic for the near future. We consider the time variable and data non-linearity when employing neural networks. Each model's salient features have been evaluated to foresee the number of COVID-19 cases in the next 10 days. The forecasting performance of employed deep learning models shown up to July 01, 2020, is more than 90% accurate, which shows the reliability of the proposed study. We hope that the present comparative analysis will provide an accurate picture of pandemic spread to the government officials so that they can take appropriate mitigation measures.
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Affiliation(s)
- Hafiz Tayyab Rauf
- Department of Computer Science, University of Gujrat, Gujrat, Pakistan
| | - M. Ikram Ullah Lali
- Department of Computer Science, University of Education, Lahore, 54770 Pakistan
| | | | - Seifedine Kadry
- Department of Mathematics and Computer Science, Faculty of Science, Beirut Arab University, Beirut, Lebanon
| | - Hanan Alolaiyan
- Department of Mathematics, King Saud University, Riyadh, 11451 Saudi Arabia
| | - Abdul Razaq
- Division of Science and Technology, Department of Mathematics, University of Education, Lahore, 54000 Pakistan
| | - Rizwana Irfan
- Department of Mathematics and Computer Science, Faculty of Science, University of Jeddah, Jeddah, Saudi Arabia
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40
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Al-Humairi SNS, Kamal AAA. Design a smart infrastructure monitoring system: a response in the age of COVID-19 pandemic. INNOVATIVE INFRASTRUCTURE SOLUTIONS 2021; 6:144. [PMCID: PMC8052533 DOI: 10.1007/s41062-021-00515-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Since the end of 2019, COVID-19 has been a challenge for the world, and it is expected that the world must take precautionary steps to tackle the virus spreading prior produces an efficient vaccine. Currently, most government efforts seek to avoid disseminating the coronavirus and forecast probable hot areas. The most susceptible to coronaviral infection are the healthcare staff due to their daily contact with potential patients. This article proposes a COVID-19 real-time system for tracking and identifying the suspected cases using an Internet of Things platform for capturing user symptoms and notify the authority. The proposed framework addressed four main components: (1) real-time symptom data collection via thermal scanning algorithm, (2) facial recognition algorithm, (3) a data analysis that uses artificial intelligence (AI) algorithm, and (4) a cloud infrastructure. A monitoring experiment was conducted to test three different ages, kid, middle, and older, considering the scanning distance influence compared with contact wearable sensors. The results show that 99.9% accuracy was achieved within a (500 ± 5) cm distance, and this accuracy tends to decrease as the distance the camera scanning and objects increased. The results also revealed that the scanning system's accuracy had been slightly changed as the environmental temperature dropped lower than 27 °C. Based on the high-temperature presence's simulated environment, the system demonstrated an effective and instant response via sending email and MQTT message to the person in charge of providing accurate identification of potential cases of COVID-19.
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Affiliation(s)
- Safaa N. Saud Al-Humairi
- Faculty of Information Sciences and Engineering, Management and Science University, 40100 Shah Alam, Selangor Malaysia
| | - Ahmad Aiman A. Kamal
- Faculty of Information Sciences and Engineering, Management and Science University, 40100 Shah Alam, Selangor Malaysia
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41
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Nguyen DC, Ding M, Pathirana PN, Seneviratne A. Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:95730-95753. [PMID: 34812398 DOI: 10.20944/preprints202004.0325.v1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/27/2021] [Indexed: 05/21/2023]
Abstract
The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.
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Affiliation(s)
- Dinh C Nguyen
- School of EngineeringDeakin University Waurn Ponds VIC 3216 Australia
| | - Ming Ding
- Data61CSIRO Eveleigh NSW 2015 Australia
| | | | - Aruna Seneviratne
- School of Electrical Engineering and TelecommunicationsUniversity of New South Wales (UNSW) Sydney NSW 2052 Australia
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42
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Srivastava V, Srivastava S, Chaudhary G, Al-Turjman F. A systematic approach for COVID-19 predictions and parameter estimation. PERSONAL AND UBIQUITOUS COMPUTING 2020; 27:675-687. [PMID: 33173450 PMCID: PMC7644415 DOI: 10.1007/s00779-020-01462-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 09/12/2020] [Indexed: 05/10/2023]
Abstract
The world is currently facing a pandemic called COVID-19 which has drastically changed our human lifestyle, affecting it badly. The lifestyle and the thought processes of every individual have changed with the current situation. This situation was unpredictable, and it contains a lot of uncertainties. In this paper, the authors have attempted to predict and analyze the disease along with its related issues to determine the maximum number of infected people, the speed of spread, and most importantly, its evaluation using a model-based parameter estimation method. In this research the Susceptible-Infectious-Recovered model with different conditions has been used for the analysis of COVID-19. The effects of lockdown, the light switch method, and parameter variations like contact ratio and reproduction number are also analyzed. The authors have attempted to study and predict the lockdown effect, particularly in India in terms of infected and recovered numbers, which show substantial improvement. A disease-free endemic stability analysis using Lyapunov and LaSalle's method is presented, and novel methods such as the convalescent plasma method and the Who Acquires Infection From Whom method are also discussed, as they are considered to be useful in flattening the curve of COVID-19.
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Affiliation(s)
- Vishal Srivastava
- Netaji Subhas Institute of Technology, Sector 3, Dwarka, New Delhi India
| | - Smriti Srivastava
- Netaji Subhas Institute of Technology, Sector 3, Dwarka, New Delhi India
| | - Gopal Chaudhary
- Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, Delhi India
| | - Fadi Al-Turjman
- Artificial Intelligence Dept., Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
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Ndiaye M, Oyewobi SS, Abu-Mahfouz AM, Hancke GP, Kurien AM, Djouani K. IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:186821-186839. [PMID: 34786294 PMCID: PMC8545289 DOI: 10.1109/access.2020.3030090] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 10/06/2020] [Indexed: 05/03/2023]
Abstract
The novel coronavirus (COVID-19), declared by the World Health Organization (WHO) as a global pandemic, has brought with it changes to the general way of life. Major sectors of the world industry and economy have been affected and the Internet of Things (IoT) management and framework is no exception in this regard. This article provides an up to date survey on how a global pandemic such as COVID-19 has affected the world of IoT technologies. It looks at the contributions that IoT and associated sensor technologies have made towards virus tracing, tracking and spread mitigation. The associated challenges of deployment of sensor hardware in the face of a rapidly spreading pandemic have been looked into as part of this review article. The effects of a global pandemic on the evolution of IoT architectures and management have also been addressed, leading to the likely outcomes on future IoT implementations. In general, this article provides an insight into the advancement of sensor-based E-health towards the management of global pandemics. It also answers the question of how a global virus pandemic has shaped the future of IoT networks.
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Affiliation(s)
- Musa Ndiaye
- Department of Electrical EngineeringCopperbelt UniversityKitwe10101Zambia
| | - Stephen S. Oyewobi
- French South African Institute of Technology (FSATI), Tshwane University of TechnologyPretoria0001South Africa
| | - Adnan M. Abu-Mahfouz
- French South African Institute of Technology (FSATI), Tshwane University of TechnologyPretoria0001South Africa
- Council for Scientific and Industrial ResearchPretoria0083South Africa
- Department of ElectricalElectronic and Computer EngineeringUniversity of PretoriaPretoria0028South Africa
| | - Gerhard P. Hancke
- College of Automation and Artificial IntelligenceNanjing University of Posts and TelecommunicationsNanjing210023China
- Department of ElectricalElectronic and Computer EngineeringUniversity of PretoriaPretoria0028South Africa
| | - Anish M. Kurien
- French South African Institute of Technology (FSATI), Tshwane University of TechnologyPretoria0001South Africa
| | - Karim Djouani
- French South African Institute of Technology (FSATI), Tshwane University of TechnologyPretoria0001South Africa
- LISSI LaboratoryUniversity Paris-Est Creteil (UPEC)94000CreteilFrance
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Ramchandani A, Fan C, Mostafavi A. DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:159915-159930. [PMID: 34786287 PMCID: PMC8545302 DOI: 10.1109/access.2020.3019989] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 08/25/2020] [Indexed: 05/08/2023]
Abstract
In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate time series and multivariate spatial time series data. Using this novel method, the proposed model can both take in a large number of heterogeneous features, such as census data, intra-county mobility, inter-county mobility, social distancing data, past growth of infection, among others, and learn complex interactions between these features. Using data collected from various sources, we estimate the range of increase in infected cases seven days into the future for all U.S. counties. In addition, we use the model to identify the most influential features for prediction of the growth of infection. We also analyze pairs of features and estimate the amount of observed second-order interaction between them. Experiments show that the proposed model obtains satisfactory predictive performance and fairly interpretable feature analysis results; hence, the proposed model could complement the standard epidemiological models for national-level surveillance of pandemics, such as COVID-19. The results and findings obtained from the deep learning model could potentially inform policymakers and researchers in devising effective mitigation and response strategies. To fast-track further development and experimentation, the code used to implement the proposed model has been made fully open source.
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Affiliation(s)
- Ankit Ramchandani
- Department of Computer Science and EngineeringTexas A&M UniversityCollege StationTX77840USA
| | - Chao Fan
- Zachry Department of Civil and Environmental EngineeringTexas A&M UniversityCollege StationTX77840USA
| | - Ali Mostafavi
- Zachry Department of Civil and Environmental EngineeringTexas A&M UniversityCollege StationTX77840USA
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