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Uddin S, Khan A, Lu H, Zhou F, Karim S, Hajati F, Moni MA. Road networks and socio-demographic factors to explore COVID-19 infection during its different waves. Sci Rep 2024; 14:1551. [PMID: 38233430 PMCID: PMC10794216 DOI: 10.1038/s41598-024-51610-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/07/2024] [Indexed: 01/19/2024] Open
Abstract
The COVID-19 pandemic triggered an unprecedented level of restrictive measures globally. Most countries resorted to lockdowns at some point to buy the much-needed time for flattening the curve and scaling up vaccination and treatment capacity. Although lockdowns, social distancing and business closures generally slowed the case growth, there is a growing concern about these restrictions' social, economic and psychological impact, especially on the disadvantaged and poorer segments of society. While we are all in this together, these segments often take the heavier toll of the pandemic and face harsher restrictions or get blamed for community transmission. This study proposes a road-network-based networked approach to model mobility patterns between localities during lockdown stages. It utilises a panel regression method to analyse the effects of mobility in transmitting COVID-19 in an Australian context, together with a close look at a suburban population's characteristics like their age, income and education. Firstly, we attempt to model how the local road networks between the neighbouring suburbs (i.e., neighbourhood measure) and current infection count affect the case growth and how they differ between delta and omicron variants. We use a geographic information system, population and infection data to measure road connections, mobility and transmission probability across the suburbs. We then looked at three socio-demographic variables: age, education and income and explored how they moderate independent and dependent variables (infection rates and neighbourhood measures). The result shows strong model performance to predict infection rate based on neighbourhood road connection. However, apart from age in the delta variant context, the other variables (income and education level) do not seem to moderate the relationship between infection rate and neighbourhood measure. The results indicate that suburbs with a more socio-economically disadvantaged population do not necessarily contribute to more community transmission. The study findings could be potentially helpful for stakeholders in tailoring any health decision for future pandemics.
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Affiliation(s)
- Shahadat Uddin
- School of Project Management, Faculty of Engineering, The University of Sydney, Forest Lodge, NSW, 2037, Australia.
| | - Arif Khan
- School of Project Management, Faculty of Engineering, The University of Sydney, Forest Lodge, NSW, 2037, Australia
| | - Haohui Lu
- School of Project Management, Faculty of Engineering, The University of Sydney, Forest Lodge, NSW, 2037, Australia
| | - Fangyu Zhou
- School of Project Management, Faculty of Engineering, The University of Sydney, Forest Lodge, NSW, 2037, Australia
| | - Shakir Karim
- School of Project Management, Faculty of Engineering, The University of Sydney, Forest Lodge, NSW, 2037, Australia
| | - Farshid Hajati
- School of Science and Technology, University of New England, Armidale, NSW, 2350, Australia
| | - Mohammad Ali Moni
- Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW, 2795, Australia
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2
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Nordin NI, Mustafa WA, Lola MS, Madi EN, Kamil AA, Nasution MD, K. Abdul Hamid AA, Zainuddin NH, Aruchunan E, Abdullah MT. Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model. Bioengineering (Basel) 2023; 10:1318. [PMID: 38002441 PMCID: PMC10669812 DOI: 10.3390/bioengineering10111318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/03/2023] [Accepted: 10/09/2023] [Indexed: 11/26/2023] Open
Abstract
Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics.
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Affiliation(s)
- Noor Ilanie Nordin
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia or (N.I.N.); (A.A.K.A.H.)
- Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Kelantan, Bukit Ilmu, Machang 18500, Kelantan, Malaysia
| | - Wan Azani Mustafa
- Faculty of Electrical Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
- Centre of Excellence for Advanced Computing, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
| | - Muhamad Safiih Lola
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia or (N.I.N.); (A.A.K.A.H.)
- Special Interest Group on Modeling and Data Analytics (SIGMDA), Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
| | - Elissa Nadia Madi
- Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Besut Campus, Besut 22200, Terengganu, Malaysia;
| | - Anton Abdulbasah Kamil
- Faculty of Economics, Administrative and Social Sciences, Istanbul Gelisim University, Cihangir Mah. Şehit Jandarma Komando Er Hakan Öner Sk. No:1 Avcılar, İstanbul 34310, Turkey;
| | - Marah Doly Nasution
- Faculty of Teacher and Education, University Muhammadiyah Sumatera Utara, Jl. Kapten Muchtar Basri No.3, Glugur Darat II, Kec. Medan Tim., Kota Medan 20238, Sumatera Utara, Indonesia;
| | - Abdul Aziz K. Abdul Hamid
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia or (N.I.N.); (A.A.K.A.H.)
- Special Interest Group on Applied Informatics and Intelligent Applications (AINIA), Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
| | - Nurul Hila Zainuddin
- Mathematics Department, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim 53900, Perak Darul Ridzuan, Malaysia;
| | - Elayaraja Aruchunan
- Department of Decision Science, Faculty of Business and Economics, University Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mohd Tajuddin Abdullah
- Fellow Academy of Sciences Malaysia, Level 20, West Wing Tingkat 20, Menara MATRADE, Jalan Sultan Haji Ahmad Shah, Kuala Lumpur 50480, Malaysia;
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3
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Johnson MR, Naik H, Chan WS, Greiner J, Michaleski M, Liu D, Silvestre B, McCarthy IP. Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions. Health Care Manag Sci 2023; 26:477-500. [PMID: 37199873 PMCID: PMC10191824 DOI: 10.1007/s10729-023-09639-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 04/20/2023] [Indexed: 05/19/2023]
Abstract
During the COVID-19 pandemic, there has been considerable research on how regional and country-level forecasting can be used to anticipate required hospital resources. We add to and build on this work by focusing on ward-level forecasting and planning tools for hospital staff during the pandemic. We present an assessment, validation, and deployment of a working prototype forecasting tool used within a modified Traffic Control Bundling (TCB) protocol for resource planning during the pandemic. We compare statistical and machine learning forecasting methods and their accuracy at one of the largest hospitals (Vancouver General Hospital) in Canada against a medium-sized hospital (St. Paul's Hospital) in Vancouver, Canada through the first three waves of the COVID-19 pandemic in the province of British Columbia. Our results confirm that traditional statistical and machine learning (ML) forecasting methods can provide valuable ward-level forecasting to aid in decision-making for pandemic resource planning. Using point forecasts with upper 95% prediction intervals, such forecasting methods would have provided better accuracy in anticipating required beds on COVID-19 hospital units than ward-level capacity decisions made by hospital staff. We have integrated our methodology into a publicly available online tool that operationalizes ward-level forecasting to aid with capacity planning decisions. Importantly, hospital staff can use this tool to translate forecasts into better patient care, less burnout, and improved planning for all hospital resources during pandemics.
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Affiliation(s)
| | - Hiten Naik
- Department of Medicine, University of British Columbia, Vancouver, Canada
| | - Wei Siang Chan
- Land and Food Systems, University of British Columbia, Vancouver, Canada
| | - Jesse Greiner
- Department of Medicine, Providence Health Care, Vancouver, Canada
| | - Matt Michaleski
- Department of Medicine, Vancouver General Hospital, Vancouver, Canada
| | - Dong Liu
- Land and Food Systems, University of British Columbia, Vancouver, Canada
| | - Bruno Silvestre
- Asper School of Business, University of Manitoba, Winnipeg, Canada
| | - Ian P. McCarthy
- Beedie School of Business, Simon Fraser University, Vancouver, Canada
- Luiss Guido Carli, Rome, Italy
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4
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Eshkiti A, Sabouhi F, Bozorgi-Amiri A. A data-driven optimization model to response to COVID-19 pandemic: a case study. ANNALS OF OPERATIONS RESEARCH 2023; 328:1-50. [PMID: 37361061 PMCID: PMC10252180 DOI: 10.1007/s10479-023-05320-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 06/28/2023]
Abstract
COVID-19 is a highly prevalent disease that has led to numerous predicaments for healthcare systems worldwide. Owing to the significant influx of patients and limited resources of health services, there have been several limitations associated with patients' hospitalization. These limitations can cause an increment in the COVID-19-related mortality due to the lack of appropriate medical services. They can also elevate the risk of infection in the rest of the population. The present study aims to investigate a two-phase approach to designing a supply chain network for hospitalizing patients in the existing and temporary hospitals, efficiently distributing medications and medical items needed by patients, and managing the waste created in hospitals. Since the number of future patients is uncertain, in the first phase, trained Artificial Neural Networks with historical data forecast the number of patients in future periods and generate scenarios. Through the use of the K-Means method, these scenarios are reduced. In the second phase, a multi-objective, multi-period, data-driven two-stage stochastic programming is developed using the acquired scenarios in the previous phase concerning the uncertainty and disruption in facilities. The objectives of the proposed model include maximizing the minimum allocation-to-demand ratio, minimizing the total risk of disease spread, and minimizing the total transportation time. Furthermore, a real case study is investigated in Tehran, the capital of Iran. The results showed that the areas with the highest population density and no facilities near them have been selected for the location of temporary facilities. Among temporary facilities, temporary hospitals can allocate up to 2.6% of the total demand, which puts pressure on the existing hospitals to be removed. Furthermore, the results indicated that the allocation-to-demand ratio can remain at an ideal level when disruptions occur by considering temporary facilities. Our analyses focus on: (1) Examining demand forecasting error and generated scenarios in the first phase, (2) exploring the impact of demand parameters on the allocation-to-demand ratio, total time and total risk, (3) investigating the strategy of utilizing temporary hospitals to address sudden changes in demand, (4) evaluating the effect of disruption to facilities on the supply chain network.
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Affiliation(s)
- Amin Eshkiti
- School of Industrial
Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Fatemeh Sabouhi
- School of Industrial
Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Ali Bozorgi-Amiri
- School of Industrial
Engineering, College of Engineering, University of Tehran, Tehran, Iran
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5
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Kamran MA, Kia R, Goodarzian F, Ghasemi P. A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms. SOCIO-ECONOMIC PLANNING SCIENCES 2023; 85:101378. [PMID: 35966449 PMCID: PMC9359548 DOI: 10.1016/j.seps.2022.101378] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 04/29/2022] [Accepted: 06/18/2022] [Indexed: 06/02/2023]
Abstract
With the discovery of the COVID-19 vaccine, what has always been worrying the decision-makers is related to the distribution management, the vaccination centers' location, and the inventory control of all types of vaccines. As the COVID-19 vaccine is highly demanded, planning for its fair distribution is a must. University is one of the most densely populated areas in a city, so it is critical to vaccinate university students so that the spread of this virus is curbed. As a result, in the present study, a new stochastic multi-objective, multi-period, and multi-commodity simulation-optimization model has been developed for the COVID-19 vaccine's production, distribution, location, allocation, and inventory control decisions. In this study, the proposed supply chain network includes four echelons of manufacturers, hospitals, vaccination centers, and volunteer vaccine students. Vaccine manufacturers send the vaccines to the vaccination centers and hospitals after production. The students with a history of special diseases such as heart disease, corticosteroids, blood clots, etc. are vaccinated in hospitals because of accessing more medical care, and the rest of the students are vaccinated in the vaccination centers. Then, a system dynamic structure of the prevalence of COVID -19 in universities is developed and the vaccine demand is estimated using simulation, in which the demand enters the mathematical model as a given stochastic parameter. Thus, the model pursues some goals, namely, to minimize supply chain costs, maximize student desirability for vaccination, and maximize justice in vaccine distribution. To solve the proposed model, Variable Neighborhood Search (VNS) and Whale Optimization Algorithm (WOA) algorithms are used. In terms of novelties, the most important novelties in the simulation model are considering the virtual education and exerted quarantine effect on estimating the number of the vaccines. In terms of the mathematical model, one of the remarkable contributions is paying attention to social distancing while receiving the injection and the possibility of the injection during working and non-working hours, and regarding the novelties in the solution methodology, a new heuristic method based on a meta-heuristic algorithm called Modified WOA with VNS (MVWOA) is developed. In terms of the performance metrics and the CPU time, the MOWOA is discovered with a superior performance than other given algorithms. Moreover, regarding the data, a case study related to the COVID-19 pandemic period in Tehran/Iran is provided to validate the proposed algorithm. The outcomes indicate that with the demand increase, the costs increase sharply while the vaccination desirability for students decreases with a slight slope.
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Affiliation(s)
- Mehdi A Kamran
- Faculty of Business and Economics, Department of Logistics, Tourism and Service Management, German University of Technology, Muscat, Oman
- Department of Industrial Engineering, Urmia University of Technology, Urmia, Iran
| | - Reza Kia
- Department of Engineering, School of Engineering and Built Environment, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Millennium Point, Curzon Street, Birmingham, B4 7XG, UK
| | - Fariba Goodarzian
- Engineering Group, School of Engineering, University of Seville, Camino de los Descubrimientos s/n, 41092 Seville, Spain
| | - Peiman Ghasemi
- Faculty of Business and Economics, Department of Logistics, Tourism and Service Management, German University of Technology, Muscat, Oman
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6
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Picture fuzzy set-based decision-making approach using Dempster-Shafer theory of evidence and grey relation analysis and its application in COVID-19 medicine selection. Soft comput 2023; 27:3327-3341. [PMID: 34108847 PMCID: PMC8178672 DOI: 10.1007/s00500-021-05909-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/24/2021] [Indexed: 12/12/2022]
Abstract
To offer better treatment for a COVID-19 patient, preferable medicine selection has become a challenging task for most of the medical practitioners as there is no such proven information regarding it. This article proposes a decision-making approach for preferable medicine selection using picture fuzzy set (PFS), Dempster-Shafer (D-S) theory of evidence and grey relational analysis (GRA). PFS is an extended version of the intuitionistic fuzzy set, where in addition to membership and non-membership grade, neutral and refusal membership grades are used to solve uncertain real-life problems more efficiently. Hence, we attempt to use it in this article to solve the mentioned problem. Previously, researchers considered the neutral membership grade of the PFS similar to the other two membership values (positive and negative) as applied to the decision-making method. In this study, we explore that neutral membership grade can be associated with probabilistic uncertainty which is measured using D-S theory of evidence and FUSH operation is applied for the aggregation purpose. Then GRA is used to measure the performance among the set of parameters which are in conflict and contradiction with each other. In this process, we propose an alternative group decision-making approach by the evidence of the neutral membership grade which is measured by the D-S theory and the conflict and contradiction among the criteria are managed by GRA. Finally, the proposed approach is demonstrated to solve the COVID-19 medicine selection problem.
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7
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Castillo O, Castro JR, Melin P. Forecasting the COVID-19 with Interval Type-3 Fuzzy Logic and the Fractal Dimension. INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2023; 25:182-197. [PMCID: PMC9486798 DOI: 10.1007/s40815-022-01351-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/30/2022] [Accepted: 06/06/2022] [Indexed: 01/02/2024]
Abstract
In this article, the prediction of COVID-19 based on a combination of fractal theory and interval type-3 fuzzy logic is put forward. The fractal dimension is utilized to estimate the time series geometrical complexity level, which in this case is applied to the COVID-19 problem. The main aim of utilizing interval type-3 fuzzy logic is for handling uncertainty in the decision-making occurring in forecasting. The hybrid approach is formed by an interval type-3 fuzzy model structured by fuzzy if then rules that utilize as inputs the linear and non-linear values of the dimension, and the forecasts of COVID-19 cases are the outputs. The contribution is the new scheme based on the fractal dimension and interval type-3 fuzzy logic, which has not been proposed before, aimed at achieving an accurate forecasting of complex time series, in particular for the COVID-19 case. Publicly available data sets are utilized to construct the interval type-3 fuzzy system for a time series. The hybrid approach can be a helpful tool for decision maker in fighting the pandemic, as they could use the forecasts to decide immediate actions. The proposed method has been compared with previous works to show that interval type-3 fuzzy systems outperform previous methods in prediction.
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8
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Alweshah M. Coronavirus herd immunity optimizer to solve classification problems. Soft comput 2023; 27:3509-3529. [PMID: 35309595 PMCID: PMC8922087 DOI: 10.1007/s00500-022-06917-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/13/2022] [Indexed: 11/28/2022]
Abstract
Classification is a technique in data mining that is used to predict the value of a categorical variable and to produce input data and datasets of varying values. The classification algorithm makes use of the training datasets to build a model which can be used for allocating unclassified records to a defined class. In this paper, the coronavirus herd immunity optimizer (CHIO) algorithm is used to boost the efficiency of the probabilistic neural network (PNN) when solving classification problems. First, the PNN produces a random initial solution and submits it to the CHIO, which then attempts to refine the PNN weights. This is accomplished by the management of random phases and the effective identification of a search space that can probably decide the optimal value. The proposed CHIO-PNN approach was applied to 11 benchmark datasets to assess its classification accuracy, and its results were compared with those of the PNN and three methods in the literature, the firefly algorithm, African buffalo algorithm, and β-hill climbing. The results showed that the CHIO-PNN achieved an overall classification rate of 90.3% on all datasets, at a faster convergence speed as compared outperforming all the methods in the literature. Supplementary Information The online version contains supplementary material available at 10.1007/s00500-022-06917-z.
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Affiliation(s)
- Mohammed Alweshah
- Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan
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9
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Chakrabortty R, Pal SC, Ghosh M, Arabameri A, Saha A, Roy P, Pradhan B, Mondal A, Ngo PTT, Chowdhuri I, Yunus AP, Sahana M, Malik S, Das B. Weather indicators and improving air quality in association with COVID-19 pandemic in India. Soft comput 2023; 27:3367-3388. [PMID: 34276248 PMCID: PMC8276232 DOI: 10.1007/s00500-021-06012-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2021] [Indexed: 12/13/2022]
Abstract
The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad. Supplementary Information The online version contains supplementary material available at 10.1007/s00500-021-06012-9.
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Affiliation(s)
- Rabin Chakrabortty
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
| | - Manoranjan Ghosh
- Centre for Rural Development and Sustainable Innovative Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal India
| | - Alireza Arabameri
- Department of Geomorphology, Tarbiat Modares University, 14117-13116 Tehran, Iran
| | - Asish Saha
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
| | - Paramita Roy
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007 Australia ,Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul, 05006 Korea ,Center of Excellence for Climate Change Research, King Abdulaziz University, P.O. Box 80234, Jeddah, 21589 Saudi Arabia ,Earth Observation Center, Institute of Climate Change, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Malaysia
| | - Ayan Mondal
- Ecology and Environmental Modelling Laboratory, Department of Environmental Science, The University of Burdwan, Burdwan, West Bengal India
| | - Phuong Thao Thi Ngo
- Institute of Research and Development, Duy Tan University, Da Nang, 550000 Vietnam
| | - Indrajit Chowdhuri
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
| | - Ali P. Yunus
- Centre for Climate Change Adaptation, National Institute for Environmental Studies, Ibaraki, 305-8506 Japan
| | - Mehebub Sahana
- School of Environment, Education and Development, University of Manchester, Oxford Road, Manchester, M13 9PL UK
| | - Sadhan Malik
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
| | - Biswajit Das
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal India
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10
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Al-Shaikh A, Mahafzah BA, Alshraideh M. Hybrid harmony search algorithm for social network contact tracing of COVID-19. Soft comput 2023; 27:3343-3365. [PMID: 34220301 PMCID: PMC8237257 DOI: 10.1007/s00500-021-05948-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/04/2021] [Indexed: 02/05/2023]
Abstract
The coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan, China, and then moved to almost every country showing an unprecedented outbreak. The world health organization declared COVID-19 a pandemic. Since then, millions of people were infected, and millions have lost their lives all around the globe. By the end of 2020, effective vaccines that could prevent the fast spread of the disease started to loom on the horizon. Nevertheless, isolation, social distancing, face masks, and quarantine are the best-known measures, in the time being, to fight the pandemic. On the other hand, contact tracing is an effective procedure in tracking infections and saving others' lives. In this paper, we devise a new approach using a hybrid harmony search (HHS) algorithm that casts the problem of finding strongly connected components (SCCs) to contact tracing. This new approach is named as hybrid harmony search contact tracing (HHS-CT) algorithm. The hybridization is achieved by integrating the stochastic hill climbing into the operators' design of the harmony search algorithm. The HHS-CT algorithm is compared to other existing algorithms of finding SCCs in directed graphs, where it showed its superiority over these algorithms. The devised approach provides a 77.18% enhancement in terms of run time and an exceptional average error rate of 1.7% compared to the other existing algorithms of finding SCCs.
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Affiliation(s)
- Ala’a Al-Shaikh
- Learning and Teaching Technology Center, Al-Balqa Applied University, Al-Salt, 19117 Jordan
| | - Basel A. Mahafzah
- Department of Computer Science, King Abdulla II School of Information Technology, The University of Jordan, Amman, 11942 Jordan
| | - Mohammad Alshraideh
- Department of Computer Science, King Abdulla II School of Information Technology, The University of Jordan, Amman, 11942 Jordan
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11
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Lai H, Khan YA, Thaljaoui A, Chammam W, Abbas SZ. RETRACTED ARTICLE: COVID-19 pandemic and unemployment rate: A hybrid unemployment rate prediction approach for developed and developing countries of Asia. Soft comput 2023; 27:615. [PMID: 34025212 PMCID: PMC8132284 DOI: 10.1007/s00500-021-05871-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/03/2021] [Indexed: 01/05/2023]
Affiliation(s)
- Han Lai
- grid.459575.f0000 0004 1761 0120School of Information Engineering, Huanghuai University, Henan, China
| | - Yousaf Ali Khan
- grid.440530.60000 0004 0609 1900Department of Mathematics and Statistics, Hazara University Mansehra, Dhodial, Pakistan ,grid.453548.b0000 0004 0368 7549School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013 China
| | - Adel Thaljaoui
- grid.449051.d0000 0004 0441 5633Department of Computer Science and Information, College of Science At Zulfi, Majmaah University, PO Box 66, Al-Majmaah, 11952 Saudi Arabia
| | - Wathek Chammam
- grid.449051.d0000 0004 0441 5633Department of Mathematics, College of Science Al-Zulfi, Majmaah University, PO Box 66, Al-Majmaah, 11952 Saudi Arabia
| | - Syed Zaheer Abbas
- grid.440530.60000 0004 0609 1900Department of Mathematics and Statistics, Hazara University Mansehra, Dhodial, Pakistan
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12
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Ramírez M, Melin P. A New Interval Type-2 Fuzzy Aggregation Approach for Combining Multiple Neural Networks in Clustering and Prediction of Time Series. INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2023; 25:1077-1104. [PMCID: PMC9669546 DOI: 10.1007/s40815-022-01426-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 10/17/2022] [Accepted: 10/20/2022] [Indexed: 03/14/2024]
Abstract
Inspired by how some cognitive abilities affect the human decision-making process, the proposed approach combines neural networks with type-2 fuzzy systems. The proposal consists of combining computational models of artificial neural networks and fuzzy systems to perform clustering and prediction of time series corresponding to the population, urban population, particulate matter (PM2.5), carbon dioxide (CO2), registered cases and deaths from COVID-19 for certain countries. The objective is to associate these variables by country based on the identification of similarities in the historical information for each variable. The hybrid approach consists of computationally simulating the behavior of cognitive functions in the human brain in the decision-making process by using different types of neural models and interval type-2 fuzzy logic for combining their outputs. Simulation results show the advantages of the proposed approach, because starting from an input data set, the artificial neural networks are responsible for clustering and predicting values of multiple time series, and later a set of fuzzy inference systems perform the integration of these results, which the user can then utilize as a support tool for decision-making with uncertainty.
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Affiliation(s)
- Martha Ramírez
- Tijuana Institute of Technology, TecNM, Calzada Tecnologico S/N, Fracc. Tomas Aquino, 22379 Tijuana, Mexico
| | - Patricia Melin
- Tijuana Institute of Technology, TecNM, Calzada Tecnologico S/N, Fracc. Tomas Aquino, 22379 Tijuana, Mexico
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13
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Jain R, Rana KB, Meena ML. An integrated multi-criteria decision-making approach for identifying the risk level of musculoskeletal disorders among handheld device users. Soft comput 2023; 27:3283-3293. [PMID: 33551675 PMCID: PMC7856850 DOI: 10.1007/s00500-021-05592-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
In work-from-home (WFH) situation due to coronavirus (COVID-19) pandemic, the handheld device (HHD) users work in awkward postures for longer hours because of unavailability of ergonomically designed workstations. This problem results in different type of musculoskeletal disorders (MSDs) among the HHD users. An integrated multi-criteria decision-making approach was offered for identifying the risk level of MSDs among HHD users. A case example implemented the proposed approach in which, firstly, the best-worst method (BWM) technique was used to prioritize and determine the relative importance (weightage) of the risk factors. The weightages of the risk factors further used to rank the seven alternatives (HHD users) using Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) technique. The outcomes of the BWM investigation showed that the three most significant risk factors responsible for MSDs are duration of working, poor working posture and un-ergonomic design. The outcome of the VIKOR technique exhibited that computer professionals were at the highest risk among all users. The risk factor priority must be used for designing a working strategy for the WFH situation which will help to mitigate the risks of MSDs.
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Affiliation(s)
- Rahul Jain
- Department of Mechanical Engineering, University Teaching Department, Rajasthan Technical University Kota, Rawatbhata Road, Akelgarh, Kota, Rajasthan 324010 India
| | - Kunj Bihari Rana
- Department of Mechanical Engineering, University Teaching Department, Rajasthan Technical University Kota, Rawatbhata Road, Akelgarh, Kota, Rajasthan 324010 India
| | - Makkhan Lal Meena
- Department of Mechanical Engineering, Malaviya National Institute of Technology, JLN Marg, Malaviya Nagar, Jaipur, Rajasthan 302017 India
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14
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Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction. Soft comput 2023; 27:3245-3282. [PMID: 33456340 PMCID: PMC7804581 DOI: 10.1007/s00500-020-05549-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
In this paper, the latest global COVID-19 pandemic prediction is addressed. Each country worldwide has faced this pandemic differently, reflected in its statistical number of confirmed and death cases. Predicting the number of confirmed and death cases could allow us to know the future number of cases and provide each country with the necessary information to make decisions based on the predictions. Recent works are focused only on confirmed COVID-19 cases or a specific country. In this work, the firefly algorithm designs an ensemble neural network architecture for each one of 26 countries. In this work, we propose the firefly algorithm for ensemble neural network optimization applied to COVID-19 time series prediction with type-2 fuzzy logic in a weighted average integration method. The proposed method finds the number of artificial neural networks needed to form an ensemble neural network and their architecture using a type-2 fuzzy inference system to combine the responses of individual artificial neural networks to perform a final prediction. The advantages of the type-2 fuzzy weighted average integration (FWA) method over the conventional average method and type-1 fuzzy weighted average integration are shown.
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15
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Multicriteria group decision making via generalized trapezoidal intuitionistic fuzzy number-based novel similarity measure and its application to diverse COVID-19 scenarios. Artif Intell Rev 2023; 56:3543-3617. [PMID: 36092823 PMCID: PMC9450847 DOI: 10.1007/s10462-022-10251-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Havoc, brutality, economic breakdown, and vulnerability are the terms that can be rightly associated with COVID-19, for the kind of impact it is having on the whole world for the last two years. COVID-19 came as a nightmare and it is still not over yet, changing its form factor with each mutation. Moreover, each unpredictable mutation causes more severeness. In the present article, we outline a decision support algorithm using Generalized Trapezoidal Intuitionistic Fuzzy Numbers (GTrIFNs) to deal with various facets of COVID-19 problems. Intuitionistic fuzzy sets (IFSs) and their continuous counterparts, viz., the intuitionistic fuzzy numbers (IFNs), have the flexibility and effectiveness to handle the uncertainty and fuzziness associated with real-world problems. Although a meticulous amount of research works can be found in the literature, a wide majority of them are based mainly on normalized IFNs rather than the more generalized approach, and most of them had several limitations. Therefore, we have made a sincere attempt to devise a novel Similarity Measure (SM) which considers the evaluation of two prominent features of GTrIFNs, which are their expected values and variances. Then, to establish the superiority of our approach we present a comparative analysis of our method with several other established similarity methods considering ten different profiles of GTrIFNs. The proposed SM is then validated for feasibility and applicability, by elaborating a Fuzzy Multicriteria Group Decision Making (FMCGDM) algorithm and it is supportedby a suitable illustrative example. Finally, the proposed SM approach is applied to tackle some significant concerns due to COVID-19. For instance, problems like the selection of best medicine for COVID-19 infected patients; proper healthcare waste disposal technique; and topmost government intervention measures to prevent the COVID-19 spread, are some of the burning issues which are handled with our newly proposed SM approach. Graphical abstract
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16
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Eken S. A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata. Soft comput 2023; 27:2645-2655. [PMID: 33100897 PMCID: PMC7570402 DOI: 10.1007/s00500-020-05387-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results.
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Affiliation(s)
- Süleyman Eken
- grid.411105.00000 0001 0691 9040Department of Information Systems Engineering, Kocaeli University, 41001 Kocaeli, Turkey
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17
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Bhoi SK, Jena KK, Mohapatra D, Singh M, Kumar R, Long HV. Communicable disease pandemic: a simulation model based on community transmission and social distancing. Soft comput 2023; 27:2717-2727. [PMID: 34483721 PMCID: PMC8406017 DOI: 10.1007/s00500-021-06168-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2021] [Indexed: 11/26/2022]
Abstract
Communicable disease pandemic is a severe disease outbreak all over the countries and continents. Swine Flu, HIV/AIDS, corona virus disease-19 (COVID-19), etc., are some of the global pandemics in the world. The major cause of becoming pandemic is community transmission and lack of social distancing. Recently, COVID-19 is such a largest outbreak all over the world. This disease is a communicable disease which is spreading fastly due to community transmission, where the affected people in the community affect the heathy people in the community. Government is taking precautions by imposing social distancing in the countries or state to control the impact of COVID-19. Social distancing can reduce the community transmission of COVID-19 by reducing the number of infected persons in an area. This is performed by staying at home and maintaining social distance with people. It reduces the density of people in an area by which it is difficult for the virus to spread from one person to other. In this work, the community transmission is presented using simulations. It shows how an infected person affects the healthy persons in an area. Simulations also show how social distancing can control the spread of COVID-19. The simulation is performed in GNU Octave programming platform by considering number of infected persons and number of healthy persons as parameters. Results show that using the social distancing the number of infected persons can be reduced and heathy persons can be increased. Therefore, from the analysis it is concluded that social distancing will be a better solution of prevention from community transmission.
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Affiliation(s)
- Sourav Kumar Bhoi
- High Performance Computing Lab, Department of Computer Science and Engineering Parala Maharaja Engineering College (Govt.), BPUT University, Berhampur, 761003 India
| | - Kalyan Kumar Jena
- High Performance Computing Lab, Department of Computer Science and Engineering Parala Maharaja Engineering College (Govt.), BPUT University, Berhampur, 761003 India
| | - Debasis Mohapatra
- High Performance Computing Lab, Department of Computer Science and Engineering Parala Maharaja Engineering College (Govt.), BPUT University, Berhampur, 761003 India
| | - Munesh Singh
- Department of CSE, PDPM Indian Institute of Information Technology Design and Manufacturing, Dumna Airport Road, 482005 Jabalpur, India
| | - Raghvendra Kumar
- Department of Computer Science and Engineering, GIET University, Gunupur, India
| | - Hoang Viet Long
- Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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18
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A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network. Soft comput 2023; 27:2685-2694. [PMID: 33230389 PMCID: PMC7675021 DOI: 10.1007/s00500-020-05452-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
We describe in this paper an approach for predicting the COVID-19 time series in the world using a hybrid ensemble modular neural network, which combines nonlinear autoregressive neural networks. At the level of the modular neural network, which is formed with several modules (ensembles in this case), the modules are designed to be efficient predictors for each country. In this case, an integrator is used to combine the outputs of the modules, in this way achieving the goal of predicting a set of countries. At the level of the ensembles, forming a part of the modular network, these are constituted by a set of modules, which are nonlinear autoregressive neural networks that are designed to be efficient predictors under particular conditions for each country. In each ensemble, the results of the modules are combined with an aggregator to achieve a better and improved result for the ensemble. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained that could be helpful in deciding the best strategies in dealing with this virus for countries in their fight against the coronavirus pandemic. In addition, the proposed approach could be helpful in proposing strategies for similar countries.
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19
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Almulihi A, Saleh H, Hussien AM, Mostafa S, El-Sappagh S, Alnowaiser K, Ali AA, Refaat Hassan M. Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction. Diagnostics (Basel) 2022; 12:diagnostics12123215. [PMID: 36553222 PMCID: PMC9777370 DOI: 10.3390/diagnostics12123215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/12/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Many epidemics have afflicted humanity throughout history, claiming many lives. It has been noted in our time that heart disease is one of the deadliest diseases that humanity has confronted in the contemporary period. The proliferation of poor habits such as smoking, overeating, and lack of physical activity has contributed to the rise in heart disease. The killing feature of heart disease, which has earned it the moniker the "silent killer," is that it frequently has no apparent signs in advance. As a result, research is required to develop a promising model for the early identification of heart disease using simple data and symptoms. The paper's aim is to propose a deep stacking ensemble model to enhance the performance of the prediction of heart disease. The proposed ensemble model integrates two optimized and pre-trained hybrid deep learning models with the Support Vector Machine (SVM) as the meta-learner model. The first hybrid model is Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) (CNN-LSTM), which integrates CNN and LSTM. The second hybrid model is CNN-GRU, which integrates CNN with a Gated Recurrent Unit (GRU). Recursive Feature Elimination (RFE) is also used for the feature selection optimization process. The proposed model has been optimized and tested using two different heart disease datasets. The proposed ensemble is compared with five machine learning models including Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (K-NN), Decision Tree (DT), Naïve Bayes (NB), and hybrid models. In addition, optimization techniques are used to optimize ML, DL, and the proposed models. The results obtained by the proposed model achieved the highest performance using the full feature set.
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Affiliation(s)
- Ahmed Almulihi
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt
- Correspondence:
| | - Ali Mohamed Hussien
- Department of Computer Science, Faculty of Science, Aswan University, Aswan 81528, Egypt
| | - Sherif Mostafa
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 34511, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Khaled Alnowaiser
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia
| | - Abdelmgeid A. Ali
- Faculty of Computers and Information, Minia University, Minia 61519, Egypt
| | - Moatamad Refaat Hassan
- Department of Computer Science, Faculty of Science, Aswan University, Aswan 81528, Egypt
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20
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Shi L, Khan YA, Tian MW. COVID-19 pandemic and unemployment rate prediction for developing countries of Asia: A hybrid approach. PLoS One 2022; 17:e0275422. [PMID: 36454804 PMCID: PMC9714828 DOI: 10.1371/journal.pone.0275422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 09/18/2022] [Indexed: 12/03/2022] Open
Abstract
Unemployment is an essential problem for developing countries, which has a direct and major role in economy of a country. Understanding the pattrens of unemployment rate is critical now a days and has drawn attention of researcher from all fields of study across the globe. As unemployment plays an important role in the planning of a country's monetary progress for policymakers and researcher. Determining the unemployment rate efficiently required an advance modeling approach. Recently,numerous studies have relied on traditional testing methods to estimate the unemployment rate. Unemployment is usually nonstationary in nature. As a result, demonstrating them using traditional methods will lead to unpredictable results. It needs a hybrid approach to deal with the prediction of unemployment rate in order to deal with the issue associated with traditional techniques. This research primary goal is to examine the effect of the Covid-19 pandemic on the unemployment rate in selected countries of Asia through advanced hybrid modeling approach, using unemployment data of seven developing countries of Asian: Iran, Sri Lanka; Bangladesh; Pakistan; Indonesia; China; and India,and compare the results with conventional modeling approaches. Finding shows that the hybrid ARIMA-ARNN model outperformed over its competitors for Asia developing economies. In addition, the best fitted model was utilised to predict five years ahead unemployment rate. According to the findings, unemployment will rise significantly in developing economies in the next years, and this will have a particularly severe impact on the region's economies that aren't yet developed.
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Affiliation(s)
- Lumin Shi
- Business School, Lishui University, Lishui, Zhejiang, China
- Philippine Christian University, Manila, Philippines
| | - Yousaf Ali Khan
- Department of Mathematics and Statistics, Hazara University Mansehra, Mansehra, Pakistan
- * E-mail:
| | - Man-Wen Tian
- National Key Project Laboratory, Jiangxi University of Engineering, Xinyu, China
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21
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Haghrah AA, Ghaemi S, Badamchizadeh MA. Fuzzy-SIRD model: Forecasting COVID-19 death tolls considering governments intervention. Artif Intell Med 2022; 134:102422. [PMID: 36462905 PMCID: PMC9557117 DOI: 10.1016/j.artmed.2022.102422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 09/27/2022] [Accepted: 10/03/2022] [Indexed: 12/14/2022]
Abstract
Modeling the trend of contagious diseases has particular importance for managing them and reducing the side effects on society. In this regard, researchers have proposed compartmental models for modeling the spread of diseases. However, these models suffer from a lack of adaptability to variations of parameters over time. This paper introduces a new Fuzzy Susceptible-Infectious-Recovered-Deceased (Fuzzy-SIRD) model for covering the weaknesses of the simple compartmental models. Due to the uncertainty in forecasting diseases, the proposed Fuzzy-SIRD model represents the government intervention as an interval type 2 Mamdani fuzzy logic system. Also, since society's response to government intervention is not a static reaction, the proposed model uses a first-order linear system to model its dynamics. In addition, this paper uses the Particle Swarm Optimization (PSO) algorithm for optimally selecting system parameters. The objective function of this optimization problem is the Root Mean Square Error (RMSE) of the system output for the deceased population in a specific time interval. This paper provides many simulations for modeling and predicting the death tolls caused by COVID-19 disease in seven countries and compares the results with the simple SIRD model. Based on the reported results, the proposed Fuzzy-SIRD model can reduce the root mean square error of predictions by more than 80% in the long-term scenarios, compared with the conventional SIRD model. The average reduction of RMSE for the short-term and long-term predictions are 45.83% and 72.56%, respectively. The results also show that the principle goal of the proposed modeling, i.e., creating a semantic relation between the basic reproduction number, government intervention, and society's response to interventions, has been well achieved. As the results approve, the proposed model is a suitable and adaptable alternative for conventional compartmental models.
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22
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Dehghandar M, Rezvani S. Classification of COVID-19 Individuals Using Adaptive Neuro-Fuzzy Inference System. JOURNAL OF MEDICAL SIGNALS & SENSORS 2022; 12:334-340. [PMID: 36726414 PMCID: PMC9885510 DOI: 10.4103/jmss.jmss_140_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 05/14/2022] [Accepted: 06/10/2022] [Indexed: 02/03/2023]
Abstract
The COVID-19 has become an important health issue in the world and has endangered human health. The purpose of this research is to use an intelligent system model of adaptive neuro-fuzzy inference system (ANFIS) using twelve variables of input for the diagnosis of COVID-19. The evaluation of the model was performed using the information of 500 patients referred to and suspected of the COVID-19. Three hundred and fifty people were used as training data and 150 people were used as test and validation data. Information on 12 important parameters of COVID-19 such as fever, cough, headache, respiratory rate, Ct-chest, medical history, skin rash, age, family history, loss of olfactory sensation and taste, digestive symptoms, and malaise was also reported in patients with severe disease. ANFIS identified COVID-19 in accuracy, sensitivity, and specificity with more than 95%, 94%, and 95%, respectively, which indicates the high efficiency of the system in the correct diagnosis of individuals. The proposed system accurately detected more than 95% COVID-19 as well as mild, moderate, and acute severity. Due to the time-constraint, limitations, and error of COVID-19 diagnostic tools, the proposed system can be used in high-precision primary detection, as well as saving time and cost.
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Affiliation(s)
- Mohammad Dehghandar
- Departments of Applied Mathematics, Payame Noor University, Tehran, Iran,Address for correspondence: Dr. Mohammad Dehghandar, Department of Applied Mathematics, Payame Noor University, PO Box 3697-19395, Tehran, Iran. E-mail:
| | - Samaneh Rezvani
- Departments of Applied Mathematics, Payame Noor University, Tehran, Iran
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23
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Fuzzified deep neural network ensemble approach for estimating cycle time range. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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24
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Ahadian S, Pishvaee MS, Jahani H. A decision support system to reorganize medical service network in pandemic. IFAC-PAPERSONLINE 2022; 55:2914-2919. [PMID: 38620794 PMCID: PMC9605707 DOI: 10.1016/j.ifacol.2022.10.174] [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: 11/05/2022]
Abstract
The advent of the Covid-19 pandemic has posed severe challenges to health care networks in various countries. The overcrowding of hospitals and the lack of medical staff and beds in multiple wards are among the main problems of governments. A new virus wave also exacerbates these problems. Also, the lack of information and the variability of the incidence rate and severity of the disease in different waves make it difficult to estimate the number of patients accurately. In this respect, this study develops a mixed-integer linear programming model to reorganize the medical service network. A fuzzy approach is employed to estimate the number of patients in each period. The result obtained from the model, apart from preventing the shortage of hospital beds, demonstrates a 60% reduction in visits to these centers.
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Affiliation(s)
- Sajjad Ahadian
- Industrial Engineering School, Iran University of Science and Technology
| | - Mir Saman Pishvaee
- Industrial Engineering School, Iran University of Science and Technology
| | - Hamed Jahani
- School of Accounting, Information Systems and Supply Chain, RMIT University, Melbourne, Australia
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25
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Oztel I, Yolcu Oztel G, Akgun D. A hybrid LBP-DCNN based feature extraction method in YOLO: An application for masked face and social distance detection. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:1565-1583. [PMID: 36313483 PMCID: PMC9589619 DOI: 10.1007/s11042-022-14073-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 10/06/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
COVID-19 is an ongoing pandemic and the WHO recommends at least one-meter social distance, and the use of medical face masks to slow the disease's transmission. This paper proposes an automated approach for detecting social distance and face masks. Thus, it aims to help the reduction of diseases transferred by respiratory droplets such as COVID-19. For this system, a two-cascaded YOLO is used. The first cascade detects humans in the environment and computes the social distance between them. Then, the second cascade detects human faces with or without a mask. Finally, red bounding boxes encircle the people's images that did not follow the rules. Also, in this paper, we propose a two-part feature extraction approach used with YOLO. The first part of the proposed feature extraction method extracts general features using the transfer learning approach. The second part extracts better features specific to the current task using the LBP layer and classification layers. The best average precision for the human detection task was obtained as 66% using Resnet50 in YOLO. The best average precision for the mask detection was obtained as 95% using Darknet19+LBP with YOLO. Also, another popular object detection network, Faster R-CNN, have been used for comparison purpose. The proposed system performed better than the literature in human and mask detection tasks.
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Affiliation(s)
- Ismail Oztel
- Computer Engineering Department, Sakarya University, Sakarya, 54050 Turkey
| | - Gozde Yolcu Oztel
- Software Engineering Department, Sakarya University, Sakarya, 54050 Turkey
| | - Devrim Akgun
- Software Engineering Department, Sakarya University, Sakarya, 54050 Turkey
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26
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Duan H, Nie W. A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19. PHYSICA A 2022; 602:127622. [PMID: 35692385 PMCID: PMC9169490 DOI: 10.1016/j.physa.2022.127622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/20/2022] [Indexed: 06/15/2023]
Abstract
The COVID-19 pandemic has lasted for nearly two years, and the global epidemic situation is still grim and growing. Therefore, it is necessary to make correct predictions about the epidemic to implement appropriate and effective epidemic prevention measures. This paper analyzes the classic Susceptible Infected Recovered Model (SIR) to understand the significance of model characteristics and parameters, and uses the differential and difference information of the grey system to put forward a grey prediction model based on SIR infectious disease model. The Laplace transform is used to calculate the model reduction formula, and finally obtain the modeling steps of the model. It is applied to large and small numerical cases to verify the validity of different orders of magnitude data. Meanwhile, data of different lengths are modeled and predicted to verify the robustness of model. Finally, the new model is compared with three classical grey prediction models. The results show that the model is significantly superior to the comparison model, indicating that the model can effectively predict the COVID-19 epidemic, and is applicable to countries with different population magnitude, can carry out stable and effective simulation and prediction for data of different lengths.
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Affiliation(s)
- Huiming Duan
- School of Science, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Weige Nie
- School of Science, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
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27
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Gohain B, Chutia R, Dutta P. Discrete similarity measures on Pythagorean fuzzy sets and its applications to medical diagnosis and clustering problems. INT J INTELL SYST 2022. [DOI: 10.1002/int.23057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Brindaban Gohain
- Department of Mathematics Dibrugarh University Dibrugarh Assam India
| | - Rituparna Chutia
- Department of Mathematics Cotton University Guwahati Assam India
| | - Palash Dutta
- Department of Mathematics Dibrugarh University Dibrugarh Assam India
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Castillo O, Castro JR, Pulido M, Melin P. Interval type-3 fuzzy aggregators for ensembles of neural networks in COVID-19 time series prediction. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022; 114:105110. [PMID: 35945944 PMCID: PMC9354327 DOI: 10.1016/j.engappai.2022.105110] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/13/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
In this work we are presenting an approach for fuzzy aggregation in ensembles of neural networks for forecasting. The aggregator is used in an ensemble to combine the outputs of the networks forming the ensemble. This is done in such a way that the total output of the ensemble is better than the outputs of the individual modules. In our approach a fuzzy system is used to estimate the weights that will be assigned to the outputs in the process of combining them in a weighted average calculation. The uncertainty in the process of aggregation is modeled with interval type-3 fuzzy, which in theory can outperform type-2 and type-1. Publicly available data sets of COVID-19 cases for several countries in the world were utilized to test the proposed approach. Simulation results of the COVID-19 data show the potential of the approach to outperform other aggregators in the literature.
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29
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Cui Z, Cai M, Xiao Y, Zhu Z, Yang M, Chen G. Forecasting the transmission trends of respiratory infectious diseases with an exposure-risk-based model at the microscopic level. ENVIRONMENTAL RESEARCH 2022; 212:113428. [PMID: 35568232 PMCID: PMC9095069 DOI: 10.1016/j.envres.2022.113428] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/30/2022] [Accepted: 05/02/2022] [Indexed: 05/03/2023]
Abstract
Respiratory infectious diseases (e.g., COVID-19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies focus on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of transmission trends. Firstly, the front two modules reproduce the movements of individuals and the droplets of infectors' expiratory activities, respectively. Then, the outputs are fed to the third module to estimate the personal exposure risk. Finally, the number of new cases is predicted in the final module. By predicting the new COVID- 19 cases in the United States, the performances of our model and 4 other existing macroscopic or microscopic models are compared. Specifically, the mean absolute error, root mean square error, and mean absolute percentage error provided by the proposed model are respectively 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models. The quantitative results reveal that our model can accurately predict the transmission trends from a microscopic perspective, and it can benefit the further investigation of many microscopic disease transmission factors (e.g., non-walkable areas and facility layouts).
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Affiliation(s)
- Ziwei Cui
- School of Intelligent System Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
| | - Ming Cai
- School of Intelligent System Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
| | - Yao Xiao
- School of Intelligent System Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
| | - Zheng Zhu
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Mofeng Yang
- Maryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland at College Park, Maryland, USA.
| | - Gongbo Chen
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China.
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30
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Perone G. Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy. THE EUROPEAN JOURNAL OF HEALTH ECONOMICS : HEPAC : HEALTH ECONOMICS IN PREVENTION AND CARE 2022; 23:917-940. [PMID: 34347175 PMCID: PMC8332000 DOI: 10.1007/s10198-021-01347-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 07/01/2021] [Indexed: 05/13/2023]
Abstract
The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic's second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box-Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020-October 13, 2020 were extracted from the website of the Italian Ministry of Health ( www.salute.gov.it ). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities' decision-making, especially in the short-term.
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Affiliation(s)
- Gaetano Perone
- Department of Management, Economics and Quantitative Methods, University of Bergamo, via dei Caniana 2, 24127, Bergamo, Italy.
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31
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Impact of the COVID-19 Pandemic on the Diagnosis and Prognosis of Melanoma. J Clin Med 2022; 11:jcm11144181. [PMID: 35887944 PMCID: PMC9321960 DOI: 10.3390/jcm11144181] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 02/06/2023] Open
Abstract
Background: Early detection of melanoma is one of the main diagnostic goals of dermatologists worldwide, due to the increasing incidence of the disease in our environment. However, the irruption of the SARS-CoV-2 pandemic has posed a challenge to global healthcare, forcing systems to focus their resources on the fight against COVID-19. Methods: Retrospective cohort study. The exposed cohort were patients diagnosed with melanoma in the year after the general confinement in Spain (15 March 2020) and the unexposed cohort were patients with melanoma diagnosed in the previous year. Results: 130 patients were included. No differences were observed between demographic characteristics in both cohorts. The mean Breslow of melanoma before the onset of the pandemic was 1.08, increasing to 2.65 in the year after the onset of the pandemic (p < 0.001). On the other hand, the percentage of melanomas in situ decreased from 38.96% to 16.98% in the year after the declaration of the state of alarm in Spain. Conclusions: The SARS-CoV-2 outbreak has led to a reduction in the early diagnosis of melanoma, with an increase in invasive melanomas with poor prognosis histological factors. This could lead to an increase in melanoma-related mortality in the coming years in our environment.
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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: 22] [Impact Index Per Article: 11.0] [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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33
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Interval Type-3 Fuzzy Aggregation of Neural Networks for Multiple Time Series Prediction: The Case of Financial Forecasting. AXIOMS 2022. [DOI: 10.3390/axioms11060251] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In this work, we present an approach for fuzzy aggregation of neural networks for forecasting. The interval type-3 aggregator is used to combine the outputs of the networks to improve the quality of the prediction. This is carried out in such a way that the final output is better than the outputs of the individual modules. In our approach, a fuzzy system is used to estimate the prediction increments that will be assigned to the output in the process of combining them with a set of fuzzy rules. The uncertainty in the process of aggregation is modeled with an interval type-3 fuzzy system, which, in theory, can outperform type-2 and type-1 fuzzy systems. Publicly available data sets of COVID-19 cases and the Dow Jones index were utilized to test the proposed approach, as it has been stated that a pandemic wave can have an effect on the economies of countries. The simulation results show that the COVID-19 data does have, in fact, an influence on the Dow Jones time series and its use in the proposed model improves the forecast of the Dow Jones future values.
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34
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A Review of the Potential of Artificial Intelligence Approaches to Forecasting COVID-19 Spreading. AI 2022. [DOI: 10.3390/ai3020028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The spread of SARS-CoV-2 can be considered one of the most complicated patterns with a large number of uncertainties and nonlinearities. Therefore, analysis and prediction of the distribution of this virus are one of the most challenging problems, affecting the planning and managing of its impacts. Although different vaccines and drugs have been proved, produced, and distributed one after another, several new fast-spreading SARS-CoV-2 variants have been detected. This is why numerous techniques based on artificial intelligence (AI) have been recently designed or redeveloped to forecast these variants more effectively. The focus of such methods is on deep learning (DL) and machine learning (ML), and they can forecast nonlinear trends in epidemiological issues appropriately. This short review aims to summarize and evaluate the trustworthiness and performance of some important AI-empowered approaches used for the prediction of the spread of COVID-19. Sixty-five preprints, peer-reviewed papers, conference proceedings, and book chapters published in 2020 were reviewed. Our criteria to include or exclude references were the performance of these methods reported in the documents. The results revealed that although methods under discussion in this review have suitable potential to predict the spread of COVID-19, there are still weaknesses and drawbacks that fall in the domain of future research and scientific endeavors.
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Mydukuri RV, Kallam S, Patan R, Al‐Turjman F, Ramachandran M. Deming least square regressed feature selection and Gaussian neuro-fuzzy multi-layered data classifier for early COVID prediction. EXPERT SYSTEMS 2022; 39:e12694. [PMID: 34230740 PMCID: PMC8250320 DOI: 10.1111/exsy.12694] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 02/10/2021] [Indexed: 05/31/2023]
Abstract
Coronavirus disease (COVID-19) is a harmful disease caused by the new SARS-CoV-2 virus. COVID-19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID-19 has affected many countries and their spread in the world has put humanity at risk. Due to the increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the coronavirus disease existence in patients. However, the accuracy was not improved, and time consumption was not minimized during the disease prediction. To address these problems, least square regressive Gaussian neuro-fuzzy multi-layered data classification (LSRGNFM-LDC) technique is introduced in this article. LSRGNFM-LDC technique performs efficient COVID prediction with better accuracy and lesser time consumption through feature selection and classification. The preprocessing is used to eliminate the unwanted data in input features. Preprocessing is applied to reduce the time complexity. Next, Deming Least Square Regressive Feature Selection process is carried out for selecting the most relevant features through identifying the line of best fit. After the feature selection process, Gaussian neuro-fuzzy classifier in LSRGNFM-LDC technique performs the data classification process with help of fuzzy if-then rules for performing prediction process. Finally, the fuzzy if-then rule classifies the patient data as lower risk level, medium risk level and higher risk level with higher accuracy and lesser time consumption. Experimental evaluation is performed by Novel Corona Virus 2019 Dataset using different metrics like prediction accuracy, prediction time, and error rate. The result shows that LSRGNFM-LDC technique improves the accuracy and minimizes the time consumption as well as error rate than existing works during COVID prediction.
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Affiliation(s)
- Rathnamma V Mydukuri
- Department of Computer Science and EngineeringKSRM College Of Engineering (A)KadapaAndhra PradeshIndia
| | - Suresh Kallam
- Department of Computer Science & EngineeringSree Vidyanikethan Engineering CollegeTirupatiAndhra PradeshIndia
| | - Rizwan Patan
- Department of Computer Science & EngineeringVelagapudi Ramakrishna Siddhartha Engineering CollegeVijayawadaAndhra PradeshIndia
| | - Fadi Al‐Turjman
- Research Center for AI and IoT, Artificial Intelligence Engineering DepartmentNear East UniversityMersinTurkey
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36
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Mendoza DE, Ochoa-Sánchez A, Samaniego EP. Forecasting of a complex phenomenon using stochastic data-based techniques under non-conventional schemes: The SARS-CoV-2 virus spread case. CHAOS, SOLITONS, AND FRACTALS 2022; 158:112097. [PMID: 35411129 PMCID: PMC8986496 DOI: 10.1016/j.chaos.2022.112097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 03/31/2022] [Accepted: 04/04/2022] [Indexed: 06/14/2023]
Abstract
Epidemics are complex dynamical processes that are difficult to model. As revealed by the SARS-CoV-2 pandemic, the social behavior and policy decisions contribute to the rapidly changing behavior of the virus' spread during outbreaks and recessions. In practice, reliable forecasting estimations are needed, especially during early contagion stages when knowledge and data are insipient. When stochastic models are used to address the problem, it is necessary to consider new modeling strategies. Such strategies should aim to predict the different contagious phases and fast changes between recessions and outbreaks. At the same time, it is desirable to take advantage of existing modeling frameworks, knowledge and tools. In that line, we take Autoregressive models with exogenous variables (ARX) and Vector autoregressive (VAR) techniques as a basis. We then consider analogies with epidemic's differential equations to define the structure of the models. To predict recessions and outbreaks, the possibility of updating the model's parameters and stochastic structures is considered, providing non-stationarity properties and flexibility for accommodating the incoming data to the models. The Generalized-Random-Walk (GRW) and the State-Dependent-Parameter (SDP) techniques shape the parameters' variability. The stochastic structures are identified following the Akaike (AIC) criterion. The models use the daily rates of infected, death, and healed individuals, which are the most common and accurate data retrieved in the early stages. Additionally, different experiments aim to explore the individual and complementary role of these variables. The results show that although both the ARX-based and VAR-based techniques have good statistical accuracy for seven-day ahead predictions, some ARX models can anticipate outbreaks and recessions. We argue that short-time predictions for complex problems could be attained through stochastic models that mimic the fundamentals of dynamic equations, updating their parameters and structures according to incoming data.
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Affiliation(s)
- Daniel E Mendoza
- Department of Civil Engineering, University of Cuenca, Av. 12 de Abril sn, CP: 010112 Cuenca, Ecuador
- Faculty of Engineering, University of Cuenca, Av.12 de Abril sn, CP: 010112 Cuenca, Ecuador
| | - Ana Ochoa-Sánchez
- School of Environmental Engineering, Faculty of Science and Technology, University of Azuay, Cuenca, Ecuador
- TRACES, University of Azuay, Cuenca, Ecuador
| | - Esteban P Samaniego
- Faculty of Engineering, University of Cuenca, Av.12 de Abril sn, CP: 010112 Cuenca, Ecuador
- Department of Water Resources and Environmental Sciences, University of Cuenca, Av. 12 de Abril sn, CP: 010151 Cuenca, Ecuador
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37
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R S, Thaseen IS, M V, M D, M A, R M, Mahendran A, Alnumay W, Chatterjee P. An efficient hardware architecture based on an ensemble of deep learning models for COVID -19 prediction. SUSTAINABLE CITIES AND SOCIETY 2022; 80:103713. [PMID: 35136715 PMCID: PMC8812126 DOI: 10.1016/j.scs.2022.103713] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 01/21/2022] [Accepted: 01/21/2022] [Indexed: 05/17/2023]
Abstract
Deep learning models demonstrate superior performance in image classification problems. COVID-19 image classification is developed using single deep learning models. In this paper, an efficient hardware architecture based on an ensemble deep learning model is built to identify the COVID-19 using chest X-ray (CXR) records. Five deep learning models namely ResNet, fitness, IRCNN (Inception Recurrent Convolutional Neural Network), effectiveness, and Fitnet are ensembled for fine-tuning and enhancing the performance of the COVID-19 identification; these models are chosen as they individually perform better in other applications. Experimental analysis shows that the accuracy, precision, recall, and F1 for COVID-19 detection are 0.99,0.98,0.98, and 0.98 respectively. An application-specific hardware architecture incorporates the pipeline, parallel processing, reusability of computational resources by carefully exploiting the data flow and resource availability. The processing element (PE) and the CNN architecture are modeled using Verilog, simulated, and synthesized using cadence with Taiwan Semiconductor Manufacturing Co Ltd (TSMC) 90 nm tech file. The simulated results show a 40% reduction in the latency and number of clock cycles. The computations and power consumptions are minimized by designing the PE as a data-aware unit. Thus, the proposed architecture is best suited for Covid-19 prediction and diagnosis.
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Affiliation(s)
- Sakthivel R
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - I Sumaiya Thaseen
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Vanitha M
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Deepa M
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Angulakshmi M
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Mangayarkarasi R
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Anand Mahendran
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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Tutsoy O, Polat A. Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks. ISA TRANSACTIONS 2022; 124:90-102. [PMID: 34412892 PMCID: PMC8349905 DOI: 10.1016/j.isatra.2021.08.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/23/2021] [Accepted: 08/05/2021] [Indexed: 05/09/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has endured constituting formidable economic, social, educational, and phycological challenges for the societies. Moreover, during pandemic outbreaks, the hospitals are overwhelmed with patients requiring more intensive care units and intubation equipment. Therein, to cope with these urgent healthcare demands, the state authorities seek ways to develop policies based on the estimated future casualties. These policies are mainly non-pharmacological policies including the restrictions, curfews, closures, and lockdowns. In this paper, we construct three model structures of the SpInItIbD-N (suspicious Sp, infected In, intensive care It, intubated Ib, and dead D together with the non-pharmacological policies N) holding two key targets. The first one is to predict the future COVID-19 casualties including the intensive care and intubated ones, which directly determine the need for urgent healthcare facilities, and the second one is to analyse the linear and non-linear dynamics of the COVID-19 pandemic under the non-pharmacological policies. In this respect, we have modified the non-pharmacological policies and incorporated them within the models whose parameters are learned from the available data. The trained models with the data released by the Turkish Health Ministry confirmed that the linear SpInItIbD-N model yields more accurate results under the imposed non-pharmacological policies. It is important to note that the non-pharmacological policies have a damping effect on the pandemic casualties and this can dominate the non-linear dynamics. Herein, a model without pharmacological or non-pharmacological policies might have more dominant non-linear dynamics. In addition, the paper considers two machine learning approaches to optimize the unknown parameters of the constructed models. The results show that the recursive neural network has superior performance for learning nonlinear dynamics. However, the batch least squares outperforms in the presence of linear dynamics and stochastic data. The estimated future pandemic casualties with the linear SpInItIbD-N model confirm that the suspicious, infected, and dead casualties converge to zero from 200000, 1400, 200 casualties, respectively. The convergences occur in 120 days under the current conditions.
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Affiliation(s)
- Onder Tutsoy
- Adana Alparslan Turkes Science and Technology University, Adana, Turkey.
| | - Adem Polat
- Çanakkale Onsekiz Mart University, Çanakkale, Turkey.
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Nagpal S, Pal R, Ashima, Tyagi A, Tripathi S, Nagori A, Ahmad S, Mishra HP, Malhotra R, Kutum R, Sethi T. Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning. Front Genet 2022; 13:858252. [PMID: 35464852 PMCID: PMC9024110 DOI: 10.3389/fgene.2022.858252] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/14/2022] [Indexed: 12/23/2022] Open
Abstract
The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present Strainflow, a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences. Strainflow was trained and validated on 0.9 million sequences for the period December, 2019 to June, 2021 and the frozen model was prospectively validated from July, 2021 to December, 2021. Strainflow captured the rise in cases 2 months ahead of the Delta and Omicron surges in most countries including the prediction of a surge in India as early as beginning of November, 2021. Entropy analysis of Strainflow unsupervised embeddings clearly reveals the explore-exploit cycles in genomic feature-space, thus adding interpretability to the deep learning based model. We also conducted codon-level analysis of our model for interpretability and biological validity of our unsupervised features. Strainflow application is openly available as an interactive web-application for prospective genomic surveillance of COVID-19 across the globe.
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40
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Drias H, Drias Y, Houacine NA, Bendimerad LS, Zouache D, Khennak I. Quantum OPTICS and deep self-learning on swarm intelligence algorithms for Covid-19 emergency transportation. Soft comput 2022; 27:1-20. [PMID: 35431641 PMCID: PMC8990503 DOI: 10.1007/s00500-022-06946-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/16/2022] [Indexed: 11/05/2022]
Abstract
In this paper, the quantum technology is exploited to empower the OPTICS unsupervised learning algorithm, which is a density-based clustering algorithm with numerous applications in the real world. We design an algorithm called Quantum Ordering Points To Identify the Clustering Structure (QOPTICS) and demonstrate that its computational complexity outperforms that of its classical counterpart. On the other hand, we propose a Deep self-learning approach for modeling the improvement of two Swarm Intelligence Algorithms, namely Artificial Orca Algorithm (AOA) and Elephant Herding Optimization (EHO) in order to improve their effectiveness. The deep self-learning approach is based on two well-known dynamic mutation operators, namely Cauchy mutation operator and Gaussian mutation operator. And in order to improve the efficiency of these algorithms, they are hybridized with QOPTICS and executed on just one cluster it yields. This way, both effectiveness and efficiency are handled. To evaluate the proposed approaches, an intelligent application is developed to manage the dispatching of emergency vehicles in a large geographic region and in the context of Covid-19 crisis in order to avoid an important loss in human lives. A theoretical model is designed to describe the issue mathematically. Extensive experiments are then performed to validate the mathematical model and evaluate the performance of the proposed deep self-learning algorithms. Comparison with a state-of-the-art technique shows a significant positive impact of hybridizing Quantum Machine Learning (QML) with Deep Self Learning (DSL) on solving the Covid-19 EMS transportation.
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Affiliation(s)
- Habiba Drias
- LRIA, USTHB, BP 32 El Alia Bab Ezzouar, Algiers, 16111 Algeria
| | - Yassine Drias
- LRIA, University of Algiers, 02 rue Didouche Mourad, Algiers, 16000 Algeria
| | | | | | - Djaafar Zouache
- LRIA, University of Bordj Bou Arréridj, El-Anasser, Bordj Bou Arréridj 34030 Algeria
| | - Ilyes Khennak
- LRIA, USTHB, BP 32 El Alia Bab Ezzouar, Algiers, 16111 Algeria
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41
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James N, Menzies M, Bondell H. Comparing the dynamics of COVID-19 infection and mortality in the United States, India, and Brazil. PHYSICA D. NONLINEAR PHENOMENA 2022; 432:133158. [PMID: 35075315 PMCID: PMC8769590 DOI: 10.1016/j.physd.2022.133158] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/06/2021] [Accepted: 01/08/2022] [Indexed: 05/07/2023]
Abstract
This paper compares and contrasts the spread and impact of COVID-19 in the three countries most heavily impacted by the pandemic: the United States (US), India and Brazil. All three of these countries have a federal structure, in which the individual states have largely determined the response to the pandemic. Thus, we perform an extensive analysis of the individual states of these three countries to determine patterns of similarity within each. First, we analyse structural similarity and anomalies in the trajectories of cases and deaths as multivariate time series. Next, we study the lengths of the different waves of the virus outbreaks across the three countries and their states. Finally, we investigate suitable time offsets between cases and deaths as a function of the distinct outbreak waves. In all these analyses, we consistently reveal more characteristically distinct behaviour between US and Indian states, while Brazilian states exhibit less structure in their wave behaviour and changing progression between cases and deaths.
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Affiliation(s)
- Nick James
- School of Mathematics and Statistics, University of Melbourne, Victoria, Australia
| | - Max Menzies
- Beijing Institute of Mathematical Sciences and Applications, Tsinghua University, Beijing, China
| | - Howard Bondell
- School of Mathematics and Statistics, University of Melbourne, Victoria, Australia
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Tiwari D, Bhati BS, Al‐Turjman F, Nagpal B. Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques. EXPERT SYSTEMS 2022; 39:e12714. [PMID: 34177035 PMCID: PMC8209956 DOI: 10.1111/exsy.12714] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 04/26/2021] [Indexed: 05/09/2023]
Abstract
Pandemic novel Coronavirus (Covid-19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid-19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid-19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid-19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)-based meta-analysis to predict the trend of epidemic Covid-19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time-series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid-19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid-19 observed symptoms, a list of Top-20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid-19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction.
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Affiliation(s)
- Dimple Tiwari
- Ambedkar Institute of Advanced Communication Technologies and Research, Govt of NCT of DelhiDelhiIndia
| | - Bhoopesh Singh Bhati
- Ambedkar Institute of Advanced Communication Technologies and Research, Govt of NCT of DelhiDelhiIndia
| | - Fadi Al‐Turjman
- Artificial Intelligence Engineering Department, Research Center for AI and IoTNear East UniversityNicosiaTurkey
| | - Bharti Nagpal
- Ambedkar Institute of Advanced Communication Technologies and Research, Govt of NCT of DelhiDelhiIndia
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Fractal Nature Bridge between Neural Networks and Graph Theory Approach within Material Structure Characterization. FRACTAL AND FRACTIONAL 2022. [DOI: 10.3390/fractalfract6030134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Many recently published research papers examine the representation of nanostructures and biomimetic materials, especially using mathematical methods. For this purpose, it is important that the mathematical method is simple and powerful. Theory of fractals, artificial neural networks and graph theory are most commonly used in such papers. These methods are useful tools for applying mathematics in nanostructures, especially given the diversity of the methods, as well as their compatibility and complementarity. The purpose of this paper is to provide an overview of existing results in the field of electrochemical and magnetic nanostructures parameter modeling by applying the three methods that are “easy to use”: theory of fractals, artificial neural networks and graph theory. We also give some new conclusions about applicability, advantages and disadvantages in various different circumstances.
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Dong T, Benedetto U, Sinha S, Fudulu D, Dimagli A, Chan J, Caputo M, Angelini G. Deep recurrent reinforced learning model to compare the efficacy of targeted local versus national measures on the spread of COVID-19 in the UK. BMJ Open 2022; 12:e048279. [PMID: 35190408 PMCID: PMC8861888 DOI: 10.1136/bmjopen-2020-048279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVES To prevent the emergence of new waves of COVID-19 caseload and associated mortalities, it is imperative to understand better the efficacy of various control measures on the national and local development of this pandemic in space-time, characterise hotspot regions of high risk, quantify the impact of under-reported measures such as international travel and project the likely effect of control measures in the coming weeks. METHODS We applied a deep recurrent reinforced learning based model to evaluate and predict the spatiotemporal effect of a combination of control measures on COVID-19 cases and mortality at the local authority (LA) and national scale in England, using data from week 5 to 46 of 2020, including an expert curated control measure matrix, official statistics/government data and a secure web dashboard to vary magnitude of control measures. RESULTS Model predictions of the number of cases and mortality of COVID-19 in the upcoming 5 weeks closely matched the actual values (cases: root mean squared error (RMSE): 700.88, mean absolute error (MAE): 453.05, mean absolute percentage error (MAPE): 0.46, correlation coefficient 0.42; mortality: RMSE 14.91, MAE 10.05, MAPE 0.39, correlation coefficient 0.68). Local lockdown with social distancing (LD_SD) (overall rank 3) was found to be ineffective in preventing outbreak rebound following lockdown easing compared with national lockdown (overall rank 2), based on prediction using simulated control measures. The ranking of the effectiveness of adjunctive measures for LD_SD were found to be consistent across hotspot and non-hotspot regions. Adjunctive measures found to be most effective were international travel and quarantine restrictions. CONCLUSIONS This study highlights the importance of using adjunctive measures in addition to LD_SD following lockdown easing and suggests the potential importance of controlling international travel and applying travel quarantines. Further work is required to assess the effect of variant strains and vaccination measures.
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Affiliation(s)
- Tim Dong
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Umberto Benedetto
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Shubhra Sinha
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Daniel Fudulu
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Arnaldo Dimagli
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jeremy Chan
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Massimo Caputo
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gianni Angelini
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
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Xiang L, Ma S, Yu L, Wang W, Yin Z. Modeling the Global Dynamic Contagion of COVID-19. Front Public Health 2022; 9:809987. [PMID: 35096753 PMCID: PMC8795671 DOI: 10.3389/fpubh.2021.809987] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/15/2021] [Indexed: 11/13/2022] Open
Abstract
The COVID-19 infections have profoundly and negatively impacted the whole world. Hence, we have modeled the dynamic spread of global COVID-19 infections with the connectedness approach based on the TVP-VAR model, using the data of confirmed COVID-19 cases during the period of March 23rd, 2020 to September 10th, 2021 in 18 countries. The results imply that, (i) the United States, the United Kingdom and Indonesia are global epidemic centers, among which the United States has the highest degree of the contagion of the COVID-19 infections, which is stable. South Korea, France and Italy are the main receiver of the contagion of the COVID-19 infections, and South Korea has been the most severely affected by the overseas epidemic; (ii) there is a negative correlation between the timeliness, effectiveness and mandatory nature of government policies and the risk of the associated countries COVID-19 epidemic affecting, as well as the magnitude of the net contagion of domestic COVID-19; (iii) the severity of domestic COVID-19 epidemics in the United States and Canada, Canada and Mexico, Indonesia and Canada is almost equivalent, especially for the United States, Canada and Mexico, whose domestic epidemics are with the same tendency; (iv) the COVID-19 epidemic has spread though not only the central divergence manner and chain mode of transmission, but also the way of feedback loop. Thus, more efforts should be made by the governments to enhance the pertinence and compulsion of their epidemic prevention policies and establish a systematic and efficient risk assessment mechanism for public health emergencies.
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Affiliation(s)
- Lijin Xiang
- School of Finance, Shandong University of Finance and Economics, Jinan, China
| | - Shiqun Ma
- School of Finance, Shandong University of Finance and Economics, Jinan, China
| | - Lu Yu
- School of Finance, Shandong University of Finance and Economics, Jinan, China
| | - Wenhao Wang
- School of Finance, Shandong University of Finance and Economics, Jinan, China
| | - Zhichao Yin
- School of Finance, Shandong University of Finance and Economics, Jinan, China
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Cui C, Li B, Wang L. The selection of COVID-19 epidemic prevention and control programs based on group decision-making. COMPLEX INTELL SYST 2022; 8:1653-1662. [PMID: 35004130 PMCID: PMC8724238 DOI: 10.1007/s40747-021-00620-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 12/10/2021] [Indexed: 01/11/2023]
Abstract
COVID-19 has been wreaking havoc on the world for close to two years. As the virus continues to mutate, epidemic prevention and control has become a long and experienced war. In the face of the sudden spread of virus strains, how to quickly and effectively formulate prevention and control plans are essential to ensuring the safety and social stability of cities. This paper is based on the characteristics, namely, its persistence and the high transmissibility of mutated strains, as well as the database of epidemic prevention and control plans formed as part of the existing prevention and control measures. Then, epidemic prevention experts select effective alternatives from the program database and rank their preferences through the preliminary analysis of the local epidemic situation. The process of the integration scheme aims to minimize the differences in an effort to maximize the needs of the local epidemic. Once the consensus ranking of the scheme is obtained, the final prevention and control scheme can be determined. The proposed method of this paper can optimize the opinions of the epidemic prevention expert group and form a consensus decision, whilst also saving time by carrying out the work effectively, which is of certain practical significance to the prevention and control effect of local outbreaks.
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Affiliation(s)
- Chunsheng Cui
- College of Computer and Information Engineering, Henan University of Economics and Law, 180 Jinshui East Road, Zhengzhou, 450046 Henan China
| | - Baiqiu Li
- College of Computer and Information Engineering, Henan University of Economics and Law, 180 Jinshui East Road, Zhengzhou, 450046 Henan China
| | - Liu Wang
- College of Computer and Information Engineering, Henan University of Economics and Law, 180 Jinshui East Road, Zhengzhou, 450046 Henan China
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Jo H, Kim J, Huang TC, Ni YL. condLSTM-Q: A novel deep learning model for predicting COVID-19 mortality in fine geographical scale. QUANTITATIVE BIOLOGY 2022. [DOI: 10.15302/j-qb-021-0276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Ahuja S, Shelke NA, Singh PK. A deep learning framework using CNN and stacked Bi-GRU for COVID-19 predictions in India. SIGNAL, IMAGE AND VIDEO PROCESSING 2022; 16:579-586. [PMID: 34335985 PMCID: PMC8302212 DOI: 10.1007/s11760-021-01988-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 06/09/2021] [Accepted: 07/12/2021] [Indexed: 05/12/2023]
Abstract
The novel coronavirus infection (COVID-19) first appeared in Wuhan, China, in December 2019. COVID-19 declared as a global pandemic by the WHO was the most rapidly spreading disease all across the world. India, the second most populated nation in the world, is still fighting it, when coronavirus reached the stage where community transmission takes place at an exponential rate. Therefore, it is crucial to examine the future trends of COVID-19 in India and anticipate how it will affect economic and social growth in a short run. In this paper, a new deep learning framework using CNN and stacked Bi-GRU has been developed for predicting and analyzing the COVID-19 cases in India. The proposed model can predict the next 30 days' new positive cases, new death cases, recovery rate and containment and health index values with high accuracy. The proposed method is compared against Gaussian process regression (GPR) model on COVID-19 datasets. The experimental result shows that the proposed framework is highly reliable for COVID-19 prediction over the GPR model.
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Detecting Covid-19 chaos driven phishing/malicious URL attacks by a fuzzy logic and data mining based intelligence system. EGYPTIAN INFORMATICS JOURNAL 2021. [PMCID: PMC8668380 DOI: 10.1016/j.eij.2021.12.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
With confusion and uncertainty ruling the world, 2020 created near-perfect conditions for cybercriminals. As businesses virtually eliminated in-person experiences, the COVID-19 pandemic changed the way we live and caused a mass migration to digital platforms. However, this shift also made people more vulnerable to cyber-crime. Victims are being targeted by attackers for their credentials or financial rewards, or both. This is because the Internet itself is inherently difficult to secure, and the attackers can code in a way that exploits its flaws. Once the attackers gain root access to the devices, they have complete control and can do whatever they want. Consequently, taking advantage of highly unprecedented circumstances created by the Covid-19 event, cybercriminals launched massive phishing, malware, identity theft, and ransomware attacks. Therefore, if we wish to save people from these frauds in times when millions have already been tipped into poverty and the rest are trying hard to sustain, it is imperative to curb these attacks and attackers. This paper analyses the impact of Covid-19 on various cyber-security related aspects and sketches out the timeline of Covid-19 themed cyber-attacks launched globally to identify the modus operandi of the attackers and the impact of attacks. It also offers a thoroughly researched set of mitigation strategies which can be employed to prevent the attacks in the first place. Moreover, this manuscript proposes a fuzzy logic and data mining-based intelligence system for detecting Covid-19 themed malicious URL/phishing attacks. The performance of the system has been evaluated against various malicious/phishing URLs, and it was observed that the proposed system is a viable solution to this problem.
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Ding W, Abdel-Basset M, Hawash H. RCTE: A reliable and consistent temporal-ensembling framework for semi-supervised segmentation of COVID-19 lesions. Inf Sci (N Y) 2021; 578:559-573. [PMID: 34305162 PMCID: PMC8294559 DOI: 10.1016/j.ins.2021.07.059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/17/2021] [Accepted: 07/17/2021] [Indexed: 12/16/2022]
Abstract
The segmentation of COVID-19 lesions from computed tomography (CT) scans is crucial to develop an efficient automated diagnosis system. Deep learning (DL) has shown success in different segmentation tasks. However, an efficient DL approach requires a large amount of accurately annotated data, which is difficult to aggregate owing to the urgent situation of COVID-19. Inaccurate annotation can easily occur without experts, and segmentation performance is substantially worsened by noisy annotations. Therefore, this study presents a reliable and consistent temporal-ensembling (RCTE) framework for semi-supervised lesion segmentation. A segmentation network is integrated into a teacher-student architecture to segment infection regions from a limited number of annotated CT scans and a large number of unannotated CT scans. The network generates reliable and unreliable targets, and to evenly handle these targets potentially degrades performance. To address this, a reliable teacher-student architecture is introduced, where a reliable teacher network is the exponential moving average (EMA) of a reliable student network that is reliably renovated by restraining the student involvement to EMA when its loss is larger. We also present a noise-aware loss based on improvements to generalized cross-entropy loss to lead the segmentation performance toward noisy annotations. Comprehensive analysis validates the robustness of RCTE over recent cutting-edge semi-supervised segmentation techniques, with a 65.87% Dice score.
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Affiliation(s)
- Weiping Ding
- School of Information Science and Technology, Nantong University, Nantong 226019, China,Corresponding author
| | - Mohamed Abdel-Basset
- Zagazig Univesitry, Shaibet an Nakareyah, Zagazig 2, 44519 Ash Sharqia Governorate, Egypt
| | - Hossam Hawash
- Zagazig Univesitry, Shaibet an Nakareyah, Zagazig 2, 44519 Ash Sharqia Governorate, Egypt
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