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Ozsahin DU, Isa NA, Uzun B, Ozsahin I. Quantifying holistic capacity response and healthcare resilience in tackling COVID-19: Assessment of country capacity by MCDM. PLoS One 2024; 19:e0294625. [PMID: 38578767 PMCID: PMC10997098 DOI: 10.1371/journal.pone.0294625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/31/2023] [Indexed: 04/07/2024] Open
Abstract
The resilience of a country during the COVID-19 pandemic was determined based in whether it was holistically prepared and responsive. This resilience can only be identified through systematic data collection and analysis. Historical evidence-based response indicators have been proven to mitigate pandemics like COVID-19. However, most databases are outdated, requiring updating, derivation, and explicit interpretation to gain insight into the impact of COVID-19. Outdated databases do not show a country's true preparedness and response capacity, therefore, it undermines pandemic threat. This study uses up-to-date evidence-based pandemic indictors to run a cross-country comparative analysis of COVID-19 preparedness, response capacity, and healthcare resilience. PROMETHEE-a multicriteria decision making (MCDM) technique-is used to quantify the strengths (positive) and weaknesses (negative) of each country's COVID-19 responses, with full ranking (net) from best to least responsive. From 22 countries, South Korea obtained the highest net outranking value of 0.1945, indicating that it was the most resilient, while Mexico had the lowest (-0.1428). Although countries were underprepared, there was a robust response to the pandemic, especially in developing countries. This study demonstrates the performance and response capacity of 22 key countries to resist COVID-19, from which other countries can compare their statutory capacity ranking in order to learn/adopt the evidence-based responses of better performing countries to improve their resilience.
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Affiliation(s)
- Dilber Uzun Ozsahin
- Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, Mersin, Turkey
- Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia/TRNC, Mersin, Turkey
| | - Nuhu Abdulhaqq Isa
- Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia/TRNC, Mersin, Turkey
- Department of Biomedical Technology, Nasarawa State College of Health Science and Technology, Keffi, Nasarawa State, Nigeria
| | - Berna Uzun
- Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, Mersin, Turkey
- Department of Statistics, Carlos III University of Madrid, Madrid, Spain
| | - Ilker Ozsahin
- Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, Mersin, Turkey
- Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia/TRNC, Mersin, Turkey
- Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, United States of America
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Alamoodi AH, Zaidan BB, Albahri OS, Garfan S, Ahmaro IYY, Mohammed RT, Zaidan AA, Ismail AR, Albahri AS, Momani F, Al-Samarraay MS, Jasim AN, R.Q.Malik. Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions. COMPLEX INTELL SYST 2023; 9:1-27. [PMID: 36777815 PMCID: PMC9895977 DOI: 10.1007/s40747-023-00972-1] [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: 07/27/2022] [Accepted: 01/01/2023] [Indexed: 02/05/2023]
Abstract
When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic's main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID-19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.
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Affiliation(s)
- A. H. Alamoodi
- Faculty of Computing and Meta-Technology (FKMT), Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
| | - B. B. Zaidan
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliu, Yunlin 64002 Taiwan, ROC
| | - O. S. Albahri
- Computer Techniques Engineering Department, Mazaya University College, Nasiriyah, Iraq
| | - Salem Garfan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia
| | - Ibraheem Y. Y. Ahmaro
- Computer Science Department, College of Information Technology, Hebron University, Hebron, Palestine
| | - R. T. Mohammed
- Department of Computing Science, Komar University of Science and Technology (KUST), Sulaymaniyah, Iraq
| | - A. A. Zaidan
- SP Jain School of Global Management, Sydney, Australia
| | - Amelia Ritahani Ismail
- Department of Computer Science, Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia
| | - A. S. Albahri
- Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - Fayiz Momani
- E-Business and Commerce Department, Faculty of Administrative and Financial Sciences, University of Petra, Amman, 961343 Jordan
| | - Mohammed S. Al-Samarraay
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia
| | | | - R.Q.Malik
- Medical Intrumentation Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq
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Jafarzadeh Ghoushchi S, Bonab SR, Ghiaci AM. A decision-making framework for COVID-19 infodemic management strategies evaluation in spherical fuzzy environment. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2023; 37:1635-1648. [PMID: 36714449 PMCID: PMC9857902 DOI: 10.1007/s00477-022-02355-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 11/30/2022] [Indexed: 06/18/2023]
Abstract
100 years after the Spanish flu, the COVID-19 crisis showed that large-scale epidemics and pandemics do not belong to the past. On the report of the World Health Organization, COVID-19 is the most significant public health problem of the twenty-first century. Like previous epidemics, the current crisis is accompanied by uncertainty, mistrust, doubt and fear, and this has led to an infodemic connection to the epidemic. So not only are we fighting an epidemic, but also, we are brawling an infodemic. To reduce the social and economic consequences and harmful effects of infodemic health, and to overcome it, we need to implement strategies against infodemic. Evaluating strategies based on multiple characteristics can be considered multi-criteria decision-making (MCDM) problem. According to the literature, there is no study that aims on proposing an integrated approach to evaluate infodemic management strategies under uncertain environment. Therefore, in this paper, an integrated framework based on the extended version of best-worst method (BWM) and Combined Compromise Solution (CoCoSo) methods under a spherical fuzzy set (SFS) is developed for the first time to address the COVID-19 infodemic management strategies selection. Initially, the criteria are weighted using the developed SFS BWM which reduces uncertainty in pairwise comparisons. In the next step, the 15 selected strategies are analyzed and ranked using SFS CoCoSo. The outputs of this paper illustrate that online tools for fact checking COVID-19 information and engage and empower communities are placed in the first and second priorities, respectively. The comparison of ranking results SFS-CoCoSo with other MCDM methods demonstrates the performance of the proposed approach and its ranking stability.
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Alnoor A, Khaw KW, Chew X, Abbas S, Khattak ZZ. The Influence of the Barriers of Hybrid Strategy on Strategic Competitive Priorities: Evidence from Oil Companies. GLOBAL JOURNAL OF FLEXIBLE SYSTEMS MANAGEMENT 2023; 24:179-198. [PMID: 37101931 PMCID: PMC9812355 DOI: 10.1007/s40171-022-00335-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 12/15/2022] [Indexed: 01/05/2023]
Abstract
This study examines the impact of the barrier of adopting hybrid strategy on strategic performance using the oil sector in Iraq as a case. International oil companies consider various strategies in order to achieve superior performance. The procedure needs to overcome certain essential barriers for the adoption of the hybrid strategy that combines the cost leadership and differentiation strategy. The questionnaire was distributed online due to the COVID-19 pandemic that led to the closure of companies in the country. Out of the 537 questionnaires answered, 483 were used for further analysis which yielded usable response rate of 90%. The structural equation modeling results confirmed that the high costs of technologies, the priority of other external matters, inadequate industry regulation, insufficient supply, organizational capabilities, strategic capabilities, and financial capabilities are significantly related to strategic performance. The researchers recommend conducting an in-depth study of the phenomenon based on theoretical and empirical foundations, especially considering the relationship between the barriers of a hybrid strategy and strategic performance based on linear and non-compensatory relationships. This research sheds light on the barriers to adopting the hybrid strategy required by the oil sector as it relies on continuous production.
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Affiliation(s)
- Alhamzah Alnoor
- Southern Technical University, Management Technical College, Basrah, Iraq
| | - Khai Wah Khaw
- School of Management, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia
| | - XinYing Chew
- School of Computer Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia
| | - Sammar Abbas
- Institute of Business Studies, Kohat University of Science and Technology, Kohat, Pakistan
| | - Zeeshan Zaib Khattak
- Institute of Business Studies, Kohat University of Science and Technology, Kohat, Pakistan
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Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment. Neural Comput Appl 2023; 35:6185-6196. [PMID: 36415285 PMCID: PMC9672551 DOI: 10.1007/s00521-022-07998-5] [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: 02/10/2022] [Accepted: 10/25/2022] [Indexed: 11/18/2022]
Abstract
This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (0.1837, 0.183, 0.230, 0.276, 0.335) for (q = 1, 3, 5, 7, 10), respectively. Ranking results indicate that Hospital (H-4) is the best-ranked hospital in all experimental scenarios. Both methods were evaluated based on systematic ranking and sensitivity analysis, thereby confirming the validity of the proposed framework.
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Albahri AS, Al-qaysi ZT, Alzubaidi L, Alnoor A, Albahri OS, Alamoodi AH, Bakar AA. A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology. Int J Telemed Appl 2023; 2023:7741735. [PMID: 37168809 PMCID: PMC10164869 DOI: 10.1155/2023/7741735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/01/2023] [Accepted: 03/16/2023] [Indexed: 05/13/2023] Open
Abstract
The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.
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Affiliation(s)
- A. S. Albahri
- Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - Z. T. Al-qaysi
- Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq
| | - Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
- ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | | | - O. S. Albahri
- Computer Techniques Engineering Department, Mazaya University College, Nasiriyah, Iraq
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia
| | - A. H. Alamoodi
- Faculty of Computing and Meta-Technology (FKMT), Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
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Sadaa AM, Ganesan Y, Khaw KW, Alnoor A, Abbas S, Chew X, Bayram GE. Based on the perception of ethics in social commerce platforms: Adopting SEM and MCDM approaches for benchmarking customers in rural communities. CURRENT PSYCHOLOGY 2022. [DOI: 10.1007/s12144-022-04069-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Albahri AS, Zaidan AA, AlSattar HA, A. Hamid R, Albahri OS, Qahtan S, Alamoodi AH. Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex
T
‐spherical fuzzy‐weighted zero‐inconsistency method. Comput Intell 2022. [DOI: 10.1111/coin.12562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Ahmed S. Albahri
- Informatics Institute for Postgraduate Studies (IIPS) Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq
| | - Aws A. Zaidan
- Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates
| | - Hassan A. AlSattar
- Department of Business Administration, College of Administrative Sciences The University of Mashreq Baghdad Iraq
| | - Rula A. Hamid
- Informatics Institute for Postgraduate Studies (IIPS) Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq
| | - Osamah S. Albahri
- Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq
| | - Sarah Qahtan
- Department of Computer Center, College of Health and Medical Techniques Middle Technical University Baghdad Iraq
| | - Abdulla H. Alamoodi
- Department of Computing, Faculty of Arts, Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia
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Alqaysi ME, Albahri AS, Hamid RA. Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9410222. [PMID: 36439957 PMCID: PMC9683965 DOI: 10.1155/2022/9410222] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/01/2022] [Accepted: 10/18/2022] [Indexed: 09/08/2024]
Abstract
Method The three-phase framework integrated the MCDM and ML to develop the diagnosis models and evaluate and benchmark the best. Firstly, the new ASD-dataset-combined medical tests and sociodemographic characteristic features is identified and preprocessed. Secondly, developing the hybrid diagnosis models using the intersection process between three FS techniques and five ML algorithms introduces 15 models. The selected medical tests and sociodemographic features from each FS technique are weighted before feeding the five ML algorithms using the fuzzy-weighted zero-inconsistency (FWZIC) method based on four psychiatry experts. Thirdly, (i) formulate a dynamic decision matrix for all developed models based on seven evaluation metrics, including classification accuracy, precision, F1 score, recall, test time, train time, and AUC. (ii) The fuzzy decision by opinion score method (FDOSM) is used to evaluate and benchmark the 15 models concerning the seven evaluation metrics. Results Results reveal that (i) the three FS techniques have obtained a size different from the others in the number of the selected features; the sets were 39, 38, and 41 out of 48 features. Each set has its weights constructed by FWIZC. Considered sociodemographic features have been mostly selected more than medical tests within FS techniques. (ii) The first three best hybrid models were "ReF-decision tree," "IG-decision tree," and "Chi2-decision tree," with score values 0.15714, 0.17539, and 0.29444. The best diagnosis model (ReF-decision tree) has obtained 0.4190, 0.0030, 0.9946, 0.9902, 0.9902, 0.9902, 0.9902, and 0.9951 for the C1=train time, C2=test time, C3=AUC, C4=CA, C5=F1 score, C6=precision, and C7=recall, respectively. The developed framework would be beneficial in advancing, accelerating, and selecting diagnosis tools in therapy with ASD. The selected model can identify severity as light, medium, or intense based on medical tests and sociodemographic weighted features.
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Affiliation(s)
- M. E. Alqaysi
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
- Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad 10021, Iraq
| | - A. S. Albahri
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - Rula A. Hamid
- College of Business Informatics, University of Information Technology and Communications (UOITC), Baghdad, Iraq
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Albahri AS, Hamid RA, Zaidan AA, Albahri OS. Early automated prediction model for the diagnosis and detection of children with autism spectrum disorders based on effective sociodemographic and family characteristic features. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07822-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Alqaysi ME, Albahri AS, Hamid RA. Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review. Int J Telemed Appl 2022; 2022:3551528. [PMID: 35814280 PMCID: PMC9270139 DOI: 10.1155/2022/3551528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/31/2022] [Accepted: 06/13/2022] [Indexed: 11/18/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is "diagnosis of ASD based on questionnaires and sociodemographic features" (n = 39). This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques. The second category consists of "diagnosis ASD based on medical and family characteristic features" (n = 1). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations, and challenges of diagnosis ASD research in utilizing AI techniques and ML algorithms that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and identifies the open issues that help accomplish the recommended solution of diagnosis ASD research. Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.
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Affiliation(s)
- M. E. Alqaysi
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
- Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad 10021, Iraq
| | - A. S. Albahri
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - Rula A. Hamid
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
- College of Business Informatics, University of Information Technology and Communications (UOITC), Baghdad, Iraq
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Zhao J, Smith T, Lavigne M, Aenishaenslin C, Cox R, Fazil A, Johnson A, Sanchez J, Hermant B. A Rapid Literature Review of Multi-Criteria Decision Support Methods in the Context of One Health for All-Hazards Threat Prioritization. Front Public Health 2022; 10:861594. [PMID: 35493347 PMCID: PMC9051240 DOI: 10.3389/fpubh.2022.861594] [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] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 03/11/2022] [Indexed: 11/24/2022] Open
Abstract
Background Multi-Criteria Decision Analysis (MCDA) is a decision support tool that can be used in public health emergency management. The use of a One Health lens in MCDA can support the prioritization of threats and interventions which cut across the human, animal, and environmental domains. Previous literature reviews have focused on creating a snapshot of MCDA methodological trends. Our study provides an update to the MCDA methods literature with key considerations from a One Health perspective and addresses the application of MCDA in an all-hazards decision-making context. Methods We conducted a literature search on MEDLINE, EMBASE, SCOPUS, the CAB database, and a limited online gray literature search in partnership with a librarian from Health Canada. Articles were limited to those published in the year 2010 or later in a high-income setting (OECD member countries). Results Sixty-two articles were included for synthesis. Of these articles, most were Canadian studies (20%); and prioritized health risks, threats, and interventions in the human domain (69%). Six commonly used prioritization criteria were identified: threat, health, intervention, strategic, social, and economic impact. Stakeholders were engaged in 85% of studies and commonly consisted of government groups, non-governmental groups, subject matter experts, and the public. While most articles (65%) included elements of One Health based on our definition, only 5 studies (9%) explicitly acknowledged One Health as a guiding principle for the study. Forty seven percentage of studies noted that MCDA was beneficial in supporting the decision-making process. Conclusion Current literature on health prioritization presents some variability in the depth of integration of the One Health framework and on the use of various MCDA methodologies given prioritization objectives. Studies which applied a comprehensive One Health approach, prioritized disparate threats, or conducted cyclical prioritizations for governing bodies were broad in scope, but sparse. The results of our review indicate the need for better guidance on the integration of a One Health approach and the use of various MCDA methods given the main prioritization objectives.
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Affiliation(s)
- Jiawei Zhao
- Risk and Capability Assessment Unit, Public Health Agency of Canada, Ottawa, ON, Canada
| | - Tiffany Smith
- Risk and Capability Assessment Unit, Public Health Agency of Canada, Ottawa, ON, Canada
| | - Melissa Lavigne
- Risk and Capability Assessment Unit, Public Health Agency of Canada, Ottawa, ON, Canada
| | - Cécile Aenishaenslin
- Department of Pathology and Microbiology, University of Montreal, Montreal, QC, Canada
- Centre de recherche en santé publique de L'Université de Montréal et du CIUSSS du Centre-Sud-de-L'Île-de-Montréal, Montréal, QC, Canada
| | - Ruth Cox
- Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, Canada
- National Wildlife Management Centre, Animal and Plant Health Agency, Woodchester Park, United Kingdom
| | - Aamir Fazil
- National Microbiology Laboratory, Public Health Agency of Canada, Ottawa, ON, Canada
| | - Ana Johnson
- Department of Public Health Sciences, Queen's University, Kingston, ON, Canada
| | - Javier Sanchez
- Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, Canada
| | - Benoit Hermant
- Risk and Capability Assessment Unit, Public Health Agency of Canada, Ottawa, ON, Canada
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Alamoodi AH, Mohammed RT, Albahri OS, Qahtan S, Zaidan AA, Alsattar HA, Albahri AS, Aickelin U, Zaidan BB, Baqer MJ, Jasim AN. Based on neutrosophic fuzzy environment: a new development of FWZIC and FDOSM for benchmarking smart e-tourism applications. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00689-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
AbstractThe task of benchmarking smart e-tourism applications based on multiple smart key concept attributes is considered a multi-attribute decision-making (MADM) problem. Although the literature review has evaluated and benchmarked these applications, data ambiguity and vagueness continue to be unresolved issues. The robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy weighted with zero inconsistency (FWZIC) is proven compared with that of other MADM methods. Thus, this study extends FDOSM and FWZIC under a new fuzzy environment to address the mentioned issues whilst benchmarking the applications. The neutrosophic fuzzy set is used for this purpose because of its high ability to handle ambiguous and vague information comprehensively. Fundamentally, the proposed methodology comprises two phases. The first phase adopts and describes the decision matrices of the smart e-tourism applications. The second phase presents the proposed framework in two sections. In the first section, the weight of each attribute of smart e-tourism applications is calculated through the neutrosophic FWZIC (NS-FWZIC) method. The second section employs the weights determined by the NS-FWZIC method to benchmark all the applications per each category (tourism marketing and smart-based tourism recommendation system categories) through the neutrosophic FDOSM (NS-FDOSM). Findings reveal that: (1) the NS-FWZIC method effectively weights the applications’ attributes. Real time receives the highest importance weight (0.402), whereas augmented reality has the lowest weight (0.005). The remaining attributes are distributed in between. (2) In the context of group decision-making, NS-FDOSM is used to uniform the variation found in the individual benchmarking results of the applications across all categories. Systematic ranking, sensitivity analysis and comparison analysis assessments are used to evaluate the robustness of the proposed work. Finally, the limitations of this study are discussed along with several future directions.
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Al-Samarraay MS, Zaidan A, Albahri O, Pamucar D, AlSattar H, Alamoodi A, Zaidan B, Albahri A. Extension of interval-valued Pythagorean FDOSM for evaluating and benchmarking real-time SLRSs based on multidimensional criteria of hand gesture recognition and sensor glove perspectives. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108284] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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A new extension of FDOSM based on Pythagorean fuzzy environment for evaluating and benchmarking sign language recognition systems. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06683-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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