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Yildizdan G, Baş E. A Novel Binary Artificial Jellyfish Search Algorithm for Solving 0–1 Knapsack Problems. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11171-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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A multi-objective chicken swarm optimization algorithm based on dual external archive with various elites. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Yang Y, Huang PQ, Kong X, Zhao J. A constrained multi-objective evolutionary algorithm assisted by an additional objective function. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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An intelligent hybrid JAYA and crow search algorithms for optimizing constrained and unconstrained problems. Soft comput 2021. [DOI: 10.1007/s00500-021-06205-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Alsalem MA, Alsattar HA, Albahri AS, Mohammed RT, Albahri OS, Zaidan AA, Alnoor A, Alamoodi AH, Qahtan S, Zaidan BB, Aickelin U, Alazab M, Jumaah FM. Based on T-spherical fuzzy environment: A combination of FWZIC and FDOSM for prioritising COVID-19 vaccine dose recipients. J Infect Public Health 2021; 14:1513-1559. [PMID: 34538731 PMCID: PMC8388152 DOI: 10.1016/j.jiph.2021.08.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 08/14/2021] [Accepted: 08/21/2021] [Indexed: 01/07/2023] Open
Abstract
The problem complexity of multi-criteria decision-making (MCDM) has been raised in the distribution of coronavirus disease 2019 (COVID-19) vaccines, which required solid and robust MCDM methods. Compared with other MCDM methods, the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) have demonstrated their solidity in solving different MCDM challenges. However, the fuzzy sets used in these methods have neglected the refusal concept and limited the restrictions on their constants. To end this, considering the advantage of the T-spherical fuzzy sets (T-SFSs) in handling the uncertainty in the data and obtaining information with more degree of freedom, this study has extended FWZIC and FDOSM methods into the T-SFSs environment (called T-SFWZIC and T-SFDOSM) to be used in the distribution of COVID-19 vaccines. The methodology was formulated on the basis of decision matrix adoption and development phases. The first phase described the adopted decision matrix used in the COVID-19 vaccine distribution. The second phase presented the sequential formulation steps of T-SFWZIC used for weighting the distribution criteria followed by T-SFDOSM utilised for prioritising the vaccine recipients. Results revealed the following: (1) T-SFWZIC effectively weighted the vaccine distribution criteria based on several parameters including T = 2, T = 4, T = 6, T = 8, and T = 10. Amongst all parameters, the age criterion received the highest weight, whereas the geographic locations severity criterion has the lowest weight. (2) According to the T parameters, a considerable variance has occurred on the vaccine recipient orders, indicating that the existence of T values affected the vaccine distribution. (3) In the individual context of T-SFDOSM, no unique prioritisation was observed based on the obtained opinions of each expert. (4) The group context of T-SFDOSM used in the prioritisation of vaccine recipients was considered the final distribution result as it unified the differences found in an individual context. The evaluation was performed based on systematic ranking assessment and sensitivity analysis. This evaluation showed that the prioritisation results based on each T parameter were subject to a systematic ranking that is supported by high correlation results over all discussed scenarios of changing criteria weights values.
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Affiliation(s)
- M A Alsalem
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - H A Alsattar
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - A S Albahri
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - R T Mohammed
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia; Faculty of Computing and Innovative Technology, Geomatika University College, Kuala Lumpur, Malaysia
| | - O S Albahri
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - A A Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia.
| | - Alhamzah Alnoor
- School of Management, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia
| | - A H Alamoodi
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia
| | - Sarah Qahtan
- Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - B B Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia; Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
| | - Uwe Aickelin
- School of Computing and Information Systems, University of Melbourne, 700 Swanston Street, Victoria 3010, Australia
| | - Mamoun Alazab
- College of Engineering, IT and Environment, Charles Darwin University, NT, Australia
| | - F M Jumaah
- Department of Advanced Applications and Embedded Systems, Intel Corporation, Plot 6 Bayan Lepas Technoplex, 11900 Pulau Pinang, Malaysia
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Albahri AS, Zaidan AA, Albahri OS, Zaidan BB, Alamoodi AH, Shareef AH, Alwan JK, Hamid RA, Aljbory MT, Jasim AN, Baqer MJ, Mohammed KI. Development of IoT-based mhealth framework for various cases of heart disease patients. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00579-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Mohammed TJ, Albahri AS, Zaidan AA, Albahri OS, Al-Obaidi JR, Zaidan BB, Larbani M, Mohammed RT, Hadi SM. Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component. APPL INTELL 2021; 51:2956-2987. [PMID: 34764579 PMCID: PMC7820530 DOI: 10.1007/s10489-020-02169-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2020] [Indexed: 01/31/2023]
Abstract
As coronavirus disease 2019 (COVID-19) spreads across the world, the transfusion of efficient convalescent plasma (CP) to the most critical patients can be the primary approach to preventing the virus spread and treating the disease, and this strategy is considered as an intelligent computing concern. In providing an automated intelligent computing solution to select the appropriate CP for the most critical patients with COVID-19, two challenges aspects are bound to be faced: (1) distributed hospital management aspects (including scalability and management issues for prioritising COVID-19 patients and donors simultaneously), and (2) technical aspects (including the lack of COVID-19 dataset availability of patients and donors and an accurate matching process amongst them considering all blood types). Based on previous reports, no study has provided a solution for CP-transfusion-rescue intelligent framework during this pandemic that has addressed said challenges and issues. This study aimed to propose a novel CP-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide an efficient CP from eligible donors to the most critical patients using multicriteria decision-making (MCDM) methods. A dataset, including COVID-19 patients/donors that have met the important criteria in the virology field, must be augmented to improve the developed framework. Four consecutive phases conclude the methodology. In the first phase, a new COVID-19 dataset is generated on the basis of medical-reference ranges by specialised experts in the virology field. The simulation data are classified into 80 patients and 80 donors on the basis of the five biomarker criteria with four blood types (i.e., A, B, AB, and O) and produced for COVID-19 case study. In the second phase, the identification scenario of patient/donor distributions across four centralised/decentralised telemedicine hospitals is identified 'as a proof of concept'. In the third phase, three stages are conducted to develop a CP-transfusion-rescue framework. In the first stage, two decision matrices are adopted and developed on the basis of the five 'serological/protein biomarker' criteria for the prioritisation of patient/donor lists. In the second stage, MCDM techniques are analysed to adopt individual and group decision making based on integrated AHP-TOPSIS as suitable methods. In the third stage, the intelligent matching components amongst patients/donors are developed on the basis of four distinct rules. In the final phase, the guideline of the objective validation steps is reported. The intelligent framework implies the benefits and strength weights of biomarker criteria to the priority configuration results and can obtain efficient CPs for the most critical patients. The execution of matching components possesses the scalability and balancing presentation within centralised/decentralised hospitals. The objective validation results indicate that the ranking is valid.
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Affiliation(s)
- Thura J. Mohammed
- grid.444506.70000 0000 9272 6490Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Malaysia ,Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - A. S. Albahri
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - A. A. Zaidan
- grid.444506.70000 0000 9272 6490Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Malaysia
| | - O. S. Albahri
- grid.444506.70000 0000 9272 6490Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Malaysia
| | - Jameel R. Al-Obaidi
- grid.444506.70000 0000 9272 6490Department of Biology, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak Malaysia
| | - B. B. Zaidan
- grid.444506.70000 0000 9272 6490Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Malaysia
| | - Moussa Larbani
- grid.34428.390000 0004 1936 893XSchool of Mathematics and Statistics, Carleton University, Ottawa, ON Canada
| | - R. T. Mohammed
- grid.11142.370000 0001 2231 800XFaculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - Suha M. Hadi
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
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Medical decision-making based on the exploration of a personalized medicine dataset. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100561] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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Albahri OS, Zaidan AA, Salih MM, Zaidan BB, Khatari MA, Ahmed MA, Albahri AS, Alazab M. Multidimensional benchmarking of the active queue management methods of network congestion control based on extension of fuzzy decision by opinion score method. INT J INTELL SYST 2020. [DOI: 10.1002/int.22322] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Osamah Shihab Albahri
- Department of Computing Universiti Pendidikan Sultan Idris, Tanjong Malim Perak Malaysia
| | - Aws Alaa Zaidan
- Department of Computing Universiti Pendidikan Sultan Idris, Tanjong Malim Perak Malaysia
| | - Mahmood M. Salih
- Department of Computer Science, Computer Science and Mathematics College Tikrit University Tikrit 34001 Iraq
| | - Bilal Bahaa Zaidan
- Department of Computing Universiti Pendidikan Sultan Idris, Tanjong Malim Perak Malaysia
| | - Maimuna A. Khatari
- Department of Computing Universiti Pendidikan Sultan Idris, Tanjong Malim Perak Malaysia
| | - Mohamed A. Ahmed
- Department of Computer Science, Computer Science and Mathematics College Tikrit University Tikrit 34001 Iraq
| | - Ahmed Shihab Albahri
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI) Tikrit University Baghdad Iraq
| | - Mamoun Alazab
- College of Engineering, IT and Environment Charles Darwin University NT 0909 Australia
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A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05020-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04325-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure. J Med Syst 2019; 43:223. [PMID: 31187288 DOI: 10.1007/s10916-019-1362-x] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 05/30/2019] [Indexed: 01/01/2023]
Abstract
Remotely monitoring a patient's condition is a serious issue and must be addressed. Remote health monitoring systems (RHMS) in telemedicine refers to resources, strategies, methods and installations that enable doctors or other medical professionals to work remotely to consult, diagnose and treat patients. The goal of RHMS is to provide timely medical services at remote areas through telecommunication technologies. Through major advancements in technology, particularly in wireless networking, cloud computing and data storage, RHMS is becoming a feasible aspect of modern medicine. RHMS for the prioritisation of patients with multiple chronic diseases (MCDs) plays an important role in sustainably providing high-quality healthcare services. Further investigations are required to highlight the limitations of the prioritisation of patients with MCDs over a telemedicine environment. This study introduces a comprehensive and inclusive review on the prioritisation of patients with MCDs in telemedicine applications. Furthermore, it presents the challenges and open issues regarding patient prioritisation in telemedicine. The findings of this study are as follows: (1) The limitations and problems of existing patients' prioritisation with MCDs are presented and emphasised. (2) Based on the analysis of the academic literature, an accurate solution for remote prioritisation in a large scale of patients with MCDs was not presented. (3) There is an essential need to produce a new multiple-criteria decision-making theory to address the current problems in the prioritisation of patients with MCDs.
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Enaizan O, Zaidan AA, Alwi NHM, Zaidan BB, Alsalem MA, Albahri OS, Albahri AS. Electronic medical record systems: decision support examination framework for individual, security and privacy concerns using multi-perspective analysis. HEALTH AND TECHNOLOGY 2018. [DOI: 10.1007/s12553-018-0278-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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