UPSI Digital Repository (UDRep)
|
UPSI Digital Repository (UDRep)
|
|
|
| ||||||||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| 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. ? 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature. |
| References |
Abdulkareem, K. H. (2020). 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 Computing and Applications, Retrieved from www.scopus.com Abdullateef, B. N., Elias, N. F., Mohamed, H., Zaidan, A. A., & Zaidan, B. B. (2016). An evaluation and selection problems of OSS-LMS packages. SpringerPlus, 5(1), 1-35. doi:10.1186/s40064-016-1828-y Abolghasemi, H., Eshghi, P., Cheraghali, A. M., Imani Fooladi, A. A., Bolouki Moghaddam, F., Imanizadeh, S., . . . Shahverdi, S. (2020). Clinical efficacy of convalescent plasma for treatment of COVID-19 infections: Results of a multicenter clinical study. Transfusion and Apheresis Science, 59(5) doi:10.1016/j.transci.2020.102875 Alamoodi, A. H., Zaidan, B. B., Zaidan, A. A., Albahri, O. S., Mohammed, K. I., Malik, R. Q., . . . Alaa, M. (2021). Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review. Expert Systems with Applications, 167 doi:10.1016/j.eswa.2020.114155 Alao, M. A., Ayodele, T. R., Ogunjuyigbe, A. S. O., & Popoola, O. M. (2020). Multi-criteria decision based waste to energy technology selection using entropy-weighted TOPSIS technique: The case study of lagos, nigeria. Energy, 201 doi:10.1016/j.energy.2020.117675 Albahri, A. S., Albahri, O. S., Zaidan, A. A., Zaidan, B. B., Hashim, M., Alsalem, M. A., . . . Baqer, M. J. (2019). Based multiple heterogeneous wearable sensors: A smart real-time health monitoring structured for hospitals distributor. IEEE Access, 7, 37269-37323. doi:10.1109/ACCESS.2019.2898214 Albahri, A. S., Al-Obaidi, J. R., Zaidan, A. A., Albahri, O. S., Hamid, R. A., Zaidan, B. B., . . . Hashim, M. (2020). Multi-biological laboratory examination framework for the prioritization of patients with COVID-19 based on integrated AHP and group VIKOR methods. International Journal of Information Technology and Decision Making, 19(5), 1247-1269. doi:10.1142/S0219622020500285 Albahri, A. S., Alwan, J. K., Taha, Z. K., Ismail, S. F., Hamid, R. A., Zaidan, A. A., . . . Alsalem, M. A. (2021). IoT-based telemedicine for disease prevention and health promotion: State-of-the-art. Journal of Network and Computer Applications, 173 doi:10.1016/j.jnca.2020.102873 Albahri, A. S., Hamid, R. A., Alwan, J., Al-qays, Z. T., Zaidan, A. A., Zaidan, B. B., . . . Madhloom, H. T. (2020). Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): A systematic review. Journal of Medical Systems, 44(7) doi:10.1007/s10916-020-01582-x Albahri, A. S., Zaidan, A. A., Albahri, O. S., Zaidan, B. B., & Alsalem, M. A. (2018). Real-time fault-tolerant mHealth system: Comprehensive review of healthcare services, opens issues, challenges and methodological aspects. Journal of Medical Systems, 42(8) doi:10.1007/s10916-018-0983-9 Albahri, O. S., Albahri, A. S., Mohammed, K. I., Zaidan, A. A., Zaidan, B. B., Hashim, M., & Salman, O. H. (2018). Systematic review of real-time remote health monitoring system in triage and priority-based sensor technology: Taxonomy, open challenges, motivation and recommendations. Journal of Medical Systems, 42(5) doi:10.1007/s10916-018-0943-4 Albahri, O. S., Albahri, A. S., Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Mohsin, A. H., . . . Shareef, A. H. (2019). Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors. IEEE Access, 7, 50052-50080. doi:10.1109/ACCESS.2019.2910411 Albahri, O. S., Al-Obaidi, J. R., Zaidan, A. A., Albahri, A. S., Zaidan, B. B., Salih, M. M., . . . Zulkifli, C. Z. (2020). Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods. Computer Methods and Programs in Biomedicine, 196 doi:10.1016/j.cmpb.2020.105617 Albahri, O. S., Zaidan, A. A., Albahri, A. S., Zaidan, B. B., Abdulkareem, K. H., Al-qaysi, Z. T., . . . Rashid, N. A. (2020). Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health, 13(10), 1381-1396. doi:10.1016/j.jiph.2020.06.028 Albahri, O. S., Zaidan, A. A., Zaidan, B. B., Hashim, M., Albahri, A. S., & Alsalem, M. A. (2018). Real-time remote health-monitoring systems in a medical centre: A review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects. Journal of Medical Systems, 42(9) doi:10.1007/s10916-018-1006-6 Almahdi, E. M., Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., & Albahri, A. S. (2019). Mobile patient monitoring systems from a benchmarking aspect: Challenges, open issues and recommended solutions. Journal of Medical Systems, 43(7) doi:10.1007/s10916-019-1336-z Almahdi, E. M., Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., & Albahri, A. S. (2019). Mobile-based patient monitoring systems: A prioritisation framework using multi-criteria decision-making techniques. Journal of Medical Systems, 43(7) doi:10.1007/s10916-019-1339-9 Alsalem, M. A., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alamoodi, A. H., Albahri, A. S., . . . Mohammed, K. I. (2019). Multiclass benchmarking framework for automated acute leukaemia detection and classification based on BWM and group-VIKOR. Journal of Medical Systems, 43(7) doi:10.1007/s10916-019-1338-x Alsalem, M. A., Zaidan, A. A., Zaidan, B. B., Hashim, M., Albahri, O. S., Albahri, A. S., . . . Mohammed, K. I. (2018). Systematic review of an automated multiclass detection and classification system for acute leukaemia in terms of evaluation and benchmarking, open challenges, issues and methodological aspects. Journal of Medical Systems, 42(11) doi:10.1007/s10916-018-1064-9 Anderson, M., McKee, M., & Mossialos, E. (2020). Covid-19 exposes weaknesses in european response to outbreaks. The BMJ, 368 doi:10.1136/bmj.m1075 Aziz, M., Fatima, R., Lee-Smith, W., & Assaly, R. (2020). The association of low serum albumin level with severe COVID-19: A systematic review and meta-analysis. Critical Care, 24(1) doi:10.1186/s13054-020-02995-3 Badi, I., & Pamucar, D. (2020). Supplier selection for steelmaking company by using combined grey-marcos methods. Decision Making: Applications in Management and Engineering, 3(2), 37-47. doi:10.31181/dmame2003037b Burnouf, T., & Seghatchian, J. (2014). Ebola virus convalescent blood products: Where we are now and where we may need to go. Transfusion and Apheresis Science, 51(2), 120-125. doi:10.1016/j.transci.2014.10.003 Catelli, R., Gargiulo, F., Casola, V., De Pietro, G., Fujita, H., & Esposito, M. (2020). Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 italian data set. Applied Soft Computing Journal, 97 doi:10.1016/j.asoc.2020.106779 Chen, L., Xiong, J., Bao, L., & Shi, Y. (2020). Convalescent plasma as a potential therapy for COVID-19. The Lancet Infectious Diseases, 20(4), 398-400. doi:10.1016/S1473-3099(20)30141-9 Coperchini, F., Chiovato, L., Croce, L., Magri, F., & Rotondi, M. (2020). The cytokine storm in COVID-19: An overview of the involvement of the chemokine/chemokine-receptor system. Cytokine and Growth Factor Reviews, 53, 25-32. doi:10.1016/j.cytogfr.2020.05.003 Deeks, J. J., Dinnes, J., Takwoingi, Y., Davenport, C., Spijker, R., Taylor-Phillips, S., . . . Cochrane COVID-19 Diagnostic Test Accuracy Group. (2020). Antibody tests for identification of current and past infection with SARS-CoV-2. Cochrane Database of Systematic Reviews, 2020(6) doi:10.1002/14651858.CD013652 Enaizan, O., Zaidan, A. A., Alwi, N. H. M., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., & Albahri, A. S. (2020). Electronic medical record systems: Decision support examination framework for individual, security and privacy concerns using multi-perspective analysis. Health and Technology, 10(3), 795-822. doi:10.1007/s12553-018-0278-7 Hall, L. O., Paul, R., Goldgof, D. B., & Goldgof, G. M. (2004). Retrieved from www.scopus.com Hanratty, B., Burton, J. K., Goodman, C., Gordon, A. L., & Spilsbury, K. (2020). Covid-19 and lack of linked datasets for care homes: The pandemic has shed harsh light on the need for a live minimum dataset. The BMJ, 369 doi:10.1136/bmj.m2463 Hernandez-Matamoros, A., Fujita, H., Hayashi, T., & Perez-Meana, H. (2020). Forecasting of COVID19 per regions using ARIMA models and polynomial functions. Applied Soft Computing Journal, 96 doi:10.1016/j.asoc.2020.106610 Hu, R., Ruan, G., Xiang, S., Huang, M., Liang, Q., & Li, J. (2020). Automated diagnosis of COVID-19 using deep learning and data augmentation on chest CT. MedRxiv, , 1-11. Retrieved from www.scopus.com Ibrahim, N. K., Hammed, H., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., . . . Alaa, M. (2019). Multi-criteria evaluation and benchmarking for young learners' english language mobile applications in terms of LSRW skills. IEEE Access, 7, 146620-146651. doi:10.1109/ACCESS.2019.2941640 Isidori, A., de Leval, L., Gergis, U., Musto, P., & Porcu, P. (2020). Management of patients with hematologic malignancies during the COVID-19 pandemic: Practical considerations and lessons to be learned. Frontiers in Oncology, 10 doi:10.3389/fonc.2020.01439 Jumaah, F. M., Zaidan, A. A., Zaidan, B. B., Bahbibi, R., Qahtan, M. Y., & Sali, A. (2018). Technique for order performance by similarity to ideal solution for solving complex situations in multi-criteria optimization of the tracking channels of GPS baseband telecommunication receivers. Telecommunication Systems, 68(3), 425-443. doi:10.1007/s11235-017-0401-5 Kalid, N., Zaidan, A. A., Zaidan, B. B., Salman, O. H., Hashim, M., Albahri, O. S., & Albahri, A. S. (2018). Based on real time remote health monitoring systems: A new approach for prioritization “Large scales data” patients with chronic heart diseases using body sensors and communication technology. Journal of Medical Systems, 42(4) doi:10.1007/s10916-018-0916-7 Kalid, N., Zaidan, A. A., Zaidan, B. B., Salman, O. H., Hashim, M., & Muzammil, H. (2018). Based real time remote health monitoring systems: A review on patients prioritization and related "big data" using body sensors information and communication technology. Journal of Medical Systems, 42(2) doi:10.1007/s10916-017-0883-4 Kaur, R., Singh, S., & Kumar, H. (2018). AuthCom: Authorship verification and compromised account detection in online social networks using AHP-TOPSIS embedded profiling based technique. Expert Systems with Applications, 113, 397-414. doi:10.1016/j.eswa.2018.07.011 Khatari, M., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., & Alsalem, M. A. (2019). Multi-criteria evaluation and benchmarking for active queue management methods: Open issues, challenges and recommended pathway solutions. International Journal of Information Technology and Decision Making, 18(4), 1187-1242. doi:10.1142/S0219622019300039 Lahby, M., Cherkaoui, L., & Adib, A. (2013). A novel ranking algorithm based network selection for heterogeneous wireless access. Journal of Networks, 8(2), 263-272. doi:10.4304/jnw.8.2.263-272 Loey, M., Smarandache, F., & Khalifa, N. E. M. (2020). A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images. A Deep Transfer Learning Model with Classical Data Augmentation and CGAN to Detect COVID-19 from Chest CT Radiography Digital Images, , 1-13. Retrieved from www.scopus.com Marano, G., Vaglio, S., Pupella, S., Facco, G., Catalano, L., Liumbruno, G. M., & Grazzini, G. (2016). Convalescent plasma: New evidence for an old therapeutic tool? Blood Transfusion, 14(2), 152-157. doi:10.2450/2015.0131-15 Martínez, V., Navarro, C., Cano, C., Fajardo, W., & Blanco, A. (2015). DrugNet: Network-based drug-disease prioritization by integrating heterogeneous data. Artificial Intelligence in Medicine, 63(1), 41-49. doi:10.1016/j.artmed.2014.11.003 Maxmen, A. (2020). How blood from coronavirus survivors might save lives. Nature, 580(7801), 16-17. doi:10.1038/d41586-020-00895-8 Miao, F., Wen, B., Hu, Z., Fortino, G., Wang, X. -., Liu, Z. -., . . . Li, Y. (2020). Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques. Artificial Intelligence in Medicine, 108 doi:10.1016/j.artmed.2020.101919 Mohammed, K. I., Jaafar, J., Zaidan, A. A., Albahri, O. S., Zaidan, B. B., Abdulkareem, K. H., . . . Alamoodi, A. H. (2020). A uniform intelligent prioritisation for solving diverse and big data generated from multiple chronic diseases patients based on hybrid decision-making and voting method. IEEE Access, 8, 91521-91530. doi:10.1109/ACCESS.2020.2994746 Mohammed, K. I., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., Albahri, A. S., . . . Hashim, M. (2019). Real-time remote-health monitoring systems: A review on patients prioritisation for multiple-chronic diseases, taxonomy analysis, concerns and solution procedure. Journal of Medical Systems, 43(7) doi:10.1007/s10916-019-1362-x Mohsin, A. H., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Albahri, A. S., Alsalem, M. A., & Mohammed, K. I. (2019). Based blockchain-PSO-AES techniques in finger vein biometrics: A novel verification secure framework for patient authentication. Computer Standards and Interfaces, 66 doi:10.1016/j.csi.2019.04.002 Napi, N. M., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., & Albahri, A. S. (2019). Medical emergency triage and patient prioritisation in a telemedicine environment: A systematic review. Health and Technology, 9(5), 679-700. doi:10.1007/s12553-019-00357-w Piechotta, V., Chai, K. L., Valk, S. J., Doree, C., Monsef, I., Wood, E. M., . . . Skoetz, N. (2020). Convalescent plasma or hyperimmune immunoglobulin for people with COVID-19: A living systematic review. Cochrane Database of Systematic Reviews, 2020(7) doi:10.1002/14651858.CD013600.pub2 Rahmatullah, B., Zaidan, A. A., Mohamed, F., & Sali, A. (2017). Multi-complex attributes analysis for optimum GPS baseband receiver tracking channels selection. Paper presented at the 2017 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017, , 2017-January 1084-1088. doi:10.1109/CoDIT.2017.8102743 Retrieved from www.scopus.com Rajak, M., & Shaw, K. (2019). Evaluation and selection of mobile health (mHealth) applications using AHP and fuzzy TOPSIS. Technology in Society, 59 doi:10.1016/j.techsoc.2019.101186 Rajam, G., Sampson, J., Carlone, G. M., & Ades, E. W. (2010). An augmented passive immune therapy to treat fulminant bacterial infections. Recent Patents on Anti-Infective Drug Discovery, 5(2), 157-167. doi:10.2174/157489110791233496 Remuzzi, A., & Remuzzi, G. (2020). COVID-19 and italy: What next? The Lancet, 395(10231), 1225-1228. doi:10.1016/S0140-6736(20)30627-9 Richardson, S., Hirsch, J. S., Narasimhan, M., Crawford, J. M., McGinn, T., Davidson, K. W., . . . Zanos, T. P. (2020). Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the new york city area. JAMA - Journal of the American Medical Association, 323(20), 2052-2059. doi:10.1001/jama.2020.6775 Saksrisathaporn, K., Bouras, A., Reeveerakul, N., & Charles, A. (2016). Application of a decision model by using an integration of AHP and TOPSIS approaches within humanitarian operation life cycle. International Journal of Information Technology and Decision Making, 15(4), 887-918. doi:10.1142/S0219622015500261 Salman, O. H., Zaidan, A. A., Zaidan, B. B., Naserkalid, & Hashim, M. (2017). Novel methodology for triage and prioritizing using "big data" patients with chronic heart diseases through telemedicine environmental. International Journal of Information Technology and Decision Making, 16(5), 1211-1245. doi:10.1142/S0219622017500225 Seising, R., & Tabacchi, M. E. (2013). Fuzziness and Medicine: Philosophical Reflections and Application Systems in Health Care, 302 Retrieved from www.scopus.com Shah, S., Diwan, S., Soin, A., Rajput, K., Mahajan, A., Manchikanti, L., . . . Gharibo, C. (2020). Evidence-based risk mitigation and stratification during covid-19 for return to interventional pain practice: American society of interventional pain physicians (asipp) guidelines. Pain Physician, 23(4 Special Issue), S161-S182. doi:10.36076/PPJ.2020/23/S161 Shen, C., Wang, Z., Zhao, F., Yang, Y., Li, J., Yuan, J., . . . Liu, L. (2020). Treatment of 5 critically ill patients with COVID-19 with convalescent plasma. JAMA - Journal of the American Medical Association, 323(16), 1582-1589. doi:10.1001/jama.2020.4783 TABISH, M., KHATOON, A., ALKAHTANI, S., ALKAHTANE, A., ALGHAMDI, J., AHMED, S. A., . . . HASNAIN, M. S. (2020). Approaches for prevention and environmental management of novel COVID-19. Environ.Sci.Pollut.Res., , 1-11. Retrieved from www.scopus.com Talal, M., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., Albahri, A. S., . . . Alaa, M. (2019). Comprehensive review and analysis of anti-malware apps for smartphones. Telecommunication Systems, 72(2), 285-337. doi:10.1007/s11235-019-00575-7 Tariq, I., AlSattar, H. A., Zaidan, A. A., Zaidan, B. B., Abu Bakar, M. R., Mohammed, R. T., . . . Albahri, A. S. (2020). MOGSABAT: A metaheuristic hybrid algorithm for solving multi-objective optimisation problems. Neural Computing and Applications, 32(8), 3101-3115. doi:10.1007/s00521-018-3808-3 Tinetti, M., Dindo, L., Smith, C. D., Blaum, C., Costello, D., Ouellet, G., . . . Naik, A. (2019). Challenges and strategies in patients' health priorities-aligned decision-making for older adults with multiple chronic conditions. PLoS ONE, 14(6) doi:10.1371/journal.pone.0218249 Vassallo, R. R., Hilton, J. F., Bravo, M. D., Vittinghoff, E., Custer, B., & Kamel, H. (2020). Recovery of iron stores after adolescents donate blood. Pediatrics, 146(1) doi:10.1542/peds.2019-3316 Wang, C. J., Ng, C. Y., & Brook, R. H. (2020). Response to COVID-19 in taiwan: Big data analytics, new technology, and proactive testing. JAMA - Journal of the American Medical Association, 323(14), 1341-1342. doi:10.1001/jama.2020.3151 Wang, L. (2020). C-reactive protein levels in the early stage of COVID-19. Medecine Et Maladies Infectieuses, 50(4), 332-334. doi:10.1016/j.medmal.2020.03.007 Yahyaie, M., Tarokh, M. J., & Mahmoodyar, M. A. (2019). Use of internet of things to provide a new model for remote heart attack prediction. Telemedicine and e-Health, 25(6), 499-510. doi:10.1089/tmj.2018.0076 Yas, Q. M., Zadain, A. A., Zaidan, B. B., Lakulu, M. B., & Rahmatullah, B. (2017). Towards on develop a framework for the evaluation and benchmarking of skin detectors based on artificial intelligent models using multi-criteria decision-making techniques. International Journal of Pattern Recognition and Artificial Intelligence, 31(3) doi:10.1142/S0218001417590029 Yas, Q. M., Zaidan, A. A., Zaidan, B. B., Rahmatullah, B., & Abdul Karim, H. (2018). Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions. Measurement: Journal of the International Measurement Confederation, 114, 243-260. doi:10.1016/j.measurement.2017.09.027 Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., Albahri, A. S., Yas, Q. M., & Hashim, M. (2018). A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: Coherent taxonomy, open issues and recommendation pathway solution. Health and Technology, 8(4), 223-238. doi:10.1007/s12553-018-0223-9 Zaidan, A. A., Zaidan, B. B., Al-Haiqi, A., Kiah, M. L. M., Hussain, M., & Abdulnabi, M. (2015). Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. Journal of Biomedical Informatics, 53, 390-404. doi:10.1016/j.jbi.2014.11.012 Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., Albahri, A. S., & Qahtan, M. Y. (2020). Multi-agent learning neural network and bayesian model for real-time IoT skin detectors: A new evaluation and benchmarking methodology. Neural Computing and Applications, 32(12), 8315-8366. doi:10.1007/s00521-019-04325-3 Zaidan, A. A., Zaidan, B. B., Hussain, M., Al-Haiqi, A. M., Mat Kiah, M. L., & Abdulnabi, M. (2015). Multi-criteria analysis for OS-EMR software selection problem: A comparative study. Decision Support Systems, 78, 15-27. doi:10.1016/j.dss.2015.07.002 Zaidan, B. B., & Zaidan, A. A. (2018). Comparative study on the evaluation and benchmarking information hiding approaches based multi-measurement analysis using TOPSIS method with different normalisation, separation and context techniques. Measurement: Journal of the International Measurement Confederation, 117, 277-294. doi:10.1016/j.measurement.2017.12.019 Zaidan, B. B., & Zaidan, A. A. (2017). Software and hardware FPGA-based digital watermarking and steganography approaches: Toward new methodology for evaluation and benchmarking using multi-criteria decision-making techniques. Journal of Circuits, Systems and Computers, 26(7) doi:10.1142/S021812661750116X Zaidan, B. B., Zaidan, A. A., Abdul Karim, H., & Ahmad, N. N. (2017). A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques. International Journal of Information Technology and Decision Making, , 1-42. doi:10.1142/S0219622017500183 Zaidan, B. B., Zaidan, A. A., Karim, H. A., & Ahmad, N. N. (2017). A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi-criteria analysis based on ‘large-scale data’. Software - Practice and Experience, 47(10), 1365-1392. doi:10.1002/spe.2465 Zughoul, O., Momani, F., Almasri, O. H., Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., . . . Hashim, M. (2018). Comprehensive insights into the criteria of student performance in various educational domains. IEEE Access, 6, 73245-73264. doi:10.1109/ACCESS.2018.2881282 |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |