1. Furusa, S. S., and Coleman, A., Factors influencing e-health implementation by medical doctors in public hospitals in Zimbabwe. SA J. Inform. Manag. 20(1):9, 2018.

2. S. Iqbal et al., Real-time-based E-health systems: Design and implementation of a lightweight key management protocol for securing sensitive information of patients. Health Technol. (Berl)., pp. 1–19, 2018.

3. Mohsin, A. H. et al., Blockchain authentication of network applications: Taxonomy, classification, capabilities, open challenges, motivations, recommendations and future directions. Comput. Stand. Interfaces, 2018.

4. Alanazi, H. O. et al., Meeting the security requirements of electronic medical records in the ERA of high-speed computing. J. Med. Syst. 39(1):165, 2015.

5. Nabi, M. S. A. et al., Suitability of using SOAP protocol to secure electronic medical record databases transmission. Int. J. Pharmacol. 6(6):959–964, 2010.

6. Kiah, M. L. M. et al., An enhanced security solution for electronic medical records based on AES hybrid technique with SOAP/XML and SHA-1. J. Med. Syst. 37(5):9971, 2013.

7. Nabi, M. S. et al., Suitability of adopting S/MIME and OpenPGP email messages protocol to secure electronic medical records. Second international conference on future generation communication technologies (FGCT 2013). 93–97, 2013.

8. Abdulnabi, M. et al., A distributed framework for health information exchange using smartphone technologies. J. Biomed. Inform. 69:230–250, 2017.

9. Zaidan, A. A. et al., Challenges, alternatives, and paths to sustainability: Better public health promotion using social networking pages as key tools. J. Med. Syst. 39(2):7, 2015.

10. Mat Kiah, M. L. et al., Design and develop a video conferencing framework for real-time telemedicine applications using secure group-based communication architecture. J. Med. Syst. 38(10): 133, 2014.

11. Zaidan, B. B. et al., A security framework for Nationwide health information exchange based on telehealth strategy. J. Med. Syst. 39(5):51, 2015.

12. Hussain, M. et al., The landscape of research on smartphone medical apps: Coherent taxonomy, motivations, open challenges and recommendations. Comput. Methods Programs Biomed. 122(3): 393–408, 2015.

13. Zaidan, B. B. et al., Impact of data privacy and confidentiality on developing telemedicine applications: A review participates opinion and expert concerns. Int. J. Pharmacol. 7(3):382–387, 2011.

14. Kiah, M. L. M. et al., MIRASS: Medical informatics research activity support system using information mashup network. J. Med. Syst. 38(4):37, 2014.

15. A. H. Mohsin et al., Based Blockchain-PSO-AES techniques in finger vein biometrics: A novel verification secure framework for patient authentication. Comput. Stand. Interfaces, 2019.

16. Hussain, M. et al., Conceptual framework for the security of mobile health applications on android platform. Telemat. Inform. 35(5), 2018.

17. Hussain, M. et al., A security framework for mHealth apps on android platform. Comput. Secur. 75:191–217, 2018.

18. Alsalem, M. A. et al., A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations. Comput. Methods Programs Biomed. 158:93–112, 2018.

19. Mohsin, A. H. et al., Real-time medical systems based on human biometric steganography: A systematic review. J. Med. Syst. 42(12):245, 2018.

20. Mohsin, A. H. et al., Real-time remote health monitoring systems using body sensor information and finger vein biometric verification: A multi-layer systematic review. J. Med. Syst. 42(12):238, 2018.

21. Albahri, O. S. et al., Systematic review of real-time remote health monitoring system in triage and priority-based sensor technology: Taxonomy, open challenges, motivation and recommendations. J. Med. Syst. 42(5), 2018.

22. Salman, O. H. et al., Novel methodology for triage and prioritizing using ‘big data’ patients with chronic heart diseases through telemedicine environmental. Int. J. Inf. Technol. Decis. Mak. 16(05): 1211–1245, 2017.

23. Shuwandy, M. L. et al., Sensor-based mHealth authentication for real-time remote healthcare monitoring system: A multilayer systematic review. J. Med. Syst. 43(2):33, 2019.

24. A. H. Mohsin et al., Based medical systems for patient’s authentication: Towards a new verification secure framework using CIA standard. J. Med. Syst., 2019.

25. Talal, M. et al., Smart home-based IoT for real-time and secure remote health monitoring of triage and priority system using body sensors: Multi-driven systematic review. J. Med. Syst. 43(3):42, 2019.

26. Cameron, J. D., Ramaprasad, A., and Syn, T., An ontology of and roadmap for mHealth research. Int. J. Med. Inform. 100:16–25, 2017.

27. Táborský, M., Linhart, A., and Skalická, H., E-health: A position statement of the European Society of Cardiology: Summary of the document prepared by the Czech Society of Cardiology. Cor et Vasa 59(2):e204–e207, 2017.

28. Varga, N., Bokor, L., and Takács, A., Context-aware IPv6 flow mobility for multi-sensor based Mobile patient monitoring and tele-consultation. Proc. Comput. Sci. 40:222–229, 2014.

29. Pawar, P.A. and Mohammad, S.P., Review of quality of service in the mobile patient monitoring systems. I2017 IEEE Region 10 Symposium (TENSYMP). 2017.

30. Paganelli, F., and Giuli, D., An ontology-based system for context-aware and configurable services to support home-based continuous care. IEEE Transactions on Information Technology in Biomedicine 15(2):324–333, 2011

31. Logan, A. G. et al., Mobile phone–based remote patient monitoring system for management of hypertension in diabetic patients. Am. J. Hypertens. 20(9):942–948, 2007.

32. Miao, F. et al., Mobihealthcare system: Body sensor network based m-health system for healthcare application. E-Health Telecommun. Syst. Netw. 1(01):12, 2012.

33. Fortier, P. and B. Viall. Development of a mobile cardiac wellness application and integrated wearable sensor suite. The fifth international conference on sensor technologies and applications. 2011.

34. Wai, A., et al., Smart wireless continence management system for elderly with dementia. In 10th International Conference on ehealth Networking, Applications and Services, HealthCom. 2008.

35. Dickerson, R.F., E.I. Gorlin, and J.A. Stankovic. Empath: a continuous remote emotional health monitoring system for depressive illness. .Proceedings of the 2nd Conference on Wireless Health. 2011. ACM.

36. Bourouis, A., Feham, M., and Bouchachia, A., Ubiquitous mobile health monitoring system for elderly (UMHMSE). arXiv preprint arXiv:1107.3695, 2011.

37. Gao, T. et al., The advanced health and disaster aid network: A light-weight wireless medical system for triage. IEEE Trans. Biomed. Circ. Syst. 1(3):203–216, 2007.

38. Jones, V., Gay, V., and Leijdekkers, P., Body sensor networks for mobile health monitoring: Experience in europe and australia. Digital Society, 2010. ICDS'10. Fourth International Conference on. 2010. IEEE.

39. Pawar, P. et al., A framework for the comparison of mobile patient monitoring systems. J. Biomed. Inform. 45(3):544–556, 2012.

40. Hussain, A. et al., Health and emergency-care platform for the elderly and disabled people in the Smart City. J. Syst. Softw. 110: 253–263, 2015.

41. Bonney, W., Mobile health technologies-theories and applications. InTech, 2016.

42. Paliwal, G. and Kiwelekar, A.W., A comparison of mobile patient monitoring systems. In International Conference on Health Information Science. 2013. Springer.

43. Massé, F., et al., Miniaturized wireless ECG-monitor for real-time detection of epileptic seizures. In Wireless health 2010. 2010. ACM.

44. Ren, Y. et al., Monitoring patients via a secure and mobile healthcare system. IEEE Wireless Commun. 17(1):59–65, 2010.

45. Koutkias, V. G. et al., A personalized framework for medication treatment management in chronic care. IEEE Trans. Inform. Technol. Biomed. 14(2):464–472, 2010.

46. Shahriyar, R., et al., Intelligent mobile health monitoring system (IMHMS). International Conference on Electronic Healthcare. 2009. Springer.

47. Angood, P. B. et al., Telemedicine at the top of the world: The 1998 and 1999 Everest extreme expeditions. Telemed. J. e-Health 6(3):315–325, 2000.

48. Lin, Y.-H. et al., A wireless PDA-based physiological monitoring system for patient transport. IEEE Trans. Inform. Technol. Biomed. 8(4):439–447, 2004.

49. Gay, V., and Leijdekkers, P., A health monitoring system using smart phones and wearable sensors. Int. J. ARM 8(2):29–35, 2007.

50. Leijdekkers, P., Gay, V., and Barin, E., Feasibility study of a non invasive cardiac rhythm management system. International Journal of Assistive Robotics and Systems, 2009.

51. Wai, A. A. P. et al., Smart wireless continence management system for persons with dementia. Telemed. e-Health 14(8):825–832, 2008.

52. Van Halteren, A. et al., Mobile patient monitoring: The mobihealth system. J. Inform. Technol. Healthcare 2(5):365–373, 2004.

53. Jones, V., et al., Mobihealth: Mobile health services based on body area networks, in M-Health. Springer; 219–236, 2006.

54. Jones, V.M., et al., Biosignal and context monitoring: distributed multimedia applications of body area networks in healthcare. in Multimedia Signal Processing, 2008 IEEE 10th Workshop on. 2008. IEEE.

55. Ostmark, A., et al.,Mobile medical applications made feasible through use of EIS platforms. In IEEE Instrumentation and Measurement Technology conference proceedings. 2003. IEEE; 1999.

56. Gao, M. et al., Cardiosentinal: A 24-hour heart care and monitoring system. J. Comput. Sci. Eng. 6(1):67–78, 2012.

57. Triantafyllidis, A., et al., An open and reconfigurable wireless sensor network for pervasive health monitoring. in Pervasive Computing Technologies for Healthcare, 2008. PervasiveHealth 2008. Second International Conference on. 2008. IEEE.

58. Pawar, P. et al., Performance evaluation of the context-aware handover mechanism for the nomadic mobile services in remote patient monitoring. Comput. Commun. 31(16):3831–3842, 2008.

59. Channabasavaiah, K., Holley, K., and Tuggle, E., Migrating to a service-oriented architecture. IBM DeveloperWorks 16: 727–728, 2003.

60. Djuknic, G. M., and Richton, R. E., Geolocation and assisted GPS. Computer 2:123–125, 2001.

61. Liu, H. et al., Survey of wireless indoor positioning techniques andsystems. IEEE Trans. Syst. Man. Cybernet.,  37(6):1067–1080, 2007.

62. Nilsson, H., Nordström, E.-M., and Öhman, K., Decision support for participatory forest planning using AHP and TOPSIS. Forests 7(5):100, 2016.

63. Motebele, M.M., Knowledge Based Decision Support System forGSC Industries Benchmarking Perspective. In  Proceedings of the World Congress on Engineering. 2018.

64. Trentesaux, D. et al., Benchmarking flexible job-shop scheduling and control systems. Contrl Eng. Pract. 21(9):1204–1225, 2013.

65. Anliker, U. et al., AMON: A wearable multiparameter medical monitoring and alert system. IEEE Trans. Inform. Technol. Biomed. 8(4):415–427, 2004.

66. Albahri, O. et al., 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. J. Med. Syst. 42(9):164, 2018.

67. Jumaah, F. et al., Decision-making solution based multimeasurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment. Measurement 118:83–95, 2018.

68. Abdullateef, B. N. et al., An evaluation and selection problems of OSS-LMS packages. SpringerPlus 5(1):248, 2016.

69. Rossi, G., In: Pavese, F., Forbes, A. B. (Eds), Data modeling for metrology and testing in measurement science. Boston: Birkhäuser, 2009.

70. Keeney, R.L. and Raiffa, H., Decisions with multiple objectives: preferences and value trade-offs. 1993: Cambridge university press.

71. Greco, S., Figueira, J., and Ehrgott, M., Multiple criteria decision analysis. 2016: Springer.

72. Malczewski, J., GIS and multicriteria decision analysis. 1999: John Wiley & Sons.

73. Petrovic-Lazarevic, S. and A. Abraham, Hybrid fuzzy-linear programming approach for multi criteria decision making problems. arXiv preprint cs/0405019, 2004.

74. Zionts, S., MCDM—If not a roman numeral, then what? Interfaces 9(4):94–101, 1979.

75. Zaidan, B. et al., A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi-criteria analysis based on ‘large-scale data’. Softw.: Pract. Exper. 47(10):1365–1392, 2017.

76. Zaidan, A. et al., Multi-criteria analysis for OS-EMR software selection problem: A comparative study. Decis. Support Syst. 78: 15–27, 2015.

77. Zaidan, A. et al., Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. J. Biomed. Inform. 53:390–404, 2015.

78. M. Khatari et al., BMulti-criteria evaluation and benchmarking for active queue management methods: Open issues, challenges and recommended pathway solutions. Int. J. Inf. Technol. Decis. Mak. S0219622019300039, 2019.

79. Yas, Q. M. et al., Towards on develop a framework for the evaluation and benchmarking of skin detectors based on artificial intelligent models using multi-criteria decision-making techniques. Int. J. Pattern Recognit. Artif. Intell. 31(03):1759002, 2017.

80. Oliveira, M., Fontes, D.B., and Pereira, T., Multicriteria decision making: A case study in the automobile industry. 2013.

81. Zaidan, B., et al., A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques. International Journal of Information Technology & Decision Making. 1–42, 2017.

82. Zaidan, B., and Zaidan, A., Software and hardware FPGA-based digital watermarking and steganography approaches: Toward new methodology for evaluation and benchmarking using multicriteria decision-making techniques. J. Circ. Syst. Comput. 26(07):1750116, 2017.

83. Zaidan, B., and Zaidan, A., Comparative study on the evaluation and benchmarking information hiding approaches based multimeasurement analysis using TOPSIS method with different normalisation, separation and context techniques. Measurement 117: 277–294, 2018.

84. Rahmatullah, B. et al., Multi-complex attributes analysis for optimum GPS baseband receiver tracking channels selection. 2017 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017 2017:1084–1088, 2017.

85. F. M. Jumaah et al., BTechnique 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. Telecommun. Syst. 1–19, 2017.

86. Yas, Q. M. et al., Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions. Measurement 114:243–260, 2018.

87. Jadhav, A. and R. Sonar. Analytic hierarchy process (AHP), weighted scoring method (WSM), and hybrid knowledge based system (HKBS) for software selection: a comparative study. in Emerging trends in engineering and technology (ICETET), 2009 2nd international conference on. 2009. IEEE.

88. Zaidan, A. A. et al., A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: Coherent taxonomy, open issues and recommendation pathway solution. Health Technol. (Berl). 8(4):223–238, 2018.

89. Alsalem, M. A. et al., 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. J. Med. Syst. 42(11): 204, 2018.

90. Tariq, I. et al., MOGSABAT: A metaheuristic hybrid algorithm for solving multi-objective optimisation problems. Neural Comput. Appl. 30:1–15, 2018.

91. Enaizan, O. et al., Electronic medical record systems: Decision support examination framework for individual, security and privacy concerns using multi-perspective analysi. Health Technol. (Berl). 2018.

92. Salih, M. M. et al., Survey on fuzzy TOPSIS state-of-the-art between 2007–2017. Comput. Oper. Res. 2018.

93. Kalid, N. et al., Based real time remote health monitoring systems: A review on patients prioritization and related" big data" using body sensors information and communication technology. J. Med. Syst. 42(2):30, 2018.

94. Diaby, V., Campbell, K., and Goeree, R., Multi-criteria decision analysis (MCDA) in health care: A bibliometric analysis. Opera. Res. Health Care 2(1–2):20–24, 2013.

95. Thokala, P. et al., Multiple criteria decision analysis for health care decision making—An introduction: Report 1 of the ISPOR MCDA emerging good practices task force. Value Health 19(1): 1–13, 2016.

96. Adunlin, G., Diaby, V., and Xiao, H., Application of multicriteria decision analysis in health care: A systematic review and bibliometric analysis. Health Expect. 18(6):1894–1905, 2015.

97. Yas, Q.M., et al., Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions. Measurement, 2017.

98. Mühlbacher, A. C., and Kaczynski, A., Making good decisions in healthcare with multi-criteria decision analysis: The use, current research and future development of MCDA. Appl. Health Econ. Health Policy 14(1):29–40, 2016.

99. Zhu, G.-N. et al., An integrated AHP and VIKOR for design concept evaluation based on rough number. Adv. Eng. Inform. 29(3):408–418, 2015.

100. Albahri, A. S. et al., Real-time fault-tolerant mHealth system: Comprehensive review of healthcare services, opens issues, challenges and methodological aspects. J. Med. Syst. 42(8. Springer US):137, 2018.

101. Albahri, O. S. et al., 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. J. Med. Syst. 42(9):164, 2018.

102. Talal, M. et al., Comprehensive review and analysis of antimalware apps for smartphones. Telecommun. Syst., 2019.

103. Zaidan, A. A. et al., Based multi-agent learning neural network and Bayesian for real-time IoT skin detectors: A new evaluation and benchmarking methodology. Neural Comput. Appl. 2019.

104. Albahri, A. S. et al., Based multiple heterogeneous wearable sensors: A smart real-time health monitoring structured for hospitals distributor. IEEE Access 7:37269–37323, 2019.

105. Albahri, O. S. et al., Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors. IEEE Access 7:50052–50080, 2019.

106. Raviv, G., Shapira, A., and Fishbain, B., AHP-based analysis of the risk potential of safety incidents: Case study of cranes in the construction industry. Saf. Sci. 91:298–309, 2017.

107. Zhao, H., Guo, S., and Zhao, H., Comprehensive benefit evaluation of eco-industrial parks by employing the best-worst method based on circular economy and sustainability. Environ. Dev. Sustain. 20(3):1229–1253, 2018.

108. Chou, S.-Y., Chang, Y.-H., and Shen, C.-Y., A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. Eur. J. Opera. Res. 189(1):132–145, 2008.

109. Singh, A. and Malik, S.K., Major MCDM Techniques and their application-A Review. IOSR Journal of Engineering (IOSRJEN), ISSN (e): 2250–3021, ISSN (p): 2278–8719 Vol, 2014. 4.

110. Jablonsky, J., MS excel based software support tools for decision problems with multiple criteria. Proc. Econ. Fin. 12:251–258, 2014.

111. Ahmad, W. N. K. W. et al., Evaluation of the external forces affecting the sustainability of oil and gas supply chain using best worst method. J. Clean. Prod. 153:242–252, 2017.

112. Gupta, H., and Barua, M. K., Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. J. Clean. Prod. 152:242–258, 2017.

113. Rezaei, J., Best-worst multi-criteria decision-making method. Omega 53:49–57, 2015.

114. Rezaei, J., Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega 64:126–130, 2016.

115. Gupta, H., Evaluating service quality of airline industry using hybrid best worst method and VIKOR. J. Air Transp. Manag. 68:35–47, 2018.

116. Opricovic, S., and Tzeng, G.-H., Extended VIKOR method in comparison with outranking methods. Eur. J. Opera. Res. 178(2):514–529, 2007.

117. Opricovic, S., and Tzeng, G.-H., Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur. J. Opera. Res. 156(2):445–455, 2004.

118. Mahjouri, M. et al., Optimal selection of Iron and steel wastewater treatment technology using integrated multi-criteria decision-making techniques and fuzzy logic. Process Saf. Environ. Protect. 107: 54–68, 2017.

119. Tian, Z.-p., Wang, J.-q., and Zhang, H.-y., An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. Applied Soft Computing, 2018.

120. Ren, J., Selection of sustainable prime mover for combined cooling, heat, and power technologies under uncertainties: An interval multicriteria decision-making approach. International Journal of Energy Research, 2018.

121. Serrai, W., et al., An efficient approach for Web service selection. in Computers and Communication (ISCC), 2016 IEEE Symposium on. 2016. IEEE.

122. Shojaei, P., Haeri, S. A. S., and Mohammadi, S., Airports evaluation and ranking model using Taguchi loss function, best-worst method and VIKOR technique. J.Air Transp. Manag. 68:4–13, 2018.

123. Serrai, W. et al., Towards an efficient and a more accurate web service selection using MCDM methods. J. Comput. Sci. 22:253–267, 2017.

124. Pamu?ar, D., Petrovi?, I., and ?irovi?, G., Modification of the best–worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers. Expert Syst. Applic. 91: 89–106, 2018.

125. Guo, S., and Zhao, H., Fuzzy best-worst multi-criteria decisionmaking method and its applications. Knowl.-Based Syst. 121:23–31, 2017.

126. Sofuoglu, M. A., and Orak, S., A novel hybrid multi criteria decision making model: Application to turning operations. Int. J. Intell. Syst. Applic. Eng. 5(3):124–131, 2017.

127. Aboutorab, H. et al., ZBWM: The Z-number extension of best worst method and its application for supplier development. Expert Syst. Applic. 107:115–125, 2018.

128. Rezaei, J., van Roekel, W. S., and Tavasszy, L., Measuring the relative importance of the logistics performance index indicators using best worst method. Transpo. Policy 68:158–169, 2018.

129. Salimi, N., and Rezaei, J., Evaluating firms’ R&D performance using best worst method. Eval. Program Plan. 66:147–155, 2018.

130. Gul, M. et al., A state of the art literature review of VIKOR and its fuzzy extensions on applications. Appl. Soft Comput. 46:60–89, 2016.

131. Chiu, W.-Y., Tzeng, G.-H., and Li, H.-L., A new hybrid MCDM model combining DANP with VIKOR to improve e-store business. Knowledge-Based Syst. 37:48–61, 2013.

132. Jahan, A. et al., A comprehensive VIKOR method for material selection. Mater. Design 32(3):1215–1221, 2011.

133. Yang, Y.-P. O., Shieh, H.-M., and Tzeng, G.-H., A VIKOR technique based on DEMATEL and ANP for information security risk control assessment. Inform. Sci. 232:482–500, 2013.

134. Cavallini, C. et al., Integral aided method for material selection based on quality function deployment and comprehensive VIKOR algorithm. Mater. Des. 47:27–34, 2013.

135. Liou, J. J. H. et al., A modified VIKOR multiple-criteria decision method for improving domestic airlines service quality. J. Air Transpo. Manag. 17(2):57–61, 2011.

136. Migdadi, M., Knowledge management enablers and outcomes in the small-and-medium sized enterprises. Indust. Manag. Data Syst. 109(6):840–858, 2009.

137. Kiah, M. L. M. et al., Open source EMR software: Profiling, insights and hands-on analysis. Comput Methods Programs Biomed. 117(2):360–382, 2014.

138. de Paiva Guimarães, M. and Martins, V.F., A checklist to evaluate Augmented Reality Applications. In 2014 XVI Symposium on Virtual and Augmented Reality (SVR). 2014. IEEE.

139. Huang, P. H., and Moh, T.-t., A non-linear non-weight method for multi-criteria decision making. Ann. Opera. Res. 248(1):239–251, 2017.

140. Kalid, N. et al., Based on real time remote health monitoring systems: A new approach for prioritization Blarge scales data patients with chronic heart diseases using body sensors and communication technology. J. Med. Syst. 42(4):69, 2018.

141. Qader, M. A. et al., A methodology for football players selection problem based on multi-measurements criteria analysis. Measurement 111:38–50, 2017.