|
UPSI Digital Repository (UDRep)
|
|
| ||||||||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| This paper aims to propose a grouping framework for benchmarking the active queue management (AQM) methods of network congestion control based on multicriteria decision-making (MCDM) techniques to assist developers of AQM methods in selecting the best AQM method. Given the current rapid development of the AQM techniques, determining which of these algorithms is better than the other is difficult because each algorithm performs better in a specific metric(s). Current benchmarking studies benchmark the AQM methods from a single incomplete prospective. In each proposed AQM method, the benchmarking was achieved with reference to some evaluation measures that are relatively close to the desired goal being followed during the development of the AQM methods. Furthermore, the benchmarking frameworks of AQM methods are complicated and challenging because of the following reasons: (1) the technical details of the AQM methods are adapted and the input parameters are selected according to the sensitivity of the AQM methods; and (2) a framework is developed and designed for simulating AQM methods, the simulated network and the collected results. For this purpose, a set of criteria for AQM comparison are determined. These criteria are performance, processing overhead and configuration. The benchmarking framework is developed based on the crossover of three groups of multi-evaluation criteria and several AQM methods as a proof of concept. The AQM families that are implemented and utilized in experiments to generate the data that are used as a proof of concept of our proposed framework are the parameter-based (pars) and fuzzy-based AQM methods. Accordingly, constructing the decision matrix (DM) that will be used to generate the final results is necessary. Subsequently, the underlying AQM methods are benchmarked and ranked using MCDM techniques, namely, integrated analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS). The validation was performed objectively. The mean�standard deviation was computed to ensure that the AQM methods ranking undergo systematic ranking. Results illustrate that (1) the integration of AHP and TOPSIS solves the AQM method benchmarking problems; (2) results of the individual TOPSIS context clearly show variances among the ranking results of the six experts; (3) the ranks of the AQM methods obtained from internal and external TOPSIS group decision-making are nearly similar, with random early detection method being ranked as the best one; and (4) in the objective validation, significant differences were found between the groups' scores, thereby indicating that the ranking results of internal and external TOPSIS group decision-making were valid. ? 2021 World Scientific Publishing Company. |
| References |
Abbasov, B., & Korukoglu, S. (2009). Effective RED: An algorithm to improve RED's performance by reducing packet loss rate. Journal of Network and Computer Applications, 32(3), 703-709. doi:10.1016/j.jnca.2008.07.001 Abdeljaber, H., Thabtah, F., Woodward, M., Jaffar, A., & Al Bazaar, H. (2014). Random early dynamic detection approach for congestion control. Baltic J.Mod.Comput., 2(1), 16-31. Retrieved from www.scopus.com Abdulkareem, K. H., Arbaiy, N., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., & Salih, M. M. (2021). 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, 33(4), 1029-1054. doi:10.1007/s00521-020-05020-4 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 Alaa, M., Albakri, I. S. M. A., Singh, C. K. S., Hammed, H., Zaidan, A. A., Zaidan, B. B., . . . Jasim, A. N. (2019). Assessment and ranking framework for the english skills of pre-service teachers based on fuzzy delphi and TOPSIS methods. IEEE Access, 7, 126201-126223. doi:10.1109/ACCESS.2019.2936898 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., 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., 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 Ali Ahammed, G. F., & Banu, R. (2010). Analyzing the performance of active queue management algorithms. International Journal of Computer Networks & Communications, 2(2), 1-19. Retrieved from www.scopus.com 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 Baklizi, M., Abdel-jaber, H., Abu-Alhaj, M. M., Abdullah, N., Ramadass, S., & Almomani, A. (2013). Dynamic stochastic early discovery: A new congestion control technique to improve networks performance. International Journal of Innovative Computing, Information and Control, 9(3), 1113-1126. Retrieved from www.scopus.com Baklizi, M., Abdel-Jaber, H., Abu-Shareha, A. A., Abualhaj, M. M., & Ramadass, S. (2014). Fuzzy logic controller of gentle random early detection based on average queue length and delay rate. International Journal of Fuzzy Systems, 16(1), 9-19. Retrieved from www.scopus.com Baklizi, M., Abdel-Jaber, H., Ramadass, S., Abdullah, L., & Anbar, M. (2012). Performance assessment of AGRED, RED and GRED congestion control algorithms. Information Technology Journal, 11(2), 255-261. doi:10.3923/itj.2012.255.261 Chebli, S., Elakkary, A., Sefiani, N., & Elalami, N. (2016). PI stabilization for congestion control of AQM routers with tuning parameter optimization. International Journal of Interactive Multimedia and Artificial Intelligence, 4(1), 52-55. Retrieved from www.scopus.com Chen, J., Hu, C., & Ji, Z. (2011). Self-tuning random early detection algorithm to improve performance of network transmission. Mathematical Problems in Engineering, 2011 doi:10.1155/2011/872347 Chen, W., Li, Y., & Yang, S. -. (2007). An average queue weight parameterization in a network supporting TCP flows with RED. Paper presented at the 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07, 590-595. doi:10.1109/ICNSC.2007.372845 Retrieved from www.scopus.com Chitra, K., & Padamavathi, D. G. (2010). Adaptive CHOKe: An algorithm to increase the fairness in internet routers. Int.J.Advanced Networking and Applications, 1(6), 382-386. Retrieved from www.scopus.com Chrysostomou, C., Pitsillides, A., Hadjipollas, G., Sekercioglu, A., & Polycarpou, M. (2003). Fuzzy explicit marking for congestion control in differentiated services networks. Paper presented at the Proceedings - IEEE Symposium on Computers and Communications, 312-319. doi:10.1109/ISCC.2003.1214139 Retrieved from www.scopus.com Chydziñski, A., & Chróst, U. (2011). Analysis of AQM queues with queue size based packet dropping. International Journal of Applied Mathematics and Computer Science, 21(3), 567-577. doi:10.2478/v10006-011-0045-7 Dai, Y., Hu, B., Su, Y., Mao, C., Chen, J., Zhang, X., . . . Cai, H. (2015). Feature selection of high-dimensional biomedical data using improved SFLA for disease diagnosis. Paper presented at the Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, 458-463. doi:10.1109/BIBM.2015.7359728 Retrieved from www.scopus.com De Campos, L. M., Cano, A., Castellano, J. G., & Moral, S. (2011). Bayesian networks classifiers for gene-expression data. Paper presented at the International Conference on Intelligent Systems Design and Applications, ISDA, 1200-1206. doi:10.1109/ISDA.2011.6121822 Retrieved from www.scopus.com 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 Etbega, M. H., Woodward, M., Abdel-Jaber, H., & Ali, A. G. (2004). A New Version of Adaptive Red with Reduced Dependency on Parameterisation, Retrieved from www.scopus.com Floyd, S., & Jacobson, V. (1993). Random early detection gateways for congestion avoidance. IEEE/ACM Transactions on Networking, 1(4), 397-413. doi:10.1109/90.251892 Hamdi, M. M., Rashid, S. A., Ismail, M., Altahrawi, M. A., Mansor, M. F., & Abufoul, M. K. (2018). Performance evaluation of active queue management algorithms in large network. Paper presented at the ISTT 2018 - 2018 IEEE 4th International Symposium on Telecommunication Technologies, doi:10.1109/ISTT.2018.8701716 Retrieved from www.scopus.com Hong, J., Joo, C., & Bahk, S. (2007). Active queue management algorithm considering queue and load states. Computer Communications, 30(4), 886-892. doi:10.1016/j.comcom.2006.10.012 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 Jumaah, F. M., Zadain, A. A., Zaidan, B. B., Hamzah, A. K., & Bahbibi, R. (2018). Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment. Measurement: Journal of the International Measurement Confederation, 118, 83-95. doi:10.1016/j.measurement.2018.01.011 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 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 Koo, J., Ahn, S., & Chung, J. (2004). A comparative study of queue, delay, and loss characteristics of AQM schemes in QoS-enabled networks. Computing and Informatics, 23(4), 317-335. Retrieved from www.scopus.com Kou, G., Ergu, D., Lin, C., & Chen, Y. (2016). Pairwise comparison matrix in multiple criteria decision making. Technological and Economic Development of Economy, 22(5), 738-765. doi:10.3846/20294913.2016.1210694 Kou, G., Ergu, D., & Shang, J. (2014). Enhancing data consistency in decision matrix: Adapting hadamard model to mitigate judgment contradiction. European Journal of Operational Research, 236(1), 261-271. doi:10.1016/j.ejor.2013.11.035 Kou, G., & Lin, C. (2014). A cosine maximization method for the priority vector derivation in AHP. European Journal of Operational Research, 235(1), 225-232. doi:10.1016/j.ejor.2013.10.019 Kou, G., Lu, Y., Peng, Y., & Shi, Y. (2012). Evaluation of classification algorithms using MCDM and rank correlation. International Journal of Information Technology and Decision Making, 11(1), 197-225. doi:10.1142/S0219622012500095 Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1-12. doi:10.1016/j.ins.2014.02.137 Kou, G., Yang, P., Peng, Y., Xiao, F., Chen, Y., & Alsaadi, F. E. (2020). Evaluation of feature selection methods for text classification with small datasets using multiple criteria decision-making methods. Applied Soft Computing Journal, 86 doi:10.1016/j.asoc.2019.105836 Kunniyur, S., & Srikant, R. (2003). End-to-end congestion control schemes: Utility functions, random losses and ECN marks. IEEE/ACM Transactions on Networking, 11(5), 689-702. doi:10.1109/TNET.2003.818183 Lin, C., Kou, G., Peng, Y., & Alsaadi, F. E. (2020). Aggregation of the nearest consistency matrices with the acceptable consensus in AHP-GDM. Annals of Operations Research, , 1-17. Retrieved from www.scopus.com Liu, S., Başar, T., & Srikant, R. (2008). TCP-illinois: A loss- and delay-based congestion control algorithm for high-speed networks. Performance Evaluation, 65(6-7), 417-440. doi:10.1016/j.peva.2007.12.007 Loukas, R., Koehler, S., Andreas, P., & Phuoc, T. (2000). Fuzzy RED: Congestion control for TCP/IP diff-serv. Paper presented at the Proceedings of the Mediterranean Electrotechnical Conference - MELECON, , 1 19-22. Retrieved from www.scopus.com Malczewski, J. (1999). GIS and Multicriteria Decision Analysis, Retrieved from www.scopus.com Mohammadi, S., Pour, H. M., Jafari, M., & Javadi, A. (2010). Fuzzy-based PID active queue manager for TCP/IP networks. Paper presented at the 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010, 434-439. doi:10.1109/ISSPA.2010.5605462 Retrieved from www.scopus.com 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 Mohammed, R. T., Yaakob, R., Zaidan, A. A., Sharef, N. M., Abdullah, R. H., Zaidan, B. B., & Dawood, K. A. (2020). Review of the research landscape of multi-criteria evaluation and benchmarking processes for many-objective optimization methods: Coherent taxonomy, challenges and recommended solution. International Journal of Information Technology and Decision Making, 19(6), 1619-1693. doi:10.1142/S0219622020300049 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 Petrovic-Lazarevic, S., & Abraham, A. (2004). Hybrid Fuzzy-Linear Programming Approach for Multi Criteria Decision Making Problems, Retrieved from www.scopus.com Qader, M. A., Zaidan, B. B., Zaidan, A. A., Ali, S. K., Kamaluddin, M. A., & Radzi, W. B. (2017). A methodology for football players selection problem based on multi-measurements criteria analysis. Measurement: Journal of the International Measurement Confederation, 111, 38-50. doi:10.1016/j.measurement.2017.07.024 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 Rawat, J., Singh, A., Bhadauria, H. S., & Virmani, J. (2015). Computer aided diagnostic system for detection of leukemia using microscopic images. Paper presented at the Procedia Computer Science, , 70 748-756. doi:10.1016/j.procs.2015.10.113 Retrieved from www.scopus.com Salem, H., Attiya, G., & El-Fishawy, N. (2016). Gene expression profiles based human cancer diseases classification. Paper presented at the 2015 11th International Computer Engineering Conference: Today Information Society what's Next?, ICENCO 2015, 181-187. doi:10.1109/ICENCO.2015.7416345 Retrieved from www.scopus.com Salih, M. M., Zaidan, B. B., Zaidan, A. A., & Ahmed, M. A. (2019). Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017. Computers and Operations Research, 104, 207-227. doi:10.1016/j.cor.2018.12.019 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 Stanojević, R., Shorten, R. N., & Kellett, C. M. (2006). Adaptive tuning of drop-tail buffers for reducing queueing delays. IEEE Communications Letters, 10(7), 570-572. doi:10.1109/LCOMM.2006.1673016 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. (2018). MOGSABAT: A metaheuristic hybrid algorithm for solving multi-objective optimisation problems. Neural Computing and Applications, 32, 1-15. Retrieved from www.scopus.com Wang, H., Kou, G., & Peng, Y. (2020). Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending. Journal of the Operational Research Society, (4), 1-12. Retrieved from www.scopus.com Wang, H. -., Wong, H. -., Zhu, H., & Yip, T. T. C. (2009). A neural network-based biomarker association information extraction approach for cancer classification. Journal of Biomedical Informatics, 42(4), 654-666. doi:10.1016/j.jbi.2008.12.010 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., 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., Alsalem, M. A., Momani, F., & Zughoul, O. (2020). Novel multiperspective hiring framework for the selection of software programmer applicants based on AHP and group TOPSIS techniques. International Journal of Information Technology and Decision Making, 19(3), 775-847. doi:10.1142/S0219622020500121 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, A. A., Zaidan, B. B., Qahtan, M. Y., Albahri, O. S., Albahri, A. S., Alaa, M., . . . Lim, C. K. (2018). A survey on communication components for IoT-based technologies in smart homes. Telecommunication Systems, 69(1), 1-25. doi:10.1007/s11235-018-0430-8 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 Zhang, H., Kou, G., & Peng, Y. (2019). Soft consensus cost models for group decision making and economic interpretations. European Journal of Operational Research, 277(3), 964-980. doi:10.1016/j.ejor.2019.03.009 Zhang, L., & Huang, X. (2015). Multiple SVM-RFE for multi-class gene selection on DNA microarray data. Paper presented at the Proceedings of the International Joint Conference on Neural Networks, , 2015-September doi:10.1109/IJCNN.2015.7280417 Retrieved from www.scopus.com |
| 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. |