MCDM APPLICATION FOR BACTERIA MEASUREMENT DEVICES
Downloads
This study explores the innovative use of gas sensors to detect bacterial infections. The fundamental reliance of this technique is on monitoring fluctuations in the quantities of biogenic gases produced by bacteria during their growth. Through analysis, these sensors can accurately identify the kind and severity of a disease by detecting chemicals such as ammonia and hydrogen sulphide. Preliminary studies indicate that gas sensors provide a rapid and non-intrusive method for diagnosing bacterial infections. Additionally, they possess a notable level of sensitivity and precision. This technology improves clinical results and reduces the transmission of infections, enabling healthcare personnel to administer the appropriate medications promptly
. Wilson AD, Baietto M. Applications, and advances in electronic-nose technologies. Sensors (Basel). 2009;9(7):5099-148. doi: 10.3390/s90705099. Epub 2009 Jun 29. PMID: 22346690; PMCID: PMC3274163. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3274163/#b21-sensors-09-05099
. Ginting, G., Fadlina, M., Siahaan, A. P. U., & Rahim, R. (2017). Technical approach of TOPSIS in decision making. Int. J. Recent Trends Eng. Res, 3(8), 58-64.
. https://www.academia.edu/download/54116947/technical- approach-of-topsis-in-decision-making.pdf
. Jasri, D. S., & Rahim, R. (2017). Decision support system best employee assessments with technique for order of preference by similarity to ideal solution. int. J. Recent TRENDS Eng. res, 3(3), 6-17.
. Shekhovtsov, A., & Kołodziejczyk, J. (2020). Do distance-based multi-criteria decision analysis methods create similar rankings?. Procedia Computer Science, 176, 3718-3729.
. Behzadian, M., Kazemzadeh, R. B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European journal of Operational research, 200(1), 198-215. https://edisciplinas.usp.br/pluginfile.php/7474539/mod_resource/content/2/PROMETHEE%20literature%20review.pdf
. Ishizaka, A., & Nemery, P. (2013). Multi-criteria decision analysis: methods and software. John Wiley & Sons.
. https://onlinelibrary.wiley.com/doi/book/10.1002/9781118644898
. Razmak, J., & Aouni, B. (2015). Decision support system and multi‐criteria decision aid: a state of the art and perspectives. Journal of Multi‐Criteria Decision Analysis, 22(1-2), 101-117.
. https://onlinelibrary.wiley.com/doi/abs/10.1002/mcda.1530
. Aruldoss, M., Lakshmi, T. M., & Venkatesan, V. P. (2013). A survey on multi criteria decision making methods and its applications. American Journal of Information Systems, 1(1), 31-43.
. Brookes, V. J., Hernandez-Jover, M., Cowled, B., Holyoake, P. K., & Ward, M. P. (2014). Building a picture: Prioritisation of exotic diseases for the pig industry in Australia using multi-criteria decision analysis. Preventive Veterinary Medicine, 113(1), 103-117.
. https://www.sciencedirect.com/science/article/pii/S0167587713003085
. Sahoo, S. K., & Goswami, S. S. (2023). A comprehensive review of multiple criteria decision-making (MCDM) Methods: advancements, applications, and future directions. Decision Making Advances, 1(1), 25-48.
. http://www.dma-journal.org/index.php/dema/article/view/7
. Sałabun, W., Wątróbski, J., & Shekhovtsov, A. (2020). Are mcda methods benchmarkable? a comparative study of topsis, vikor, copras, and promethee ii methods. Symmetry, 12(9), 1549.
. https://www.mdpi.com/2073-8994/12/9/1549
. Wątróbski, J., Bączkiewicz, A., & Sałabun, W. (2022). pyrepo-mcda—Reference objects based MCDA software package. SoftwareX, 19, 101107.
. https://www.sciencedirect.com/science/article/pii/S2352711022000711
. Mukhametzyanov, I. (2023). On the conformity of scales of multidimensional normalization: An application for the problems of decision making. Decision Making: Applications in Management and Engineering, 6(1), 399-341.
. http://www.dmame-journal.org/index.php/dmame/article/view/493
. Paradowski, B., & Sałabun, W. (2021). Are the results of MCDA methods reliable? Selection of materials for Thermal Energy Storage. Procedia Computer Science, 192, 1313-1322.
. https://www.sciencedirect.com/science/article/pii/S1877050921016240
. Odu, G. O. (2019). Weighting methods for multi-criteria decision making technique. Journal of Applied Sciences and Environmental Management, 23(8), 1449-1457.
. https://www.ajol.info/index.php/jasem/article/view/189641
. Stević, Ž., Zavadskas, E. K., Tawfiq, F. M., Tchier, F., & Davidov, T. (2022). Fuzzy multicriteria decision-making model based on Z numbers for the evaluation of information technology for order picking in warehouses. Applied Sciences, 12(24), 12533.
. https://www.mdpi.com/2076-3417/12/24/12533
. Stević, Ž., Ulutaş, A., Korucuk, S., Memiş, S., Demir, E., Topal, A., & Karamaşa, Ç. (2023). Supply Chain Management (SCM) Breakdowns and SCM Strategy Selection during the COVID-19 Pandemic Using the Novel Rough MCDM Model. Complexity, 2023.
. https://www.hindawi.com/journals/complexity/2023/3478719/
. Kizielewicz, B., Więckowski, J., & Wątrobski, J. (2021). A study of different distance metrics in the TOPSIS method. In Intelligent Decision Technologies: Proceedings of the 13th KES-IDT 2021 Conference (pp. 275-284). Springer Singapore.
. https://link.springer.com/chapter/10.1007/978-981-16-2765-1_23
. Ali, A., Ullah, K., & Hussain, A. (2023). An approach to multi-attribute decision-making based on intuitionistic fuzzy soft information and Aczel-Alsina operational laws. Journal of decision analytics and intelligent computing, 3(1), 80-89.
. http://www.jdaic-journal.org/index.php/about/article/view/28
. Khan, M. R., Ullah, K., & Khan, Q. (2023). Multi-attribute decision-making using Archimedean aggregation operator in T-spherical fuzzy environment. Reports in Mechanical Engineering, 4(1), 18-38.
. https://www.rme-journal.org/index.php/asd/article/view/97
. Yatsalo, B., Korobov, A., Radaev, A., Qin, J., & Martínez, L. (2021). Ranking of independent and dependent fuzzy numbers and intransitivity in fuzzy MCDA. IEEE Transactions on Fuzzy Systems, 30(5), 1382-1395.
. https://ieeexplore.ieee.org/abstract/document/9352551/
. Saltelli, A., Tarantola, S., & Chan, K. (1999). A role for sensitivity analysis in presenting the results from MCDA studies to decision makers. Journal of Multi‐Criteria Decision Analysis, 8(3), 139-145.
. Zheng, J., Egger, C., & Lienert, J. (2016). A scenario-based MCDA framework for wastewater infrastructure planning under uncertainty. Journal of environmental management, 183, 895-908.
. https://www.sciencedirect.com/science/article/pii/S0301479716306776
. Iooss, B., & Saltelli, A. (2017). Introduction to sensitivity analysis. Handbook of uncertainty quantification, Ghanem R, Higdon D, Owhadi H.
. https://link.springer.com/referenceworkentry/10.1007/978-3-319-11259-6_31-1
. Bozanic, D., Tešić, D., Marinković, D., & Milić, A. (2021). Modeling of neuro-fuzzy system as a support in decision-making processes. Reports in Mechanical Engineering, 2(1), 222-234.
. https://www.rme-journal.org/index.php/asd/article/view/58
. Bozanic, D., Tešić, D., Marinković, D., & Milić, A. (2021). Modeling of neuro-fuzzy system as a support in decision-making processes. Reports in Mechanical Engineering, 2(1), 222-234.
. https://www.rme-journal.org/index.php/asd/article/view/58
. Hyde, K., Maier, H. R., & Colby, C. (2003). Incorporating uncertainty in the PROMETHEE MCDA method. Journal of Multi‐Criteria Decision Analysis, 12(4‐5), 245-259.
. https://onlinelibrary.wiley.com/doi/abs/10.1002/mcda.361
. Cacuci, D. G., & Ionescu-Bujor, M. (2004). A comparative review of sensitivity and uncertainty analysis of large-scale systems—II: statistical methods. Nuclear science and engineering, 147(3), 204-217.
. https://www.tandfonline.com/doi/abs/10.13182/04-54CR
. Cacuci, D. G., Ionescu-Bujor, M., & Navon, I. M. (2005). Sensitivity and uncertainty analysis, volume II: applications to large-scale systems. CRC press.
. Saltelli, A., Tarantola, S., & Chan, K. S. (1999). A quantitative model-independent method for global sensitivity analysis of model output. Technometrics, 41(1), 39-56.
. https://www.tandfonline.com/doi/abs/10.1080/00401706.1999.10485594
. Saltelli, A., Ratto, M., Tarantola, S., & Campolongo, F. (2005). Sensitivity analysis for chemical models. Chemical reviews, 105(7), 2811-2828.
. https://pubs.acs.org/doi/full/10.1021/cr040659d
. Triantaphyllou, E., & Sánchez, A. (1997). A sensitivity analysis approach for some deterministic multi‐criteria decision‐making methods. Decision sciences, 28(1), 151-194.
. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-5915.1997.tb01306.x
Copyright (c) 2024 Maryam Khalid Shaalan, Humam Abduljabbar Abdulkhalik, Naba Ndhm Amran
This work is licensed under a Creative Commons Attribution 4.0 International License.
