Page 114 - IJEEE-2023-Vol19-ISSUE-1

110 | Abed, Wali, & Alaziz

particles,” Powder Technol., vol. 239, pp. 12–20, May decision tree and fuzzy classifier,” Int. J. Adv. Manuf.
2013. doi: 10.1016/J.POWTEC.2013.01.052. Technol. 2016 899, vol. 89, no. 9, pp. 3487–3494, Aug.
[17] M. D. Mikhailov and A. P. S. Freire, “The drag 2016. doi: 10.1007/S00170-016-9307-8.
coefficient of a sphere: An approximation using Shanks [30] V. N. Vapnik, “The Nature of Statistical Learning
transform,” Powder Technol., vol. 237, pp. 432–435, Mar. Theory,” Nat. Stat. Learn. Theory, 2000. doi:
2013. doi: 10.1016/J.POWTEC.2012.12.033. 10.1007/978-1-4757-3264-1.
[18] N. S. Cheng, “Comparison of formulas for drag [31] V. Franc and V. Hlavác, “Vector machine multi-class
coefficient and settling velocity of spherical particles,” support,” Proc. - Int. Conf. Pattern Recognit., vol. 2, pp.
Powder Technol., vol. 189, no. 3, pp. 395–398, Feb. 2009. 236–239, 2002. doi: 10.1109/ICPR.2002.1048282.
doi: 10.1016/J.POWTEC.2008.07.006. [32] Karim Egab et al., “ Study the Effect of Heat Transfer
[19] R. Barati, S. A. A. S. Neyshabouri, and G. Ahmadi, Coefficient and Thermal Conductivity on Cracked Pipes
“Development of empirical models with high accuracy for Carrying Pressurized Fluid,” Int. J. Eng. Technol. , vol. 8,
estimation of drag coefficient of flow around a smooth no. 2019, pp. 275–282, 2019.
sphere: An evolutionary approach,” Powder Technol., vol. [33] M. Ghasemi, M. Zarei, A. Foroutannia, and S. Jafari,
257, pp. 11–19, May 2014. doi: “Study of functional connectivity of central motor system
10.1016/J.POWTEC.2014.02.045. in Parkinson’s disease using copula theory,” Biomed.
[20] A. B. Figueiredo, R. M. Baptista, F. B. de F. Rachid, Signal Process. Control, vol. 65, no. November 2020, pp.
and G. C. R. Bodstein, “A straightforward strategy for leak 102320, 2021. doi: 10.1016/j.bspc.2020.102320.
localization in two-phase gas pipelines,” J. Nat. Gas Sci.
Eng., vol. 94, Oct. 2021. doi:
10.1016/j.jngse.2021.104061.
[21] J. V. N. de Sousa, C. H. Sodré, A. G. B. de Lima, and S.
R. de F. Neto, “Numerical Analysis of Heavy Oil-Water
Flow and Leak Detection in Vertical Pipeline,” Adv. Chem.
Eng. Sci., vol. 03, no. 01, pp. 9–15, 2013. doi:
10.4236/aces.2013.31002.
[22] M. De Vasconcellos Araújo, S. R. De Farias Neto, A.
G. B. De Lima, and F. Daylane Tavares De Luna,
“Hydrodynamic study of oil leakage in pipeline via CFD,”
Adv. Mech. Eng., vol. 2014, 2014. doi:
10.1155/2014/170178.
[23] O. Akinsete and A. Oshingbesan, “Leak detection in
natural gas pipelines using intelligent models,” Soc. Pet.
Eng. - SPE Niger. Annu. Int. Conf. Exhib. 2019. NAIC
2019, no. April, 2019, doi: 10.2118/198738-MS.
[24] R. R. Sharma, “Gas Leakage Detection in Pipeline by
SVM classifier with Automatic Eddy Current based Defect
Recognition Method,” J. Ubiquitous Comput. Commun.
Technol., vol. 3, no. 3, pp. 196–212, 2021. doi:
10.36548/jucct.2021.3.004.
[25] Y. Shen and W. Cheng, “A Tree-Based Machine
Learning Method for Pipeline Leakage Detection,” Water
(Switzerland), vol. 14, no. 18, 2022. doi:
10.3390/w14182833.
[26] H. Salim, M. Alaziz, and T. Abdalla, “Human Activity
Recognition Using The Human Skeleton Provided by
Kinect,” Iraqi J. Electr. Electron. Eng., vol. 17, no. 2, pp.
183–189, 2021. doi: 10.37917/ijeee.17.2.20.
[27] N. Mashhadi, I. Shahrour, N. Attoue, J. El Khattabi, and
A. Aljer, “Use of machine learning for leak detection and
localization in water distribution systems,” Smart Cities,
vol. 4, no. 4, pp. 1293–1315, 2021. doi:
10.3390/smartcities4040069.
[28] J. Kemba, K. Gideon, and C. N. Nyirenda, “Leakage
detection in Tsumeb east water distribution network using
EPANET and support vector regression,” 2017 IST-Africa
Week Conf. IST-Africa 2017, Nov. 2017. doi:
10.23919/ISTAFRICA.2017.8102401.
[29] A. Krishnakumari, A. Elayaperumal, M. Saravanan,
and C. Arvindan, “Fault diagnostics of spur gear using