Decision Rule Prediction for Assessment of Rotor Spun Cotton Yarn Strength Using Rough Set

References

• Banerjee, D., A. Ghosh, and S. Das. 2013. Yarn strength modelling using genetic fuzzy expert system. Journal of the Institution of Engineers (India): Series E 93 (2):83–90. doi:10.1007/s40034-013-0010-0.
   Google Scholar
• Bisaria, J., N. Srivastava, and K. R. Pardasani. 2009. A rough set model for sequential pattern mining with constraints. International Journal of Computer and Network Security 1 (1):1–7.
   Google Scholar
• Bogdan, J. E. 1967. The prediction of cotton yarn strength. Textile Research Journal 37 (6):536–37. doi:10.1177/004051756703700617.
   Web of Science ®Google Scholar
• Cerchiari, S. C., A. Teurya, J. P. Pinto, G. L. Torres, L. Sauer, and E. H. Zorzate. 2006. Data mining in distribution consumer database using rough sets and self-organizing maps. Proceedings of the 2006 IEEE Power Systems Conference and Exposition. New Jersey-USA: IEEE Press.
   Google Scholar
• Chen, D., D. W. Cui, C. X. Wang, and Z. R. Wang. 2006. A rough set-based hierarchical clustering algorithm for categorical data. International Journal of Information Technology 12 (3):149–59.
   Google Scholar
• Chouchoulas, A., and Q. Shen. 2001. Rough set-aided keyword reduction for text categorisation. Applied Artificial Intelligence 15 (9):843–73. doi:10.1080/088395101753210773.
   Web of Science ®Google Scholar
• Czyzewski, A., and R. Krolikowski. 2001. Neuro-rough control of masking thresholds for audio signal enhancement. Neuro-Computing 36 (1–4):5–27. doi:10.1016/S0925-2312(00)00333-7.
   Google Scholar
• Das, S., and A. Ghosh. 2021. Rough set-based decision tool for classification of cotton yarn neps. Journal of Institution of Engineers (India): Series E 102 (1):1–10. doi:10.1007/s40034-020-00173-2.
   Google Scholar
• Das, S., A. Ghosh, A. Majumdar, and D. Banerjee. 2013. Yarn engineering using hybrid artificial neural network and genetic algorithm model. Fibres and Polymers 14 (7):1220–26. doi:10.1007/s12221-013-1220-2.
   Web of Science ®Google Scholar
• Geng, Z., and Z. Qunxiong. 2006. A new rough set-based heuristic algorithm for attribute reduct. Proceedings of the 6th World Congress on Intelligent Control and Automation, Wuhan-China, Wuhan University of Technology Press.
   Google Scholar
• Ghosh, A., and P. Chatterjee. 2010. Prediction of cotton yarn properties using support vector machine. Fibres and Polymers 11 (1):84–88. doi:10.1007/s12221-010-0084-y.
   Web of Science ®Google Scholar
• Hvidsten, T. R., B. Wilczynski, A. Kryshtafovych, J. Tiuryn, J. Komorowski, and K. Fidelis. 2005. Discovering regulatory binding-site modules using rule-based learning. Genome Research 6 (15):856–66. doi:10.1101/gr.3760605.
   Google Scholar
• Jelonek, J., and J. Stefanowski. 1997. Feature subset selection for classification of histological images. Artificial Intelligence in Medicine 9 (3):227–39. doi:10.1016/S0933-3657(96)00375-2.
   PubMed Web of Science ®Google Scholar
• Jin, G., Q. Li, and C. Zhu. 2016. Determining the influence of processing parameters on spun bonded nonwoven pore shape using rough set theory. Journal of Fiber Science and Technology 72 (9):184–94. doi:10.2115/fiberst.fiberst.2016-0028.
   Web of Science ®Google Scholar
• Johnson, D. S. 1974. Approximation algorithms for combinatorial problems. Journal of Computer and System Sciences 9 (3):256–78. doi:10.1016/S0022-0000(74)80044-9.
   Web of Science ®Google Scholar
• Kostek, B. 1999. Soft computing in acoustics, applications of neural networks, fuzzy logic and rough sets to physical acoustics. In Studies in fuzziness and soft computing, ed. K. Janusz, 165–206. Heidelberg: Physica-Verlag.
   Google Scholar
• Liu, G., and W. Yu. 2008. The application of rough set theory in worsted roving procedure. In Innovations and advanced techniques in systems, computing sciences and software engineering, ed. K. Elleithy, 309–12. Netherland: Springer Dordrecht.
   Google Scholar
• Majumdar, A. 2010. Modelling of cotton yarn hairiness using adaptive neuro-fuzzy inference systems. Indian Journal of the Fibre & Textile Research 35:121–27.
   Web of Science ®Google Scholar
• Majumdar, P. K., and A. Majumdar. 2004. Prediction of ring spun cotton yarn elongation using mathematical, statistical and artificial neural network models. Textile Research Journal 74 (7):652–55. doi:10.1177/004051750407400717.
   Web of Science ®Google Scholar
• Majumdar, A., P. K. Majumdar, and B. Sarkar. 2005. Application of adaptive neuro-fuzzy system for the prediction of cotton yarn strength from HVI fibre properties. Journal of the Textile Institute 96 (1):55–60. doi:10.1533/joti.2004.0062.
   Google Scholar
• Marcus, S. 1994. Tolerance rough sets, cech topologies, learning processes. Bulletin of the Polish Academy of Sciences, Technical Sciences 42 (3):471–87.
   Google Scholar
• Parthalain, N. M., and Q. Shen. 2009. Exploring the boundary region of tolerance rough sets for feature selection. Pattern Recognition 42 (5):655–67. doi:10.1016/j.patcog.2008.08.029.
   Web of Science ®Google Scholar
• Pawlak, Z. 1982. Rough sets. International Journal of Computer and Information Sciences 11 (5):341–56. doi:10.1007/BF01001956.
   Web of Science ®Google Scholar
• Pawlak, Z. 1991. Rough sets: Thoretical aspects of reasoning about data. Boston, MA: Kluwer Academic.
   Google Scholar
• Sai, Y., P. Nie, R. Xu, and J. Huang. 2006. A rough set approach to mining concise rules from In Consistent data, IEEE International Conference on Granular Comp, Atlanta, G.A., U.S.A.
   Google Scholar
• Slowinski, R. 1992. Rough classification of HSV patients. In Intelligent decision support, handbook of applications and advances of the rough set theory, ed. R. Slowinski, Vol. 11, 77–93. Dordrecht: Springer.
   Google Scholar
• Slowinski, R., and C. Zopounidis. 1995. Applications of the rough set approach to evaluation of bankruptcy risk. Intelligent System in Accounting, Finance and Management 4 (1):27–41. doi:10.1002/j.1099-1174.1995.tb00078.x.
   Google Scholar
• Wu, C., Y. Yue, M. Li, and O. Adjei. 2004. The rough set theory and applications. Engineering Computations 21 (5):488–511. doi:10.1108/02644400410545092.
   Web of Science ®Google Scholar
• Zurek, W., I. Fredrych, and S. Zakrzewski. 1987. A method of predicting the strength and breaking strain of cotton yarn. Textile Research Journal 57 (8):439–44. doi:10.1177/004051758705700802.
   Web of Science ®Google Scholar