PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

J

• Abirami, R. S., & Kumar, G. S. (2022). Comparative study based on analysis of coronavirus disease (COVID-19) detection and prediction using machine learning models. SN Computer Science, 3(1), Article 79. https://doi.org/10.1007/s42979-021-00965-2

• AlArjani, A., Nasseef, M. T., Kamal, S. M., Rao, B. V. S., Mahmud, M., & Uddin, M. S. (2022). Application of mathematical modeling in prediction of COVID-19 transmission dynamics. Arabian Journal for Science and Engineering, 47(8), 10163-10186. https://doi.org/10.1007/s13369-021-06419-4

• Ali, M. I. (2012). Another view on reduction of parameters in soft sets. Applied Soft Computing, 12(6), 1814-1821. https://doi.org/10.1016/j.asoc.2012.01.002

• Alotaibi, A. S. (2022). Hybrid model based on relief algorithm and k-nearest neighbor for erythemato-squamous diseases forecasting. Arabian Journal for Science and Engineering, 47(2), 1299-1307. URL https://doi.org/10.1007/s13369-021-05921-z

• Ansari, G. A., Bhat, S. S., Ansari, M. D., Ahmad, S., Nazeer, J., & Eljialy, A. E. M. (2023). Performance evaluation of machine learning techniques (MLT) for heart disease prediction. Computational and Mathematical Methods in Medicine, 2023, Article 8191261. https://doi.org/10.1155/2023/8191261

• Bhat, S. S., Selvam, V., Ansari, G. A., Ansari, M. D., & Rahman, M. H. (2022). Prevalence and early prediction of diabetes using machine learning in North Kashmir: A case study of District Bandipora. Computational Intelligence and Neuroscience, 2022, Article 2789760. https://doi.org/10.1155/2022/2789760

• Boukhatem, C., Youssef, H. Y., & Nassif, A. B. (2022). Heart disease prediction using machine learning. In 2022 Advances in Science and Engineering Technology International Conferences (ASET) pp. 1-6. IEEE Publishing. https://doi.org/10.1109/ASET53988.2022.9734880

• Cagman, N., & Enginoglu, S. (2010). Soft set theory and uni-int decision making. European Journal of Operational Research, 207(2), 848-855. https://doi.org/10.1016/j.ejor.2010.05.004

• Chen, D., Tsang, E. C. C., Yeung, D. S., & Wang, X. (2005). The parameterization reduction of soft sets and its applications. Computers & Mathematics with Applications, 49(5), 757-763. https://doi.org/10.1016/j.camwa.2004.10.036

• Gupta, G., Gupta, A., Jaiswal, V., & Ansari, M. D. (2018). A review and analysis of mobile health applications for alzheimer patients and caregivers. In 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 171-175). IEEE Publishing. https://doi.org/10.1109/PDGC.2018.8745995

• Herawan, T., Rose, A. N. M., & Deris, M. M. (2009). Soft set theoretic approach for dimensionality reduction. In D. Ślęzak, T. Kim, Y. Zhang, J. Ma & K. Chung (Eds.), Database Theory and Application (pp. 171-178). Springer. https://doi.org/10.1007/978-3-642-10583-8_20

• Jindal, H., Agrawal, S., Khera, R., Jain, R., & Nagrath, P. (2021). Heart disease prediction using machine learning algorithms. In IOP conference series: materials science and engineering (Vol. 1022, No. 1, p. 012072). IOP Publishing. https://doi.org/10.1088/1757-899X/1022/1/012072

• Kannan, K., & Menaga, A. (2022). Some efficient algorithms on the parameter reduction of soft sets for decision making problems. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 92(3), 319-329. https://doi.org/10.1007/s40010-021-00730-3

• Khan, A., & Zhu, Y. (2019). An improved algorithm for normal parameter reduction of soft set. Journal of Intelligence and Fuzzy Systems, 37(2), 2953-2968. https://doi.org/10.3233/JIFS-190071

• Kong, Z., Gao, L., Wang, L., & Li, S. (2008). The normal parameter reduction of soft sets and its algorithm. Computers and Mathematics with Applications, 56(12), 3029-3037. https://doi.org/10.1016/j.camwa.2008.07.013

• Kong, Z., Zhang, G. D., & Wang, L. F. (2014). Normal parameter reduction in soft set based on harmony search algorithm. Applied Mechanics and Materials, 644-650, 2173-2176. https://doi.org/10.4028/www.scientific.net/AMM.644-650.2173

• Kovkov, D. V., Kolbanov, V. M., & Molodtsov, D. A. (2007). Soft sets theory-based optimization. Journal of Computer and Systems Sciences International, 46(6), 872-880. https://doi.org/10.1134/S1064230707060032

• Ma, X., Sulaiman, N., Qin, H., Herawan, T., & Zain, J. M. (2011). A new efficient normal parameter reduction algorithm of soft sets. Computer & Mathematics with Applications, 62(2), 588-598. https://doi.org/10.1016/j.camwa.2011.05.038

• Maji, P. K., Roy, A. R., & Biswas, R. (2002). An application of soft sets in a decision making problem. Computers & Mathematics with Applications, 44(8), 1077-1083.

• Mezzatesta, S., Torino, C., Meo, P. D., Fiumara, G., & Vilasi, A. (2019). A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis. Computer Methods and Programs in Biomedicine, 177, 9-15. https://doi.org/10.1016/j.cmpb.2019.05.005

• Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.

• Mohammed, M. A. T., Sadiq, A. S., Arshah, R. A., Ernawan, F., & Mirjalili, S. M. (2017). Soft set decision/forecasting system based on hybrid parameter reduction algorithm. Journal of Telecommunication, Electronic and Computer Engineering, 9(2-7), 143-148.

• Molodtsov, D. (1999). Soft set theory - First results. Computers & Mathematics with Applications, 37(4-5), 19-31. https://doi.org/10.1016/S0898-1221(99)00056-5

• Muhammad, L. J., Al-Shourbaji, I., Haruna, A. A., Mohammed, I. A., Ahmad, A., & Jibrin, M. B. (2021). Machine learning predictive models for coronary artery disease. SN Computer Science, 2(5), Article 350. https://doi.org/10.1007/s42979-021-00731-4

• Palaniappan, S., Raghavi, V., David, B., & Nisha, S. P. (2021). Prediction of epidemic disease dynamics on the infection risk using machine learning algorithms. SN Computer Science, 3(1), Article 47. https://doi.org/10.1007/s42979-021-00902-3

• Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11(5), 341-356. https://doi.org/10.1007/BF01001956

• Pawlak, Z. (1991). Rough Sets: Theoretical Aspects of Reasoning about Data (Vol. 9). Springer Science & Business Media.

• Rahman, M. M., Islam, M. M., Manik, M. M. H., Islam, M. R., & Al-Rakhami, M. S. (2021). Machine learning approaches for tackling novel Coronavirus (COVID-19) pandemic. SN Computer Science, 2(5), Article 384. https://doi.org/10.1007/s42979-021-00774-7

• Rose, A. N. M., Herawan, T., & Deris, M. M. (2010). A framework of decision making based on maximal supported sets. In L. Zhang, B. L. Lu & J. Kwok (Eds.), Advances in Neural Networks - ISNN 2010 (pp. 473-482). Springer. https://doi.org/10.1007/978-3-642-13278-0_61

• Sadiq, A. S., Tahir, M. A., Ahmed, A. A., & Alghushami, A. (2020). Normal parameter reduction algorithm in soft set based on hybrid binary particle swarm and biogeography optimizer. Neural Computing and Applications, 32(16), 12221-12239. https://doi.org/10.1007/s00521-019-04423-2

• Shah, D., Patel, S., & Bharti, S. K. (2020). Heart disease prediction using machine learning techniques. SN Computer Science, 1(6), Article 345. https://doi.org/10.1007/s42979-020-00365-y

• Sharma, D., Gotlieb, N., Farkouh, M. E., Patel, K., Xu, W., & Bhat, M. (2022). Machine learning approach to classify cardiovascular disease in patients with nonalcoholic fatty liver disease in the UK Biobank Cohort. Journal of the American Heart Association, 11(1), Article e022576. https://doi.org/10.1161/JAHA.121.022576

• Wang, S. H., Wu, K., Chu, T., Fernandes, S. L., Zhou, Q., Zhang, Y. D., & Sun, J. (2021). SOSPCNN: Structurally optimized stochastic pooling convolutional neural network for tetralogy of fallot recognition. Wireless Communications and Mobile Computing, 2021, Article 5792975. https://doi.org/10.1155/2021/5792975

• Xie, N. X. (2016). An algorithm on the parameter reduction of soft sets. Fuzzy Information and Engineering, 8(2), 127-145. https://doi.org/10.1016/j.fiae.2016.06.001

• Yuksel, S., Dizman, T., Yildizdan, G., & Sert, U. (2013). Application of soft sets to diagnose the prostate cancer risk. Journal of Inequalities and Applications, 2013(1), Article 229. https://doi.org/10.1186/1029-242X-2013-229

• Zhang, Y., Lu, S., Zhou, X., Yang, M., Wu, L., Liu, B., Phillips, P., & Wang, S. (2016). Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: Decision tree, k-nearest neighbors, and support vector machine. Simulation, 92(9), 861-871. https://doi.org/10.1177/0037549716666962