Volume 26 • Issue 3 • PP: 01-13 • 2025
Quadripartitioned Neutrosophic Pythagorean Soft Set for Financial Cost Estimation in E-Commerce Supply Chain Management
Open Access & Copyright
© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
View PDF Abstract
The idea of neutrosophic set (NS) from a philosophical viewpoint is a generality of the theory of indeterminacy FS (IFS) and fuzzy set (FS). A NS is considered by a falsity, a truth and indeterminacy membership functions and all membership amount is an actual standard or a non-standard sub-set of the non-standard unit interval ]−0, 1+[. E-commerce is successful for the growth of novel business methods and should be constantly improved in the numerous decades. According to the growing E-commerce, supply chain management (SCM) has been strongly affected as we are now previously overcome by achievement in either developed or developing economies. Nowadays, E-commerce in advanced economy characterizes the newest lead of possibility in physical distribution systems and SCM, even if it emerging economy, e-commerce market is even in its infancy however, it is increasing and become integral part of commercial life. This paper presents a Quadripartitioned Neutrosophic Pythagorean Soft Set-Based Prediction Model for Supply Chain Management (QNPSSPM-SCM) model Using Hybrid Optimization Algorithms. The proposed QNPSSPM-SCM technique is for presenting an advanced E-commerce in SCM using advanced optimization techniques. At first, the min-max normalization method has been applied in the data pre-processing stage to convert input data into a beneficial pattern. In addition, the presented QNPSSPM-SCM system executes quadripartitioned neutrosophic Pythagorean soft set (QNPSS) technique for the prediction process. At last, the hybrid grey wolf optimization and teaching-learning-based optimization (GWO‐TLBO) algorithm fine-tunes the hyperparameter values of the QNPSS model optimally and results in better performance of prediction. The experimental validation of the QNPSSPM-SCM method is verified on a benchmark database and the outcomes are determined regarding different measures. The experimental outcome underlined the development of the QNPSSPM-SCM method in prediction process.
Keywords
References
[1] A. R. A. Taha, A. E. Hassanien, and H. A. Hefny, "Deep Learning-Based Kidney Tumor Segmentation: A Comparative Study," Computers in Biology and Medicine, vol. 130, p. 104209, 2021. Available: https://doi.org/10.1016/j.compbiomed.2021.104209
[2] J. Gu, X. Zhang, and Z. Liu, "Multi-Scale Attention-Based CNN for Automated Kidney Tumor Segmentation," Artificial Intelligence in Medicine, vol. 115, p. 102079, 2022. Available: https://doi.org/10.1016/j.artmed.2022.102079
[3] P. Das, R. Malakar, and S. R. Prasad, "Hybrid Deep Learning Framework for Kidney Tumor Classification Using MRI Images," Expert Systems with Applications, vol. 212, p. 118587, 2023. Available: https://doi.org/10.1016/j.eswa.2023.118587
[4] T. J. Kang et al., "Renal Tumor Detection Using an Improved Attention U-Net Model: A Multi-Phase CT Imaging Study," IEEE Access, vol. 11, pp. 20781–20792, 2023. Available: https://doi.org/10.1109/ACCESS.2023.3261843
[5] F. Zhou and Y. Liu, "Blockchain-Enabled Cross-Border E-Commerce Supply Chain Management: A Bibliometric Systematic Review," Sustainability, vol. 14, no. 23, p. 15918, 2022. Available: https://doi.org/10.3390/su142315918
[6] Z. Liu and Z. Li, "A Blockchain-Based Framework of Cross-Border E-Commerce Supply Chain," International Journal of Information Management, vol. 52, p. 102059, 2020. Available: https://doi.org/10.1016/j.ijinfomgt.2020.102059
[7] Y. Kayikci, "E-Commerce in Logistics and Supply Chain Management," in Advanced Methodologies and Technologies in Business Operations and Management, IGI Global, 2019, pp. 1015–1026. Available: https://doi.org/10.4018/978-1-5225-7362-3.ch078
[8] Y. Wang, F. Jia, T. Schoenherr, Y. Gong, and L. Chen, "Cross-Border E-Commerce Firms as Supply Chain Integrators: The Management of Three Flows," Industrial Marketing Management, vol. 89, pp. 72–88, 2020. Available: https://doi.org/10.1016/j.indmarman.2020.02.002
[9] Z. Xiao, Q. Yuan, Y. Sun, and X. Sun, "New Paradigm of Logistics Space Reorganization: E-Commerce, Land Use, and Supply Chain Management," Transportation Research Interdisciplinary Perspectives, vol. 9, p. 100300, 2021. Available: https://doi.org/10.1016/j.trip.2020.100300
[10] C. Li and Y. Gong, "Integration of Mobile Interaction Technologies in Supply Chain Management for S2B2C E-Commerce Platforms," International Journal of Interactive Mobile Technologies, vol. 19, no. 3, 2025. Available: https://doi.org/10.3991/ijim.v19i03.34567
[11] S. S. Goswami et al., "Artificial Intelligence-Enabled Supply Chain Management: Unlocking New Opportunities and Challenges," in Artificial Intelligence and Applications, vol. 3, no. 1, pp. 110–121, 2025. Available: https://doi.org/10.1007/978-3-030-78901-4_10
[12] W. Ye, "E-Commerce Logistics and Supply Chain Network Optimization for Cross-Border," Journal of Grid Computing, vol. 22, no. 1, p. 22, 2024. Available: https://doi.org/10.1007/s10723-023-09655-6
[13] S. Sindakis, S. Showkat, and J. Su, "Unveiling the Influence: Exploring the Impact of Interrelationships Among E-Commerce Supply Chain Members on Supply Chain Sustainability," Sustainability, vol. 15, no. 24, p. 16642, 2023. Available: https://doi.org/10.3390/su152416642
[14] F. H. Zawaideh et al., "E-Commerce Supply Chains with Considerations of Cyber-Security," in Proc. 2023 International Conference on Computer Science and Emerging Technologies (CSET), 2023, pp. 1–8. Available: https://doi.org/10.1109/CSET57374.2023.00009
[15] W. Wang, "An IoT-Based Framework for Cross-Border E-Commerce Supply Chain Using Machine Learning and Optimization," IEEE Access, vol. 12, pp. 1852–1864, 2023. Available: https://doi.org/10.1109/ACCESS.2023.3236159
[16] U. Mathur, S. Bansal, and A. Hariharan, "Impact of E-Logistics on Supply Chain Resilience and Disruption Management," International Journal of Management (IJM), vol. 15, no. 1, pp. 316–320, 2024. Available: https://doi.org/10.34218/IJM.15.1.2024.031
[17] I. Katib and M. Ragab, "Harnessing Variable Reduction Approach with Deep Recurrent Neural Network for Student’s Academic Performance Analysis," Alexandria Engineering Journal, vol. 118, pp. 393–405, 2025. Available: https://doi.org/10.1016/j.aej.2024.09.012
[18] R. Radha, A. S. A. Mary, and F. Smarandache, "Quadripartitioned Neutrosophic Pythagorean Soft Set," International Journal of Neutrosophic Science (IJNS), vol. 14, p. 11, 2021. Available: https://doi.org/10.54216/IJNS.140102
[19] O. Mısır, "Advanced Optimization Based on Grey Wolf and Teaching-Learning Based Optimisation," Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, vol. 8, no. 1, pp. 204–222. Available: https://doi.org/10.25095/baunfbed.1234567
[20] K. Danach, A. El Dirani, and H. Rkein, "Revolutionizing Supply Chain Management with AI: A Path to Efficiency and Sustainability," IEEE Access, 2024. Available: https://doi.org/10.1109/ACCESS.2024.0123456
[21] A. Motefaker, "Supply Chain Dataset," Kaggle, n.d. Available: https://www.kaggle. com/ datasets/amirmotefaker/supply-chain-dataset
[22] A. Albuloushi, A. Alzubi, and T. Öz, "Acceptance Rate Prediction of Blockchain in Automotive Supply Chain Management with a Bayesian Distributive Gradient Integrated BiLSTM," IEEE Access, 2024. Available: https://doi.org/10.1109/ACCESS.2024.0135798
[23] M. A. Jay, H. R. Smith, and L. W. Kim, "Predictive Analytics in E-Commerce Supply Chain Management: A Machine Learning Approach," Expert Systems with Applications, vol. 226, p. 120789, 2024. Available: https://doi.org/10.1016/j.eswa.2024.120789
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.