Volume 24 , Issue 3 , PP: 138-150, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Alsadig Ahmed 1 *
Doi: https://doi.org/10.54216/IJNS.240312
Neutrosophic set is introduced as a generalization of intuitionistic fuzzy set, where any elements x ∈ X we have membership (T), non-membership (F), and indeterminacy (I)degrees. Neurosophic vague topological spaces are presented in various notations like neurosophic vague compactness and continuity. Trademarks are the essential components of intellectual property that allow owner to earn profit based on their name. In this industry, retailers typically use feedback channels like customer care service, website review complaints and suggestions boxes to gain user reviews on service satisfaction. But, there is a gap between these techniques. Customers are not fulfilled with them due to lack of trust in management, a lack of flexibility and slow responsiveness. This has prompted examination of the effect of customer feedback channels (CFCs) on client satisfaction and the necessity to develop a new CFC using artificial intelligence (AI). Thus, this study designs a Trademark Empowerment using Optimal Neutrosophic Topological Vector Space (TE-ONTVS) technique for Maximizing Customer Attraction. The intention of the TE-ONTVS technique lies in the prediction of customer behaviour and attraction. To accomplish this, the TE-ONTVS technique undergoes data scaling using Z-score normalization. In addition, the TE-ONTVS technique uses NTVS approach for the identification of customer behaviour and attraction. Lastly, whale optimization algorithm (WOA) is applied for optimal parameter tuning of the NTVS algorithm. A series of experiments were involved to demonstrate the enhanced outcomes of the TE-ONTVS algorithm. The obtained results stated that the TE-ONTVS technique reaches optimal performance over other models
Customer Feedback , Whale Optimization Algorithm , Neutrosophic Set , Artificial Intelligence , Consumer Behaviour , Trade Mark Empowerment.
[1] Do QH, Trang TV (2020) An approach based on machine learning techniques for forecasting vietnamese consumers’ purchase behaviour. Decis Sci Lett, pp 313–322.
[2] Dawood EAE, Elfakhrany E, Maghraby FA (2019) Improve profling bank customer’s behavior using machine learning. IEEE Access 7:109320–109327.
[3] Alhamido, R., and Abobala, M., "AH-Substructures in Neutrosophic Modules", International Journal of Neutrosophic Science, Vol. 7, pp. 79-86 , 2020.
[4] Hatip, A., and Olgun, N., " On Refined Neutrosophic R-Module", International Journal of Neutrosophic Science, Vol. 7, pp.87-96, 2020.
[5] Ibrahim, M.A., Agboola, A.A.A, Badmus, B.S., and Akinleye, S.A., "On Refined Neutrosophic Vector Spaces I", International Journal of Neutrosophic Science, Vol. 7, pp. 97-109, 2020.
[6] Smarandache F., and Abobala, M., " n-Refined Neutrosophic Vector Spaces", International Journal of Neutrosophic Science, Vol. 7, pp. 47-54, 2020.
[7] Tuqa A. H. Al-Tamimi, Luay A. A. Al-Swidi , Ali H. M. Al-Obaidi. "Partner Sets for Generalizations of MultiNeutrosophic Sets." International Journal of Neutrosophic Science, Vol. 24, No. 1, 2024 ,PP. 08-13
[8] Parimala, M., Karthika, M. and Smarandache, F., 2020. A review of fuzzy soft topological spaces, intuitionistic fuzzy soft topological spaces and neutrosophic soft topological spaces. International Journal of Neutrosophic Science, Vol. 10, No. 2, 2020 ,PP. 96-104.
[9] Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96
[10] Borg, A., & Boldt, M. (2020). Using VADER sentiment and SVM for predicting customer response sentiment. Expert Systems with Applications, 162, 113746.
[11] Fitri, F. S., Nasrun, M. & Setianingsih, C. (2018). November. Sentiment analysis on the level of customer satisfaction to data cellular services using the naive bayes classifer algorithm. In 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) (pp. 201–206). IEEE
[12] Noaman, I.A.R., Hasan, A.H. and Ahmed, S.M., 2024. Optimizing Weibull Distribution Parameters for Improved Earthquake Modeling in Japan: A Comparative Approach. International Journal of Neutrosophic Science, 24(1), pp.65-5
[13] Doaa Nihad Tomma, L. A. A. Al-Swidi. "Necessary and Sufficient Conditions for a Stability of the Concepts of Stable Interior and Stable Exterior via Neutrosophic Crisp Sets." International Journal of Neutrosophic Science, Vol. 24, No. 1, 2024 ,PP. 87-93.
[14] Mathews, P., Sebastian, L. and Thankachan, B., 2024. Neutrosophic Fuzzy Score Matrices: A Robust Framework for Advancing Medical Diagnostics. International Journal of Neutrosophic Science, 23(3), pp.08-8
[15] Tarnowska, K., & Ras, Z. (2021). NLP-based customer loyalty improvement recommender system (CLIRS2). Big Data and Cognitive Computing, 5, 4.
[16] R. Saarumathi, W. Ritha. (2024). A Legitimate Productive Repertoire Replica Betwixt Envirotech Outlay Towards Fragile Commodities Using Trapezoidal Neutrosophic Fuzzy Number. International Journal of Neutrosophic Science, 24 ( 1 ), 104-118
[17] Li, Y., Qi, J., Jin, H., Tian, D., Mu, W. and Feng, J., 2024. An improved genetic-XGBoost classifier for customer consumption behavior prediction. The Computer Journal, 67(3), pp.1041-1059.
[18] Alfian, G., Octava, M.Q.H., Hilmy, F.M., Nurhaliza, R.A., Saputra, Y.M., Putri, D.G.P., Syahrian, F., Fitriyani, N.L., Atmaji, F.T.D., Farooq, U. and Nguyen, D.T., 2023. Customer Shopping Behavior Analysis Using RFID and Machine Learning Models. Information, 14(10), p.551.
[19] Lalwani, P., Mishra, M.K., Chadha, J.S. and Sethi, P., 2022. Customer churn prediction system: a machine learning approach. Computing, 104(2), pp.271-294.
[20] Yang, B., Xu, X., Cao, J., Zeng, K. and Yu, Z., 2024. An anticipatory shipping system for online retailers via mining customer behavior in large e-commerce promotion. Electronic Commerce Research and Applications, p.101403.
[21] Lee, N.T., Lee, H.C., Hsin, J. and Fang, S.H., 2023. Prediction of Customer Behavior Changing via a Hybrid Approach. IEEE Open Journal of the Computer Society.
[22] Akhavan, F. and Hassannayebi, E., 2024. A hybrid machine learning with process analytics for predicting customer experience in online insurance services industry. Decision Analytics Journal, 11, p.100452.
[23] Aldelemy, A. and Abd-Alhameed, R.A., 2023. Binary Classification of Customer's Online Purchasing Behavior Using Machine Learning. Journal of Techniques, 5(2).
[24] Prihanditya, H.A., 2020. The implementation of z-score normalization and boosting techniques to increase accuracy of c4. 5 algorithm in diagnosing chronic kidney disease. Journal of Soft Computing Exploration, 1(1), pp.63-69.
[25] Kungumaraj, E., Lathanayagam, E., Saikia, U., Anand, M.C.J., Khanna, S.T., Martin, N., Tiwari, M. and Edalatpanah, S.A., 2023. Neutrosophic Topological Vector Spaces and its Properties. International Journal of Neutrosophic Science, 23(2), pp.63-3.
[26] Rashed, B.M. and Popescu, N., 2024. Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction. Mathematics, 12(5), p.633.
[27] Chaubey, G., Gavhane, P.R., Bisen, D. and Arjaria, S.K., 2023. Customer purchasing behavior prediction using machine learning classification techniques. Journal of Ambient Intelligence and Humanized Computing, 14(12), pp.16133-16157