PREDICTIVE ANALYTICS FOR FOLK ART MATERIAL REQUIREMENTS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6880Keywords:
Predictive Analytics, Folk Art, Sustainability, Material Forecasting, Hybrid Machine Learning, ARIMA, XG Boost, LSTM, Cultural Economy, Decision-Support SystemAbstract [English]
Predictive analytics is a more rational direction of converting material planning to the old folk art manufacturing systems. The use of experiential estimation tends to create shortages in materials, excessive stocking and unviable use of resources further enhanced by seasons and change in demand in the market. To overcome such difficulties, a hybrid forecasting model was created that would have incorporated ARIMA, XGBoost, and LSTM models to forecast the material demand information based on multi-source data, such as past production data, climate indices, and sightseeing event schedules. The ensemble approach is also successful in capturing both linear and nonlinear demand patterns with a high prediction accuracy (R 2 = 0.96) and minimal error in the forecasting process minimized by 14.5 percent relative to the best single model. Pilot tests in Madhubani, Pattachitra and Warli clusters revealed that the number of wastages in procurement was decreased by 22 percent and the Sustainability Efficiency Index (SEI) was also enhanced by 18 percent indicating improved alignment of the production and material availability. The cloud-based device-supported dashboard will help artisans and cooperatives decide in an informed and eco-friendly manner by supplying real-time visualisations and procurement advice, as well as sustainability analytics. The balance between the precision and cultural authenticity of technologies is one of the other gains of the framework, where predictive intelligence is not in conflict with traditional craftsmanship but is rather its complement. The incorporation of sustainability constraints into the optimization layer also makes the model more consistent with the principles of the circular economy and UN Sustainable Development Goals (SDG 12). All in all, the study provides a scalable and culture adaptable model that improves efficiency, heritage value and fosters environmentally friendly development of folk art ecosystems.
References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., and others. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv preprint. https://doi.org/10.48550/arXiv.1603.04467
Al-Khazraji, L. R., Abbas, A. R., Jamil, A. S., and Hussain, A. J. (2023). A Hybrid Artistic Model Using DeepDream Model and Multiple Convolutional Neural Network Architectures. IEEE Access, 11, 101443–101459. DOI: https://doi.org/10.1109/ACCESS.2023.3309419
Chen, G., Wen, Z., and Hou, F. (2023). Application of Computer Image Processing Technology in Old Artistic Design Restoration. Heliyon, 9, e21366. https://doi.org/10.1016/j.heliyon.2023.e21366 DOI: https://doi.org/10.1016/j.heliyon.2023.e21366
Chen, X. L., Zou, D., Xie, H. R., Cheng, G., and Liu, C. X. (2022). Two Decades of Artificial Intelligence in Education: Contributors, Collaborations, Research Topics, Challenges, and Future Directions. Educational Technology and Society, 25, 28–47. DOI: https://doi.org/10.1007/s10639-022-11209-y
Del Bonifro, F., Gabbrielli, M., Lisanti, G., and Zingaro, S. P. (2020). Student Dropout Prediction. In I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, and E. Millán (Eds.), Lecture Notes in Computer Science (pp. 129–140). Cham, Switzerland: Springer. DOI: https://doi.org/10.1007/978-3-030-52237-7_11
Dobbs, T., and Ras, Z. (2022). On Art Authentication and the Rijksmuseum Challenge: A Residual Neural Network Approach. Expert Systems with Applications, 200, 116933. https://doi.org/10.1016/j.eswa.2022.116933 DOI: https://doi.org/10.1016/j.eswa.2022.116933
Elgammal, A., Liu, B., Elhoseiny, M., and Mazzone, M. (2017). CAN: Creative Adversarial Networks—Generating “Art” by Learning About Styles and Deviating From Style Norms. arXiv preprint. https://doi.org/10.48550/arXiv.1706.07068
Leonarduzzi, R., Liu, H., and Wang, Y. (2018). Scattering Transform and Sparse Linear Classifiers for Art Authentication. Signal Processing, 150, 11–19. https://doi.org/10.1016/j.sigpro.2018.03.012 DOI: https://doi.org/10.1016/j.sigpro.2018.03.012
Lou, Y. Q. (2023). Human Creativity in the AIGC Era. Journal of Design Economics and Innovation, 9, 541–552. DOI: https://doi.org/10.1016/j.sheji.2024.02.002
McCormack, J., Gifford, T., and Hutchings, P. (2019). Autonomy, Authenticity, Authorship, and Intention in Computer Generated Art. In Proceedings of EvoMUSART: International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar), 35–50. Cham, Switzerland. DOI: https://doi.org/10.1007/978-3-030-16667-0_3
Niyogisubizo, J., Liao, L. C., Nziyumva, E., Murwanashyaka, E., and Nshimyumukiza, P. C. (2022). Predicting Student Dropout in University Classes Using Two-Layer Ensemble Machine Learning Approach: A Novel Stacked Generalization. Computers and Education: Artificial Intelligence, 3, 100066. DOI: https://doi.org/10.1016/j.caeai.2022.100066
Pérez, B., Castellanos, C., and Correal, D. (2018). Predicting Student Drop-Out Rates Using Data Mining Techniques: A Case Study. In A. D. Orjuela-Cañón, J. C. Figueroa-García, and J. D. Arias-Londoño (Eds.), Communications in Computer and Information Science, 111–125. Cham, Switzerland: Springer. DOI: https://doi.org/10.1007/978-3-030-03023-0_10
Rios-Campos, C., Cánova, E. S. M., Zaquinaula, I. R. A., Zaquinaula, H. E. A., Vargas, D. J. C., Peña, W. S., Idrogo, C. E. T., and Arteaga, R. M. Y. (2023). Artificial Intelligence and Education. South Florida Journal of Development, 4, 641–655. DOI: https://doi.org/10.46932/sfjdv4n2-001
Wan Yaacob, W. F., Mohd Sobri, N., Nasir, S. A. M., Norshahidi, N. D., and Wan Husin, W. Z. (2020). Predicting Student Dropout in Higher Institutions Using Data Mining Techniques. Journal of Physics: Conference Series, 1496, 012005. DOI: https://doi.org/10.1088/1742-6596/1496/1/012005
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Copyright (c) 2025 Abhishek Pathak, Dr. M. Saravanan, Pooja Srishti, Mary Praveena J, Mahesh Kurulekar, Madhur Taneja
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