[1]Gandhi, M. A., Maharram, V. K., Raja, G., Sellapaandi, S. P., Rathor, K., & Singh, K. (2023, July). A novel method for exploring the store sales forecasting using fuzzy Pruning LS-SVM approach. In 2023 2nd International Conference on Edge Computing and Applications (ICECAA) (pp. 537-543). IEEE.

[2]Basha, C. Z., Bhavana, N., Bhavya, P., & Sowmya, V. (2020, July). Rainfall prediction using machine learning & deep learning techniques. In 2020 international conference on electronics and sustainable communication systems (ICESC) (pp. 92-97).

[3]Sohrabpour, V., Oghazi, P., Toorajipour, R., & Nazarpour, A. (2021). Export sales forecasting using artificial intelligence. Technological Forecasting and Social Change, 163, 120480.

[4]Jung, Y., Jung, J., Kim, B., & Han, S. (2020). Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities: Case study of South Korea. Journal of Cleaner Production, 250, 119476.

[5]Isabona, J., Imoize, A. L., Ojo, S., Karunwi, O., Kim, Y., Lee, C. C., & Li, C. T. (2022). Development of a multilayer perceptron neural network for optimal predictive modeling in urban microcellular radio environments. Applied Sciences, 12(11), 5713.

[6]Revathi, B. S., & Kowshalya, A. M. (2022). A REVIEW ON IMAGE CAPTIONING SYSTEM FROM ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND DEEP LEARNING TECHNIQUES. I-Manager's Journal on Image Processing, 9(3).

[7]Fildes, R., Ma, S., & Kolassa, S. (2022). Retail forecasting: Research and practice. International Journal of Forecasting, 38(4), 1283-1318.

[8]Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021). Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity, 25, 1315-1360.

[9]Charles, M., & Ochieng, S. B. (2023). Strategic outsourcing and firm performance: a review of literature. International Journal of Social Science and Humanities Research (IJSSHR) ISSN, 2959-7056.

[10]Raji, M. A., Olodo, H. B., Oke, T. T., Addy, W. A., Ofodile, O. C., & Oyewole, A. T. (2024). Real-time data analytics in retail: A review of USA and global practices. GSC Advanced Research and Reviews, 18(3), 059-065.

[11]Korkmaz, C., Kocas, H. E., Uysal, A., Masry, A., Ozkasap, O., & Akgun, B. (2020, November). Chain fl: Decentralized federated machine learning via blockchain. In 2020 Second international conference on blockchain computing and applications (BCCA) (pp. 140-146). IEEE.

[12]Islam, S., & Amin, S. H. (2020). Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques. Journal of Big Data, 7(1), 65.

[13]Chelliah, B. J., Latchoumi, T. P., & Senthilselvi, A. (2024). Analysis of demand forecasting of agriculture using machine learning algorithm. Environment, Development and Sustainability, 26(1), 1731-1747.

[14]Xu, W., Cao, Y., & Chen, R. (2024). A multimodal analytics framework for product sales prediction with the reputation of anchors in live streaming e-commerce. Decision Support Systems, 177, 114104.

[15]Zaki, A. M., Khodadadi, N., Lim, W. H., & Towfek, S. K. (2024). Predictive analytics and machine learning in direct marketing for anticipating bank term deposit subscriptions. American Journal of Business and Operations Research, 11(1), 79-88.

[16]Rao, F. A., Muneer, A., Almaghthawi, A., Alghamdi, A., Fati, S. M., & Ghaleb, E. A. A. (2023). BMSP-ML: big mart sales prediction using different machine learning techniques. IAES International Journal of Artificial Intelligence, 12(2), 874.

[17]Ratre, S., & Jayaraj, J. (2023, January). Sales prediction using arima, facebook’s prophet and xgboost model of machine learning. In Machine Learning, Image Processing, Network Security and Data Sciences: Select Proceedings of 3rd International Conference on MIND 2021 (pp. 101-111). Singapore: Springer Nature Singapore.

[18]Chen, S., Lan, F., Liu, M., Ye, T., Xiao, K., Zheng, P., ... & Kong, D. (2019, November). Cash flow forecasting model for electricity sale based on deep recurrent neural network. In 2019 IEEE International Conference on Power Data Science (ICPDS) (pp. 67-70). IEEE.

[19]Majhi, B., & Naidu, D. (2021). Differential evolution based radial basis function neural network model for reference evapotranspiration estimation. SN Applied Sciences, 3(1), 56.

[20]Loureiro, A. L., Miguéis, V. L., & Da Silva, L. F. (2018). Exploring the use of deep neural networks for sales forecasting in fashion retail. Decision Support Systems, 114, 81-93.

[21]Piciu, L., Damian, A., Tapus, N., Simion-Constantinescu, A., & Dumitrescu, B. (2018, September). Deep recommender engine based on efficient product embeddings neural pipeline. In 2018 17th RoEduNet conference: networking in education and research (RoEduNet) (pp. 1-6). IEEE.

[22]Krishna, A., Akhilesh, V., Aich, A., & Hegde, C. (2018, December). Sales-forecasting of retail stores using machine learning techniques. In 2018 3rd international conference on computational systems and information technology for sustainable solutions (CSITSS) (pp. 160-166). IEEE.

[23]Bandara, K., Shi, P., Bergmeir, C., Hewamalage, H., Tran, Q., & Seaman, B. (2019). Sales demand forecast in e-commerce using a long short-term memory neural network methodology. In Neural Information Processing: 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, Proceedings, Part III 26 (pp. 462-474). Springer International Publishing.

[24]Kim, T., & Kim, H. Y. (2019). Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data. PloS one, 14(2), e0212320.

[25]Lu, W., Li, J., Li, Y., Sun, A., & Wang, J. (2020). A CNN‐LSTM‐based model to forecast stock prices. Complexity, 2020(1), 6622927.