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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Sineglazov, Victor | - |
| dc.contributor.author | Синєглазов, Віктор Михайлович | - |
| dc.contributor.author | Novikov, Mikhaylo | - |
| dc.contributor.author | Новіков, Mихайло Сергійович | - |
| dc.date.accessioned | 2024-02-15T09:55:20Z | - |
| dc.date.available | 2024-02-15T09:55:20Z | - |
| dc.date.issued | 2023-12-27 | - |
| dc.identifier.citation | Sineglazov V. M. Determination of Marketing Parameters for Building a Demand Forecasting Model using Neural Networks / V. M. Sineglazov, M. S. Novikov // Electronics and Control Systems, N 4(78) – Kyiv: ТОВ «Альянт», 2023. – pp. 44–51 | uk_UA |
| dc.identifier.issn | 1990-5548 | - |
| dc.identifier.uri | https://er.nau.edu.ua/handle/NAU/62256 | - |
| dc.description | [1] Armstrong J. Scott, and Kesten C. Green. "Demand forecasting II: Evidence-based methods and checklists," 2017. URL: https://faculty.wharton.upenn.edu/wp-content/uploads/2017/05/JSA-Demand-Forecasting-89-clean.pdf [2] Silveira Netto, Carla Freitas & Brei, Vinicius Andade, Demand Forecasting in Marketing: Methods, Types of Data, and Future Research, 2017. [3] C. Ingle, D. Bakliwal, J. Jain, P. Singh, P. Kaleand V. Chhajed, "Demand Forecasting: Literature Review on Various Methodologies," 2021, 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2021, pp. 1–7, https://doi.org/10.1109/ICCCNT51525.2021.9580139. [4] eWorldFulfillment, "Demand Forecasting: Methods, Models, and Examples." eWorldFulfillment, 2021. URL: https://eworldfulfillment.com/blog/demand-forecasting-methods/ [5] E. S. Gardner, “Exponential smoothing: The state of the art—Part II”, International Journal of Forecasting, vol. 22, no. 4, pp. 637–666, Oct. 2006. [Online]. Available: https://doi.org/10.1016/j.ijforecast.2006.03.005 [6] A. Mitra, A. Jain, A. Kishore, and et al. “A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach,” Oper. Res. Forum, 3, 58 (2022). https://doi.org/10.1007/s43069-022-00166-4 [7] M. Seyedan, & F. Mafakheri, “Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities,” Journal of Big Data, vol. 7, Article number: 53, 2020. https://doi.org/10.1186/s40537-020-00329-2. [8] A. Aktepe, E. Yanık, & S. Ersöz, “Demand forecasting application with regression and artificial intelligence methods in a construction machinery company,” J Intell Manuf, 32, 1587–1604, 2021. https://doi.org/10.1007/s10845-021-01737-8 [9] A. Mitra, A. Jain, A. Kishore, and et al., “A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach,” Oper. Res. Forum, 3, 58, 2022. https://doi.org/10.1007/s43069-022-00166-4 [10] Guoping Xu, Hanqiang Cao, Youli Dong, Chunyi Yue, Kexin Li, and Yubing Tong, “Focal Loss Function based DeepLabv3+ for Pathological Lymph Node Segmentation on PET/CT,” Proceedings of the 2020 2nd International Conference on Intelligent Medicine and Image Processing, 2020 pp. 24–28. https://doi.org/10.1145/3399637.3399651. [11] G. Behera, A. Bhoi,, A. K. Bhoi, “A Comparative Analysis of Weekly Sales Forecasting Using Regression Techniques,” In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 431, 2022. Springer, Singapore. https://doi.org/10.1007/978-981-19-0901-6_4 [12] T. Gopalakrishnan, Ritesh Choudhary, and Sarada Prasad, "Prediction of Sales Value in Online shopping using Linear Regression," 2018 4th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, 2018, pp. 1–6, https://doi.org/10.1109/CCAA.2018.8777620. [13] D. Schiessl, H. B. A. Dias, & J. C. Korelo, “Artificial intelligence in marketing: a network analysis and future agenda.” J Market Anal, 10, 207–218, 2022. https://doi.org/10.1057/s41270-021-00143-6 [14] Jun Zhu, Jianfei Chen, Wenbo Hu, and Bo Zhang, “Big Learning with Bayesian methods,” National Science Review, vol. 4, Issue 4, July 2017, pp. 627–651, https://doi.org/10.1093/nsr/nwx044 [15] G.M. Allenby, E.T. Bradlow, E.I. George, et al. “Perspectives on Bayesian Methods and Big Data,” Cust. Need. And Solut. 1, 169–175, 2014. https://doi.org/10.1007/s40547-014-0017-9 [16] M. Scutari, C. Vitolo, & A. Tucker, “Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation,” Stat Comput, 29, 1095–1108, 2019. https://doi.org/10.1007/s11222-019-09857-1 [17] D. G. Rasines & G. A. Young, “Empirical Bayes and Selective Inference,” J Indian Inst Sci, 102, 1205–1217, 2022. https://doi.org/10.1007/s41745-022-00286-0 [18] L. Alzubaidi, J. Zhang, A. J. Humaidi, and et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, futuredirections,” J Big Data, 8, 53, 2021. https://doi.org/10.1186/s40537-021-00444-8 [19] Nan Zheng and Pinaki Mazumder, "Fundamentals and Learning of Artificial Neural Networks," in Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design, IEEE, 2020, pp. 11–60, https://doi.org/10.1002/9781119507369.ch2. [20] R. Zese, E. Bellodi, M. Fraccaroli, F. Riguzzi, E. Lamma, 2022, “Neural Networks and Deep Learning Fundamentals,” In: Micheloni, R., Zambelli, C. (eds) Machine Learning and Non-volatile Memories. Springer, Cham. https://doi.org/10.1007/978-3-031-03841-9_2 [21] O. A. Montesinos López, A. Montesinos López, & J. Crossa, “Fundamentals of Artificial Neural Networks and Deep Learning,” In: Multivariate Statistical Machine Learning Methods for Genomic Prediction. 2022, Springer, Cham. https://doi.org/10.1007/978-3-030-89010-0_10 [22] Wei Wang & Ruyi Yang, “Enterprise Network Marketing Prediction Using the Optimized GA-BP Neural Network,” Complexity, 2020, Article No. 6682296. https://doi.org/10.1155/2020/6682296. | uk_UA |
| dc.description.abstract | This article is devoted to finding marketing parameters for building a demand forecasting model using neural networks using real data. The work deals with the problem of modeling product demand on the market in marketing using artificial intelligence and machine learning methods. The main features of existing approaches to building models of products on the market, their advantages and disadvantages are shown. The need for their improvement has been identified. A new methodology for solving the problem is presented. The model's demonstrated ability to predict consumer demand based on a variety of marketing parameters helps businesses plan inventory, production, and personnel more effectively and can lead to significant cost savings and improved efficiency. | uk_UA |
| dc.description.abstract | Cтаттю присвячено знаходженню маркетингових параметрів для побудови моделі прогнозування попиту за допомогою нейронних мереж з використанням реальних даних. У роботі розглянуто проблему в області моделювання попиту товару на ринку в маркетингу за допомогою методів штучного інтелекту та машинного навчання. Показано основні особливості існуючих підходів до побудови моделей товарів на ринку, їх переваги та недоліки. Виявлено потребу у їх вдосконаленні. Представлено нову методологію для розв’язання задачі. Продемонстровано здатність моделі успішно прогнозувати споживчий попит на основі різноманітних маркетингових параметрів, що допомагає підприємствам ефективніше планувати запаси, виробництво та персонал і може призвести до значної економії коштів та підвищенню ефективності. | uk_UA |
| dc.language.iso | uk | uk_UA |
| dc.publisher | National Aviation University | uk_UA |
| dc.relation.ispartofseries | Electronics and Control Systems;№4(78) | - |
| dc.relation.ispartofseries | Електроніка та системи управління;№4(78) | - |
| dc.subject | determination of marketing parameters | uk_UA |
| dc.subject | forecasting | uk_UA |
| dc.subject | neural networks | uk_UA |
| dc.subject | regression models | uk_UA |
| dc.subject | multilayer perceptron | uk_UA |
| dc.subject | визначення маркетингових параметрів | uk_UA |
| dc.subject | прогнозування | uk_UA |
| dc.subject | нейронні мережі | uk_UA |
| dc.subject | регресійні моделі | uk_UA |
| dc.subject | багатошаровий персептрон | uk_UA |
| dc.title | Determination of Marketing Parameters for Building a Demand Forecasting Model using Neural Networks | uk_UA |
| dc.title.alternative | Визначення маркетингових параметрів для побудови моделі прогнозування попиту за допомогою нейронних мереж | uk_UA |
| dc.type | Article | uk_UA |
| dc.subject.udc | 004.855.5(045) | uk_UA |
| dc.identifier.doi | 10.18372/1990-5548.78.18263 | - |
| Appears in Collections: | Наукові публікації та матеріали кафедри авіаційних комп'ютерно-інтегрованих комплексів (НОВА) | |
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