Open Access
Issue |
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
|
|
---|---|---|
Article Number | 01089 | |
Number of page(s) | 11 | |
DOI | https://doi.org/10.1051/e3sconf/202339101089 | |
Published online | 05 June 2023 |
- K. Lutoslawski, M. Hernes, J. Radomska, M. Hajdas, E. Walaszczyk and A. Kozina, “Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network,” in IEEE Access, vol. 9, pp. 146123–146136, (2021) [CrossRef] [Google Scholar]
- Kilimci, Zeynep Hilal, A. Okay Akyuz, Mitat Uysal, Selim Akyokus, M. Ozan Uysal, Berna Atak Bulbul, and Mehmet Ali Ekmis. “An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain.” Complexity (2019) [Google Scholar]
- Saha, Priyam & Gudheniya, Nitesh & Mitra, Rony & Das, Dyutimoy & Narayana, Sushmita & Tiwari, Manoj “Demand Forecasting of a Multinational Retail Company using Deep Learning Frameworks”. IFAC-PapersOnLine. 55. 395–399. 10.1016/j.ifacol.2022.09.425. [CrossRef] [Google Scholar]
- Golabek, Marta & Senge, Robin & Neumann, Rainer “Demand Forecasting using Long Short-Term Memory Neural Networks”, (2020).. [Google Scholar]
- P S Smirnov and VA Sudakov, “Forecasting new product demand using machine learning”,(2021) [Google Scholar]
- Mobina Mousapour Mamoudan, Zahra Mohammadnazari, Ali Ostadi & Ali Esfahbodi “Food products pricing theory with application of machine learning and game theory approach”, International Journal of Production Research (2022) [Google Scholar]
- P. Meulstee and M. Pechenizkiy, “Food Sales Prediction: “If Only It Knew What We Know”, IEEE International Conference on Data Mining Workshops, Pisa, Italy, 2008, pp. 134–143, (2008) [Google Scholar]
- Sushil Punia, Sonali Shankar, Predictive analytics for demand forecasting: A deep learning-based decision support system, Knowledge-Based Systems, Volume 258, 109956, ISSN 0950-7051, (2022). [CrossRef] [Google Scholar]
- Lorente-Leyva, LL.; Alemany Diaz, MDM.; Peluffo-Ordonez, DH.; Herrera-Granda, ID. “A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry”. Lecture Notes in Computer Science. 131–142, (2021). [Google Scholar]
- Schmidt, A.; Kabir, M.W.U.; Hoque, M.T. Machine Learning Based Restaurant Sales Forecasting. Mach. Learn. Knowl. Extr. 4, 105–130,(2022). [CrossRef] [Google Scholar]
- Akbar Abbaspour Ghadim Bonab. “A comparative study of demand forecasting based on machine learning methods with time series approach”. Journal of Applied Research on Industrial Engineering, 9, 3, 331–353,(2022). [Google Scholar]
- Hast, Matteus. “Evaluation of machine learning algorithms for customer demand prediction of in-flight meals.” (2019). [Google Scholar]
- Yerragudipadu Subbarayudu, Alladi Sureshbabu “Distributed Multimodal Aspective on Topic Model Using Sentiment Analysis for Recognition of Public Health Surveillance” Expert Clouds and Applications, Singapore Print ISBN 978-981-16-2125-3 Online ISBN 978-981-16-2126-0 (2021) [Google Scholar]
- Syed Umar, Yerragudipadu Subbarayudu, K. Kiran Kumar, N. Bashwanth, “Designing of Dynamic Re-clustering Leach Protocol for Calculating Total Residual Time and Performance”, International Journal of Electrical and Computer Engineering (IJECE) Vol.7, No.3, pp. 1286–1292 ISSN: 2088-8708, (2017) [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.