Research Article A Method for Predicting Production Costs Based on Data Fusion from Multiple Sources for Industry 4.0: Trends and Applications of Machine Learning Methods Masoud Soleimani , 1 Hossein Naderian , 2 Amir Hossein Afshinfar , 3 Zoha Savari , 4 Mahtab Tizhari, 5 and Seyed Reza Agha Seyed Hosseini 6 1 Department of Computer Engineering, University of Isfahan, Isfahan, Iran 2 Amirkabir University of Technology, Tehran, Iran 3 Department of Economics, Shahid Chamran University, Ahvaz, Iran 4 Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran 5 Department of Industrial Engineering & Management Systems, Amirkabir University of Technology, Tehran, Iran 6 California Miramar University, School of Business, San Diego, CA, USA Correspondence should be addressed to Hossein Naderian; h.naderian@aut.ac.ir Received 4 July 2022; Revised 3 November 2022; Accepted 15 April 2023; Published 10 October 2023 Academic Editor: Hye•Jin Kim Copyright © 2023 Masoud Soleimani et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tere is a growing need for manufacturing processes that improve product quality and production rates while reducing costs. With the advent of multisensory information fusion technology, individuals can acquire a broader range of information. Several data fusion and machine learning methods have been discussed in this article within the context of the Industry 4.0 paradigm. Depending on its purpose, a prognostic method can be categorized as descriptive, predictive, or prescriptive. ANN and CNN models are applied to predicting production costs using neural networks based on multisource information fusion, and mul• tisource information fusion theory is examined and applied to ANNs and CNNs. In this study, ANN and CNN predictions have been compared. CNN has demonstrated more remarkable skill in predicting the six cost categories than ANN. When predicting the true value of each cost category, CNN is superior to ANN. As a result, CNN’s forecast error for the current month’s total income is 0.0234. Because of its improved prediction accuracy and more straightforward training technique, CNN is better suited to incorporating information from several sources. Furthermore, both neural networks overestimate indirect costs, including direct material costs and item consumption prices. 1.Introduction In manufacturing, the fourth industrial revolution refers to a general movement to adopt new communication systems and protocols, cyber security norms, display devices that can display multiple devices simultaneously, mobile and com• pact communication devices with ever•increasing compu• tation capabilities, and artifcial intelligence methods. As this international trend has grown, the Internet has expanded to permeate every facet of human life, including economics and social life [1–3]. Digital technologies have also been widely implemented within industrial manufacturing procedures and investments due to this paradigm shift. Essentially, the smart factories of tomorrow will be built on the convergence of the physical and digital worlds. Despite the growing popularity of deep learning and neural networks, there are still obstacles to combining multiple sources of data and information. Deep learning and neural networks remain challenging when combining information from multiple sources. In decision•making, Bayesian reasoning provides a rigorous method for quantifying uncertainty [4]. Bayesian inference quantifes uncertainty by combining multiple data Hindawi Computational Intelligence and Neuroscience Volume 2023, Article ID 6271241, 12 pages https://doi.org/10.1155/2023/6271241