Published 10 Oct 2023
Interpretable Methods of Artificial Intelligence Algorithms
Research Article | Open Access
Volume 2023 | Article ID 6271241 | https://doi.org/10.1155/2023/6271241
- Seyed Reza Agha Seyed Hosseini
- Masoud Soleimani
- Hossein Naderian
- Amir Hossein Afshinfa
- Zoha Savari
- Mahtab Tizhari
Abstract
There 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 multisource 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 compact communication devices with ever-increasing computation capabilities, and artificial 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 quantifies uncertainty by combining multiple data sources and considering uncertainty related to model parameters. With multiple data sources and a Bayesian framework, Chandra and Kapoor proposed a method for transfer learning based on neural networks. They used the Markov chain Monte Carlo method to get samples from the posterior distribution in a multisource Bayesian transfer learning framework. Despite the ambiguous experimental results, the framework offers a robust probability-based foundation for decision-making. Pattern recognition and artificial intelligence communities have focused on self-centered activity recognition due to its wide application to human systems, such as dietary and physical activity assessment and patient monitoring [5, 6]. The authors created a simple probability table based on a knowledge-driven multisource fusion architecture to provide frequent information regarding self-centered activities (ADLs) in everyday life. Using statistics and support vector machines based on information theory, a well-trained convolutional neural network creates a set of text labels from regular information and other sensor data. The proposed method can accurately recognize several previously challenging sedentary activities, including 15 predefined ADL categories. Compared to previous methods, this method provides better results when applied to data collected using wearable devices. This research has not yet been widely adopted, despite an average accuracy of 85.4% for 15 ADLs.
Several robotics-related research domains have recently benefited from artificial neural networks (ANNs) because of their superior spatial feature abstraction and keyframe prediction capabilities. An ANN is a connectionist model, which makes them inherently wrong at making long-term plans, thinking logically, and making multistep decisions. In their study, Zuo et al. developed an enhanced ANN (SANN) model of state calculator and result (SOAR) that combines the feature detection abilities of ANN with the long-term cognitive planning capabilities of SOAR [7]. A logical planning module is added to the classic ANN to improve its performance by imitating the cognitive operation of the human brain. The SOAR planning probability vector was merged with the original feature array of data via a data fusion module [7–12]. Experiments have shown that the suggested architecture is efficient and accurate and has excellent potential for more challenging tasks requiring quick categorization, planning, and learning. It is possible to recognize grasping sequences when multiple objects are involved and perform metaobject cooperative grabbing. However, the benefits of these applications are limited [3]. A diagnosis based on data fusion is an exciting application of the Industrial Internet of Things for the efficient use of motor monitoring data. A multimodal neural network (DRMNN) based on dynamic routing was introduced by Wang et al. to follow the concept of deep multimodal learning (MDL) [8, 9]. They proposed a strategy for dimensionality reduction and invariant feature capture using vibration and stator current signals to extract multimodal features from multisource data. The decision-making layer implements a dynamic routing method to assign appropriate weights to various modes based on the relative relevance of each mode. DRMNN is practical and durable in a motor test platform trial.
To implement robot demonstration programming, Wang et al. suggested an implicit interaction technique based on forearm sEMG (surface electromyography) and inertial multisource information fusion [10]. An M-DDPG method for modifying assembly parameters was presented based on the demonstrator’s demonstrations and lessons learned to improve adaptability to diverse assembly components. To improve generalization performance and accuracy of gesture identification, they proposed an improved PCNN (1D-PCNN) based on one-dimensional convolution and pooling to extract feature inertia and EMG. Previous studies found that retailers’ prior disclosure of imprecise information flow would reduce the supply chain’s profitability and cost retailers’ money. By mentioning the possibility of manufacturers infiltrating and confronting uneconomical or economical manufacturing, Zhao and Li expand the study on information sharing. Manufacturing costs do not have to be addressed when retailers expropriate manufacturers and share demand information with producers [11]. A further incentive may be provided by producers to retailers in order to increase the accuracy of their demand estimations.
The manufacturer infringes and experiences production diseconomy, the retailer benefits from information exchange, and the manufacturer benefits from minimal production combined with exceptional conditions. It has not been examined whether retailers gain more from the following factors when demand becomes more variable or when demand signals become more accurate [6]. There is a tendency in the educational publishing industry to create a great deal of stock for “on-demand manufacturing,” but modifying the item might lead to obsolescence problems. He et al. addressed two distinct but related problems [12]. A variety of printed items are forecasted and managed using predictive models. Demand estimates can now be more precise, and inventory obsolescence can be reduced. Also, educational publishing merchants benefit from contracts that have knowledge asymmetries.