Universities play an essential role in preparing human resources for the industry of the future. By providing the proper knowledge, they can ensure that graduates will be able to adapt to the ever-changing industrial sector. However, to achieve this, the courses provided by academia must cover the current and future industrial needs by considering the trends in scientific research and emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing (EC). This work presents the survey results conducted among academics to assess the current state of university courses, regarding the level of knowledge and skills provided to students about the Internet of Things, Artificial Intelligence, and Edge Computing. The novelty of the work is that (a) the research was carried out in several European countries, (b) the current curricula of universities from different countries were analyzed, and (c) the results present the teachers’ perspective. To conduct the research, the analysis of the relevant literature took place initially to explore the issues of the presented subject, which will increasingly concern the industry in the near future. Based on the literature review results and analysis of the universities’ curricula involved in this study, a questionnaire was prepared and shared with academics. The outcomes of the analysis reveal the areas that require more attention from scholars and possibly modernization of curricula.
Industry 4.0 corresponds to the Fourth Industrial Revolution, resulting from technological innovation and research multidisciplinary advances. Researchers aim to contribute to the digital transformation of the manufacturing ecosystem both in theory and mainly in practice by identifying the real problems that the industry faces. Researchers focus on providing practical solutions using technologies such as the Industrial Internet of Things (IoT), Artificial Intelligence (AI), and Edge Computing (EC). On the other hand, universities educate young engineers and researchers by formulating a curriculum that prepares graduates for the industrial market. This research aimed to investigate and identify the industry’s current problems and needs from an educational perspective. The research methodology is based on preparing a focused questionnaire resulting from an extensive recent literature review used to interview representatives from 70 enterprises operating in 25 countries. The produced empirical data revealed (1) the kind of data and business management systems that companies have implemented to advance the digitalization of their processes, (2) the industries’ main problems and what technologies (could be) implemented to address them, and (3) what are the primary industrial needs and how they can be met to facilitate their digitization. The main conclusion is that there is a need to develop a taxonomy that shall include industrial problems and their technological solutions. Moreover, the educational needs of engineers and researchers with current knowledge and advanced skills were underlined.
<p>A Fuzzy Cognitive Map (FCM) is a graph-based tool for knowledge representation that intends to model any complex system through an interactive structure of nodes interacting with each other through causal relationships. Owing to their flexibility and inherent interpretability, FCMs have been used in various modeling and prediction tasks, particularly in situations where humans make final decisions, such as industrial anomaly detection. However, FCMs can unintentionally absorb spurious correlations presented in collected data during development, leading to poor prediction accuracy and interpretability. To address this limitation, this article proposes a novel framework for constructing FCMs based on the Liang-Kleeman Information Flow (L-K IF) analysis, a causal inference tool. The actual causal relationships are identified from the data using an automatic causal search algorithm, and these are then imposed as constraints in the FCM learning procedure to rule out spurious correlations and improve the predictive and explanatory power of the model. Numerical simulations were conducted by comparing the proposed approach with state-of-the-art FCM-based models, thereby demonstrating the promising performance of the developed FCM.</p>
<p>A Fuzzy Cognitive Map (FCM) is a graph-based tool for knowledge representation that intends to model any complex system through an interactive structure of nodes interacting with each other through causal relationships. Owing to their flexibility and inherent interpretability, FCMs have been used in various modeling and prediction tasks, particularly in situations where humans make final decisions, such as industrial anomaly detection. However, FCMs can unintentionally absorb spurious correlations presented in collected data during development, leading to poor prediction accuracy and interpretability. To address this limitation, this article proposes a novel framework for constructing FCMs based on the Liang-Kleeman Information Flow (L-K IF) analysis, a causal inference tool. The actual causal relationships are identified from the data using an automatic causal search algorithm, and these are then imposed as constraints in the FCM learning procedure to rule out spurious correlations and improve the predictive and explanatory power of the model. Numerical simulations were conducted by comparing the proposed approach with state-of-the-art FCM-based models, thereby demonstrating the promising performance of the developed FCM.</p>
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