Application of machine learning and rough set theory in lean maintenance decision support system development

Antosz, Katarzyna; Jasiulewicz-Kaczmarek, Małgorzata; Paśko, Łukasz; Zhang, Chao; Wang, Shaoping

doi:10.17531/ein.2021.4.12

Cited by 28 publications

(4 citation statements)

References 96 publications

(99 reference statements)

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“…The rough set theory that Pawlak [37] put forward serves as a powerful mathematical technique for addressing information and knowledge that are imprecise, inconsistent, and incomplete without any assumptions and additional adjustments. Due to its innovative approach, distinct methodology, and straightforward operation, rough set theory has gained prominence in various fields such as intelligence information processing (e.g., [44,45]), pattern recognition (e.g., [46,47]), knowledge acquisition (e.g., [48]), and decision support analysis (e.g., [49]), among others. Note that this list is not intended to be comprehensive.…”

Section: Rough Set Theorymentioning

confidence: 99%

Preparedness Indicator System for Education 4.0 with FUCOM and Rough Sets

Almacen

Castilla

Gonzales

et al. 2023

Systems

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In view of the recent education sectoral transition to Education 4.0 (EDUC4), evaluating the preparedness of higher education institutions (HEIs) for EDUC4 implementation remains a gap in the current literature. Through a comprehensive review, seven criteria were evaluated, namely, human resources, infrastructure, financial, linkages, educational management, learners, and health and environment. This work offers two crucial contributions: (1) the development of an EDUC4 preparedness indicator system and (2) the design of a computational structure that evaluates each indicator and computes an aggregate preparedness level for an HEI. Using the full consistency method (FUCOM) to assign the priority weights of EDUC4 criteria and the rough set theory to capture the ambiguity and imprecision inherent in the measurement, this study offers an aggregate EDUC4 preparedness index to holistically capture the overall preparedness index of an HEI towards EDUC4. An actual case study is presented to demonstrate the applicability of the proposed indicator system. After a thorough evaluation, the results indicate that human resources were the most critical criterion, while health and environment ranked last. Insights obtained from the study provide HEIs with salient information necessary for decision making in various aspects, including the design of targeted policies and the allocation of resources conducive to implementing EDUC4 initiatives. The proposed indicator system can be a valuable tool to guide HEIs in pursuing EDUC4, resulting in a more effective and efficient implementation of this educational paradigm.

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Section: Rough Set Theorymentioning

confidence: 99%

Preparedness Indicator System for Education 4.0 with FUCOM and Rough Sets

Almacen

Castilla

Gonzales

et al. 2023

Systems

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“…The regression-based studies available in the literature can be divided according to the type of method used to estimate fuel consumption. Machine learning models based on support vector machines [9,22], neural networks [23], or decision trees [24] are widely used. Some studies also use fuzzy logic algorithms [25] or classical regression for modeling [26].…”

Section: Literature Review On Fuel Consumption Modelingmentioning

confidence: 99%

Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface

Rykała,

Grzelak,

Rykała

et al. 2023

Energies

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As a result of ever-growing energy demands, motor vehicles are among the largest contributors to overall energy consumption. This has led researchers to focus on fuel consumption, which has important implications for the environment, the economy, and geopolitical stability. This article presents a comprehensive analysis of various fuel consumption modeling methods, with the aim of identifying parameters that significantly influence fuel consumption. The scientific novelty of this article lies in its use of low-cost technology, i.e., an OBD-II interface paired with a mobile phone, combined with modern mathematical modeling methods to create an accurate model of the fuel consumption of a vehicle. A vehicle test drive was performed, during which variations in selected parameters were recorded. Based on the obtained data, a model of the vehicle’s fuel consumption was built using three forecasting methods: a multivariate regression model, decision trees, and neural networks. The results show that the multivariate regression model obtained the lowest MSE, MAR, and MRSE coefficients, indicating that this was the best forecasting method among those tested. Sufficient forecast error results were obtained using neural networks, with increases of approximately 73%, 10%, and 131% in MSE, MAE, and MRAE, respectively, compared to regression results. The worst results were obtained with the decision tree model, with increases of approximately 163%, 21%, and 92% in MSE, MAE, and MRAE compared to the regression results.

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“…The research likely sheds light on strategies to maintain and enhance the quality of education in the country. In contrast, Antosz et al (2021) delve into an industrial context, employing machine learning and rough set theory to develop a lean maintenance decision support system. By analyzing large datasets, the study aims to improve maintenance practices and decision-making in industrial systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Charting the Future of Philippines Education: Navigating the Intersection of K-12 Education, the Fourth Industrial Revolution (IR 4.0), and Internationalization

Eslit

2023

Preprint

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This paper explores the dynamic interplay of K-12 education, the Fourth Industrial Revolution (IR 4.0), and internationalization in the Philippines. Through qualitative content analysis, key themes emerged, shedding light on the impact of K-12 education on global competitiveness, challenges in embracing IR 4.0 technologies, and opportunities arising from internationalization efforts. The findings underscore the significance of curriculum alignment with international standards, comprehensive digital literacy training, and fostering a global mindset to prepare students for the digitally-driven, interconnected world. Understanding the implications of these themes is crucial for the Philippines' education system, as it navigates towards a transformative and globally competitive future. Further, this research offers evidence-based recommendations and addresses research gaps, contributing to a broader understanding of how the convergence of these forces shapes the future of Philippine education. Future research avenues may explore long-term impacts on workforce preparedness, innovative IR 4.0 integration, and the effectiveness of internationalization efforts on educational outcomes. Eventually, this study inspires an education system that nurtures learners equipped to thrive in an ever-evolving global landscape.

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Application of machine learning and rough set theory in lean maintenance decision support system development

Cited by 28 publications

References 96 publications

Preparedness Indicator System for Education 4.0 with FUCOM and Rough Sets

Preparedness Indicator System for Education 4.0 with FUCOM and Rough Sets

Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface

Charting the Future of Philippines Education: Navigating the Intersection of K-12 Education, the Fourth Industrial Revolution (IR 4.0), and Internationalization

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