An approach of ontology and knowledge base for railway maintenance

Maintenance methods have become automated and innovative, especially with the transition to maintenance 4.0. However, social issues such as coronavirus disease of 2019 (COVID-19) and the war in Ukraine have caused significant departures of maintenance experts, resulting in the loss of enormous know-how. As part of this work, we will propose a solution by exploring the knowledge and expertise of these experts for the purpose of sharing and conservation. In this perspective, we have built a knowledge base based on experience and feedback. The proposed method illustrates a case study based on the single excitation configuration interaction (SECI) method to optimally capture the explicit and tacit knowledge of each technician, as well as the theoretical basis, the model of Nonaka and Takeuchi

Implementing sharing platform based on ontology using a sequential recommender system

While recommender systems have shown success in many fields, accurate recommendations in industrial settings remain challenging. In maintenance, existing techniques often struggle with the “cold start” problem and fail to consider differences in the target population's characteristics. To address this, additional user information can be incorporated into the recommendation process. This paper proposes a recommender system for recommending repair actions to technicians based on an ontology (knowledge base) and a sequential model. The approach utilizes two ontologies, one representing failure knowledge and the other representing asset attributes. The proposed method involves two steps: i) calculating score similarity based on ontology domain knowledge to make predictions for targeted failures and ii) generating Top-N repair actions through collaborative filtering recommendations for targeted failures. An additional module was implemented to evaluate the recommender system, and results showed improved performance

Towards a hybrid recommendation approach using a community detection and evaluation algorithm

In social learning platforms, community detection algorithms are used to identify groups of learners with similar interests, behavior, and levels. While, recommendation algorithms personalize the learning experience based on learners' profile information, including interests and past behavior. Combining these algorithms can improve the recommendation quality by identifying learners with similar needs and interests for more accurate and relevant suggestions. Community detection enhances recommendations by identifying groups of learners with similar needs and interests. Leveraging their similarities, recommendation algorithms generate more accurate suggestions. In this article, we propose a novel approach that combines community detection and recommendation algorithms into a single framework to provide learners with personalized recommendations and opportunities for collaborative learning. Our proposed approach consists of three steps: first, applying the maximal clique-based algorithm to detect learning communities with common characteristics and interests; second, evaluating learners within their communities using static and dynamic evaluation; and third, generating personalized recommendations within each detected cluster using a recommendation system based on correlation and co-occurrence. To evaluate the effectiveness of our proposed approach, we conducted experiments on a real-world dataset. Our results show that our approach outperforms existing methods in terms of modularity, precision, and accuracy

Towards an Approach Based on Adjusted Genetic Algorithms to Improve the Quantity of Existing Data in the Context of Social Learning

In the current era, multiple disciplines struggle with the scarcity of data, particu-larly in the area of e-learning and social learning. In order to test their ap-proaches and their recommendation systems, researchers need to ensure the availability of large databases. Nevertheless, it is sometimes challenging to find-out large scale databases, particularly in terms of education and e-learning. In this article, we outline a potential solution to this challenge intended to improve the quantity of an existing database. In this respect, we suggest genetic algo-rithms with some adjustments to enhance the size of an initial database as long as the generated data owns the same features and properties of the initial data-base. In this case, testing machine learning and recommendation system ap-proaches will be more practical and relevant. The test is carried out on two da-tabases to prove the efficiency of genetic algorithms and to compare the struc-ture of the initial databases with the generated databases. The result reveals that genetic algorithms can achieve a high performance to improve the quantity of existing data and to solve the problem of data scarcity

Towards an Evolution of E-Learning Recommendation Systems: From 2000 to Nowadays

In e-learning, recommendation systems have proven to be highly efficient for improving learners' performance and knowledge. They can manage the different pedagogical resources and simplify the workload for the instructor and learners as well. Throughout the years, recommendation systems in e-learning have wit-nessed a major evolution since the 2000s. Several aspects have been developed, including techniques involved, test data (...). In this respect, this paper analyses the evolution of recommendation systems in e-learning since 2000 with a focus on the evolution sides. It furthermore addresses areas not fully addressed to date. A set of recommendation systems is identified and then analysed in order to define techniques used, as well as algorithms deployed

Mobile Serious Game Design using User Experience: Modeling of Software Product Line Variability

In recent years, gamification has emerged as a new approach to increasing learner engagement. It covers a very wide range of games with very different purposes and with many fields of application. However, most of the gamification solutions proposed do not adopt the same modeling approach and little attention has been paid to mobile serious games (MSG) belonging to different pedagogical contexts. In order to overcome these difficulties, we have developed in this paper a generic model based on the Software Product Line (SPL) approach to manage the common and variable points of the MSG product set. We have also focused on the User eXperience (UX) concept to study the aspects that most affect the player’s experience in the context of MSGs. These aspects have been modeled in the form of features in the SPL Feature Model. MSG designers can use the model proposed during the development process, both to manage variability and to create an effective and fun learning environment

A New Algorithm to Detect and Evaluate Learning Communities in Social Networks: Facebook Groups

This article aims to present a new method of evaluating learners by communities on Facebook groups which based on their interactions. The objective of our study is to set up a community learning structure according to the learners' levels. In this context, we have proposed a new algorithm to detect and evaluate learning communities. Our algorithm consists of two phases. The first phase aims to evaluate learners by measuring their degrees of ‘Safely’. The second phase is used to detect communities. These two phases will be repeated until the best community structure is found. Finally, we test the performance of our proposed approach on five Facebook groups. Our algorithm gives good results compared to other community detection algorithms