Forecasting learning in electrical engineering and informatics: An ontological approach

Abstract

This research explores the vital role of ontology in learning forecasting in electrical engineering and informatics. As formally defined models of knowledge, ontologies are critical in organizing concepts for predictive learning. More than just an inquiry, our research reveals complex interconnections centered on Internet of Things (IoT) design, the semantic web, and knowledge modeling. Applications demonstrate the practical significance of ontologies in fostering intelligent connections, advancing information production, and improving interactions between computers, devices, and humans. This research introduces a comprehensive forecasting learning ontology to highlight the importance of ontologies in education, scientific inquiry, and developing systems for predictive analysis. Ontologies provide a structured framework for understanding the essence of predictive learning, encompassing key elements such as ideas, terminology, methodology, algorithms, data preprocessing, assessment, validation, data sources, application environments, interactions with technology, and learning processes. Emphasizing ontologies as indispensable instruments that drive technological development, our work underscores structured representation, semantic interoperability, and knowledge integration. In summary, this research improves the understanding of ontologies in forecasting by explaining practical applications and revealing new perspectives. Its unique contribution lies in its specific applications and natural consequences, laying the foundation for the future progress of ontology and learning forecasting, especially in educational contexts.