Shaping the Future of Intelligent Data Science and Engineering Knowledge Applications
Keywords:
Intelligent systems, data science, knowledge engineering, machine learning, semantic reasoning, automation, decision support, knowledge graphs, computational intelligenceAbstract
The integration of intelligent systems into data science and engineering knowledge applications is reshaping how complex problems are approached and solved. With advancements in artificial intelligence, machine learning, and automated reasoning, intelligent data science is transitioning from traditional statistical analysis to context-aware, adaptive decision-making. This paper explores the emerging landscape of intelligent knowledge-driven systems, discussing current frameworks, challenges, and opportunities for future applications. Special attention is given to architectural innovations, semantic enrichment, and autonomous knowledge discovery methods.
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