ModelSelect Data and Results

Artificial intelligence (AI) and machine learning (ML) have revolutionized various domains, enabling faster, more accurate task execution in areas like healthcare, agriculture, and supply chain management. Despite the rapid growth of AI, selecting suitable models poses a challenge, especially for Research Software Engineers (RSEs) who often lack deep expertise in the field. This study addresses this challenge by proposing a structured decision model to guide RSEs in choosing AI models tailored to their needs.

The proposed framework integrates multi-criteria decision-making (MCDM) principles and combines objective performance metrics with subjective user sentiment analysis. By collecting data from public sources like documentation, repositories, and community feedback, the model facilitates trade-off analysis, optimizing for constraints such as computational efficiency and long-term maintainability. Using Design Science Research (DSR) methodology, the model was developed and validated through real-world case studies, demonstrating its effectiveness in enhancing decision-making accuracy.