A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction.

Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there's a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs.

Unraveling the effect of Cu doping on the structural and morphological properties and photocatalytic activity of ZrO.

Pristine ZrO and doped with different concentrations of Copper (0-7 %) were synthesized using a sol-gel combustion route. Several advanced techniques like XRD, EDX, TEM, XPS, P.L.

Optical Band Gap Tuning, DFT Understandings, and Photocatalysis Performance of ZnO Nanoparticle-Doped Fe Compounds.

Iron-doped Zinc oxide nanoparticles were produced by the sol-gel combustion method. This study aims to see how iron doping affects the structural, optical, and photocatalytic characteristics of ZnO composites. XRD examined all samples to detect the structural properties and proved that all active materials are a single hexagonal phase.

The differential measure for Pythagorean fuzzy multiple criteria group decision-making.

Pythagorean fuzzy sets (PFSs) proved to be powerful for handling uncertainty and vagueness in multi-criteria group decision-making (MCGDM). To make a compromise decision, comparing PFSs is essential. Several approaches were introduced for comparison, e.