Vibhu Paudyal, Ellie Gunner, Matthew Bowen, Inderpal Dehele, Atif Shamim, Khalid Khan, Gail Fleming
National recruitment system for pre-registration pharmacist trainees in England and Wales: context, process and outcomes
The International journal of pharmacy practice
recent years rise various machine learning methods ultraviolet near infrared uvnir spectral analysis impressive determination intricate systems however uvnir spectral analysis based traditional machine learning requires independent training tedious parameter tuning different samples tasks result training highquality model often complicated timeconsuming large language model llm one cuttingedge achievements deep learning parameter size order billion llm extract abstract information input use effectively even without additional training using simple natural language prompts llm accomplish tasks never seen completely new domains look forward utilizing capability spectral analysis reduce timeconsuming operational difficulties study used uvnir spectral analysis predict concentration chemical oxygen demand cod three different water samples including complex wastewater extracting characteristic bands spectrum input llm concentration prediction compared cod prediction results different models water samples discussed effects different experiments setting llm results show even brief prompts prediction llm wastewater achieved best performance r2 rmse equal 0931 10966 exceed best results traditional models r2 rmse correspond 0920 11854 result indicates llm simpler operation less timeconsuming ability approach even surpass traditional machine learning models uvnir spectral analysis conclusion study proposed new method uvnir spectral analysis based llm preliminary demonstrated potential llm application