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Opis: Background/Objectives: Identifying and decomposing compound ingredients within meal plans presents meal customization and nutritional analysis challenges. It is essential for accurately identifying and replacing problematic ingredients linked to allergies or intolerances and helping nutritional evaluation. Methods: This study explored the effectiveness of three large language models (LLMs)—GPT-4o, Llama-3 (70B), and Mixtral (8x7B), in decomposing compound ingredients into basic ingredients within meal plans. GPT-4o was used to generate 15 structured meal plans, each containing compound ingredients. Each LLM then identified and decomposed these compound items into basic ingredients. The decomposed ingredients were matched to entries in a subset of the USDA FoodData Central repository using API-based search and mapping techniques. Nutritional values were retrieved and aggregated to evaluate accuracy of decomposition. Performance was assessed through manual review by nutritionists and quantified using accuracy and F1-score. Statistical significance was tested using paired t-tests or Wilcoxon signed-rank tests based on normality. Results: Results showed that large models—both Llama-3 (70B) and GPT-4o—outperformed Mixtral (8x7B), achieving average F1-scores of 0.894 (95% CI: 0.84–0.95) and 0.842 (95% CI: 0.79–0.89), respectively, compared to an F1-score of 0.690 (95% CI: 0.62–0.76) from Mixtral (8x7B). Conclusions: The open-source Llama-3 (70B) model achieved the best performance, outperforming the commercial GPT-4o model, showing its superior ability to consistently break down compound ingredients into precise quantities within meal plans and illustrating its potential to enhance meal customization and nutritional analysis. These findings underscore the potential role of advanced LLMs in precision nutrition and their application in promoting healthier dietary practices tailored to individual preferences and needs.
Ključne besede: artificial intelligence, food analysis, LLM, Ilama, GPT, mixtral, ingredient identification, ingredient decomposition, personalized nutrition, meal customization, nutritional analysis, dietary planning
Objavljeno v DKUM: 08.05.2025; Ogledov: 0; Prenosov: 2
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Opis: Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability. This novel approach has not been explored before in using pretrained transformers for diabetes classification on tabular data. Methods: The study used the Pima Indians Diabetes dataset to investigate Type 2 diabetes mellitus. Python and Jupyter Notebook were employed for analysis, with the NiaARM framework for association rule mining. LightGBM and the dalex package were used for performance comparison and feature importance analysis, respectively. SHAP was used for local interpretability. OpenAI GPT version 3.5 was utilized for outcome prediction and interpretation. The source code is available on GitHub. Results: NiaARM generated 350 rules to predict diabetes. LightGBM performed better than the GPT-based model. A comparison of GPT and NiaARM rules showed disparities, prompting a similarity score analysis. LightGBM’s decision making leaned heavily on glucose, age, and BMI, as highlighted in feature importance rankings. Beeswarm plots demonstrated how feature values correlate with their influence on diagnosis outcomes. Discussion: Combining association rule mining with GPT for Type 2 diabetes mellitus classification yields limited effectiveness. Enhancements like preprocessing and hyperparameter tuning are required. Interpretation challenges and GPT’s dependency on provided rules indicate the necessity for prompt engineering and similarity score methods. Variations in feature importance rankings underscore the complexity of T2DM. Concerns regarding GPT’s reliability emphasize the importance of iterative approaches for improving prediction accuracy.
Ključne besede: GPT, association rule mining, classification, interpretability, diagnostics
Objavljeno v DKUM: 26.11.2024; Ogledov: 0; Prenosov: 252
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Opis: Diplomska naloga obravnava uporabo umetne inteligence v oglaševanju in primerja njeno učinkovitost s tradicionalnimi oglaševalskimi metodami. Namen raziskave je bil ugotoviti, kako različne starostne skupine sprejemajo oglase, ustvarjene z umetno inteligenco, v primerjavi s tradicionalnimi oglasi, ter kako te preference vplivajo na njihove nakupne odločitve. Metode uporabljene v pričujoči raziskavi so analize že obstoječih študij v kombinaciji z anketnim vprašalnikom, ki smo ga izvedli. Pomagale so nam križne tabele v programu SPSS. Rezultati so pokazali, da starejši odjemalci raje vidijo tradicionalne oglase, medtem ko so oglasi, ustvarjeni z UI, ocenjeni kot bolj kreativni. Vendar pa so mlajši odjemalci kljub temu raje izbrali tradicionalne oglase pri svojih nakupnih odločitvah. To nakazuje, da kreativnost sama po sebi ni dovolj za povečanje učinkovitosti oglasov, saj odjemalci še vedno vrednotijo pristnost in zaupanje tradicionalnih oglasov. Ugotovitve kažejo na potrebo po kombinaciji kreativnih elementov umetne inteligence z elementi tradicionalnega oglaševanja. Umetna inteligenca ima velik potencial za preoblikovanje oglaševalske industrije, vendar je ključ do uspeha v strateškem pristopu, ki upošteva specifične potrebe in preference ciljnih odjemalcev.
Ključne besede: umetna inteligenca, oglaševanje, marketinško komuniciranje, Chat GPT, marketing
Objavljeno v DKUM: 26.09.2024; Ogledov: 0; Prenosov: 92
Celotno besedilo (3,53 MB)