Jul 2026· 2026 IEEE 9th International Conference on Big Data and Artificial Intelligence (BDAI)· pp. 502-509· 0 citations· 16 references
Abstract
Dietary recommendation involves trade-offs among cost, nutritional coverage, and food diversity, yet existing methods focus on single-day optimization, leaving weekly diversity, interpretability, andpersonalized adaptation insufficiently explored. This study introduces a framework integrating Formal Concept Analysis (FCA) with Pareto multi-objective optimization. Formal concepts extracted from a food-nutrient context serve as interpretable semantic units, each specifying an explicit food set and its shared nutrients. Pareto sorting preserves trade-off solutions, while user profiles and a greedy weekly strategy with novelty rewards construct seven-day meal plans. Experiments with 450 foods and 15 nutrients across six profiles demonstrate weekly food variety of 40-82 items, nutrient coverage of 84.8%-92.4%, and higher diversity and interpretability than Linear Programming, Genetic Algorithm, and Graph Neural Network baselines.
Personalized nutrition recommendations encounter difficulties in aligning users′ dietary preferences with appropriate foods in extensive nutritional databases comprising thousands of products. This research introduces a multinutrient clustering framework that examines 8790 foods from the USDA National Nutrient Database for Standard Reference, Release 28, utilizing 23 nutritional attributes, including macronutrients, vitamins, and minerals. We thoroughly analyze K‐means and agglomerative clustering algorithms across various configurations (k = 2 − 8), finding that K‐means with eight clusters yields optimal performance, achieving a silhouette score of 0.273 and semantically interpretable dietary categories. The suggested technique shows a 46.4% improvement over suggestions based on popularity and a 273.0% improvement over recommendations based on a single nutrient. This was shown by a thorough evaluation utilizing Precision@K, NDCG, and fivefold cross‐validation (mean silhouette 0.265 ± 0.006). Users can set their own dietary preferences using nutrient sliders, six preset configurations (high protein, low carb, high fiber, low sodium, high calcium, and low calorie), and three configurable weighting strategies (equal, prioritized 3×, and focus‐only). The system then gives them real‐time recommendations (in less than 2 s) with clear similarity scores. A systematic examination with 50 automated test questions shows that the system works well in a wide range of dietary situations. The huge effect sizes (46.4% and 273.0% improvements) suggest that the results are statistically significant. This research connects computational nutrition studies with real‐world dietary advice. It offers an open‐source, understandable system for evidence‐based meal planning that fills important holes in current prediction and classification methods by allowing personalized, multinutrient food suggestions with clear reasoning behind the choices.
Rajkumar Sarker, Kazi Farhan Hasan Tanjim· International journal of foo...· 0 citations
Experimental results demonstrate that the proposed PLAN'EAT Nutrition Advisor can efficiently generate personalized, nutritionally compliant, and diverse weekly meal plans while maintaining transparent expert-rule-driven optimization.
Dimitris Tsolakidis, Vasilis Stamatis, L. Gymnopoulos et al.· Frontiers in Nutrition· 0 citations
Dietary labels, calorie targets and broad food-group advice do not by themselves show whether nutrient functions are preserved when foods are excluded or substituted. This study developed a proposed nine-domain nutrient-function architecture to translate adult nutrient requirements into auditable food-source decisions across contrasting dietary preferences and calorie tiers.
The architecture was developed through a design-based, criterion-referenced process beginning with the declared nutrient, dependency, safety and implementation requirement universe; mapping recurrent substitution failures; clustering failures by shared planning action; and applying eight domain-retention criteria. Eleven source-availability archetypes were crossed with four maintenance calorie-tier anchors to generate 44 internal model-evaluation scenarios. Foods consumed periodically were converted to average daily equivalents. Candidate food quantities were iteratively reconciled against tier energy bands, benchmark adequacy, domain coverage, source dependencies, upper-limit screens and practical portions. Compatible ordinary foods were considered first; unresolved gaps or narrow-source vulnerabilities then triggered evaluation of alternative foods, verified fortification, targeted sources or conditional interpretation.
The model generated 1,012 non-energy scenario-nutrient rows. Sodium was retained as a safety/contextual output, leaving 22 ordinary adequacy nutrients and 968 ordinary adequacy rows. Final evaluated scenarios met a mean of 20.64 of 22 ordinary nutrients, with a scenario range of 19–21 and variant means of 20.25–21.00. Tier means increased from 20.00 in Lite to 21.00 in High-Output Maintenance. Iron and zinc each produced eight near-adequacy findings; no ordinary nutrient produced a moderate or major gap. Vitamin D remained conditional in all scenarios. Fortified plant milk, fortified nutritional yeast, algal EPA + DHA, iodized salt and a capped selenium source materially affected selected outputs. The safety screen produced 484 no-UL, 374 within-UL, 47 watch and 151 exceedance flags; most exceedance flags involved form-sensitive vitamin A, niacin and folate comparisons or sodium. Thirty-three scenarios showed moderate and 11 high sensitivity to the tested assumptions.
The proposed architecture generated broad modelled benchmark coverage while making compensatory sources, conditional findings, source-form safety issues and broader food-matrix pathways visible. These internally constructed scenarios demonstrate feasibility under declared assumptions, not independent validation, clinical efficacy, real-world adherence or universal individual sufficiency.
Nutritional deficiencies remain a significant global public health challenge, while existing dietary assessment and recipe recommendation systems often operate independently, limiting their ability to provide integrated and personalized nutritional guidance. This article presents an integrated artificial intelligence framework developed using Sri Lankan dietary reference intake guidelines, culturally specific food composition data, and a curated corpus of Sri Lankan recipes to support household nutritional guidance. The proposed framework was developed as part of this Sri Lankan study to address local nutritional challenges while providing a methodology that can be adapted to other countries by replacing country-specific dietary reference standards, food composition databases, and recipe repositories with those of other countries. The nutritional model employed adequacy ratio-based features with a random forest classifier, achieving 88.12% accuracy and a macro area under the curve of 0.91. The semantic module used Sentence-Bidirectional Encoder Representations from Transformers embeddings with fuzzy ingredient matching to achieve 86.84% classification accuracy under stratified cross-validation. By linking predicted deficiencies to context-aware recipes, the system transforms analytical insights into actionable meal recommendations. The results demonstrated that the framework achieved stable performance and showed strong potential for practical application.
L. Weerasinghe, Methusala Perera, Shahmi Mohamed et al.· Artificial Intelligence in H...· 0 citations
The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary management. However, in the domain of food systems, autonomous agents face a unique and persistent challenge: the"Systemic Information Asymmetry"between visual appearance and intrinsic nutritional composition. Existing benchmarks primarily focus on coarse-grained classification tasks, such as food category recognition, which fail to evaluate the intricate reasoning chain required for real-world dietary management -- specifically, the ability to traverse from identifying hidden ingredients to estimating physical mass, and finally synthesizing safety-critical medical advice. In this paper, we introduce OmniFood-Bench, a comprehensive benchmark constructed from the MM-Food-100K dataset. Unlike previous works, OmniFood-Bench evaluates VLMs across three progressive capabilities: Basic Perception (Ingredients&Cooking Methods), Quantitative Reasoning (Portion Size&Nutritional Profiling), and Safety-Critical Advisory (Disease-Specific Recommendations). We evaluate six state-of-the-art VLMs, including gpt-5.1, gemini-3-flash, and qwen3-vl-8B. Our extensive experiments reveal a startling"Semantic-Physical Gap": while models achieve near-human accuracy in naming dishes, they exhibit catastrophic failure in mass estimation and frequently hallucinate benign advice for high-risk diabetic profiles. This work establishes a rigorous standard for trustworthiness in autonomous agents deployed for public health. The code and datasets are available in: https://anonymous.4open.science/r/OmniFood-Bench-7D0B
Qian Jiang, Zhecheng Shi, Jingpu Yang et al.· 1 citation
This paper presents the development of a diet plan generator system that uses user-specific parameters such as age, weight, gender, activity level, dietary goals, and food preferences to recommend a structured meal plan.
S. Uma· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
