Exploring AI-Based System for African Food Weight-Loss Recommendations
Bibliographic record
Abstract
This research leverages artificial intelligence to design an African food recommendation system for weight loss. The rationale for designing this system was based on our recently published study on the design of socio-cultural food recognition systems for Africans. Based on our previous study, results revealed that users considered the socio-cultural food recognition system to provide nutritional value and would require a robust system with more African foods. Hence, to tailor our findings to effective dietary planning where obesity could be a concern, we propose the current system given the health implications of additional foods for specific users (that is, overweight users). Our current study is in three phases. The first phase will focus on validating some African foods with dieticians to determine their appropriateness for weight loss and better alternatives based on calories and other important metrics. Additionally, we will invite dieticians and some overweight users to evaluate some low-fidelity (Lo-fi) prototypes for the design requirement elicitation of the final prototype. The second phase will involve the development of our AI models (computer vision and large language models) and their evaluation. Furthermore, we will leverage the design requirements gathered from the lo-fi prototype study together with the AI models to develop a high-fidelity (Hi-fi) AI system that will run on mobile devices (the final prototype). Consequently, a post-study evaluation will be conducted with dieticians and overweight users to obtain subjective feedback. Hence, findings from this study will provide design recommendations for integrating African foods into existing and related large-scale AI-based systems in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".