Nutritional Menu Planning: A Hybrid Approach and Preliminary Tests

Chávez-Bosquez, Oscar; Marchi, Jerusa; Pozos-Parra, Pilar

doi:10.13053/rcs-82-1-8

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…Finally, we propose to model real-life scenarios, taking examples from different areas such as decision-making from strategic planning [7], or menu planning from nutrition sciences [8]. By this, real scenarios will be validated by experts of each field and comparison of operators will be more interesting and valuable.…”

Section: Resultsmentioning

confidence: 99%

“…Algorithm 1 is implemented in language M using GNU Octave 3.6.2, as it is an open alternative to Matlab R with similar characteristics and performance [14]. We based on existing source code 4 , and we design a User Interface (UI) based on the Belief Merger prototype 5 described in [7]. Figure 1 outlines the UML diagram of the Belief Reviser where main classes and its relations are depicted.…”

Section: Software Prototypementioning

confidence: 99%

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Implementing Δ<sub>ps</sub> (PS-Merge) Belief Merging Operator for Belief Revision

Chávez-Bosquez

Pozos-Parra

2017

CyS

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Belief merging aims at combining information from multiple sources while belief revision studies strategies for retracting information in order to maintain consistency when the addition of new evidence to a belief base makes it inconsistent. An ordering of the sentences in the belief base is used to determine priorities among sentences so that those with lower priority can be identified and retracted. This ordering can be difficult to generate and maintain. To address this difficulty, in this paper we show how to generate automatically an ordering of the belief base sentences through the implementation of a belief merging operator. We extend the ∆ps (PS-Merge) belief merging operator in order to consider constraints, then we use this extension, called ∆ps µ (∆ps under constraints), as a strategy for belief revision. We treat new evidence as a constraint and apply the extended merging operator to obtain the revised belief base. We propose several properties of this operator when compared to other two belief revision operators solving four examples described as real-life scenarios. Finally we show a software prototype based on this approach, called Belief Reviser, freely accessible online.

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Section: Resultsmentioning

confidence: 99%

Section: Software Prototypementioning

confidence: 99%

Implementing Δ<sub>ps</sub> (PS-Merge) Belief Merging Operator for Belief Revision

Chávez-Bosquez

Pozos-Parra

2017

CyS

Self Cite

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“…Rule-based systems: Early attempts at automating meal planning predominantly relied on rule-based systems. These systems utilized predefined rules and constraints, often based on nutritional guidelines and dietary requirements, to generate meal plans [5][6][7]. While effective in ensuring basic nutritional adequacy, these systems lacked the flexibility to account for individual preferences or lifestyle changes, often leading to static and repetitive meal recommendations.…”

Section: Related Workmentioning

confidence: 99%

“…Traditional systems often use predefined rules and criteria, creating meal plans based on user profiles. For instance, they might suggest a low-carb meal plan for someone with diabetes or a vegetarian plan for users with specific dietary restrictions [5][6][7][8][9]. Despite their utility, these systems suffer from inflexibility and a lack of adaptability to changing user needs.…”

Section: Introductionmentioning

confidence: 99%

Delighting Palates with AI: Reinforcement Learning’s Triumph in Crafting Personalized Meal Plans with High User Acceptance

Amiri,

Sarani Rad,

2024

Nutrients

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Eating, central to human existence, is influenced by a myriad of factors, including nutrition, health, personal taste, cultural background, and flavor preferences. The challenge of devising personalized meal plans that effectively encompass these dimensions is formidable. A crucial shortfall in many existing meal-planning systems is poor user adherence, often stemming from a disconnect between the plan and the user’s lifestyle, preferences, or unseen eating patterns. Our study introduces a pioneering algorithm, CFRL, which melds reinforcement learning (RL) with collaborative filtering (CF) in a unique synergy. This algorithm not only addresses nutritional and health considerations but also dynamically adapts to and uncovers latent user eating habits, thereby significantly enhancing user acceptance and adherence. CFRL utilizes Markov decision processes (MDPs) for interactive meal recommendations and incorporates a CF-based MDP framework to align with broader user preferences, translated into a shared latent vector space. Central to CFRL is its innovative reward-shaping mechanism, rooted in multi-criteria decision-making that includes user ratings, preferences, and nutritional data. This results in versatile, user-specific meal plans. Our comparative analysis with four baseline methods showcases CFRL’s superior performance in key metrics like user satisfaction and nutritional adequacy. This research underscores the effectiveness of combining RL and CF in personalized meal planning, marking a substantial advancement over traditional approaches.

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“…Automatic meal planning using advanced Artificial Intelligence (AI) techniques has been studied to help users design good diet plans and to make cooking easy. Chávez-Bosquez et al (2014) propose a hybrid optimization approach for meal planning. This approach integrates mathematical programming as the first module and belief merging as the second module to combine the nutrition guidelines with the user's food desires.…”

Section: Ai Techniques For Meal Planningmentioning

confidence: 99%

Meal Planning for Alzheimer's Disease Using an Ontology-Assisted Multiple Criteria Decision-Making Approach

Amiri

Roy

2022

International Journal of E-Health and Medical Communications

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As healthy diets and nutrition are crucial for people with Alzheimer's disease (AD), caregivers of patients with AD need to provide a balanced diet with the correct nutrients to boost the health and well-being of patients. However, this is challenging as they are likely to suffer from aging-related problems (such as teeth or gum problems) that make eating more uncomfortable; the planners, who are usually patients' family members, generally face high pressure, a busy schedule, and little experience. To help unprofessional caregivers of AD plan meals with the right nutrition and flavors, in this paper, the authors propose a meal planning mechanism that uses a multiple criteria decision-making approach to integrate various factors that affect a caregiver's choice of meals for AD patients. Ontology-based knowledge has been used to model personal preferences and characteristics and customize general diet recommendations. Case studies have demonstrated the feasibility and usability of the proposed approach.

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Nutritional Menu Planning: A Hybrid Approach and Preliminary Tests

Cited by 8 publications

References 22 publications

Implementing Δ<sub>ps</sub> (PS-Merge) Belief Merging Operator for Belief Revision

Implementing Δ<sub>ps</sub> (PS-Merge) Belief Merging Operator for Belief Revision

Delighting Palates with AI: Reinforcement Learning’s Triumph in Crafting Personalized Meal Plans with High User Acceptance

Meal Planning for Alzheimer's Disease Using an Ontology-Assisted Multiple Criteria Decision-Making Approach

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