Mobile Application of Food Recommendation For Allergy Baby Using Rule-Based Technique

Shari, Anis Amilah; Pajar, Norelyana Ahmad; Sabri, Nurbaity; Noordin, Mohd Rahmat Mohd; Zainudin, F.; Shari, Asma Shazwani; Ahmad, Aminah

doi:10.1109/i2cacis.2019.8825026

Cited by 10 publications

(1 citation statement)

References 17 publications

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“…Performance of proposed SVMTAFRS is compared with the RBFRS reported in [27] in terms of accuracy of the FRS. Accuracy of the RBFRS reported in [27] is 96.579% whereas the proposed SVMTAFRS works with a higher accuracy of 99.379%. Hence, efficacy of designed SVMTAFRS is superior compared to the RBFRS.…”

Section: Performance Comparative Studymentioning

confidence: 99%

An intelligent time aware food recommender system using support vector machine

Panwar,

Sharma,

Mahela

et al. 2024

IJEECS

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This paper formulated a support vector machine powered time-aware food recommender system (SVMTAFRS) to recommend healthy food for the customers. The rated food item incorporates the user preference (UP) in terms of calories, nutrition factor, and all food contents required for a healthy diet. This also takes into account the user age, time of day and week day while predicting the food rating. The SVMTAFRS involves two steps for computation of user identity document (UID) and predicted food rating (PFR). UID is computed considering the customer age (CA), UP in terms of calories and suitable weight factors. PFR is computed considering the UID and time of day (TOD). PFR for week end day is computed by multiplying the PFR by week end multiplying factor (WEMF). Support vector machine (SVM) is used for recommending the suitable healthy food for customer in terms of correct values of PFR. Efficacy of PFR is tested in terms of mean absolute error (MAE) and root mean squared error (RMSE). This is established that performance of the SVMTAFRS is superior compared to the rule-based food recommender system (RBFRS).

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Section: Performance Comparative Studymentioning

confidence: 99%