Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process

Author:

Wang Haoxiang

Abstract

Hybrid data mining processes are employed in recent days on several applications to achieve a better prediction and classification rate along with customer satisfaction. Hybrid data mining processes are the combination of different form of data considered for a neural network decision. In some cases, the different form of data represents image along with numerical data. In the proposed work, a food recommendation system is developed with respect to the flavour taste of the customer and considering the review comments of previous customers. The suggestions given by the users are taken into account as a feedback layer in the neural network for fine tuning the accuracy of the prediction process. The architectural design of the proposed model is employed with an ADNet (Adaptively Dense Convolutional Neural Network) algorithm to enable the usage of low range features in an efficient way. To verify the performance of the developed model, a pizza flavour recommender dataset is employed in the work for analysis. The experimental work analysis indicates that the ADNet algorithm works in a better way on a hybrid data analysis than the traditional DenseNet and ResNet algorithms.

Publisher

Inventive Research Organization

Subject

General Earth and Planetary Sciences,General Environmental Science

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A User Preference-Based Food Recommender System using Artificial Intelligence;2024 2nd International Conference on Disruptive Technologies (ICDT);2024-03-15

2. Intelligent System to Maximize Customer Experience in Restaurants;2023 5th International Conference on Advancements in Computing (ICAC);2023-12-07

3. A Survey on Recommendation Systems using Collaborative Filtering Techniques;2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT);2023-01-23

4. Cross Domain Movie Recommendation System using Personalized Preference Transfer;2022 International Conference on Edge Computing and Applications (ICECAA);2022-10-13

5. Recommendation and Rating System using Machine Learning;2022 International Conference on Edge Computing and Applications (ICECAA);2022-10-13

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