A Linear General Type-2 Fuzzy-Logic-Based Computing With Words Approach for Realizing an Ambient Intelligent Platform for Cooking Recipe Recommendation

PROJECT TITLE:

A Linear General Type-2 Fuzzy-Logic-Based Computing With Words Approach for Realizing an Ambient Intelligent Platform for Cooking Recipe Recommendation

ABSTRACT:

This paper addresses the need to boost transparency in ambient intelligent environments by developing a lot of natural ways in which of interaction, that allow the users to communicate simply with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient intelligence vision aims to understand digital environments that adapt to users in a very responsive, transparent, and context-aware manner so as to reinforce users’ comfort. It is, therefore, appropriate to use the paradigm of “computing with words” (CWWs), that aims to mimic the power of humans to communicate transparently and manipulate perceptions via words. One in all the daily activities that would increase the comfort levels of the users (particularly people with disabilities) is cooking and performing tasks within the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal selections whereas providing restricted personalization features through the use of intrusive user interfaces. Herein, we present an application, that transparently interacts with users based mostly on a completely unique CWWs approach so as to predict the recipe’s problem level and to advocate an appropriate recipe relying on the user’s mood, appetite, and spare time. The proposed CWWs framework relies on linear general kind-2 (LGT2) fuzzy sets, that linearly quantify the linguistic modifiers within the third dimension in order to better represent the user perceptions whereas avoiding the drawbacks of kind-one and interval type-a pair of fuzzy sets. The LGT2-primarily based CWWs framework will learn from user experiences and adapt to them so as to ascertain a lot of natural human-machine interaction. We have a tendency to have carried various real-world experiments with varied users in the University of Essex intelligent flat. The comparison analysis between interval sort-two fuzzy sets and LGT2 fuzzy sets demonstrates up to fifty five.forty threep.c improvement when general t- pe-a pair of fuzzy sets are used than when interval sort-two fuzzy sets are used instead. The quantitative and qualitative analysis each show the success of the system in providing a natural interaction with the users for recommending food recipes where the quantitative analysis shows the high statistical correlation between the system output and therefore the users’ feedback; the qualitative analysis presents social science evaluation confirming the strong user acceptance of the system.

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