A Computing with Words Framework for Ambient Intelligence

Bilgin, Aysenur; Hagras, Hani; Malibari, Areej; Alghazzawi, Daniyal; Alhaddad, Mohammed J.

doi:10.1109/smc.2013.492

Cited by 8 publications

(13 citation statements)

References 16 publications

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“…(Source: http://www.oxforddictionaries.com/definition/english/sortal) Hence, 'sortal range' in this paper can be defined to be the numerical domain (universe of discourse) of the variable in question whose values can be sorted. For example, for variable 'hot' assuming that the numerical domain is [20,30], the values in this domain can be sorted as in 24<25.…”

Section: Linear General Type-2 Fuzzy Sets (Lgt2 Fss)mentioning

confidence: 99%

“… Due to the concise design of LGT2 FSs, they are more advantageous than IT2 FSs regarding the convenience in learning and adaptation aspect of fuzzy membership functions. It has been shown in [20] that LGT2 FSs can easily accommodate the changes in the inputs and can dynamically represent human perceptions as they reflect on the experienced information rather than data collected through surveys ahead of time.…”

Section: B Benefits Of Lgt2 Fssmentioning

confidence: 99%

“…Given that a zSlice ̃ is equivalent to an IT2 set with particular height in the third dimension ℎ = / where 1 ≤ ≤ , and I is the number of zSlices, we can infer ̃̃ and ̃̃ are special cases of IT2 FSs having heights ℎ̃= / and ℎ̃= / , respectively, where , = 1 … . The adapted definition of Jaccard similarity in Equation (3) to be applied on individual zSlices ̃̃ and ̃̃ is shown in Equation (4) [20]. …”

Section: Granulation In the Proposed Cwws Frameworkmentioning

confidence: 99%

“…= 0, = 5, = 5) in their level in the third dimension. Hence, we use weighting factor , which denotes the effect of difference in the zLevels of two LGT2 FSs, as follows [20]:…”

Section: Granulation In the Proposed Cwws Frameworkmentioning

confidence: 99%

“…Likewise, the closer the zSlices are to each other, the more weight for similarity the zSlices have. The final local similarity measure, denoted (̃,̃), is based on weighted average calculation as shown in Equation (6) [20]:…”

Section: Granulation In the Proposed Cwws Frameworkmentioning

confidence: 99%

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

Bilgin

Hagras

Helvert

et al. 2016

IEEE Trans. Fuzzy Syst.

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Abstract-This paper addresses the need to enhance transparency in Ambient Intelligent Environments by developing more natural ways of interaction, which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient Intelligence vision aims to realize digital environments that adapt to users in a responsive, transparent and context aware manner in order to enhance users' comfort. It is therefore appropriate for employing the paradigm of 'Computing With Words' (CWWs), which aims to mimic the ability of humans to communicate transparently and manipulate perceptions via words. One of the daily activities that would increase the comfort levels of the users (especially people with disabilities) is cooking and performing tasks in the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices while providing limited personalization features through the use of intrusive user interfaces. Herein, we present an application, which transparently interacts with users based on a novel CWWs approach in order to predict the recipe's difficulty level and to recommend an appropriate recipe depending on the user's mood, appetite and spare time. The proposed CWWs framework is based on Linear General Type-2 (LGT2) Fuzzy Sets, which linearly quantify the linguistic modifiers in the third dimension in order to better represent the user perceptions while avoiding the drawbacks of type-1 and interval type-2 fuzzy sets. The LGT2 based CWWs framework can learn from user experiences and adapt to them in order to establish more natural human-machine interaction. improvement when general type-2 fuzzy sets are used than when interval type-2 fuzzy sets are used instead. The quantitative and qualitative analysis both 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 the users' feedback; and the qualitative analysis presents social science evaluation confirming the strong user acceptance of the system. Index Terms-ambient intelligence, computing with words, general type-2 fuzzy sets I. INTRODUCTIONhe recent years have witnessed a rapid increase in the miniaturization of computers which enabled to embed computing throughout our spaces in familiar objects such as home appliances (e.g. washing machines, refrigerators, etc.), portable devices (e.g. mobile phones, tablets, etc.), cars, etc. In addition, the advances in communications allowed such devices to be networked and connected to the Internet. Among the highlights of the connected and miniaturized devices bring to real life is the opportunity of customization and therefore personalization. Lately, it is getting more important to deliver personalized content in areas such as food planning which is becoming an important personal issue that affects the ...

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Section: Linear General Type-2 Fuzzy Sets (Lgt2 Fss)mentioning

confidence: 99%

Section: B Benefits Of Lgt2 Fssmentioning

confidence: 99%

Section: Granulation In the Proposed Cwws Frameworkmentioning

confidence: 99%

“…= 0, = 5, = 5) in their level in the third dimension. Hence, we use weighting factor , which denotes the effect of difference in the zLevels of two LGT2 FSs, as follows [20]:…”

Section: Granulation In the Proposed Cwws Frameworkmentioning

confidence: 99%

Section: Granulation In the Proposed Cwws Frameworkmentioning

confidence: 99%

See 3 more Smart Citations