Fuzzy Ontology Based Document Feature Vector Modification Using Fuzzy Tree Transducer

Fallah, Mohammad K; Moghari, Somaye; Nazemi, Eslam; Zahedi, M. M.

doi:10.1109/sitis.2008.90

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Usually, synthesis and analysis of systems by computational models is associated with uncertainty modeling, and approximate reasoning [2,10]. Fuzzy logic is capable of overcoming the vagueness, imprecision and absence of concrete data for generating robust models to drive decisions from uncertainties [41].…”

Section: Related Workmentioning

confidence: 99%

Synthesizing fuzzy tree automata

Moghari

2022

RAIRO-Theor. Inf. Appl.

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Fuzzy tree automata are mathematical devices for modeling and analyzing vaguely defined tree structures. The behavior of a fuzzy tree automaton generates a fuzzy tree language by mapping a set of regular trees on a ranked alphabet to fuzzy membership values. It calculates the membership grade of trees using a set of rules that process their structural characteristics. This paper deals with constructing fuzzy tree automata models that their behavior satisfies a set of given logical propositions (called properties) on the structure of trees. Our goal is uncertainty modeling by synthesizing fuzzy tree automata whose behavior is described by fuzzy linguistic variables. In this regard, we first provide several patterns and heuristic tricks and techniques for constructing fuzzy tree automata that satisfy simple properties. Then, we develop a method for modeling complex propositional formulas based on the conversion of a logical formula into a computation tree, as well as a step-by-step combination of models.

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Section: Related Workmentioning

confidence: 99%

Synthesizing fuzzy tree automata

Moghari

2022

RAIRO-Theor. Inf. Appl.

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“…Now, the proof is straightforward. δ(q 1 , α) = 0.9, r 2 : δ(q 1 , q 4 , λ) = 0.8, r 3 : δ(q 2 , q 4 , λ) = 0.8, r 4 : δ(q 3 , q 4 , λ) = 0.8, r 5 : δ(q 4 , q 2 , λ) = 0.8, r 6 : δ(q 1 , q 1 , q 3 , σ ) = 0.7, r 7 : δ(q 1 , q 2 , q 3 , σ ) = 0.7, r 8 : δ(q 1 , q 3 , q 2 , σ ) = 0.6, r 9 : δ(q 1 , q 4 , q 4 , σ ) = 0.6, r 10 : δ(q 2 , q 1 , q 3 , σ ) = 0.6, r 11 : δ(q 2 , q 2 , q 3 , σ ) = 0.6, r 12 : δ(q 2 , q 3 , q 2 , σ ) = 0.6, r 13 : δ(q 2 , q 4 , q 4 , σ ) = 0.2, r 14 : δ(q 3 , q 1 , q 2 , σ ) = 0.3, r 15 : δ(q 3 , q 2 , q 2 , σ ) = 0.3, r 16 : δ(q 3 , q 3 , q 3 , σ ) = 0.3, r 17 : δ(q 3 , q 4 , q 4 , σ ) = 0.2, r 18 : δ(q 4 , q 1 , q 4 , σ ) = 0.1, r 19 : δ(q 4 , q 2 , q 4 , σ ) = 0.1, r 20 : δ(q 4 , q 3 , q 4 , σ ) = 0.1, r 21 : δ(q 4 , q 4 , q 4 , σ ) = 1}. Now, for each i ∈ {1, 2, 3, 4} we have f * (q i ) = i − 1 and then f * (r 16 M = ( , Q, , δ, , ρ, β) be an FFTA.…”

Section: Lemmamentioning

confidence: 99%

“…This paper introduces the concept of similarity-based minimizing DFFTA and a method for handeling the trade-off between the amount of states that can be merged and the quality of preserving the behavior of system. Similarity plays an essential role in taxonomy, recognition, case based reasoning and many other fields [15,38,40]. We use the concept of similarity or approximate equality [31,41] modeled on classes of fuzzy sets to define similarity between some DFFTA that approximately accept the same fuzzy tree language.…”

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Similarity-based minimization of fuzzy tree automata

Moghari

Zahedi

2015

J. Appl. Math. Comput.

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This paper presents a contribution to the problem of similarity-based minimization of deterministic fuzzy finite tree automata (DFFTA). The main question is: how to minimize the number of states of a complete and reduced DFFTA such that the languages of the original automaton and the minimized one be similar but not necessarily equal? Based on extended concepts of fuzzy distance and similarity measures on L-fuzzy sets, we introduce the notion of similarity-based minimal (s-minimal) DFFTA which approximately accepts a fuzzy tree language. Then, a solution for handeling the trade-off between the amount of reduction and the quality of preserving the behavior of system is presented. The paper deals with fuzzy tree automata over complete lattices, but identical results can also be obtained in a more general context for fuzzy tree automata over complete residuated lattices, lattice-ordered monoids, and even for weighted automata over commutative semirings.

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LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart Cities

Moghari,

Fallah,

Gorgin

et al. 2024

Smart Cities

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The increasing use of mobile networks is an opportunity to collect and model users’ movement data for extracting knowledge about life and health while considering privacy leakage risk. This study aims to approximate the lifestyles of urban residents, employing statistical information derived from their movements among various Points of Interest (PoI). Our investigations comprehend a multidimensional analysis of key urban factors to provide insights into the population’s daily routines, preferences, and characteristics. To this end, we developed a framework called LEAF that models lifestyles by interpreting anonymized cell phone mobility data and integrating it with information from other sources, such as geographical layers of land use and sets of PoI. LEAF presents the information in a vector space model capable of responding to spatial queries about lifestyle. We also developed a consolidated lifestyle pattern framework to systematically identify and analyze the dominant activity patterns in different urban areas. To evaluate the effectiveness of the proposed framework, we tested it on movement data from individuals in a medium-sized city and compared the results with information collected through surveys. The RMSE of 5.167 between the proposed framework’s results and survey-based data indicates that the framework provides a reliable estimation of lifestyle patterns across diverse urban areas. Additionally, summarized patterns of criteria ordering were created, offering a concise and intuitive representation of lifestyles. The analysis revealed high consistency between the two methods in the derived patterns, underscoring the framework’s robustness and accuracy in modeling urban lifestyle dynamics.

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Fuzzy Ontology Based Document Feature Vector Modification Using Fuzzy Tree Transducer

Cited by 3 publications

References 17 publications

Synthesizing fuzzy tree automata

Synthesizing fuzzy tree automata

Similarity-based minimization of fuzzy tree automata

LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart Cities

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