An Extension of Fuzzy Linguistic Summarization Considering Probabilistic Uncertainty

Aydoğan, Sena; Akay, Diyar; Boran, Fatih Emre; Yager, Ronald R.

doi:10.1142/s0218488518500101

Cited by 6 publications

(1 citation statement)

References 44 publications

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“…First, Zadeh [ 62 ], Yager [ 65 ], Bosc, and Lietard [ 66 ] proposed using scalar cardinality to compute the degree of truth. The scalar cardinality-based methods have been widely used in the application of linguistic summarization as their computational cost is very low [ 67 ]. Scalar cardinality-based methods for Type-I sentences are scalar cardinality-based truth degree calculation by Zadeh [ 62 ], truth degree based on Ordered Weighted Averaging (OWA) operator [ 65 ], and Choquet integral-based truth degree [ 66 , 68 ].…”

Section: Linguistic Summarization Techniquesmentioning

confidence: 99%

Linguistic summarization of visual attention and developmental functioning of young children with autism spectrum disorder

Öztürk,

Aydoğan,

Kök

et al. 2024

Health Inf Sci Syst

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Diagnosing autism spectrum disorder (ASD) in children poses significant challenges due to its complex nature and impact on social communication development. While numerous data analytics techniques have been proposed for ASD evaluation, the process remains time-consuming and lacks clarity. Eye tracking (ET) data has emerged as a valuable resource for ASD risk assessment, yet existing literature predominantly focuses on predictive methods rather than descriptive techniques that offer human-friendly insights. Interpretation of ET data and Bayley scales, a widely used assessment tool, is challenging for ASD assessment of children. It should be understood clearly to perform better analytic tasks on ASD screening. Therefore, this study addresses this gap by employing linguistic summarization techniques to generate easily understandable summaries from raw ET data and Bayley scales. By integrating ET data and Bayley scores, the study aims to improve the identification of children with ASD from typically developing children (TD). Notably, this research represents one of the pioneering efforts to linguistically summarize ET data alongside Bayley scales, presenting comparative results between children with ASD and TD. Through linguistic summarization, this study facilitates the creation of simple, natural language statements, offering a first and unique approach to enhance ASD screening and contribute to our understanding of neurodevelopmental disorders.

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