A Diagnosis Aid System of Autism in a Multi-Valued Framework

Kacem, Saoussen Bel Hadj; Borgi, Amel; Othman, Sami

doi:10.1142/9789813146976_0066

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…In a further work, we will also test the suitability of unbalanced multi-sets in the diagnosis of childhood autism as described in the Childhood Autism Rating Scale (CARS). A tool was already implemented by Bel-Haj Kacem et al in collaboration with the child psychiatry department of Razzi hospital in Tunisia but it uses uniform multi-sets [25]. An adaptation of this proposal is required to deal with unbalanced multi-sets.…”

Section: Discussionmentioning

confidence: 99%

Camphor odor recognition within unbalanced multi-sets

Chaoued

Borgi²,

Laurent

2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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In fuzzy logic context, some works deal with the camphor odor perception. In this paper, we present a novel rule-based decision system for the camphor odor recognition within unbalanced multiset. Our first contribution consists in an adaptation of fuzzy knowledge representation and inference rules to the multi-valued logic context. The second contribution concerns the improvement of the knowledge base by changing facts representation and adding new rules. This proposition provides satisfactory results in term of precision, recall, F-measure and accuracy.

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Section: Discussionmentioning

confidence: 99%

Camphor odor recognition within unbalanced multi-sets

Chaoued

Borgi²,

Laurent

2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…In [30], the authors proposed to deal with autism diagnosis within uniform multi-sets. They extracted autism symptoms from the decision-making algorithm of DSM5 [9].…”

Section: Autism Diagnosismentioning

confidence: 99%

Symbolic Approximate Reasoning Within Unbalanced Multi-sets: Application to Autism Diagnosis

Chaoued¹,

Borgi²,

Laurent³

2017

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)

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Knowledge-Based Systems aim to reason and solve complex problems with a knowledge base, i.e. facts and rules. The reasoning process can be based on approximate reasoning. It allows deducing new facts from imprecise information. In this work, we focus on dealing with unbalanced linguistic terms in the multi-valued logic context. In this paper, we introduce a new method for approximate reasoning within unbalanced terms. This approach is based on a Generalized Modus Ponens model using Generalized Symbolic Modifiers. Moreover, we implement a tool for autism diagnosis based on the KBS RAMOLI with our inference engine.

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