Поиск по всему репозиторию:

Показать краткое описание

dc.contributor.authorChohra, Amine
dc.contributor.authorKanaoui, Nadia
dc.contributor.authorMadani, Kurosh
dc.coverage.spatialBrestru_RU
dc.date.accessioned2023-11-24T07:32:13Z
dc.date.available2023-11-24T07:32:13Z
dc.date.issued2006
dc.identifier.citationChohra, A. Image Representation Based Hybrid Intelligent Diagnosis Approach for Computer Aided Diagnosis (CAD) Systems / Amine Chohra, Nadia Kanaoui, Kurosh Madani // International Conference on Neural Networks and Artificial Intelligence : proceedings, Brest, 31 May – 2 June, 2006 / Edited: V. Golovko [et al.]. – Brest : BSTU, 2006. – P. 168–139 : il. – Bibliogr.: p. 173–174 (19 titles).ru_RU
dc.identifier.urihttps://rep.bstu.by/handle/data/37192
dc.descriptionЧохра Амин, Канауи Надия, Мадани Курош. Гибридный интеллектуальный диагностический подход на основе представления изображений для систем автоматизированной диагностикиru_RU
dc.description.abstractComputer Aided Diagnosis (CAD) is one of the most interesting and most difficult dilemma dealing in one hand with expert (human) knowledge consideration. On the other hand, fault diagnosis is a complex and fuzzy cognitive process and soft computing approaches as modular neural networks and fuzzy logic, have shown great potential in the development of decision support systems. In this paper, a brief survey on fault diagnosis systems, knowledge representations, and modular neural networks is given. From the classification and decisionmaking problem analysis, a hybrid intelligent diagnosis approach is suggested from signal to image conversion (image representation). In this approach, each image is divided in several sub-images (local indicators) which are classified by global approximators MultiLayer feedforward Perceptron networks (MLP) and by local approximators Radial Basis Function networks (RBF). Then, the suggested approach is developed in biomedicine for a CAD, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.ru_RU
dc.language.isoenru_RU
dc.publisherBrSTUru_RU
dc.subjectdecision supportru_RU
dc.subjectподдержка принятия решенийru_RU
dc.subjectknowledge representationru_RU
dc.subjectпредставление знанийru_RU
dc.subjectclassification and decision-makingru_RU
dc.subjectклассификация и принятие решенийru_RU
dc.subjectsoft computingru_RU
dc.subjectмягкие вычисленияru_RU
dc.subjectfuzzy logicru_RU
dc.subjectнечеткая логикаru_RU
dc.subjectmodular neural networksru_RU
dc.subjectмодульные нейронные сетиru_RU
dc.titleImage Representation Based Hybrid Intelligent Diagnosis Approach for Computer Aided Diagnosis (CAD) Systemsru_RU
dc.title.alternativeГибридный интеллектуальный диагностический подход на основе представления изображений для систем автоматизированной диагностикиru_RU
dc.typeНаучный доклад (Working Paper)ru_RU


Файлы в этом документе

Thumbnail

Данный элемент включен в следующие коллекции

Показать краткое описание