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In this paper, two hybrid intelligent approaches are suggested for computer aided diagnosis systems in a biomedicine application: auditory diagnosis based on auditory brainstem response test. Indeed, these approaches are developed through the hybrid intelligent system 1 (HIS_1) and the hybrid intelligent system (HIS_2), based on neural classification and fuzzy decision-making of global image and subdivided...
In this paper, the main objective is to give a methodology to design hybrid intelligent diagnosis systems for a large field of biomedicine and industrial applications. At first, a brief description on diagnosis tasks in such applications is presented. Second, diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems...
In this paper, an automated fault diagnosis system essentially based on neural networks and fuzzy logic, in a hybrid scheme, is suggested. First, a signal classification and image classification, resulting in a signal diagnosis and image diagnosis respectively, are developed. Such dual-classification is then exploited in a fuzzy system 1 to ensure a satisfactory reliability to medical diagnosis and...
In this paper, a neural hybrid image classification for intelligent diagnosis systems from signal to image conversion (image representation) is suggested. Such hybrid approach (multiple models) mainly aims to ensure a satisfactory reliability for faults diagnosis systems, and particularly for medical diagnosis. Thus, an overview is given on how neural global and local approximators are interesting...
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