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Design of Multi-disease Diagnosis Processor using Hypernetworks Technique

Year: 2009 Volume: 3 Issue: 2 326 - 329 Pages

Abstract: In this paper, we propose disease diagnosis hardware architecture by using Hypernetworks technique. It can be used to diagnose 3 different diseases (SPECT Heart, Leukemia, Prostate cancer). Generally, the disparate diseases require specified diagnosis hardware model for each disease. Using similarities of three diseases diagnosis processor, we design diagnosis processor that can diagnose three different diseases. Our proposed architecture that is combining three processors to one processor can reduce hardware size without decrease of the accuracy.

Self-Assembling Hypernetworks for Cognitive Learning of Linguistic Memory

Year: 2008 Volume: 2 Issue: 1 13 - 17 Pages

Abstract: Hypernetworks are a generalized graph structure representing higher-order interactions between variables. We present a method for self-organizing hypernetworks to learn an associative memory of sentences and to recall the sentences from this memory. This learning method is inspired by the “mental chemistry" model of cognition and the “molecular self-assembly" technology in biochemistry. Simulation experiments are performed on a corpus of natural-language dialogues of approximately 300K sentences collected from TV drama captions. We report on the sentence completion performance as a function of the order of word-interaction and the size of the learning corpus, and discuss the plausibility of this architecture as a cognitive model of language learning and memory.

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Design of Multi-disease Diagnosis Processor using Hypernetworks Technique

Self-Assembling Hypernetworks for Cognitive Learning of Linguistic Memory