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IJSTR >> Volume 9 - Issue 4, April 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Enriched Neutrosophic Clustering With Knowledge Of Chaotic Crow Search Algorithm For Alzheimer Detection In Diverse Multidomain Environment

[Full Text]



Dhanusha C, Dr. A V Senthil Kumar



Alzheimer, neutrosophic clustering, chaos map, crow search algorithm, smart home, unsupervised learning, indeterminacy



The persons with alzheimer disease are severely impaired with their daily activities. Elderly peoples with mild cognitive impairment also have such issue but in a mild range. Alzheimer is a kind of dementia which results in chronic illness related with progressive memory loss, abstract thinking and lessen the intellectual abilities and cognitive loss. But detection of Alzheimer’s at earlier stages may stop further progression and automated detection and classification models are pretty challenging task. This paper focuses on developing an nature inspired unsupervised learning model whose objective is to handle the indeterminacy and ambiguity while clustering the dataset for alzheimer disease detection. This work uses two different heterogenous Multidomain datasets for prediction of alzheimer, CASAS dataset comprised of Activities of Daily living of the smart home residents and OASIS dataset consist of Clinical Dataset. This work proposed a neutrosophic clustering enabled with the intelligence of chaotic crow search algorithm. The neutrosophic clustering handles the indeterminacy by introducing three degrees of membership truthiness, falsity and indeterminacy. The centroids are optimized by applying chaotic crow search algorithm based on the inspiration of crow’s memory of food stored location and its searching strategy. The issue of local optima in conventional crow search algorithm is overwhelmed by applying chaos mapping which offers the ability of global optima to produce best solution in selecting most prominent cluster centroids. The performance results proved the efficacy of the proposed work in treating presence of noisy data and indeterminacy for better alzheimer disease detection.



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