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Publication Type : Journal Article
Publisher : IIOAB JOURNAL
Source : IIOAB JOURNAL, 7(9), pp.787-794.
Campus : Chennai
School : School of Engineering
Department : Computer Science and Engineering
Year : 2016
Abstract : Alzheimer’s disease (AD) refers to a neuro-degenerative chaos that is a general kind of dementia which leads to memory loss, and lack of cognitive functioning and so on. Magnetic Resonance Imaging (MRI) is popularly utilized for human body imagings. MRI is a fundamentally non-invasive method giving high degree clarity on the soft tissue inside the brain better than conventional Computed Tomography (CT), ultrasound, Positron Emission Tomography (PET), etc. SVM is a kind of ANN (artificial neural network) which has got training from supervised learning methods and had showed the benefits of decreasing the training-testing error and hence producing greater recognition precision. This paper investigates empirically PSO’s (Particle Swarm Optimization’s) effectiveness towards selection of features.
Cite this Research Publication : Sountharrajan, S., Thangaraj, P. and Suganya, E., 2016. PARTICLE SWARM OPTIMIZED FEATURE SELECTION FOR ALZHEIMER CLASSIFICATION. IIOAB JOURNAL, 7(9), pp.787-794.