Back close

PARTICLE SWARM OPTIMIZED FEATURE SELECTION FOR ALZHEIMER CLASSIFICATION

Publication Type : Journal Article

Publisher : IIOAB JOURNAL

Source : IIOAB JOURNAL, 7(9), pp.787-794.

Url : https://www.researchgate.net/publication/341068433_PARTICLE_SWARM_OPTIMIZED_FEATURE_SELECTION_FOR_ALZHEIMER_CLASSIFICATION

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.

Admissions Apply Now