Performance Comparison of Neural Classifiers for Face Recognition System Using GLCM Features
A.Johndhanaseely , S.Himavathi
Sediment in hydraulic flow plays significant role because of complexity of its bed and the flow from multi direction with the variation of its forces. Accretion and erosion at river bed, banks, dams and power intake structures are caused due to sediment transport gradient in the flow or otherwise. Therefore prediction of sediment transport is much significant for the sustainable functioning of the structure and planning of the canals training works, reservoir intakes and capacity sustenance. Sediment transport pattern in the Himalayan River is complex and sediment sampling in these rivers are often difficult. Sediment load in the river varies spatially as well as temporarily. For the Himalayan Rivers, reliable and consistent sediment rating equations are rare. The change in the flow rate and sediment concentration is very rapid and unpredictable. This research paper describes prediction of sediment inflow based on the published data. Empirical equations in mathematical form are proposed based on the data sample of 1312 observations.
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[A.Johndhanaseely, S.Himavathi (2016), Performance Comparison of Neural Classifiers for Face Recognition System Using GLCM Features, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-4, Issue-1, Page No-15-18], (ISSN 2347 - 5552). www.ijircst.org
Department of EEE, Pondicherry Engineering College, Puducherry - 605014, India, (email@example.com).