Performance Comparison of Neural Classifiers for Face Recognition System Using GLCM Features

Abstract

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.

Authors and Affiliations

A. Johndhanaseely, S. Himavathi

Keywords

Related Articles

Hybrid Application Development using Ionic Framework & AngularJS

A new framework is being used nowadays in order to develop cross platform application since it is extremely cumbersome to form applications for various platforms specifically due to the complications of using Java, Objec...

Noise Exposure in Printing House

The purpose of this paper was to find if noise exposure (dBA) in a printing house met the recommended exposure limits (REL) as recommended in the National Institute for Occupational and Safety and Health’s (NIOSH) Criter...

Mutual Coupling Reduction Using 8x8 MIMO Antenna for MM Wave Applications

A 8x8 multiple input multiple output antenna is developed for the applications of MM wave. this proposed model has 8 ports on the single structure of antenna system. The proposed design gives a triple bands k-band (14.6...

Privacy-Preserving Services for Social Networks: A Review Paper

The popular and frequently used Online Social Networks (OSNs) all have a conceptually centralized design, in which a single organization holds unprecedented amounts of personal information in terms of amount, variety, ge...

Implementation Of Vehicle Diagnostic And Tracking Tool Using Android

The objective of our project is to develop a system to keep a track on the vehicle and the person driving the vehicle .This novel and ingenious technique facilitates the owner of the vehicle to ensure the person driving...

Download PDF file
  • EP ID EP748752
  • DOI -
  • Views 42
  • Downloads 0

How To Cite

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 and Technology, 4(1), -. https://europub.co.uk/articles/-A-748752