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

Application of Fermat’s Little Theorem in Congruence Relation Modulo n

According to Fermat's little theorem, for any p is a prime integer and gcdx,p=1, then the congruence xp-1≡1mod n is true, if we remove the restriction that gcdx,p=1, we may declarexp-1≡xmod p. For every integer x. Euler...

Investigative Study on the Properties of Hollow Concrete Blocks

The utilization of workmanship structures is as yet broad all through the world. Hollow concrete blocks have supplanted customary bricks in late development as a result of the upsides of higher bearing limit, farmland in...

Landslide Detection and Alert System Using PSoC

Landslide mainly happens especially due to heavy rainfall which leads to considerable loss of life, communication damage, damage to agricultural and forestlands, In this paper we are implementing with PSoC development ha...

VANET-OLSR Cooperative Cross-Layer Detection for Black hole Attacks

In this study, we address the issue of detecting hot spot problem targeting Multi Point Relays (MPRs) using Vehicular Ad hoc channels Reactive Routing protocol (VANET-OLSR). To identify network-related threats, a watchdo...

Fuzzy Modified TOPSIS for Supplier Selection Problem in Supply Chain Management

Nowadays, global market is highly competitive. Major part of the capital is spent on purchasing raw material/semi finished items. The strategic decision of supply chain is to minimize the expenses on the purchase of item...

Download PDF file
  • EP ID EP748752
  • DOI -
  • Views 41
  • 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