Face recognition is a type of biometric software application that can identify a specific
individual in a digital image by analyzing and comparing patterns. It is the process of
identifying an individual using their facial features and expressions.
In this paper we proposed a face recognition system using Stationary Wavelet Transform
(SWT) with Neural Network, the SWT are applied into five levels for feature facial
extraction with probabilistic Neural Network (PNN) , the system produced good results
and then we improved the system by using two manner in Neural Network (PNN) and
Support Vector Machine(SVM) so we find that the system performance is more better
after using SVM where the result shows the performance of the system is better based on
the recognition rate measurement.
Face Recognition Using Stationary wavelet transform and Neural Network with Support Vector Machine
Stationary Wavelet Transform (SWT)
Support Vector Machine (SVM)
Publication Date
Tue Nov 20 2018
Journal Name
Infection And Immunity
Acknowledgment of ad hoc reviewers
EDITORIAL
The editors of Infection and Immunity are grateful to the following individuals
who provided reviews during the period from October 2017 through September 2018. Thank you for your invaluable support.
Publication Date
Mon Apr 24 2017
Journal Name
Spe Kingdom Of Saudi Arabia Annual Technical Symposium And Exhibition
Optimization of Infill Drilling in Whicher Range Field in Australia
Abstract<p>Now that most of the conventional reservoirs are being depleted at a rapid pace, the focus is on unconventional reservoirs like tight gas reservoirs. Due to the heterogeneous nature and low permeability of unconventional reservoirs, they require a huge number of wells to hit all the isolated hydrocarbon zones. Infill drilling is one of the most common and effective methods of increasing the recovery, by reducing the well spacing and increasing the sweep efficiency. However, the problem with drilling such a large number of wells is the determination of the optimum location for each well that ensures minimum interference between wells, and accelerates the recovery from the field. Detail</p>
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machine learning
reservoir simulation
asset and portfolio management
cumulative production
transient drainage area
production rate
reservoir model
infill well
tight gas reservoir
Modeling & Simulation
(15)
(8)
Publication Date
Tue Jan 30 2024
Journal Name
Iraqi Journal Of Science
Further properties of the fuzzy complete a-fuzzy normed algebra
Character of a-fuzzy normed algebra
Structure of a-fuzzy normed algebra
ω^*-fuzzy topology on afb(Z
C)
Fuzzy Tychonoff Theorem
fuzzy Gelfand transform
In this paper further properties of the fuzzy complete a-fuzzy normed algebra have been introduced. Then we found the relation between the maximal ideals of fuzzy complete a-fuzzy normed algebra and the associated multiplicative linear function space. In this direction we proved that if is character on Z then ker is a maximal ideal in Z. After this we introduce the notion structure of the a-fuzzy normed algebra then we prove that the structure, st(Z) is -fuzzy closed subset of fb(Z, ) when (Z, , , ) is a commutative fuzzy complete a-fuzzy normed algebra with identity e.