Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : propagation


Artificial Neural Networks Based Fingerprint Authentication

Abbas H. Issa

Engineering and Technology Journal, 2015, Volume 33, Issue 5, Pages 1255-1271

Fingerprint authentication and recognition is an important subject that has been widely used in various applications because of its reliability and accuracy in the process of authenticating and recognizing the person's identity. In this paper, an Intelligent Fingerprint Authentication Model (IFAM) based upon the neural network has been proposed. The proposed work consists of two main phases which are the features extraction and the authentication. The features extraction phase has been regarded via proposing a statistical and geometrical approach for determining and isolating the features of the fingerprint images. The proposed approach is called the Features Ring Approach which is abbreviated by FRA. The approach creates a circular ring centered at the core point of the fingerprint to bind the valuable features that are invariant under rotation and translation. The radius of the outer circle of the ring is suggested to be variable to give a variable area for the established circular ring.
The authentication phase of IFAM suggests the neural network to hold the job of verification of the extracted feature patterns resulted by FRA for a fingerprint image of certain person. This is done using a neural network trained with a collection of features patterns extracted from fingerprint images. Backpropagation (BP) is suggested as a training algorithm for the structured neural network.

Intrusion Detection and Attack Classifier Based on Three Techniques: A Comparative Study

Adel Sabry Issa; Adnan Mohsin Abdulazeez Brifcani

Engineering and Technology Journal, 2011, Volume 29, Issue 2, Pages 386-412

Different soft-computing based methods have been proposed in recent years
for the development of intrusion detection systems. The purpose of this work is to
development, implement and evaluate an anomaly off-line based intrusion
detection system using three techniques; data mining association rules, decision
trees, and artificial neural network, then comparing among them to decide which
technique is better in its performance for intrusion detection system. Several
methods have been proposed to modify these techniques to improve the
classification process. For association rules, the majority vote classifier was
modified to build a new classifier that can recognize anomalies. With decision
trees, ID3 algorithm was modified to deal not only with discreet values, but also
to deal with numerical values. For neural networks, a back-propagation algorithm
has been used as the learning algorithm with different number of input patterns
(118, 51, and 41) to introduce the important knowledge about the intruder to the
neural networks. Different types of normalization methods were applied on the
input patterns to speed up the learning process. The full 10% KDD Cup 99 train
dataset and the full correct test dataset are used in this work. The results of the
proposed techniques show that there is an improvement in the performance
comparing to the standard techniques, furthermore the Percentage of Successful
Prediction (PSP) and Cost Per Test (CPT) of neural networks and decision trees
are better than association rules. On the other hand, the training time for neural
network takes longer time than the decision trees.