Computer
Salah Sabah Abed; Mohammed Natiq Fadhil
Abstract
The most attractive study framework among academics is Software Define Networking Networking SDN, which aims to create the Internet with an architecture- independent architecture that will lead to the most significant advances in the network field. This can solve many network problems to deal with high ...
Read More ...
The most attractive study framework among academics is Software Define Networking Networking SDN, which aims to create the Internet with an architecture- independent architecture that will lead to the most significant advances in the network field. This can solve many network problems to deal with high demand changes and reduce Replenishment, changes and less manual work. Because of the limited architecture of traditional networks, which requires modifications in the basic design, network expansion has been mature and slow. Since 2010, until now, the ODL - OpenDayLight model has been proposed to solve most of the problems that guide network engineers in the process of managing complex high-volume networks by top research-oriented universities around the world. Now is the time to turn dreams into reality by putting the presented ideas into action, which will result in a solution that meets the expectations of the researcher regarding the process of managing complex networks and all forms of networks. This document is an attempt to assist researchers in implementing a software identification network infrastructure on which the research community may focus on further analysis and development. We demonstrated an incremental approach to implementing ODL - OpenDayLight Controller (ODL is a JVM program and can run from any operating system and device as long as it supports Java) from the Software Define Network, as well as creating and executing required scenarios, and illustrate the working nature of ODL - OpenDayLight compared to ryu contrlloer, in this paper Research.
Computer
Ashraf Tahseen Ali; Hasanen Abdullah; Mohammed Natiq Fadhil
Abstract
Biometrics signs are the most important factor in the human recognition field and considered an effective technique for person authentication systems. Voice recognition is a popular method to use due to its ease of implementation and acceptable effectiveness. This research paper will introduce a speaker ...
Read More ...
Biometrics signs are the most important factor in the human recognition field and considered an effective technique for person authentication systems. Voice recognition is a popular method to use due to its ease of implementation and acceptable effectiveness. This research paper will introduce a speaker recognition system that consists of preprocessing techniques to eliminate noise and make the sound smoother. For the feature extraction stage, the method Mel Frequency Cepstral Coefficient (MFCC) is used, and in the second step, the four features (FF) Mean, Standard Division, Zero-Cross and Amplitude, which added to (MFCC) to improve the results. For data representation, vector quantization has been used. The evaluation method (k-fold cross-validation) has been used. Supervised machine learning (SML) is proposed using Quadratic Discriminant Analysis (QDA) classification algorithms. And the results obtained by the algorithm (QDA) varied between 98 percent and 98.43 percent, depending on the way of features extraction that was used. These results are satisfactory and reliable.
Ashraf Tahseen Ali; Hasanen Abdullah; Mohammed Natiq Fadhil
Abstract
Finger veins are different from other biometric signs; it is a special characteristic of the human body. The challenge for an imposter to explore and comprehend it, since the veins are below the skin, it is impossible to tell which one is, and which one stands out because the person has more than one ...
Read More ...
Finger veins are different from other biometric signs; it is a special characteristic of the human body. The challenge for an imposter to explore and comprehend it, since the veins are below the skin, it is impossible to tell which one is, and which one stands out because the person has more than one finger to examine. Impostor recognition based on applying three machine-learning methods will be presented in this article, and then there is a discussion at preprocessing, Linear Discriminant Analysis (LDA) for feature extraction, and k fold cross-validation as an evaluation method. These measures were carried out on two different datasets, which are the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) Dataset and the University of Twente Finger Veins (UTFV) dataset. The classifier with the best results was Support Vector Machine (SVM) and Linear Regression (LR) had the lowest classifier accuracy.
Ashraf Tahseen Ali; Hasanen Abdullah; Mohammed Natiq Fadhil
Abstract
As compared to other conventional biometrics systems, voice is a unique and important metric, where it is used in many vital fields as the security and communication domains that do not need to be expensive to achieve. The purpose of this article is to see how machine learning (ML) algorithms perform ...
Read More ...
As compared to other conventional biometrics systems, voice is a unique and important metric, where it is used in many vital fields as the security and communication domains that do not need to be expensive to achieve. The purpose of this article is to see how machine learning (ML) algorithms perform for speaker Authentication to recognize impostors. To boost the audios usable in real environments, it was suggested the preprocessing of audio, like noise decreasing and voiced improving. Mel Frequency Cepstral Coefficients (MFCC) and the four features (Amplitude, Zero Crossing, Mean, and Standard Division) are extracted for all audio metrics, straight beside their differentials and accelerations. Then, Vector Quantization (VQ) is applied to these files. The algorithms were prepared and examined on two datasets, by applying k-fold cross-validation. The preparation for testing and comparing the three (ML) approaches are as follows: Support Vector Machine (SVM), One Rule (One-R), Linear Regression (LR). The result of the (SVM) algorithm average accuracy of 96.33 percent was superior.