Preferred Language
Articles
/
H0L_6JoBMeyNPGM3mtYF
Heterogeneous Traffic Management in SDN-Enabled Data Center Network Using Machine Learning-SPIKE Model
...Show More Authors

Software-Defined Networking (SDN) has evolved network management by detaching the control plane from the data forwarding plane, resulting in unparalleled flexibility and efficiency in network administration. However, the heterogeneity of traffic in SDN presents issues in achieving Quality of Service (QoS) demands and efficiently managing network resources. SDN traffic flows are often divided into elephant flows (EFs) and mice flows (MFs). EFs, which are distinguished by their huge packet sizes and long durations, account for a small amount of total traffic but require disproportionate network resources, thus causing congestion and delays for smaller MFs. MFs, on the other hand, have a short lifetime and are latency-sensitive, but they account for the vast bulk of traffic in data center networks. The incorrect use of network resources by EFs frequently disturbs the performance of MFs. To meet these issues, precise classification of network traffic has become crucial. This classification enables traffic-aware routing techniques. This paper offers a novel model for classifying SDN traffic into MF and EF using a spike neural network. Once identified, traffic is routed based on the classification results. For MF, the model uses the Dijkstra algorithm. For EF, the Widest Dijkstra algorithm is used. This model solves the difficulties of traffic heterogeneity in SDNs by integrating advanced classification techniques and strategic routing algorithms. It enables desirable resource allocation, eliminates congestion, and increases network performance and dependability. The models used have proven their efficiency by outperforming the traditional Software Defined Network and other algorithms in terms of: throughput by 60%, and 20%, bandwidth utilization by 5%, and 7%, packet loss by 50%, and latency by 60%, respectively.

Scopus Crossref
View Publication Preview PDF
Quick Preview PDF
Publication Date
Wed Jan 01 2020
Journal Name
Periodicals Of Engineering And Natural Sciences
Analyzing big data sets by using different panelized regression methods with application: Surveys of multidimensional poverty in Iraq
...Show More Authors

Poverty phenomenon is very substantial topic that determines the future of societies and governments and the way that they deals with education, health and economy. Sometimes poverty takes multidimensional trends through education and health. The research aims at studying multidimensional poverty in Iraq by using panelized regression methods, to analyze Big Data sets from demographical surveys collected by the Central Statistical Organization in Iraq. We choose classical penalized regression method represented by The Ridge Regression, Moreover; we choose another penalized method which is the Smooth Integration of Counting and Absolute Deviation (SICA) to analyze Big Data sets related to the different poverty forms in Iraq. Euclidian Distanc

... Show More
View Publication
Scopus (13)
Scopus
Publication Date
Tue Jun 01 2021
Journal Name
International Journal Of Nonlinear Analysis And Applications
A proposed method for cleaning data from outlier values using the robust rfch method in structural equation modeling
...Show More Authors

View Publication Preview PDF
Scopus (5)
Scopus
Publication Date
Fri Jun 01 2018
Journal Name
Arpn Journal Of Engineering And Applied
Product development from 3D scanner To CNC machine in reverse engineering
...Show More Authors

The purpose of this article is to introduce reverse engineering procedure (REP). It can achieved by developing an industrial mechanical product that had no design schemes throughout the 3D-Scanners. The aim of getting a geometric CAD model from 3D scanner is to present physical model. Generally, this used in specific applications, like commercial plan and manufacturing tasks. Having a digital data as stereolithography (STL) format. Converting the point cloud be can developed as a work in programming by producing triangles between focuses, a procedure known as triangulation. Then it could be easy to manufacture parts unknown documentation and transferred the information to CNC-machines. In this work, modification was proposed and used in RE

... Show More
Scopus
Publication Date
Sat Oct 01 2022
Journal Name
Al–bahith Al–a'alami
KNOWLEDGE MANAGEMENT AND MEDIA MANAGEMENT
...Show More Authors

To track scientific developments and achievements, for example, that (achieved) after the Second World War until this moment, make each of us in absolute amazement. He invented the computer, discovered the genetic factor (DNA), and discovered the drawing of the human genetic map, going up to the moon, penetrating outer space by satellites, getting close to distant planets, producing jet planes, microprocessors, and lasers, in addition to enabling a person to create a layer of The material is extremely thin and extremely imaginative. It has also become possible for a person to "dig lines that do not exceed 20 billion meters of thickness." The human being was also able to collect things an atom and build an efficient and high-precision con

... Show More
View Publication Preview PDF
Crossref
Publication Date
Mon Aug 01 2016
Journal Name
Journal Of Engineering
Prediction of Monthly Fluoride Content in Tigris River using SARIMA Model in R Software
...Show More Authors

The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2,0,0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlation coefficien

... Show More
Publication Date
Mon Aug 01 2016
Journal Name
Journal Of Engineering
Prediction of Monthly Fluoride Content in Tigris River using SARIMA Model in R Software
...Show More Authors

The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2, 0, 0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlat

... Show More
View Publication Preview PDF
Publication Date
Sun Dec 01 2019
Journal Name
مجلة الاستاذ-
The Effect of Using PBLA on Iraqi EFL Academic Students` Learning Achievement
...Show More Authors

Assessment should give more attention on the learning achievement of the curriculum. Portfolio- based learning assessment (PBLA) is utilized in language learning materials recently .It is assessment tools to test the learners’ learning for instance open-ended problem solving, and creative and critical thinking, imaginative, reflective, have the capacity to apply their information in new issues, and to express oral and composing. This paper aims to discover the impact of utilizing (PBLA) on students’ performance at College of Education (Ibn –Rushd) in English Department the third stage. To fulfill the aim, a sample of the study is (64) EFL students of two groups. The study used a test and an instrument design .The test group ut

... Show More
View Publication Preview PDF
Publication Date
Mon Mar 31 2025
Journal Name
International Journal Of Advanced Technology And Engineering Exploration
Breast cancer survival rate prediction using multimodal deep learning with multigenetic features
...Show More Authors

Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

... Show More
View Publication Preview PDF
Scopus Crossref
Publication Date
Wed Aug 05 2026
Journal Name
Technology In Agronomy
Modeling wheat harvest index using stacking ensemble learning and seeder operational parameters
...Show More Authors

View Publication
Crossref
Publication Date
Wed May 17 2023
Journal Name
International Journal Of Computational Intelligence Systems
Prediction of ROP Zones Using Deep Learning Algorithms and Voting Classifier Technique
...Show More Authors
Abstract<p>Retinopathy of prematurity (ROP) can cause blindness in premature neonates. It is diagnosed when new blood vessels form abnormally in the retina. However, people at high risk of ROP might benefit significantly from early detection and treatment. Therefore, early diagnosis of ROP is vital in averting visual impairment. However, due to a lack of medical experience in detecting this condition, many people refuse treatment; this is especially troublesome given the rising cases of ROP. To deal with this problem, we trained three transfer learning models (VGG-19, ResNet-50, and EfficientNetB5) and a convolutional neural network (CNN) to identify the zones of ROP in preterm newborns. The dataset to train th</p> ... Show More
View Publication
Scopus (19)
Crossref (22)
Scopus Clarivate Crossref