Preferred Language
Articles
/
vxdINo8BVTCNdQwCB2LO
Multi-objective of wind-driven optimization as feature selection and clustering to enhance text clustering
...Show More Authors

Text Clustering consists of grouping objects of similar categories. The initial centroids influence operation of the system with the potential to become trapped in local optima. The second issue pertains to the impact of a huge number of features on the determination of optimal initial centroids. The problem of dimensionality may be reduced by feature selection. Therefore, Wind Driven Optimization (WDO) was employed as Feature Selection to reduce the unimportant words from the text. In addition, the current study has integrated a novel clustering optimization technique called the WDO (Wasp Swarm Optimization) to effectively determine the most suitable initial centroids. The result showed the new meta-heuristic which is WDO was employed as the multi-objective first time as unsupervised Feature Selection (WDOFS) and the second time as a Clustering algorithm (WDOC). For example, the WDOC outperformed Harmony Search and Particle Swarm in terms of F-measurement by 93.3%; in contrast, text clustering's performance improves 0.9% because of using suggested clustering on the proposed feature selection. With WDOFS more than 50 percent of features have been removed from the other examination of features. The best result got the multi-objectives with F-measurement 98.3%.

Scopus Crossref
View Publication Preview PDF
Quick Preview PDF
Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Journal Of Science
Using K-mean Clustering to Classify the Kidney Images
...Show More Authors

      This study has applied digital image processing on three-dimensional C.T. images to detect and diagnose kidney diseases.  Medical images of different cases of kidney diseases were compared with those of   healthy cases. Four different kidneys disorders, such as stones, tumors (cancer), cysts, and renal fibrosis were considered in additional to healthy tissues. This method helps in differentiating between the healthy and diseased kidney tissues. It can detect tumors in its very early stages, before they grow large enough to be seen by the human eye. The method used for segmentation and texture analysis was the k-means with co-occurrence matrix. The k-means separates the healthy classes and the tumor classes, and the affected

... Show More
View Publication Preview PDF
Scopus Crossref
Publication Date
Mon Jan 10 2022
Journal Name
Iraqi Journal Of Science
Genetic Algorithm based Clustering for Intrusion Detection
...Show More Authors

Clustering algorithms have recently gained attention in the related literature since
they can help current intrusion detection systems in several aspects. This paper
proposes genetic algorithm (GA) based clustering, serving to distinguish patterns
incoming from network traffic packets into normal and attack. Two GA based
clustering models for solving intrusion detection problem are introduced. The first
model coined as handles numeric features of the network packet, whereas
the second one coined as concerns all features of the network packet.
Moreover, a new mutation operator directed for binary and symbolic features is
proposed. The basic concept of proposed mutation operator depends on the most
frequent value

... Show More
View Publication Preview PDF
Publication Date
Wed Feb 08 2023
Journal Name
Iraqi Journal Of Science
Watershed Transform Based on Clustering Techniques to Extract Brain Tumors in MRI
...Show More Authors

In this work, watershed transform method was implemented to detect and extract tumors and abnormalities in MRI brain skull stripped images. An adaptive technique has been proposed to improve the performance of this method.Watershed transform algorithm based on clustering techniques: K-Means and FCM were implemented to reduce the oversegmentation problem. The K-Means and FCM clustered images were utilized as input images to the watershed algorithm as well as of the original image. The relative surface area of the extracted tumor region was calculated for each application. The results showed that watershed trnsform algorithm succeedeed to detect and extract the brain tumor regions very well according to the consult of a specialist doctor a

... Show More
View Publication Preview PDF
Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Journal Of Science
Fuzzy Based Clustering for Grayscale Image Steganalysis
...Show More Authors

Fuzzy Based Clustering for Grayscale Image Steganalysis

View Publication Preview PDF
Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Journal Of Science
A Genetic Based Optimization Model for Extractive Multi-Document Text Summarization
...Show More Authors

Extractive multi-document text summarization – a summarization with the aim of removing redundant information in a document collection while preserving its salient sentences – has recently enjoyed a large interest in proposing automatic models. This paper proposes an extractive multi-document text summarization model based on genetic algorithm (GA). First, the problem is modeled as a discrete optimization problem and a specific fitness function is designed to effectively cope with the proposed model. Then, a binary-encoded representation together with a heuristic mutation and a local repair operators are proposed to characterize the adopted GA. Experiments are applied to ten topics from Document Understanding Conference DUC2002 datas

... Show More
View Publication Preview PDF
Publication Date
Thu Oct 01 2015
Journal Name
Engineering And Technology Journal
Genetic Based Optimization Models for Enhancing Multi- Document Text Summarization
...Show More Authors

View Publication
Crossref
Publication Date
Fri Oct 02 2015
Journal Name
American Journal Of Applied Sciences
Advances in Document Clustering with Evolutionary-Based Algorithms
...Show More Authors

Document clustering is the process of organizing a particular electronic corpus of documents into subgroups of similar text features. Formerly, a number of conventional algorithms had been applied to perform document clustering. There are current endeavors to enhance clustering performance by employing evolutionary algorithms. Thus, such endeavors became an emerging topic gaining more attention in recent years. The aim of this paper is to present an up-to-date and self-contained review fully devoted to document clustering via evolutionary algorithms. It firstly provides a comprehensive inspection to the document clustering model revealing its various components with its related concepts. Then it shows and analyzes the principle research wor

... Show More
View Publication
Scopus (2)
Crossref (2)
Scopus Crossref
Publication Date
Wed Sep 23 2020
Journal Name
Artificial Intelligence Research
Hybrid approaches to feature subset selection for data classification in high-dimensional feature space
...Show More Authors

This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe

... Show More
View Publication
Crossref
Publication Date
Sun Nov 01 2020
Journal Name
International Journal Of Engineering
Pavement Maintenance Management Using Multi-objective Optimization: (Case Study: Wasit Governorate-Iraq)
...Show More Authors

View Publication
Scopus (4)
Scopus Clarivate Crossref
Publication Date
Sun Mar 28 2021
Journal Name
Iraqi Journal For Electrical And Electronic Engineering
A Light Weight Multi-Objective Task Offloading Optimization for Vehicular Fog Computing
...Show More Authors

Most Internet of Vehicles (IoV) applications are delay-sensitive and require resources for data storage and tasks processing, which is very difficult to afford by vehicles. Such tasks are often offloaded to more powerful entities, like cloud and fog servers. Fog computing is decentralized infrastructure located between data source and cloud, supplies several benefits that make it a non-frivolous extension of the cloud. The high volume data which is generated by vehicles’ sensors and also the limited computation capabilities of vehicles have imposed several challenges on VANETs systems. Therefore, VANETs is integrated with fog computing to form a paradigm namely Vehicular Fog Computing (VFC) which provide low-latency services to mo

... Show More
View Publication
Scopus (8)
Crossref (5)
Scopus Crossref