Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Haitham Salman Chyad
Mustansiriyah University
Computer Science & IT Electrical & Electronics Engineering

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Palm print recognition based on harmony search algorithm Raniah Ali Mustafa; Haitham Salman Chyad; Rafid Aedan Haleot
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4113-4124

Abstract

Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input related to the harmony search algorithm for extracting the features of the palmprint images through using many parameters for the harmony search algorithm, Finally, Gaussian distribution has been used for computing distance between features for region palm print images, in order to recognize the palm print images for persons by training and testing a set of images, The scheme which has been proposed using palmprint databases, was provided by College of Engineering Pune (COEP), the Hong Kong Polytechnic University (HKPU), Experimental results have shown the effectiveness of the suggested recognition system for palm print with regards to the rate of recognition that reached approximately 92.60%.
Cloud resources modelling using smart cloud management Haitham Salman Chyad; Raniah Ali Mustafa; Dena Nadir George
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3286

Abstract

Cloud computing complexity is growing rapidly with the advancements that it is witnessing. It has created a requirement to simplify the process of configuring cloud and re-configuring it when required, it also involves tasks like auto scaling of infrastructure, elastic computing and maintaining the health of the servers. The proposed method introduces a smart cloud management using knowledge base, which models the resources of cloud; it handles service level agreement and its evaluations. The proposed knowledge base supports representational state transfer (REST/RESTful) services to store and manipulate different cloud aspects like type of application, business configuration, and metrics value and its type; it also implements the strategy for efficient resource management for smart clouds. The proposed architecture consists of smart cloud engine (which provides autonomous services, which help to exploit cloud resources for service optimization and to perform service automation), knowledge base (KB) (provide a cloud ontology which will help in the management of resources and provides intelligence to the smart cloud), server and cloud enrolment, designated monitoring tool and moderator. The resulted module is easy to integrate with any of the existing cloud management tool or orchestrator. As It is developed using REST protocol and extensible markup language (XML) language it is also easy to integrate with existing monitoring tool or application programming interface (APIs).
Enhancement in privacy preservation in cloud computing using apriori algorithm Raniah Ali Mustafa; Haitham Salman Chyad; Jinan Redha Mutar
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1747-1757

Abstract

Cloud computing provides advantages, like flexibly of space, security, cost optimization, accessibility from any remote location. Because of this factor cloud computing is emerging as in primary data storage for individuals as well as organisations. At the same time, privacy preservation is an also a significant aspect of cloud computing. In regrades to privacy preservation, association rule mining was proposed by previous researches to protect the privacy of users. However, the algorithm involves creation of fake transaction and this algorithm also fails to maintain the privacy of data frequency. In this research an apriori algorithm is proposed to enhance the privacy of encrypted data. The proposed algorithm is integrated with elagmal cryptography and it does not require fake transactions. In this way, the proposed algorithm improves the data protection as well as query privacy and it hides data frequency. Result analysis shows that the proposed algorithm improves the privacy as compared to previously proposed association rule mining and the algorithm also shows 3% to 5% improvement in performance when compared to other existing algorithms. This performance analysis with varying number of the data and fake transactions shows that the proposed algorithm doesn’t require fake transactions, like data privacy association rule mining.
Human ear print recognition based on fusion of difference theoretic texture and gradient direction pattern features Kawther Thabt Saleh; Raniah Ali Mustafa; Haitham Salman Chyad
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1017-1029

Abstract

Human ear recognition can be defined as a branch of biometrics that uses images of the ears to identify people. This paper provides a new ear print recognition approach depending on the combination of gradient direction pattern (GDP2) and difference theoretic texture features (DTTF) features. The region of interest (ROI), the gray scale of the ear print was cut off, noise removal by the median filter, histogram equalization, and local normalization (LN) are the first steps in this approach. After the image has been processed, it is used as input for the fusion of GDP2 and DTTF for extracting the features of ear print images. Lastly, the Gaussian distribution (GD) was utilized to compute the distance among fusion feature vectors (FV) for ear print images for recognizing ear print images for people using a set of images that had been trained and tested. The unconstrained ear recognition challenge (UERC) database, which comprises 330 subjects for ear print images, provides the approach that was suggested by employing ear print databases. Furthermore, experimental results on images from a benchmark dataset reveal that statistical-rely super-resolution methods outperform other algorithms in ear recognition accuracy, which was around 93.70% in this case.
Co-Authors Dena Nadir George Jinan Redha Mutar Kawther Thabt Saleh Rafid Aedan Haleot Raniah Ali Mustafa