DIYAR QADER ZEEBAREE was born in Akre, Duhok, Kurdistan, Iraq, in 1985. He received the B.S. degree in computer science from the University of Nawroz, in 2012, the M.S. degree in computer information systems (CIS) from Near East University, North of Cyprus, Turkey, in 2014, and the Ph.D. degree in computer science from University Technology Malaysia (UTM), in 2020. He is currently working as the Director of the Research Center, Duhok Polytechnic University (DPU). He is the author of one book and more than 30 articles. His research interests include artificial neural networks, machine learning, deep learning, medical image analysis, and image processing. Dr. Zeebaree was a recipient of the IEEE-International Conference on Advanced Science and Engineering (ICOASE) Best Symposium Paper Award, in 2019

Soft Computing::

Usable solutions to complex computational problems

Intelligence Systems::

The capacity to learn from experience, security, connectivity, the ability to adapt according to current data and the capacity for remote monitoring and management.

Machine Learning:

Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.

Segmentation Medical Images:

In the context of image analysis, segmentation refers to the process of separating the distinguished image objects from the image background, i.e. partitioning the image into meaningful regions and then selecting the wanted region which is known as Region of interest (ROI)


Represented through audio, video, and animation in addition to traditional media (i.e., text, graphics drawings, images and medical image).

Publication Journal
Feb, 2021
Robust watermarking scheme based LWT and SVD using artificial bee colony optimization

Indonesian Journal of Electrical Engineering and Computer Science (Issue: 2) (Volume: 21)

This paper proposes a watermarking method for grayscale images, in which lifting wavelet transform and singular value decomposition are exploited based on multi-objective artificial bee colony optimization to produce a robust watermarking method. Furthermore, for increasing security encryption of the watermark is done prior to the embedding operation. In the proposed scheme, the actual image is altered to four sub-band over three levels of lifting wavelet transform then the singular value of the watermark image is embedded to the singular value of LH sub-band of the transformed original image. In the embedding operation, multiple scaling factors are utilized on behalf of the single scaling element to get the maximum probable robustness without changing watermark lucidity. Multi-objective artificial bee colony optimization is utilized for the determination of the optimal values for multiple scaling components, which are examined against various types of attacks. For making the proposed scheme more secure, the watermark is encrypted chaotically by logistic chaotic encryption before embedding it to the host (original) image. The experimental results show excellent imperceptibility and good resiliency against a wide range of image processing attacks.

Dec, 2020
Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features

Computers, Materials and Continua (Issue: 3) (Volume: 66)

Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed. This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer, which was attained in two stages. First, several images were generated from a single image using the pre-processing method. The median and Wiener filters were utilized to lessen the speckle noise and enhance the ultrasound image texture. This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes. Second, the fusion mechanism allowed the production of diverse features from different filtered images. The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated. The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images, respectively. The proposed method achieved very high accuracy (98%), sensitivity (98%), and specificity (99%). As a result, the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy, sensitivity, and specificity.

Dec, 2020
The Applications of Discrete Wavelet Transform in Image Processing: A Review


Wavelet transform (WT) for image processing is one of the most popular methods in the frequency domain, the information of images could be represented as a group based on this method. The aim of this paper is to provide a wide-ranging review of the survey found able on wavelet-based image processing applications approaches. This paper reviews the newly published works on applying waves to image processing depending on the analysis of multiple solutions. the wavelet transformation reviewed in detail including wavelet function, integrated wavelet transformation, discrete wavelet transformation, rapid wavelet transformation, DWT properties, and DWT advantages. After reviewing the basics of wavelet transformation theory, various applications of wavelet are reviewed and multi-solution analysis, including image compression, image reduction, image optimization, and image watermark. In addition, we present the concept and theory of quadruple waves for the future progress of wavelet transform applications and quadruple solubility applications. The main contribution of this paper will be beneficial for scholars to execute effective image processing applications approaches.

Dec, 2020
Evaluating Data Mining Classification Methods Performance in Internet of Things Applications


The world is passing through the stage of the superiority of science and technology. The impact of this superiority in human life cannot be hidden. There is no doubt that the societies that have acquired information and knowledge are the ones who rule the world and lead the scene in the developed and modern countries. The development of the advanced applications in the field of the Internet of Things (IoT) with the development of information and communication technologies make the IoT have the ability to link physical entities and support interaction with the human element. The data that are generated by IoT is a huge data that has a high commercial value, also the algorithms of data mining can be applied on the IoT to get the hidden data. In this paper, a systematic method is presented to review the extraction of defined data classification. The latest algorithms of classification must be analyzed to be applied on the big data. These algorithms had been reviewed and the challenges had been discussed also in terms of data accuracy to choose the most accurate algorithm. According to the reviewed papers in the fields of smart environment, healthcare and agriculture, the highest accuracy results were found.

Nov, 2020
Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images

IEEE Access (Issue: 2020) (Volume: 8)

Segmentation of the breast region and pectoral muscle are fundamental subsequent steps in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region and pectoral muscle are considered a difficult task, particularly in mammogram images because of artefacts, homogeneity among the region of the breast and pectoral muscle, and low contrast along the region of breast boundary, the similarity between the texture of the Region of Interest (ROI), and the unwanted region and irregular ROI. This study aims to propose an improved threshold-based and trainable segmentation model to derive ROI. A hybrid segmentation approach for the boundary of the breast region and pectoral muscle in mammogram images was established based on thresholding and Machine Learning (ML) techniques. For breast boundary estimation, the region of the breast was highlighted by eliminating bands of the wavelet transform. The initial breast boundary was determined through a new thresholding technique. Morphological operations and masking were employed to correct the overestimated boundary by deleting small objects. In the medical imaging field, significant progress to develop effective and accurate ML methods for the segmentation process. In the literature, the imperative role of ML methods in enabling effective and more accurate segmentation method has been highlighted. In this study, an ML technique was built based on the Histogram of Oriented Gradient (HOG) feature with neural network classifiers to determine the region of pectoral muscle and ROI. The proposed segmentation approach was tested by utilizing 322, 200, 100 mammogram images from mammographic image analysis society (mini-MIAS), INbreast, Breast Cancer Digital Repository (BCDR) databases, respectively. The experimental results were compared with manual segmentation based on different texture features. Moreover, evaluation and comparison for the boundary of the breast region and pectoral muscle segmentation have been done separately. The experimental results showed that the boundary of the breast region and the pectoral muscle segmentation approach obtained an accuracy of 98.13% and 98.41% (mini-MIAS), 100%, and 98.01% (INbreast), and 99.8% and 99.5% (BCDR), respectively. On average, the proposed study achieved 99.31% accuracy for the boundary of breast region segmentation and 98.64% accuracy for pectoral muscle segmentation. The overall ROI performance of the proposed method showed improving accuracy after improving the threshold technique for background segmentation and building an ML technique for pectoral muscle segmentation. More so, this article also included the ground-truth as an evaluation of comprehensive similarity. In the clinic, this analysis may be provided as a valuable support for breast cancer identification.

Nov, 2020
Comparison of VPN Protocols at Network Layer Focusing on Wire Guard Protocol

International Journal of Interactive Mobile Technologies (iJIM) (Issue: 18) (Volume: 14)

The key point of this paper is to assess and look over the top of the line network layer-based VPN (Virtual Private Network) protocols because data link layer is hardly ever found to be in use in organizations, the reason is because of its exceedingly high charge. Virtual Private Network (VPN) is commonly used in business situations to provide secure communication channels over public infrastructure such as Internet. Virtual Private Networks (VPNs) provide a secure encrypted communication between remote networks worldwide by using Internet Protocol (IP) tunnels and a shared medium like the Internet. The paper follows and sets standards for different types of protocols and techniques, the VPN (Virtual Private Network) architectural feature is made to deliver dependable and safe network that is not in line with regular networks that provide a higher trust and a higher secure channel between user and organization. The current study took place to summaries the usage of existing VPNs protocol and to show the strength of every VPN. Through different studies that have been made by other researchers as well as an extra focus on the state of art protocol, Wire guard. It is also worthy to mention that wire guard compared with other protocols such as IPsec and GRE. The studies show the WireGuard being a better choice in terms of other well-known procedures to inaugurate a secure and trusted VPN.

Nov, 2020
Effect of Multi-Core Processors on CPU-Usage with Heavy-Load Problem Solving

International Journal of Innovation, Creativity and Change. (Issue: 7) (Volume: 13)

Traditional approaches to enhance the execution of a processor utilised more transistors on chips and a rising clock rate. Conversely, this result has attained its limitation. As a result of excess heat and utilisation of more power, chip developers have signalled a shift in microprocessor design from single core to multi-core processors. Multi-core is an integrated circuit chip that utilises more than one core place within a single processor. This approach is used to split the computational activity of a threaded application and disperse it over multiple execution cores so that the computer can benefit from a better performance of the system. In this paper we propose the effect of a multi-core processor on CPU-usage; we address this problem by designing a parallel application by using client/server principles. We divided the workload among multiple cores at the server side for testing performance in terms of execution time and CPU-usage. This paper is based on the Matrix multiplication case study; all algorithms related to this case study are implemented using Borland C++ Builder language. Experimental results show that our algorithms are computationally fast, which is reducing the execution time and maximising throughput; also it is the finest algorithm to full utilisation of the available processing cores.

Nov, 2020
Pipelined Parallel Processing Implementation based on Distributed Memory Systems

International Journal of Innovation, Creativity and Change (Issue: 7) (Volume: 13)

Complex problems take a long time to solve, with low efficiency and performance. So, to overcome these barriers and problems, studies have headed towards a way to divide the problem into independent parts, to solve and remedy each of the parts separately in a way that each component can implement part of it, with simultaneous problems with others. Parallel processors are computer systems made up of several processing units connected over some interconnection networks and the programs needed to perform the processing. .Parallel processing and pipelining approaches are duals of each other and if there is pipelining of a calculation, it can further be executed in parallel. Both exploit concurrency accessibility in the computation using various methods. In this paper, we implemented pipelined parallel processing on distributed memory by taking advantage of both parallel processing and the pipeline approach. We proposed a parallel application design using client/servers’ principles and divided the workload among multiple hosts at the servers’ side. This paper is based on merging and sorting case studies and all the algorithms related to this case study are implemented by using Borland C++ Builder language. Experimental results showed much-improved performance and throughput.

Sep, 2020
Image Steganography Based on Swarm Intelligence Algorithms: A Survey

Test Engineering and Management (Volume: 83)

Information security and confidentiality are the prime concern of any type of communication. The techniques that utilizing inconspicuous digital media such as text, audio, video and image for hiding confidential data in it are collectively called Steganography. The key challenge of steganographic system design is to maintain a fair trade-off between, security, robustness, higher bit embedding rate and imperceptibility. Thus, with the massive progress in digital technology, to transmit secret messages through the internet effective steganography algorithms are required. However, the object which has been used to hide secret messages within may be exposed by compression or any type of noise which leads to extract secret message incorrectly. Therefore, utilizing the non-traditional basics for information security is required, such as swarm intelligence algorithms which are focused as a new aspect to achieve better security. In this paper, a survey of recent swarm intelligence algorithms based on steganography is covered. The objective function for swarm intelligence algorithms is realized in a way that the quality and robustness of the object that has been used for hiding messages are acceptable. With a particular emphasis on the main purpose and the objective of the proposed method based on the particular swarm intelligence algorithm has been reviewed. To present a more secure, efficient steganography algorithm based on swarm intelligence algorithms for future work, this will be helpful.

Sep, 2020
Management of Wireless Communication Systems Using Artificial Intelligence-Based Software Defined Radio

International Journal of Interactive Mobile Technologies (iJIM) (Issue: 13) (Volume: 14)

The wireless communication system was investigated by novel methods, which produce an optimized data link, especially the software-based methods. Software-Defined Radio (SDR) is a common method for developing and implementing wireless communication protocols. In this paper, SDR and artificial intelligence (AI) are used to design a self-management communication system with variable node locations. Three affected parameters for the wireless signal are considered: channel frequency, bandwidth, and modulation type. On one hand, SDR collects and analyzes the signal components while on the other hand, AI processes the situation in real-time sequence after detecting unwanted data during the monitoring stage. The decision was integrated into the system by AI with respect to the instantaneous data read then passed to the communication nodes to take its correct location. The connectivity ratio and coverage area are optimized nearly double by the proposed method, which means the variable node location, according to the peak time, increases the attached subscriber by a while ratio.

Jun, 2020
A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

The Journal of Applied Science and Technology Trends (JASTT) (Issue: 3) (Volume: 1)

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

Jun, 2020
Information Hiding: A Tools for Securing Biometric Information

Kansay University (Issue: 4) (Volume: 62)

The recognition of human beings via the utilization of biometric characteristics is currently the latest innovative trend. Previous years have witnessed great interest in biometric recognition due to its security significance. Amongst the presently used biometric recognition, the fingerprint is categorized to be a pragmatic technique. Techniques that are inclusive of steganography and watermarking are used in enhancing biometric data security. Watermarking is a technique of having the information implanted into a carrier file, to avert the infringement of music copyright proprietorship, image or video folders. Nevertheless, the method entails the hiding of data and is known as steganography. The current research gives an overview of techniques employed in the protection of biometric information that is contained in fingerprints. We have included thorough comprehensive biometric methodologies. In addition, the benefits and drawbacks, and the uses of biometric methods are also explicated.

Jun, 2020
Hiding Image by Using Contourlet Transform

Test Engineering and Management (Volume: 83)

The goal of the paper is to provide a practical conceptual framework based on hiding of watermarks embedded within coefficients image disintegrated by contourlet transform, that is characterized by extra sustenance for the potential and actual security of the hiding methods, and strengthen distribution techniques of watermarking inside the cover image; thus this can be done through achieving a kind of merge between those techniques and others techniques of image processing. A case study approach was used to allow the algorithms of the recommended system (non-blind) through concealing the watermarking which is resulted in the possibility of altering the watermarking dimensions with the fixed cover measurements and reciprocally. Another important practical implication is that the quartet tried to divide the techniques into four sections before it has been embedded inside the cover-image then after they have been split up, they distribute them among the fragments of the watermark within the cover-image regarding the quartet try the method. The techniques of using the three suggested algorithms by employing the watermarks cover image of various dimensions displayed that, the correlation factor ratio preceding and succeeding the process of hiding of the cover-image has been exceeded 0.99%. The most striking outcome to emerge from the data is that the result of this research which is in the watermark measured before and after the process depending on the PSNR, SNR, MSE, NC.

Jun, 2020
Wavelet Applications in Medical Images: A Review

Test Engineering and Management (Issue: 1) (Volume: 83)

This paper is review of wavelet applications in medical images. The main task of image processing is image demising wavelet transforms are used to apply medical images to image compression. The main aim of digital images compression is to decrease the rate of data transfer. Images of living objects are using different modal such as X-ray, Magnetic Resonance Imaging (MRI), Computed Tomography (CT). We present a number of methods using wavelet applications in medical images and discussed which technique is best for finding Peak-Signal to Noise-Ratio (PSNR).

May, 2020
Building Smart Cities Applications based on IoT Technologies: A Review

Kansay University (Issue: 03) (Volume: 62)

The smart cities concept highlights the use of Information and Communication Technology (ICT) to increase the quality and build more efficient technological solutions in different urban network services of a city life. One of the most famous models used to make a smart city is Internet Of Things (IOT), this is of the IOT abilities to build and organize intelligent solutions for smart cities. The concept of IOT is integrating sensors into everyday objects and through specific protocols interconnecting them over the internet for communication and exchanging information to provide various services for urban citizens. The objective of this paper is to show and discuss how cities’ projects can be developed to smart based on IOT uses

May, 2020
Machine Learning Supervised Algorithms of Gene Selection: A Review

Kansay University (Issue: 3) (Volume: 62)

Machine learning (ML) and data mining have established several effective applications in gene selection analysis. This paper review of machine learning supervised algorithms of gene selection. The high dimension that mean select a gene technique before submitting data to classifier. Supervised learning is learning involves an expert well-versed in the environment. We present a number of methods using machine learning supervised algorithms of gene selection data set leukaemia, colon, lymphoma. Furthermore, compare which algorithms is the best for using gene selection. Finding the accuracy of data set selection. The classification accuracy with minimum number of genes is improved better than further filtering and combination.

May, 2020
A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

The Journal of Applied Science and Technology Trends (JASTT) (Issue: 2) (Volume: 1)

Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

Apr, 2020

Kansay University (Issue: 03) (Volume: 62)

In this study, a human face recognition technique based on statistical features using a neural network technique is presented. In the pre-processing stage image edges have been detected. Subsequently, a new technique for two-dimension gray image to one-dimension vector is proposed. Then, seven features have been extracted depending on statistical analysis. This work describes is based on four statistical characteristics (mean, standard deviation, skewness, kurtosis) for feature extraction. These features can address image capture problems because it is main tasks are not affected by the rotation, zoom, and transfer of images taken from before the control cameras. After that, the image details have been extracted using wavelet transformations. Elman Neural Network (ENN) is used in this study for face identification. Finally, the proposed study has been implemented using MATLAB R2013a and Microsoft Excel database to preserve the information of the required people, this can be achieved by utilizing the principle of distance between facial points.

May, 2019
Enhance the Mammogram Images for Both Segmentation and Feature Extraction Using Wavelet Transform

2019 International Conference on Advanced Science and Engineering (ICOASE),IEEE

Breast cancer (BC) is a main killer disease for women and men. It can be cured and controlled only if it is detected at its early detection. BC initial identification can be realized by the help of computer support identification approaches. From the detailed study on previous researches, it is found that, there is no system producing high accuracy because of one or more reasons. Absence of effective preprocessing is the discussed reason that obstructs the detection accuracy of Computer-aided diagnosis (CAD) method. Noise removal and contrast enhancement are the two types of preprocessing. There is no system performs the preprocessing on mammogram image. This work is an attempt to develop an enhanced preprocessing method for CAD of breast cancer by incorporating suitable noise reduction and contrast enhancement methods in the conventional CAD system. Contrast enhancement after noise reduction double enhances the mammogram image and the proposed methods MSE value for the mammogram image mdb072 has been 1.44% reduced. Reduction in MSE increases the PSNR to 0.16%. Many mammogram images have been tested and the result shows that, increase in contrast, decrease in mean square error and increase in peak signal to noise ratio when comparing to existing methods Keywords—breast cancer, mammogram image, computer-aided diagnosis, wavelet transform, noise reduction

May, 2019
Machine learning and Region Growing for Breast Cancer Segmentation

2019 International Conference on Advanced Science and Engineering (ICOASE),IEEE

One of the main causes of increased mortality among women is breast cancer. The ultrasound scan is the most widely used method for diagnosing geological disease i.e. breast cancer. The first step for identifying the abnormality of the breast cancer (malignant from benign), is the extraction of the region of interest (ROI). In order to achieve this, a new approach to breast ROI extraction is proposed for the purpose of reducing false positive cases (FP). The proposed model was built based on the local pixel information and neural network. It includes two stages namely, training and testing. In the training stage, a trained model was built by extracting the number of batches from both ROI and background. The testing stage involved scanning the image with a fixed size window to detect the ROI from the background. Afterwards, a distance transform was used to identify the ROI and remove non-ROI. Experiments were conducted on the on-data set with 250 ultrasound images (150 benign and 100 malignant) the preliminary results show that the proposed method achieves a success rate of about 95.4% for breast contour extraction. The performance of the proposed solution also has been compared with the existing solutions that have been used to segment different types of images Keywords—breast cancer, ultrasound image, segmentation, machine learning, trainable segmentation

May, 2019
Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer

2019 International Conference on Advanced Science and Engineering (ICOASE),IEEE

In developing countries breast cancer has been found to be one of the diseases that threatens the lives of women, and that is why finding ways of detecting efficiently is of great importance. The detection of breast cancer at an early stage through self-examination is very difficult. In this study, we proposed a new descriptor that can help to identify the abnormality of the breast by enhancing the features of LBP texture and enhance the LPB descriptor by using a new threshold that can help to identify the important information for the detection of abnormal cases. In the next stage, the significant features are extracted from the breast tumours images that have been segmented. Such features could be found in frequency or spatial domain. The extracted features for diagnosing tumour automatically, are additional and different from those features which the radiologist extracts manually. The proposed method demonstrates the possibility of using the LBP based texture feature with the new proposed method for categorising ultrasound images, which registered a high accuracy of 96%, the sensitivity of 94%, specificity of 97%. Keywords—breast cancer, ultrasound image, segmentation, machine learning, trainable segmentation.

Oct, 2018
Gene Selection and Classification of Microarray Data Using Convolutional Neural Network

2018 International Conference on Advanced Science and Engineering (ICOASE)-IEEE

Gene expression profiles could be generated in large quantities by utilizing microarray techniques. Currently, the task of diagnosing diseases relies on gene expression data. One of the techniques which helps in this task is by utilizing deep learning algorithms. Such algorithms are effective in the identification and classification of informative genes. These genes may subsequently be used in predicting testing samples’ classes. In cancer identification, the microarray data typically possesses minimal samples number with a huge feature collection size which are hailing from gene expression data. Lately, applications of deep learning algorithms are gaining much attention to solve various challenges in artificial intelligence field. In the present study, we investigated a deep learning algorithm based on the convolutional neural network (CNN), for classification of microarray data. In comparison to similar techniques such as Vector Machine Recursive Feature Elimination and improved Random Forest (mSVM-RFE-iRF and varSeIRF), CNN showed that not all the data have superior performance. Most of experimental results on cancer datasets indicated that CNN is superior in terms of accuracy and minimizing gene in classifying cancer comparing with hybrid mSVM-RFE-iRF. Keywords: Deep Learning; Convolutional Neural Network (CNN); Microarray Cancer Data; Classification

Oct, 2018
Multi-Level of DNA Encryption Technique Based on DNA Arithmetic and Biological Operations

2018 International Conference on Advanced Science and Engineering (ICOASE)-IEEE

Networks have evolved very rapidly, which allow secret data transformation speedily through the Internet. However, the security of secret data has posed a serious threat due to openness of these networks. Thus, researchers draw their attention on cryptography field for this reason. Due to the traditional cryptographic techniques which are vulnerable to intruders nowadays. Deoxyribonucleic Acid (DNA) considered as a promising technology for cryptography field due to extraordinary data density and vast parallelism. With the help of the various DNA arithmetic and biological operations are also Blum Blum Shub (BBS) generator, a multi-level of DNA encryption algorithm is proposed here. The algorithm first uses the dynamic key generation to encrypt sensitive information as a first level; second, it uses BBS generator to generate a random DNA sequence; third, the BBS-DNA sequence spliced with a DNA Gen Bank reference to produce a new DNA reference. Then, substitution, permutation, and dynamic key are used to scramble the new DNA reference nucleotides locations. Finally, for further enhanced security, an injective mapping is established to combine encrypted information with encrypted DNA reference using Knight tour movement in Hadamard matrix. The National Institute of Standard and Technology (NIST) tests have been used to test the proposed algorithm. The results of the tests demonstrate that they effectively passed all the randomness tests of NIST which means they can effectively resist attack operations. Keywords—DNA Cryptography, Blum Blum Shub generator, Hadamard Matrix, Knight Tour, Randomness.

Sep, 2018

Science Journal of University of Zakho (Volume: Vol 6 No 3 (2018): September, 30)

Through the huge growth of heavy computing applications which require a high level of performance, it is observed that the interest of monitoring operating system performance has also demanded to be grown widely. In the past several years since OS performance has become a critical issue, many research studies have been produced to investigate and evaluate the stability status of OSs performance. This paper presents a survey of the most important and state of the art approaches and models to be used for performance measurement and evaluation. Furthermore, the research marks the capabilities of the performance-improvement of different operating systems using multiple metrics. The selection of metrics which will be used for monitoring the performance depends on monitoring goals and performance requirements. Many previous works related to this subject have been addressed, explained in details, and compared to highlight the top important features that will very beneficial to be depended for the best approach selection. KEYWORDS: OS performance, thread, multiprocessor, transaction memory.

Jan, 2017
Combination of K-means clustering with Genetic Algorithm: A review

International Journal of Applied Engineering Research ISSN 0973–4562 (Issue: 24) (Volume: 12)

In the past few decades, a detailed and extensive research has been carried out on K-Means combine with genetic algorithm for clustering of using this combine technique; to focuses on studying the efficiency and effectiveness of most article. The basic aim of this article is to gather a complete and detailed summary and a clear well explained idea of various methods and algorithms. The calculation of the number of clusters in a data user was done automatically. Representation of operator in GA was developed and group based crossover was done to fix the number of clusters. The problem on the large scale was segregated in to various mini problems through the researchers. To solving small-scale combinatorial optimization. Improving the assembling quality with less time complexity and minimization of the total distance that is travelled by the salesman are also discussed. Overall, almost K-means algorithm with GA have high performance quality of clustering with minimum time and evolution process converge fast compared with anthers technique do not combined GA with k-means cluster Keywords: Clustering; Genetic Algorithm; K-Means; Combine of K-Means and Genetic Algorithm; Data Mining;

May, 2016
Contourlet Transformation For Data Hiding

LAP LAMBERT Academic Publishing

In the last years the subject of hiding information about approving property right has been effective, many of the algorithms appeared to work on developing efficient techniques of practical hiding of right. In the recent years many technologies are becoming depended for protecting its possessiveness and proving its back profit and due to the recent development in the field of digital documentation just the science of watermarking is developed enormously for the embedding the information this kind of field (proof of property). Thus the process of hiding the owning data within document is accelerated with the development thats gaining in the methods of representation documents. Its also clear that many transformations are appeared in the recent years to represent the digital images including wavelet and curved and finally contourlet transformations.