Zahraa Hussein

University of Babylon

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Text Encryption Approach Using DNA Computation and Hyperchaotic System

Siham OleiwiTuama Sahar Adel Kadum Zahraa Hussein

“Security” has formed into a critical concern in the digital data storage and transfer via communication that is not secure channels, which can be addressed by employing trusted encryption methods. Because of its remarkable superior qualities, such as simplicity, high security, and speed faster, chaos-based encryption algorithms are becoming more prominent. Furthermore, employing DNA in cryptography opens a new area due to three factors: storage capacity, computation power, and parallelism. “This paper for text encryption, a hyperchaotic sequence and a DNA sequence are used together. The intensity levels of an input text are translated to a serial binary digit stream, and the hyperchaotic sequence scrambles this bitstream globally. To achieve a robust encryption performance, DNA operations are done between the hyperchaotic sequence and the DNA sequence. The findings of the experiment show that the encryption algorithm outperforms state-of-the-art approaches in terms of quality, security, and robustness. resistant to brute force attacks. and has a bigger key space.

Key space

Robustness

Bitstream

10.1109/it-ela52201.2021.9773674

Cite

Citations (3)

Improved Smart Forecasting Model to Combat Coronavirus using Machine Learning

2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (2022)

Akmam Majed Mosa Ebtehal Akeel Hamed Zahraa Hussein Refed Adnan Jaleel

Coronavirus (COVID) is one of the world's most devastating diseases, affecting the lives of millions of people around the globe. Accurate and timely detection of the COVID virus is critical for human survival. The standard medical history of diagnosing COVID disease has been deemed unreliable in several ways. Noninvasive methods such as Machine Learning (ML) are efficient and reliable for classifying healthy persons and people with COVID disease. In this paper, we improved a Smart Forecasting Model using Machine Learning (SFML) for COVID prediction (positive, negative) by using COVID data. We applied, two algorithms of feature selection (correlation and gain ratio), two supervised ML algorithms (Random forest and support vector machine) and the technique of cross-validation was applied for evaluating the SFML, such as specificity, accuracy, F-measure, sensitivity, and running time. The proposed SFML may easily distinguish COVID infected individuals from healthy individuals. The proposed SFML has been tested on a full set of COVID features as well as a smaller set of features. The results reveal that reducing COVID features has an impact on SMFL performance measures.

Supervised Learning

Feature (linguistics)

Coronavirus

10.1109/icacite53722.2022.9823435

Cite

Citations (3)

Correlation among network centrality metrics in complex networks

Saad Talib Hasson Zahraa Hussein

Complex networks represent one of the corner stones and play a central role in several Computer Science domains. Research in these networks represents a multidisciplinary approach due to the requirements to implement the statistical mechanics with graph theory and other techniques. The key property in the complex networks are their centrality measures. Network centrality is having a high impact on the network behaviors, dynamicity, and information spreading can deliver significant information about its organizations. Several metrics are developed to estimate the node centrality in complex networks. Each node centrality measure reflects its topological importance in the network among others. Adjacency matrix is used to derive and perform all the centrality measures based on several mathematical computations. Most of these measures may behave similarly in their statistical analyses. So some of these measures can be considered as redundant due to these and their complexity. This study tries to investigate the correlation between any pair of six selected centrality measures. This approach may advise to use the strongly correlated low-complexity metric as an approximation instead of the high complexity one. To perform this study a correlation analysis study is implemented on 6 estimated centrality measures for three different datasets. The alternate measures are selected according to their correlation coefficients strengths.

Adjacency matrix

Katz centrality

Network theory

Network Analysis

Network controllability

PageRank

10.1109/iec49899.2020.9122930

Cite

Citations (6)

Improved Model for Skin Illnesses Classification Utilizing Gray-Level Co-occurrence Matrix and Convolution Neural Network

Journal of Advances in Information Technology (2023)

Zahraa Hussein Ahmed Mohammed Hussein Ghadeer I. Maki Hadeel Qasem Gheni

Skin conditions are more common than other illnesses.Skin issues can be brought on by viruses, bacteria, allergies, fungi, etc.The detection of skin diseases has been made better by lasers and photonics since it is now quicker and more precise.However, such a diagnosis is pricey.A system for automated dermatology screening is built with the use of computer vision.Using the Gray Level Co-occurrence Matrix (GLCM) and Convolution Neural Network (CNN) provides an improved model for accurately diagnosing skin disorders.In order to classify skin photos, CNN is used by the model to extract features from the images using GLCM.The high-level features utilizing the statistical features were retrieved via GLCM separately because the photos utilized in the research are for skin conditions.Once merged, these features created a high-accuracy categorization.Two distinct classification processes are used to categorize photos into 13 diseases: First, the Deep Neural Network (DNN) classifier obtains 96.69% accuracy, 96.2% recall, 96.2% precision, and 96.2% F1-score in terms of performance evaluation measures.Second, accuracy, recall, precision, and F1-score are the performance evaluation metrics for the Multiple Support Vector Machine (MSVM) classifiers.The model outperforms other cutting-edge models in terms of accuracy and effectiveness when compared to them.This work thus indicates the capability of GLCM and CNN for the classification of skin diseases and their prospective uses in the healthcare sector.

Gray (unit)

Convolution (computer science)

10.12720/jait.14.6.1273-1279

Cite

Citations (3)

The Representation of Refined Neutrosophic Matrices By Refined Neutrosophic Linear Functions

International Journal of Neutrosophic Science (2022)

Mohammad Abobala M. Bisher ..R. Iqbal Doewes Zahraa Hussein

The aim of this paper is to solve the problem of representing refined neutrosophic matrices by linear functions, where it describes the structure of refined neutrosophic linear transformations that represent refined neutrosophic matrices. On the other hand, this work introduces a novel algorithm to compute a basis of any refined neutrosophic vector space depending on the classical basis of its corresponding classical vector space.

Basis (linear algebra)

Representation

Linear space

10.54216/ijns.190131

Cite

Citations (1)