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Detection of Pseudomonas aeruginosa in Clinical Samples Using PCR Targeting ETA and gyrB Genes
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Pseudomonas aeruginosa has variety of virulence factors that contribute to its pathogenicity. Therefore, rapid detection with high accuracy and specificity is very important in the control of this pathogenic bacterium. To evaluate the accuracy and specificity of Polymerase Chain Reaction (PCR) assay, ETA and gyrB genes were targeted to detect pathogenic strains of P. aeruginosa. Seventy swab samples were taken from patients with infected wounds and burns in two hospitals in Erbil and Koya cities in Iraq. The isolates were traditionally identified using phenotypic methods, and DNA was extracted from the positive samples, to apply PCR using the species specific primers targeting ETA, the gene encoding for exotoxin A, and gyrB gene. The results of this study indicate that 100% of P. aeruginosa isolates harbored the gyrB gene, whereas 74% of these isolates harbored ETA gene. However, the specificity of PCR for detection of P. aeruginosa based on the both genes was 100%, since no amplified product obtained using DNA extracted from other bacterial species. Hence by considering the importance of rapid detection of this bacterium due to the presence of problems in biochemical methods, PCR targeting multiple virulence genes is suggested in identification of pathogenic strains of P. aeruginosa isolated from some infections which should speed diagnosis of an antimicrobial therapy.

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Publication Date
Wed Dec 01 2021
Journal Name
Journal Of Physics: Conference Series
Disc damage likelihood scale recognition for Glaucoma detection
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Abstract<p>Glaucoma is a visual disorder, which is one of the significant driving reason for visual impairment. Glaucoma leads to frustrate the visual information transmission to the brain. Dissimilar to other eye illness such as myopia and cataracts. The impact of glaucoma can’t be cured; The Disc Damage Likelihood Scale (DDLS) can be used to assess the Glaucoma. The proposed methodology suggested simple method to extract Neuroretinal rim (NRM) region then dividing the region into four sectors after that calculate the width for each sector and select the minimum value to use it in DDLS factor. The feature was fed to the SVM classification algorithm, the DDLS successfully classified Glaucoma d</p> ... Show More
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Publication Date
Mon Oct 30 2023
Journal Name
Iraqi Journal Of Science
Machine Learning Approach for Facial Image Detection System
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HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023

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Publication Date
Sun Jun 05 2016
Journal Name
Baghdad Science Journal
Developing an Immune Negative Selection Algorithm for Intrusion Detection in NSL-KDD data Set
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With the development of communication technologies for mobile devices and electronic communications, and went to the world of e-government, e-commerce and e-banking. It became necessary to control these activities from exposure to intrusion or misuse and to provide protection to them, so it's important to design powerful and efficient systems-do-this-purpose. It this paper it has been used several varieties of algorithm selection passive immune algorithm selection passive with real values, algorithm selection with passive detectors with a radius fixed, algorithm selection with passive detectors, variable- sized intrusion detection network type misuse where the algorithm generates a set of detectors to distinguish the self-samples. Practica

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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Microwave Nondestructive Testing for Defect Detection in Composites Based on K-Means Clustering Algorithm
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Publication Date
Mon Jan 01 2024
Journal Name
Fifth International Conference On Applied Sciences: Icas2023
A modified Mobilenetv2 architecture for fire detection systems in open areas by deep learning
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This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.

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Publication Date
Sun Oct 21 2018
Journal Name
Al-kindy College Medical Journal
Evaluation of the clinical examination in thediagnosis of acute medial collateral ligament injury of the knee joint in comparism with examination under general anesthesia
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Background: Tear of MCL of the knee is a frequent problem among knee ligaments injuries.Injuries to the MCL are usually caused by contact on the outside of the knee and are accompanied by sharp pain on the inside of the knee. Contrary to most other knee ligaments the medial collateral ligament (MCL) has an excellent ability to heal, being fairly large and well vascularised structure. The vast majority of isolated medial ligament injuries heal without significant long-term problems

Objectives: is to compare between the early clinical examination, and assessment under general anesthesia (GA), and to find out the best methods to assess the MCL tear especially in suspected cases.

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Publication Date
Fri Dec 03 2021
Journal Name
Baghdad Journal Of Biochemistry And Applied Biological Sciences
A clinical-statistical study on COVID-19 infection and death status at the Alshifaa Healthcare Center/ Baghdad
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Background: COVID-19 is an ongoing disease that caused, and still causes, many challenges for humanity. In fact, COVID-19 death cases reached more than 4.5 million by the end of August 2021, although an improvement in the medical treatments and pharmaceutical protocols was obtained, and many vaccines were released. Objective: To, statistically, analyze the data of COVID-19 patients at Alshifaa Healthcare Center (Baghdad, Iraq). Methods: In this work, a statistical analysis was conducted on data included the total number, positive cases, and negative cases of people tested for COVID-19 at the Alshifaa Healthcare Center/Baghdad for the period 1 September – 31 December 2020. The number of people who got the test was 1080, where 424 w

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Publication Date
Thu Feb 09 2023
Journal Name
Artificial Intelligence Review
Community detection model for dynamic networks based on hidden Markov model and evolutionary algorithm
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Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over

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Publication Date
Tue Jan 31 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
Survey on intrusion detection system based on analysis concept drift: Status and future directions
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Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

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Publication Date
Sat Apr 30 2022
Journal Name
Revue D'intelligence Artificielle
Performance Evaluation of SDN DDoS Attack Detection and Mitigation Based Random Forest and K-Nearest Neighbors Machine Learning Algorithms
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

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