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A Novel Anomaly Intrusion Detection Method based on RNA Encoding and ResNet50 Model
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Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a new RNA encoding method and ResNet50 Model, where the encoding is done by splitting the training records into different groups. These groups are protocol, service, flag, and digit, and each group is represented by the number of RNA characters that can represent the group's values. The RNA encoding phase converts network traffic records into RNA sequences, allowing for a comprehensive representation of the dataset. The detection model, utilizing the ResNet architecture, effectively tackles training challenges and achieves high detection rates for different attack types. The KDD-Cup99 Dataset is used for both training and testing. The testing dataset includes new attacks that do not appear in the training dataset, which means the system can detect new attacks in the future. The efficiency of the suggested anomaly intrusion detection system is done by calculating the detection rate (DR), false alarm rate (FAR), and accuracy. The achieved DR, FAR, and accuracy are equal to 96.24%, 6.133%, and 95.99%. The experimental results exhibit that the RNA encoding method can improve intrusion detection.

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Publication Date
Mon Jun 30 2025
Journal Name
Acta Logistica
A business continuity-based framework for risk management in smart supply chains: a fuzzy multi-criteria decision-making approach
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The aim of this study is to develop a novel framework for managing risks in smart supply chains by enhancing business continuity and resilience against potential disruptions. This research addresses the growing uncertainty in supply chain environments, driven by both natural phenomena-such as pandemics and earthquakes—and human-induced events, including wars, political upheavals, and societal transformations. Recognizing that traditional risk management approaches are insufficient in such dynamic contexts, the study proposes an adaptive framework that integrates proactive and remedial measures for effective risk mitigation. A fuzzy risk matrix is employed to assess and analyze uncertainties, facilitating the identification of disr

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Publication Date
Tue Jan 01 2019
Journal Name
Australian Journal Of Mathematical Analysis And Applications
Formulation of approximate mathematical model for incoming water to some dams on Tigris and Euphrates Rivers using spline function
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n this paper, we formulate three mathematical models using spline functions, such as linear, quadratic and cubic functions to approximate the mathematical model for incoming water to some dams. We will implement this model on dams of both rivers; dams on the Tigris are Mosul and Amara while dams on the Euphrates are Hadetha and Al-Hindya.

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Publication Date
Wed Aug 10 2022
Journal Name
Mathematics
Modeling and Analysis of the Influence of Fear on the Harvested Modified Leslie–Gower Model Involving Nonlinear Prey Refuge
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Understanding the effects of fear, quadratic fixed effort harvesting, and predator-dependent refuge are essential topics in ecology. Accordingly, a modified Leslie–Gower prey–predator model incorporating these biological factors is mathematically modeled using the Beddington–DeAngelis type of functional response to describe the predation processes. The model’s qualitative features are investigated, including local equilibria stability, permanence, and global stability. Bifurcation analysis is carried out on the temporal model to identify local bifurcations such as transcritical, saddle-node, and Hopf bifurcation. A comprehensive numerical inquiry is carried out using MATLAB to verify the obtained theoretical findings and und

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Publication Date
Mon Sep 22 2025
Journal Name
Journal Of Physical Education
The Effect of Constructive Learning Model on Cognitive Achievement and Learning dribbling Skill in Soccer for Secondary School Students
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The research aimed at identifying the effect of using constructive learning model on academic achievement and learning soccer dribbling Skill in 2nd grade secondary school students. The researcher used the experimental method on (30) secondary school students; 10 selected for pilot study, 20 were divided into two groups. The experimental group followed constructive learning model while the controlling group followed the traditional method. The experimental program lasted for eight weeks with two teaching sessions per week for each group. The data was collected and treated using SPSS to conclude the positive effect of using constructive learning model on developing academic achievement and learning soccer dribbling Skill in 2nd grade seconda

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Publication Date
Fri Feb 08 2019
Journal Name
Journal Of The College Of Education For Women
COMPARATIVE STUDY FOR EDGE DETECTION OF NOISY IMAGE USING SOBEL AND LAPLACE OPERATORS
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Many approaches of different complexity already exist to edge detection in
color images. Nevertheless, the question remains of how different are the results
when employing computational costly techniques instead of simple ones. This
paper presents a comparative study on two approaches to color edge detection to
reduce noise in image. The approaches are based on the Sobel operator and the
Laplace operator. Furthermore, an efficient algorithm for implementing the two
operators is presented. The operators have been applied to real images. The results
are presented in this paper. It is shown that the quality of the results increases by
using second derivative operator (Laplace operator). And noise reduced in a good

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Publication Date
Mon Jan 29 2024
Journal Name
Al-rafidain Journal Of Medical Sciences ( Issn 2789-3219 )
Role of Ultrasound in Antenatal Detection of Echogenic Amniotic Fluid and Pregnancy Outcome
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Background: Ultrasound is a valuable tool for evaluating fetal problems throughout pregnancy. Amniotic fluid anomalies have been associated with unfavorable maternal, fetal, and obstetrical outcomes. Objective: To determine the effect of echogenic amniotic fluid during term pregnancy on the presence of meconium stain liquor and pregnancy outcome. Methods: A cross-sectional study was conducted on 1080 term pregnant women who visited Al-Elwiya Maternity Teaching Hospital from May 1st, 2021, to May 1st, 2023. Ultrasound was used to analyze echogenic amniotic fluid and turbid liquor. The liquor state was tested either after an artificial membrane rupture in the vaginal delivery trial or during a cesarean section. Results: Echogenic amni

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Publication Date
Sun Jun 30 2024
Journal Name
International Journal Of Intelligent Engineering And Systems
Eco-friendly and Secure Data Center to Detection Compromised Devices Utilizing Swarm Approach
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Modern civilization increasingly relies on sustainable and eco-friendly data centers as the core hubs of intelligent computing. However, these data centers, while vital, also face heightened vulnerability to hacking due to their role as the convergence points of numerous network connection nodes. Recognizing and addressing this vulnerability, particularly within the confines of green data centers, is a pressing concern. This paper proposes a novel approach to mitigate this threat by leveraging swarm intelligence techniques to detect prospective and hidden compromised devices within the data center environment. The core objective is to ensure sustainable intelligent computing through a colony strategy. The research primarily focusses on the

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Publication Date
Tue Dec 20 2022
Journal Name
2022 4th International Conference On Current Research In Engineering And Science Applications (iccresa)
Noise Detection and Removing in Heart Sound Signals via Nuclear Norm Minimization Problems
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Heart sound is an electric signal affected by some factors during the signal's recording process, which adds unwanted information to the signal. Recently, many studies have been interested in noise removal and signal recovery problems. The first step in signal processing is noise removal; many filters are used and proposed for treating this problem. Here, the Hankel matrix is implemented from a given signal and tries to clean the signal by overcoming unwanted information from the Hankel matrix. The first step is detecting unwanted information by defining a binary operator. This operator is defined under some threshold. The unwanted information replaces by zero, and the wanted information keeping in the estimated matrix. The resulting matrix

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Publication Date
Wed Dec 01 2021
Journal Name
Iraqi Journal Of Veterinary Sciences
Isolation and molecular detection of enterotoxigenic Staphylococcus aureus from raw milk of cows
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Publication Date
Sat Mar 25 2017
Journal Name
Biomedical And Pharmacology Journal
Detection of Cytomegalovirus and Epstein Barr Virus in Placental Tissues of Aborted Women
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Introduction The abortions reasons in several circumstances yet are mysterious, nevertheless the bacterial toxicities signify a main reason in abortion, where germs seems to be the utmost elaborate pathogens (Khameneh et.al., 2014) and (Oliver and Overton ,2014). Between numerous germs, Humano

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