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Toward Constructing a Balanced Intrusion Detection Dataset
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Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the efficiency of our algorithm, several machine learning algorithms have been applied on combined dataset with and without using BMCD algorithm. The experimental results have concluded that BMCD provides an effective solution to imbalanced intrusion detection and outperforms the state-of-the-art intrusion detection methods.

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
The Effect Of Optimizers On The Generalizability Additive Neural Attention For Customer Support Twitter Dataset In Chatbot Application
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When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat

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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
New Approach in Detection MAC Spoofing in a WiFi LAN
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Medium Access Control (MAC) spoofing attacks relate to an attacker altering the manufacturer assigned MAC address to any other value. MAC spoofing attacks in Wireless Fidelity (WiFi) network are simple because of the ease of access to the tools of the MAC fraud on the Internet like MAC Makeup, and in addition to that the MAC address can be changed manually without software. MAC spoofing attacks are considered one of the most intensive attacks in the WiFi network; as result for that, many MAC spoofing detection systems were built, each of which comes with its strength and weak points. This paper logically identifies and recognizes the weak points
and masquerading paths that penetrate the up-to-date existing detection systems. Then the

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Publication Date
Wed Apr 02 2014
Journal Name
Journal Of Theoretical And Applied Information Technology
TUMOR BRAIN DETECTION THROUGH MR IMAGES: A REVIEW OF LITERATURE
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Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by usin

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Publication Date
Sat Aug 01 2015
Journal Name
2015 37th Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (embc)
Tsallis entropy as a biomarker for detection of Alzheimer's disease
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Publication Date
Sat Nov 10 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Evaluation of the Families, Attitudes toward Environment Pollution
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Objectives: To evaluate the families’ attitudes toward environment pollution, and determine the relationship
between families’ attitudes towards environment pollution and their demographic characteristics of age,
education, type of family, and socioeconomic status.
Methodology: A descriptive design is carried throughout the present study to evaluate families’ attitudes toward
environment pollution for the period of October 5th2013 to May 7th2014. A non-probability "purposive" sample of
(110) families’ is selected. The sample is comprised of two groups; (75) urban families’ and (35) rural ones. An
evaluation tool is designed and constructed for the purpose of the study. It is consisted of (4) main parts;
dem

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Publication Date
Wed Jan 09 2019
Journal Name
Journal Of The College Of Education For Women
The Attitudes Toward Extremism Among University Students Baghdad
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The Present study aims to investigate the attitude toward extremism of the university student and to find differences with three variables, gender field of study ,grade), For the purposes of the study on(3) level scale of the attitude towards extremism , and subjected to validity and credibility ,the scale was designed for ( 590) students sample (237) males ,and (357) females Results shown that students has a mild attitude towards extremism compared with the average of the scale attitude towards Religious extremism occupied the number one level ,followed by social extremism and lastly political extremism in Results also shown different in gender (males ,females) with the males having the granter attitude towards extremism as for the othe

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Publication Date
Thu Nov 08 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Evaluation of nurses’ practices toward orthopaedic wound infection
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Objective(s): To evaluate of nurses practice toward orthopaedic wound infection and to determine the
relationship between orthopaedic nurses practice and their demographic data characteristic
Methodology: A descriptive study was carried out at orthopaedic wards of Baghdad Teaching Hospital started
from February 1
st, 2011 to August 30th, 2011. A non-probability sample of (39) orthopaedic nurses who were
working in orthopaedic wards were selected from Baghdad Teaching Hospital. The data were collected through
the use of questionnaire , which consists of two parts (1)Demographic data form that consists of a(10) items
and (2) orthopaedic nurses practice form that consists of (4)sections contain (69) items, by mean of di

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Publication Date
Sun Mar 19 2023
Journal Name
Journal Of Educational And Psychological Researches
Predominant Beliefs toward Coronavirus of Baghdad University Students
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Abstract

The current research aims to identify the most prominent beliefs about coronavirus of Baghdad University students, as well as to identify the prominent beliefs toward coronavirus of male and female students. To achieve the research objectives, a questionnaire of (15) items was administered to a sample of (600) male and female students collected from ten different colleges at the university of Baghdad. The findings of the research illustrated that item (8) took priority as students believe there is a misleading about coronavirus spreading by governments, item (2) which indicated that coronavirus is man-made took level two, followed by item (10), it proposes that coronavirus emerged from the American milit

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Publication Date
Fri Jan 01 2021
Journal Name
Towards Implementation Of Sustainability Concepts In Developing Countries
Toward Resiliency Through Sustainable Urban Formation in Baghdad
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Publication Date
Wed Aug 30 2023
Journal Name
Baghdad Science Journal
Deep Learning-based Predictive Model of mRNA Vaccine Deterioration: An Analysis of the Stanford COVID-19 mRNA Vaccine Dataset
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The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA

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