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Anomaly Detection Approach Based on Deep Neural Network and Dropout
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   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss function to enforce the proposed model in multiple classification, including five labels, one is normal and four others are attacks (Dos, R2L, U2L and Probe). Accuracy metric was used to evaluate the model performance. The proposed model accuracy achieved to 99.45%. Commonly the recognition time is reduced in the NIDS by using feature selection technique. The proposed DNN classifier implemented with feature selection algorithm, and obtained on accuracy reached to 99.27%.

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Publication Date
Tue Nov 30 2021
Journal Name
Research Journal Of Pharmacy And Technology
Diagnostic approach to Perimenopausal bleeding by Pipelle or Dilatation and Curettage
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Perimenopausal bleeding, is a very common problem, which is an alarming symptom for both; women and their doctors because of the rising fears of cellular changes or tumor of endometrium. In our study we tried to prove that collecting endometrial samples using the outpatient method of Pipelle is as effective as collecting the endometrial samples in the traditional method of Dilation and Curettage (DandC) in operation theatre which necessitates general anesthesia. Ninety four patients more than 40 years old were included in the study, all of them were complaining of abnormal uterine bleeding (pregnant ladies and ladies using hormonal contraception were excluded from the study) and endometrial samples were collected first in outpatient

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Publication Date
Sun Oct 15 2023
Journal Name
Sumer 3
Treatment of shallow and deep white spot lesions with three different mouthwashes evaluated by laser fluorescence (an in vitro study)
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This research aims to find how three different types of mouthwashes affect the depth of artificial white spot lesions. Teeth with various depths of white spot lesions were immersed in either splat mouthwash, Biorepair mouthwash, Sensodyne mouthwash, or artificial saliva (control)twice daily for one minute for 4 weeks and 8 weeks at 37°C. After this immersion procedure, lesion depth was measured using a diagnosed pen score. A one-way analysis of variance, Dunnett T3 and Tukey's post hoc α = .05 were used to analyze the testing data. Splat mouthwash enhanced the WSL remineralization and made the lowest ΔF compared with other mouthwashes in shallow and deep enamel after 4 and 8 weeks of treatment. In the repair groups, after 4 weeks

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Publication Date
Sun Jan 27 2019
Journal Name
Journal Of The College Of Education For Women
Cyber Crime and its Risks on Children
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Cybercrime and risks to children between the problems and solutions( An analytical study in the light of international, Arab and national statistics).
Lies the problem of the study to identify a new type of crime is different from the same traditional character of the crimes or what was customary since human creation up to the information revolution and we enter the era of globalization, which is also called (cyber crime) and their negative impact on all segments of society, especially children, as they the day of the most important social security threats, for all local and international communities alike , and those risks require collective action to various sectors and segments of society ,especially the educated classes in order t

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Publication Date
Wed Apr 25 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
On Reliability Estimation for the Exponential Distribution Based on Monte Carlo Simulation
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        This Research deals with estimation the reliability function for two-parameters Exponential distribution, using different estimation methods ; Maximum likelihood, Median-First Order Statistics, Ridge Regression, Modified Thompson-Type Shrinkage and Single Stage Shrinkage methods. Comparisons among the estimators were made using Monte Carlo Simulation based on statistical indicter mean squared error (MSE) conclude that the shrinkage method perform better than the other methods

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Publication Date
Wed Feb 10 2016
Journal Name
Scientific Reports
Experimental demonstration on the deterministic quantum key distribution based on entangled photons
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As an important resource, entanglement light source has been used in developing quantum information technologies, such as quantum key distribution(QKD). There are few experiments implementing entanglement-based deterministic QKD protocols since the security of existing protocols may be compromised in lossy channels. In this work, we report on a loss-tolerant deterministic QKD experiment which follows a modified “Ping-Pong”(PP) protocol. The experiment results demonstrate for the first time that a secure deterministic QKD session can be fulfilled in a channel with an optical loss of 9 dB, based on a telecom-band entangled photon source. This exhibits a conceivable prospect of ultilizing entanglement light source in real-life fiber-based

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Publication Date
Fri Feb 04 2022
Journal Name
Neuroquantology
Detecting Damaged Buildings on Post-Hurricane Satellite Imagery based on Transfer Learning
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In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa

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Publication Date
Sat Sep 01 2018
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Environmental Taxes Effectiveness on controlling The Pollution reduction levels due To the Activity Contracted Foreign Oil Companies in Iraq: An applied research in the general commission of taxes
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     Environmental Tax is deemed as one of the most important tools that can be used to eliminate the problem of oil –based  environment pollution resulted out of oil products processes and this has been significantly approved by the experience in those leading countries in the field of protecting the environment against pollution whereas oil-producing countries which are rather awkward in maintaining the environment such as Iraq , suffer from notorious environmental effects pertaining to oil product processes.

     The problem of the research is represented the increased and constant rise in the volume of the environmental pollutants resulted from the processes managed by the intern

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Publication Date
Sun Mar 31 2013
Journal Name
Inventi Impact: Artificial Intelligence
SIMULATION OF IDENTIFICATION AND CONTROL OF SCARA ROBOT USING MODIFIED RECURRENT NEURAL NETWORKS
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This paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett

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Publication Date
Sat Sep 23 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
E·voiution of Topology and Wei,ght-s. of Neural Netwo·rks. Using.Semi Genetic Operators
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Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
Multi-Criteria Optimization for Governmental Projects Priority Ranking Depending on Fuzzified Experts’ Opinion using Hygiene Approach
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Each organization struggles to exploit each possible opportunity for gaining success and continuing with its work carrier. In this field, organization success can be concluded by fulfilling end user requirements combined with optimizing available resources usage within a specified time and acceptable quality level to gain maximum profit. The project ranking process is governed by the multi-criteria environment, which is more difficult for the governmental organization because other organizations' main target is maximizing profit constrained with available resources. The governmental organization should consider human, social, economic and many more factors. This paper focused on building a multi-criteria optimizing proje

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