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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
Wed Sep 22 2021
Journal Name
Samarra Journal Of Pure And Applied Science
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 eff

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Publication Date
Sun Sep 07 2008
Journal Name
Baghdad Science Journal
Detection of Pseudomonas aeruginosa in Hospital Contamination
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The Present investigation includes the isolation and identification of Pseudomonas aeruginosa for different cases of hospital contamination from 1/ 6/2003 to 30/9/2004, the identification of bacteria depended on morphological , cultural and biochemical characters, 37 of isolates were diagnosed from 70 smears from wounds and burns beside 25 isolates were identified from 200 smears taken from operation theater and hospital wards including the floors , walls , sources of light and operation equipment the sensitivity of all isolates to antibiotic were done , which exhibited complete sensitivity to Ciprofloxacin , Ceftraixon, Tobromycin and Gentamysin ,while they were complete resist to Amoxcillin , Tetracyclin , Nitrofurantion , Clindamycin C

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Publication Date
Fri Mar 31 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Detection of Cholesterol in Suaeda Baccata (Chenopodiaceae)
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This study detects the presence of cholesterol in an Iraqi plant named Suaeda baccata Forsk of the family Chenopodiacae, wildly and widely grown in Iraq. The absence of any publication concerning the sterol content of this Suaeda specie, and the industrial importance of cholesterol depending on its role as a precursor in the synthesis of some hormones, like progesterone, acquired this study its value. The investigations revealed the presence of cholesterol that was proved by TLC together with the standard compound cholesterol, and anisaldehyde spray reagent using three different solvent systems, then authenticated by HPLC, in which the reten

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Publication Date
Mon Feb 01 2021
Journal Name
International Journal Of Electrical And Computer Engineering (ijece)
Differential evolution detection models for SMS spam
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With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative

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Publication Date
Wed Mar 02 2022
Journal Name
Journal Of Educational And Psychological Researches
The Effectiveness of a Training Program Based on Employing the Educational Portal in Developing the Skills of the Administrative Body in Basic Education Schools in Dhofar Governorate
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The objective of the study was to identify the effectiveness of a training program based on the employment of the educational portal in the development of the skills of the administrative body in the basic education schools in Dhofar Governorate. To achieve the goal, a training program was designed that includes the skills of the educational portal to be met by the administrative body and measuring its effectiveness. A questionnaire to identify training needs, and a notecard to evaluate performance. The study sample consisted of (70) individuals of the administrative staff. The researchers used the descriptive-analytical method to identify the training needs, design and build the training program and the experimental method for applying

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Publication Date
Wed Aug 30 2023
Journal Name
Baghdad Science Journal
Post COVID-19 Effect on Medical Staff and Doctors' Productivity Analysed by Machine Learning
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The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. T

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Publication Date
Tue Jun 24 2025
Journal Name
Food And Bioprocess Technology
Classification of Apple Slices Treated by Atmospheric Plasma Jet for Post-harvest Processes Using Image Processing and Convolutional Neural Networks
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Abstract<p>Apple slice grading is useful in post-harvest operations for sorting, grading, packaging, labeling, processing, storage, transportation, and meeting market demand and consumer preferences. Proper grading of apple slices can help ensure the quality, safety, and marketability of the final products, contributing to the post-harvest operations of the overall success of the apple industry. The article aims to create a convolutional neural network (CNN) model to classify images of apple slices after immersing them in atmospheric plasma at two different pressures (1 and 5 atm) and two different immersion times (3 and again 6 min) once and in filtered water based on the hardness of the slices usin</p> ... Show More
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Publication Date
Mon Jun 01 2015
Journal Name
The European Physical Journal Applied Physics
A carbon nanotubes photoconductive detector for middle and far infrared regions based on porous silicon and a polyamide nylon polymer
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Publication Date
Mon Jul 01 2019
Journal Name
International Journal Of Swarm Intelligence Research
A New Strategy Based on GSABAT to Solve Single Objective Optimization Problem
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This article proposes a new strategy based on a hybrid method that combines the gravitational search algorithm (GSA) with the bat algorithm (BAT) to solve a single-objective optimization problem. It first runs GSA, followed by BAT as the second step. The proposed approach relies on a parameter between 0 and 1 to address the problem of falling into local research because the lack of a local search mechanism increases intensity search, whereas diversity remains high and easily falls into the local optimum. The improvement is equivalent to the speed of the original BAT. Access speed is increased for the best solution. All solutions in the population are updated before the end of the operation of the proposed algorithm. The diversification f

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Publication Date
Sun Dec 31 2023
Journal Name
Sumer Journal For Pure Science
COVID-19Disease Diagnosis using Artificial Intelligence based on Gene Expression: A Review
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