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2022 iraqi International Conference on Communication and Information Technologies, IICCIT 2022

作者： Ghurkan, Dhurgham Kareem Abdulrahman, Amer A. Middle Technical University Informatics Institute for Post Graduate Studies Iraqi Commission for Computers and Informatics Technical Institute-Suwaira Dept of Computer Baghdad Iraq Dept. of Computer Baghdad Iraq

ISBN: (纸本)9781665472203

Distributed Denial of Service (DDoS) attacks are intended to drain network resources and increase malicious traffic. This paper describes the mechanism and methods of DDoS attacks. By studying and analyzing a data set of this type, more than one parameter is collected, preprocessed, and expanded. In this work, experiments were carried out by building RF and C4.5 classification models and using the GridSearchCV class for the purpose of optimizing the parameters of these techniques are used. Then, an Extra tree classifier is used to identify the feature and reduce the dimensions according to the importance of each feature, eliminating the noise value that occurs in the feature data. Use the accuracy of the confusion matrix to illustrate the evaluation of the model, and finally check that the evaluation model is effective and can be trusted. © 2022 IEEE.

关键词： Denial-of-service attack

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Bird Image Dataset Classification using Deep Convolutional Neural Network Algorithm

Bird Image Dataset Classification using Deep Convolutional N...

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Data Science and Intelligent Computing (ICDSIC), International Conference on

作者： Maysaa H. Abdulameer Huda W. Ahmed Israa S. Ahmed Informatics Institute for Post-Graduation Studies (IIPS) Iraqi Commission for Computer& Informatics (ICCI) Baghdad Iraq

The conventional system that is now in place was majorly used in practical problems, especially in classification problems such as lack of adaptability, weak classification accuracy, and unsatisfying results. Deep Learning (DL) was offered as a solution that could address such problems and increase accuracy as in image classification. This research explains a new intelligent system for bird type determination that estimated from bird shape images based on external appearance using multitasking and Convolutional Neural Network (CNN) is proposed. The importance of the proposed model that can obtain a good and satisfying accuracy from using CNN which performed well for the most abundant class through extracting important features. The typical structure of the proposed system consists of two stages: The first stage is the pre-processing stage and the classification stage used to give, which are gives the type and the name of the bird image. The proposed system used (BIRDS 400) species image classification. The train directory contains 58388 training images and the test directory contains 2000 test images both of size 224 × 224 × 3 in jpg format. The directories in train and test images are partitioned into 400 sub directories, one for each species (bird name). The Proposed Convolution Neural Network (CNN), which has 15 layers and gives high percentages of accuracy Precision near to 0.99%, Recall scale is 0.95% and F-measure 93%, the proposed deep CNN algorithm has proven its efficiency in classifying the bird dataset by comparing it with other machine learning classification algorithms which are ((NaiveBase, Random Forest, Decision Tree, and K-NN Algorithm).

关键词： Training Machine learning algorithms Shape Neural networks Birds Feature extraction Classification algorithms

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Early Prediction of Chronic Heart Disease Based on Electronic Triage Dataset by using Machine Learning

Early Prediction of Chronic Heart Disease Based on Electroni...

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2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Muhammed, Saleh Mahdi Abdul-Majeed, Ghassan Mahmoud, Mahmoud Shuker Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Department of Computer Science Baghdad Iraq University of Baghdad College of Engineering Department of Computer Science Baghdad Iraq Al/Mansour University College Department of Communications Baghdad Iraq

ISBN: (纸本)9798350341881

Chronic Heart Disease (CHD) is the main reason for mortality around the world, constituting about 15% of all natural deaths, which confirms the importance of early detection to mitigate these rates. CHD can be detected by measuring multiple health parameters such as blood pressure, SpO2, ECG, and other parameters. Therefore, there is a persistent need for effective and precise prediction methods. Traditional techniques for prediction and analysis are limited due to the complexity of the data and correlations. First approach for developing these techniques is by using machine learning (ML) algorithms. ML can examine vast amounts of patient information, including medical records, test outcomes, and imaging scans to identify patterns and predict the possibility of developing heart disease or having a cardiac event. This study aims to improve the accuracy of prediction by utilizing ML techniques to predict CHD using medical data, reduce the incidence of misdiagnosis, and ultimately save lives. Supervised learning techniques, such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), AdaBoost, Logistic Regression (LR), K-Nearest Neighbor (KNN) and others are employed to identify correlations in CHD data and enhance prediction rates. The results indicate that SVM-derived probabilistic models outperform other algorithms in predicting CHD and the accuracy of the model was 96.2%, as revealed through various performance evaluation metrics. © 2023 IEEE.

关键词： Support vector machines

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Weighting Heart Disease Criteria Using Multi-Criteria Decision-Making

Weighting Heart Disease Criteria Using Multi-Criteria Decisi...

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2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Muhammed, Saleh Mahdi Abdul-Majeed, Ghassan Mahmoud, Mahmoud Shuker Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Department of Computer Science Baghdad Iraq University of Baghdad College of Engineering Department of Computer Science Baghdad Iraq Al-Mansour University College Department of Communications Baghdad Iraq

ISBN: (纸本)9798350341881

Heart disease describes a group of conditions that affect the heart and there are many different factors including lifestyle, medical history, family history, and so on that may cause this complex disease. It is therefore important to consider multiple criteria, such as smoking status, blood pressure, and cholesterol levels to make informed decisions about patient priority management and treatment of HD. Multicriteria Decision Making (MCDM) allows us to identify and weigh the importance of each criterion. This approach can enhance the quality of patient care and improve clinical outcomes. Therefore, weighting HD criteria using MCDM is an essential step in achieving better healthcare outcomes in the management of HD. The FWZIC method was used in this study to weigh 11 heart disease parameters with the assistance of four cardiologists. The results of the criterion weights were without any difference in the preferences of the respective expert. Therefore, this method is flexible, robust, and useful in complex real-world problems. © 2023 IEEE.

关键词： Blood pressure

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Student Abnormal Behavior Detection Using Dlib Combined With YOLO Models

Student Abnormal Behavior Detection Using Dlib Combined With...

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2022 International Conference on Intelligent Technology, System and Service for Internet of Everything, ITSS-IoE 2022

作者： Alkhalisy, Muhanad Abdul Elah Abd, Saad Hameed Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Al-Mansur University College Department of Computer Science Baghdad Iraq

ISBN: (数字)9781665459983

ISBN: (纸本)9781665459983

In the last years, educational technology has advanced tremendously. To better serve their students, schools and universities are moving online. AI-based proctoring solutions have also grabbed the industry, and online proctoring services are rising. Online proctoring systems (OPS) generally use online tools to ensure the examination is conducted with integrity. This study shows how to develop a multi-model system using computer vision to avoid and analyze anomalous student behavior during exams. The system incorporates mouth open/close detection, object identification, head posture assessment, and face detection utilizing Dlib facial landmarks and Yolo models. Our system also proposed a novel way of analyzing student behavior by analyzing the student's computer screen using a deep neural network model learned from our developed dataset, "AppScreenshotDS."This work achieves good detection accuracy, where the "Tasks Detection"model achieves 0.87 mean Average Precision (mAP) on the developed dataset and the "Behavioral Analyses Model"achieves 0.95 accuracies. We conclude that our proposal provides a novel method for detecting abnormal student behavior by analyzing what is on the student's computer screen using deep learning. © 2022 IEEE.

关键词： Students

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Secure Wireless Sensor Network Authentication by Utilized Modified SHA3 Hash Algorithm

Secure Wireless Sensor Network Authentication by Utilized Mo...

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2022 International Conference on Data Science and Intelligent Computing, ICDSIC 2022

作者： Jafer, Azhar Sadiq Hussein, Khalid A. Naif, Jolan Rokan College of Education Mustanisiryah University Department of Computer Science Baghdad Iraq Iraqi Commission for Computers & Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq

ISBN: (纸本)9798350334883

Wireless Sensor Network is an integration between software and hardware network that makes use a number of sensors to identify physical phenomena. However, WSN is easily attacked by attackers because WSNs uses wireless communications together with their insufficient tamper resistance. Commonly, sensor nodes are more concerned regarding two major security problems which are node authentication and the privacy preserving. A well-structured authentication method is able to be sure that not any unauthorized node can fraudulently participate and obtain important information from WSNs. Hence, many schemes have been proposed to protect communications in WSNs by improving security of authentication. In this work we intended to improve password protection through authentication process by using Modified SHA3 that combined Chaotic maps and SHA-3 hash algorithm to create a hybrid method that have advantage of these techniques. The proposed model is also have been implemented on hardware to simulate real operation © 2022 IEEE.

关键词： Authentication

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Implementing a Modified Low-complexity U-Net Model for Segmenting Brain Tumors

Implementing a Modified Low-complexity U-Net Model for Segme...

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2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Al-Murshidawy, Mawj Abdul-Ameer Al-Shamma, Omran Information Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Department of Computer Science Baghdad Iraq University of Information Technology and Communications Scientific Affairs Department Baghdad Iraq

ISBN: (纸本)9798350341881

Accurate segmenting of brain tumors has become a challenge recently. Manual segmentation needs experts with high skills and takes a long time. This challenge motivates several researchers to introduce various models to solve the problem with different degrees of success. The main issue with these models is that the model accuracy increased proportionally with model complexity, which may develop further difficulties in the improvement stage. Thus, this paper aims to implement a low-complexity model with several layers less than the standard U-Net, achieving higher accuracy than the state-of-the-art models. The proposed model has obtained more than a 40% reduction in layers and better performance than the available models. The dataset BRAST 2018 is employed with a total image of 7750. The proposed model attained a 0.9222 dice score, 98.99% accuracy, and 0.1889 loss value. The findings indicate that this model has promising applications and can be used in clinical settings to segment different brain tumor sub-regions. Such models facilitate the tumor localization and classification stages. © 2023 IEEE.

关键词： Brain

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Behavior Analysis of Machine Learning Algorithms for Botnets Detection

Behavior Analysis of Machine Learning Algorithms for Botnets...

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Information Technology, Applied Mathematics and Statistics (ICITAMS), International Conference on

作者： Huda H. Ali Ruwaida M. Yas Maysaa H. Abdulameer Computer Engneering Techneques Imam Al-Kadhum College (IKC) Baghdad Iraq Informatics Institute for Post-Graduation Studies Iraqi Commission for Computer & Informatic Baghdad Iraq

ISBN: (数字)9798350335118

ISBN: (纸本)9798350335125

Botnets constitute a significant cybersecurity threat becoming increasingly sophisticated and challenging to detect. In addition, they are used for various illegal activities, such as spreading viruses, conducting DDoS attacks, click fraud, phishing, and identity theft. This study focuses on the latest advancements in machine learning-based botnet detection research. It initially describes how ML technology is implemented in network security research and describes the properties regarding the botnet’s structure prior to the introduction of ML for botnet detection. Next, the study analyzes and summarizes the most commonly used ML algorithms and their security features in botnet detection. Finally, it concludes by summarizing the current ML solutions for describing, detecting, and researching botnets, as well as discussing how to evaluate the accuracy of botnet detection techniques.

关键词： Surveys Machine learning algorithms Systematics Botnet Phishing Network security Feature extraction

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The Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks 3rd

The Effects of Weather Conditions on Data Transmission in Fr...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Kadhim, Aqeel A. Naif, Jolan R. Salman, Muna M. Hoomod, Haider K. Computer Techniques Engineering Department Imam AlKadhim University College Baghdad Iraq Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Information Technology Directorate Ministry of Science and Technology Baghdad Iraq College of Education Computer Science Department Al-Mustansiriya University Baghdad Iraq

ISBN: (纸本)9789819708918

It is necessary to collect data measured in open lands, whether for example for monitoring purposes or for border protection. The process of data transmission from active nodes and its next level to the cluster head (CH), followed by follow-up to arrive at the base station (BS), with safety and acceptability, are the main objectives of wireless sensor networks (WSNs). The key benefits of wireless systems are low-cost system installation and loss-free data transfer. Weather conditions in the spacious area affect wireless data transmission that is done between sensor nodes in free space. This study attempted to ascertain the influence of atmospheric and meteorological conditions on the loss of data when utilizing the wireless data transmission systems in a wireless sensor network. For this reason, the volume of data transfer losses that happen in free space because of three types of weather conditions is the main discussion point. The meteorological criteria that can be considered in this research are temperature, clear weather, dust, and rain as the weather elements that affect the iraqi climate. The correlation analysis's outcomes show that the amount of precipitation and the size of raindrops have a main effect on the losses of data transmission. From a practical point of view, there is no confirmed statistical evidence of the amount of loss in the volume of transmitted data as a result of the amount of rainfall with the use of different transmission frequencies. In this research, we designed an objective routing protocol called Fuzzy Clustering with Ant Colony System FCACS, which has been utilized as a simulation program using the MATLAB 2018b application. The practical results of the proposed protocol for data transmission in free space and under different weather conditions were examined, and the simulation results were compared with those of two well-known routing protocols, which are DEEC and Z-SEP. In terms of the quantity of data received from the CH or BS a

关键词： Data transfer

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A proactive grey wolf optimization for improving bioinformatic systems with high dimensional data

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International Journal of Information Technology (Singapore) 2024年第8期16卷 4797-4814页

作者： Alsaeedi, Ali Hakem Al-Shammary, Dhiah Hadi, Suha Mohammed Ahmed, Khandakar Ibaida, Ayman AlKhazraji, Nooruldeen College of Computer Science and Information Technology University of Al-Qadisiyah Diwaniyah Iraq Informatics Institute for Postgraduate Studies Iraqi Commission for Computer and Informatics Baghdad Iraq Intelligent Technology Innovation Lab Victoria University Melbourne VIC Australia Digital Department of America’s Land Schlumberger Technology Corporation Sugar Land 77478 TX United States Footscray Australia

This paper introduces a new methodology for optimization problems, combining the Grey Wolf Optimizer (GWO) with Simi-stochastic search processes. Intelligent optimizations represent an advanced approach in machine learning and computer applications, aiming to reduce the number of features used in the classification process. Optimizing bioinformatics datasets is crucial for information systems that classify data for intelligent tasks. The proposed A-Proactive Grey Wolf Optimization (A-GWO) solves stagnation in GWO by applying a dual search with a Simi-stochastic search. This target is achieved by distributing the population into two groups using a different search technique. The model's performance is evaluated using two benchmarks: the Evolutionary Computation Benchmark (CEC 2005) and seven popular biological datasets. A-GWO demonstrates highly improved efficiency in comparision to the original GWO and Particle Swarm Optimization (PSO). Specifically, it enhances exploration in 66% of CEC functions and achieves high accuracy in 70% of biological datasets. © The Author(s) 2024.

关键词： Bioinformatic systems Data mining Features selection Grey wolf optimization

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