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

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

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

关键词： Wireless communication Resistance Wireless sensor networks Chaotic communication Authentication Passwords Sensor phenomena and characterization

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

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

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.

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

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

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.

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

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.

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The design and implementation of open educational resource platform for IIPS 3

The design and implementation of open educational resource p...

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3rd International Conference on Engineering Technology and its Applications, IICETA 2020

作者： Hameed, Muna R. El-Ameer, Amer S. Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq

ISBN: (纸本)9781728182315

Open Educational Resource (OER) can present more opportunities for learning than those presented by classical classrooms and help to make the learning more efficient. However, Higher education systems in Iraq today as well as in other developing countries suffer from a lack of teaching quality and education resources quality because of lacking competences, and experiences to improve such resources, So that, it cannot meet the requirements of the open learning goals. To solve these problems, we investigate the design and implementation of the OER platform for IIPS(informatics institute for postgraduate studies). In this research, we present the main principles of the platform design, based on technical requirements analysis of the platforms;to gain large-scale online-learning support. Finally, we implement our OER platform. © 2020 IEEE.

关键词： Developing countries

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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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Intelligent triage method for early diagnosis autism spectrum disorder (ASD) based on integrated fuzzy multi-criteria decision-making methods

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informatics in Medicine Unlocked 2023年 36卷

作者： Joudar, Shahad Sabbar Albahri, A.S. Hamid, Rula A. Informatics Institute for Postgraduate Studies (IIPS) Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq University of Technology Baghdad Iraq College of Business Informatics University of Information Technology and Communications (UOITC) Baghdad Iraq

Background: Autism spectrum disorder (ASD) symptoms and severity levels vary from patient to patient, so treatment and healthcare will vary. However, little attention has been given to developing an autistic triage method for ASD patients concerning four issues: hybrid triage criteria, multi-selection criteria problems, criteria importance, and trade-off based on the inverse relationship between autistic criteria. Therefore, this study aims to develop a new method for triaging ASD patients and classifying them according to their severity of disorder using Fuzzy Multi-Criteria Decision Making (fMCDM) methods. Methods: Two methodology phases have been conducted: the first phase is to identify and preprocess the ASD dataset, including 988 autistic patients with 42 medical and Sociodemographic criteria. In the second phase, two fMCDM methods were used to develop the triage method. The fuzzy Delphi Method (FDM) is used to select the most influential criteria among the 42 based on thirteen psychologists in the psychological field. Then Fuzzy-Weighted Zero-Inconsistency (FWZIC) is used to assign weights to the important criteria according to four psychologists' opinions. Accordingly, the Processes for Triaging Autism Patients (PTAP) method has been developed for the first time for triaging and classifying patients into three severity levels: minor, moderate, and urgent. Results: For the preprocessed phase, 538 out of 988 patients were obtained as a new ASD dataset underwent data cleaning to capture only autism patients. For the second phase, the FDM results have selected 19 out of 42 criteria and can control the bias of psychologists' opinions, FWZIC has assigned the appropriate weights for the 19 criteria, and the PTAP method triages the 538 patients into three severity levels: 36 minor injuries, 432 moderate injuries, and 70 urgent injuries. More complex statistical analyses have been presented using MedCalc statistical software. Three physicians in the psychological fie

关键词： ASD Autism Fuzzy Delphi FWZIC MCDM Triage

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Techniques for secure distributed systems

Techniques for secure distributed systems

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2019 Imam Al-Kadhum International Conference for Modern Applications of Information and Communication Technology, MAICT 2019

作者： Ali, Huda H. Shaker, Shaimaa H. Iraq Iraqi Commission for Computer and Informatics Information Institute for Postgraduate Studies Iraq University of Technology

In recent networked world, more of the times computers work not isolation. They work with each other in order to satisfy communication purpose, processing, transfer of data, saving, etc., the systems can be described as distributed when it contain several systems work together in harmonic way with each other that are geographically lies over far distance. The security of distributed systems is very important issues and it may be describe as the most difficult part in the design for a system. In this paper the basic principles of distributed system is presented, then the security vulnerabilities are shown and the recent technique that used for solving the security problems are discussed and also analysis for their performance is done where the advantages and disadvantages with robustness for each technique are shown. So as conclusion for the results that are gotten by researchers, the selection of security technique depends on what really need to be protected, type of distributed systems and available resources. Although there are several efficient techniques, but the most powerful one is using of biometric and encryption for authentication and security purposes. © Published under licence by IOP Publishing Ltd.

关键词： Authentication

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A Comparative Study of Text Classification Using Artificial Intelligence Models

A Comparative Study of Text Classification Using Artificial ...

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Current Research in Engineering and Science Applications (ICCRESA), International Conference on

作者： Maysaa H. Abdulameer Mahmood Z. Abdullah Informatics Institute for Post-Graduation Studies Iraqi Commission for Computer & informatics Baghdad Iraq College of Engineering Al-Mustansiriyah University Baghdad Iraq

The presented study provides experiments for carrying out empirical studies related to deep learning, especially convolution networks, concerning novel dataset classification (video and audio). The study will utilize the architecture of the text classification problem in the document domain with the datasets related to documents collected from many projects on real document cases. The process of building a new dataset and the existence of such data followed the urgent need that is specialized in educational lectures. Test and evaluate the two new datasets (Audio and Video) with the published dataset Book dataset by using deep learning methods in classification and machine learning algorithms, then compare the Accuracy results for each dataset with both classification methods, which present that new datasets bring better results than Book dataset which are (95% and 90 % ) for new datasets and 83% for Book dataset by using Deep learning, in machine learning algorithms the results are (91%, 77%, and 46 % ). Finally, the two classification techniques are used to maximize the benefits of the classification process. In the selection process, the system will choose deep learning for classifying the datasets than using machine learning algorithms.

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