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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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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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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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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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New Cloud Computing Authentication Based on Secure Hash Algorithm (SHA-3) and Lightweight Sosemanuk Algorithm 1

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Niaf, Jolan Rokan Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Hoomod, Haider K. Salman, Muna Muhsen Iraqi Commission for Computers & Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Techniques Engineering Department Imam Al-Kadhim College Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq College of Education Al-Mustansiriyah University Baghdad Iraq Ministry of Science and Technology Department of Information Technology Baghdad Iraq

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

The term "cloud computing" refers to a vast network of linked, virtually hosted, and easily updatable computer resources. That are capable of consisting of a number of different apps and offering a number of different services to customers, and that have the potential to do so. Customers are required to pay for the cost of using the Services in accordance with the company's "pay-for-use only" policy. However, there are certain firms, such as Google Inc, that provide customers with particular free storage space even though the services provided by the company are limited, specifically with regard to the public cloud. A third-party service provider frequently owns and maintains the infrastructure required to run cloud computing services. But because more and more data and files pertaining to individuals and businesses are being stored in the cloud, worries have started to grow about how safe it actually is. Among these are the protection of software for virtualization and distributed computing, the safety of application software, the administration of identities, and the authentication and restriction of access. Nevertheless, efficient user authentication is the most essential need of cloud computing, as it limits unauthorized usage of a cloud server. Cloud computing has become increasingly popular in recent years. In this research, we present a new authentication method between users and online computing systems that is based on the suggested secure hash algorithm (SHA-3) and makes use of the lightweight Sosemanuk method. We created this technique. That system is going to be known as the SoSha3 system. The design that has been proposed for the system deals with data in three different sizes: 128, 256, and 512 bits. In order to make these authentication systems suitable for cloud computing at high rates, some modifications were made to them. The three-dimensional chaotic system, also known as the Lorenz system, will be combined with the authentication architecture tha

关键词： Cloud security

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A Comparative Study of Text Classification using Artificial Intelligence Models 4

A Comparative Study of Text Classification using Artificial ...

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4th International Conference on Current Research in Engineering and Science Applications, ICCRESA 2022

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

ISBN: (纸本)9798350334944

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. © 2022 IEEE.

关键词： Classification (of information)

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Efficient Key Management in Lightweight Cryptography: A New Hashing-Based Approach

Efficient Key Management in Lightweight Cryptography: A New ...

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International Symposium on Multidisciplinary studies and Innovative Technologies (ISMSIT)

作者： Haider H. Al-Mahmood Saad N. Alsaad Computer Science Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Department of Computer Science College of Science University of Mustansiriyah Baghdad Iraq

ISBN: (数字)9798350354423

ISBN: (纸本)9798350354430

Lightweight encryption methods are introduced to overcome the challenges of high-power consumption and high memory usage in the traditional cryptography system. The primary goal of the lightweight encryption technique is to make it suitable for use in devices provided with lower power and small memories. The importance of lightweight cryptosystem is its ability to provide suitable security for data encryption without consuming high level of power and without need for big memories. As the devices with lower battery life and supplied low memory become widespread, encryption key management problems start to emerge. This may lead to a high possibility of an attack on the encryption key and break the encryption system. In this context, this paper proposes a novel hashing- based approach for key generation in lightweight cryptography. The approach involves generating significant hash digits from a seed input to the hashing system, and then using hash code slicing to select different characters and numbers that can be used as a seed for the key generating system. The practical results, which demonstrate that a small seed length can effectively generate a large key length, are a testament to the effectiveness of the proposed approach. These results can be used in the encryption process to reduce the initial total bits, thereby enhancing the efficiency of the system.

关键词： Codes Scalability Bibliographies Memory management Encryption Batteries

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Construct an Efficient DDoS Attack Detection System Based on RF-C4.5-GridSearchCV

Construct an Efficient DDoS Attack Detection System Based on...

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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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