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

作者： Sallibi, Anas D. Alheeti, Khattab M. Ali University of Anbar College of Computer and Information Technology Department of Computer Sciences Anbar Iraq University of Anbar College of Computer and Information Technology Department of Computer Networking Systems Anbar Iraq

ISBN: (纸本)9798350341881

Autism Spectrum Disorder (ASD) is a neurological condition that permanently affects a person's ability to learn languages, speak, think clearly, and interact with others. In the embryonic stages, its symptoms typically emerge. The conditions of ASD can be improved by identifying and treating them early. ASD is either genetic or caused by environmental factors. Currently, the only methods utilized to diagnose ASD are clinically based-tests. In addition to requiring extended diagnostic time, this will also result in a sharp increase in medical expenses. Machine Learning (ML) approaches are being used to supplement traditional methods to increase diagnoses' accuracy and response time. The paper presents a review of the recent literature on different ML approaches for the classification and identification of ASD in children and the study of the characteristics of ASD. The concept of ASD and the techniques used to discover it are explained;at the end of the paper. The most prominent techniques used in ML to identify children with ASD were discussed in detail. Finally, The main objective of our study is to focus on whether the child is at risk of developing ASD spectrum disorder in its early stages And highlight the importance of early diagnosis using ML methods to reduce the symptoms of the disease. © 2023 IEEE.

关键词： Diagnosis

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Detection of Autism Spectrum Disorder by Using Common Machine Learning Algorithms 16

Detection of Autism Spectrum Disorder by Using Common Machin...

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16th International Conference on Developments in esystems Engineering, DeSE 2023

ISBN: (纸本)9798350381344

Communication, social interaction, behavior, and sensory processing are all negatively impacted by the neurodevelopmental disease known as autism spectrum disorder (ASD). The word 'spectrum' is included in the name because there is such a broad variety of symptoms and degrees of impairment. The health and well-being of children with autism spectrum disorder (ASD) can be greatly improved by early diagnosis and intervention. The current detection techniques are based on the opinion of specialists, which is subjective and expensive. In this research, we suggest a machine learning strategy for ASD diagnosis that incorporates a suite of machine learning techniques. Using it has the potential to save money and increase detection efficiency. To begin, we performed pre-processing to extract characteristics from the data. Then, classify the data with a 0.997% accuracy using four machine learning (ML)-based methods: The Random Forest, logistic regression, support vector machine (SVM) and K-nearest neighbors algorithm (KNN). The findings of this research provide promising evidence for the efficacy of the machine learning classification technique used to diagnose ASD at an early age. © 2023 IEEE.

关键词： Support vector machines

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Lung Cancer Intelligent Prediction System Using CNN

Lung Cancer Intelligent Prediction System Using CNN

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2023 International Conference on Decision Aid Sciences and Applications, DASA 2023

作者： Al-Shouka, Tabreer T. Alheeti, Khattab M. Ali College of Computer and Information Technology University of Anbar Computer Sciences Department Anbar Iraq College of Computer and Information Technology University of Anbar Department of Computer Networking Systems Anbar Iraq

ISBN: (纸本)9798350342055

The mortality rate from Lung Cancer is high, and the disease is notoriously difficult to treat. It was the third most frequent mortality reason for women and the leading cause of male mortality, according to data compiled by the International Agency for Research on Cancer (IARC) Cancer Research Facility. Additionally, they provided rates of cancer occurrence and mortality for a total of 36 different cancers occurring in 185 different countries. Typically, early tumor detection is essential for effective therapy. Early cancers can be efficiently examined to help patients recover quickly. Traditional medical imaging techniques, such as X-rays, CT scans, MRIs, etc., offer little promise for lung tumor identification. CNNs are capable of exact image processing. This paper provides a probabilistic deep 2D CNN diagnosis method for lung cancer (CNNs). This algorithm is considered the best in image classification, which gives better results, making the proposed system more efficient. The accuracy values obtained are 86%, the recall of 92%, and the F1-score is 87%. © 2023 IEEE.

关键词： computerized tomography

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K-Nearest Neighbor Algorithm for Efficient Heart Disease Classification System 15

K-Nearest Neighbor Algorithm for Efficient Heart Disease Cla...

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15th International Conference on Developments in esystems Engineering, DeSE 2023

作者： Abdalkafor, Ahmed Subhi Alheeti, Khattab M. Ali University of Anbar Career Development Center Anbar Iraq University of Anbar Department of Computer Networking Systems Anbar Iraq

ISBN: (纸本)9798350335149

Healthcare is considered significant topic in the recent research area. However, one of the most commonly diseases which is heart diseases disease. The possibility of early detection to reduce the number of deaths because it is difficult to predict a heart disorder quickly. Recently, many researchers focused on the implementation of several feature extraction techniques and the help of artificial intelligence algorithms to classify this disease, but classification accuracy remained the only difference between these studies. In this paper, the proposed work for the classification of heart diseases was implemented and tested after selecting methods and techniques for data pre-processing and extracting important features that led to obtaining a competitive classification accuracy that reached higher than 93.5% compared to related studies. This finding encourages us and other field researchers to use methods for feature extraction and other strategies described in this paper to classify other diseases. © 2023 IEEE.

关键词： Diseases

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A Review Paper: Security for Supervisory Control and Data Acquisition SCADA Based on DNP3 16

A Review Paper: Security for Supervisory Control and Data Ac...

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16th International Conference on Developments in esystems Engineering, DeSE 2023

作者： Jasim, Amenh A. Alheeti, Khattab M. Ali University of Anbar College of Computer and Information Technology Computer Science Department Ramadi Iraq University of Anbar College of Computer Sciences and Information Technology Computer Networking Systems Department Ramadi Iraq

ISBN: (纸本)9798350381344

SCADA (Supervisory Control and Data Acquisition) is a crucial ICS used in gas pipelines, power grids, and healthcare, this study discusses the importance of intrusion detection systems (IDS) systems, highlighting the DNP3 protocol for communication, IDS intrusion detection systems, and assaults. It compares and debates past research on the topic, emphasizing the need to detect irregularities and maintain safety in SCADA systems, as security breaches pose significant risks. © 2023 IEEE.

关键词： Intrusion detection

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A Comparative Study for SDN Security Based on Machine Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第11期17卷 131-140页

作者： Alheeti, Khattab M Ali Alzahrani, Abdulkareem Alamri, Maha Kareem, Aythem Khairi Al Dosary, Duaa Computer Networking Systems Department University of Anbar Anbar Iraq Faculty of Computer Science and Information Technology Al Baha University Al Baha Saudi Arabia Department of Heet Education General Directorate of Education in Anbar Ministry of Education Anbar Iraq

In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related to formal networks is their stable essence, which makes them incapable of meeting the requirements of nodes recently inserted into the network. Thus, formal networks are substituted by a Software Defined Network (SDN). The latter can be utilized to construct a structure for intensive data applications like big data. In this paper, a comparative investigation of Deep Neural Network (DNN) and Machine Learning (ML) techniques that uses various feature selection techniques is undertaken. The ML techniques employed in this approach are decision tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM). The proposed approach is tested experimentally and evaluated using an available NSL–KDD dataset. This dataset includes 41 features and 148,517 samples. To evaluate the techniques, several estimation measurements are calculated. The results prove that DT is the most accurate and effective approach. Furthermore, the evaluation measurements indicate the efficacy of the presented approach compared to earlier studies. © 2023, International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Decision trees

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Comprehensive Study of Side-Channel Analysis (CyberSecurity) 16

Comprehensive Study of Side-Channel Analysis (CyberSecurity)

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16th International Conference on Developments in esystems Engineering, DeSE 2023

作者： Obaid, Zaid M. Alheeti, Khattab M. Ali University of Anbar College of Computer Sciences and Information Technology Dept.Computer Science Anbar Iraq University of Anbar College of Computer Sciences and Information Technology Computer Networking Systems Department Anbar Iraq

ISBN: (纸本)9798350381344

In the ever-evolving environment of digital security, the value of cryptographic systems in preserving data and communications cannot be emphasized. However, the introduction of side-channel attacks has presented a substantial hazard, leveraging minor information breaches from the physical implementations of cryptographic algorithms. This research, via a thorough examination of literature spanning several years, tries to consolidate and classify the diverse approaches employed in side-channel assaults. This research dives into the fundamental weaknesses that these assaults exploit, spanning elements such as power usage, electromagnetic radiation, timing problems, and auditory emissions. To demonstrate the real-world ramifications of side-channel vulnerabilities, the paper presents realistic examples and case studies, providing light on the possible repercussions of these assaults. By classifying side-channel assaults depending on their strategy and identifying important instances within each category, this research elucidates the broad landscape of side-channel threats. It also investigates the severe effect of these attacks on cryptographic systems and its implications for general security, highlighting the critical need for comprehensive countermeasures. This thorough research gives a full evaluation of side-channel assaults, concentrating on their methodology, effect, and the increasing realm of response tactics. Understanding the subtleties of side-channel attacks is crucial to strengthening the security of cryptographic systems. As such, this study offers as a helpful resource for academics, practitioners, and policymakers contending with the evolving environment of side-channel assaults. It emphasizes the continual necessity for study and joint attempts to reinforce cryptography solutions in a more linked culture. © 2023 IEEE.

关键词： Cybersecurity

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Enhancing Wireless Sensor Network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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Methods for Predictive Performance Improvement of Deep Learning systems for Aerial Building Roof Detection with Multispectral Images

Methods for Predictive Performance Improvement of Deep Learn...

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2023 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement systems and Applications, CIVEMSA 2023

作者： Churi, A. Megherbi, D.B. University of Massachusetts Center for Human/Machine Intelligence Networking Distributed Systems Department of Electrical and Computer Engineering LowellMA United States

ISBN: (纸本)9781665434454

In this paper, we present a dilated convolutional neural network-based deep learning system for the challenging problem of building accurate roof pixel detection from city aerial multispectral images in the visible and infrared spectral bands. The proposed system uses an automatic spectral band selection strategy and an automatic histogram-based threshold-finding method to improve the network's prediction accuracy performance. Current existing systems train a single model on all the cities of interest data at once. These systems also usually use only RGB bands and avoid post-processing of the predicted images for increased prediction accuracies. In this paper, we present the following solutions to these object detection challenges, 1) we propose and implement a separate model for each city dataset, 2) we propose an optimal band selection strategy to choose the most informative visible-infrared bands from multispectral images, 3) we propose an adaptive and automatic histogram-based optimal threshold finding technique for building roof object segmentation. Our results show that having a separate model for each city rather than only one model for all the combined cities increases the system's performance by 20.77% in building roof pixel prediction in any of the cities considered. We show that our system also has a 35.6% increase in F1 score compared to its state-of-The-Art counterpart, the winning implementation of the SpaceNet building detection national challenge, which took place in 2017. © 2023 IEEE.

关键词： Object recognition

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Cloud Intrusion Detection System Based on SVM

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第11期17卷 101-114页

作者： Alheeti, Khattab M. Ali Lateef, Ali Azawii Abdu Alzahrani, Abdulkareem Imran, Azhar Al Dosary, Duaa Computer Networking Systems Department University of Anbar Anbar Iraq Human Resources Department University of Anbar Anbar Iraq Computer Engineering and Science Department Al Baha University Al Baha Saudi Arabia Department of Creative Technologies Air University Islamabad Pakistan

The demand for better intrusion detection and prevention solutions has elevated due to the current global uptick in hacking and computer network attacks. The Intrusion Detection System (IDS) is essential for spotting network attacks and anomalies, which have increased in size and scope. A detection system has become an effective security method that monitors and investigates security in cloud computing. However, several existing methods have faced issues such as low classification accuracy, high false positive rates, and low true positive rates. To solve these problems, a detection system based on Support Vector Machine (SVM) is proposed in this paper. In this method, the SVM classifier is utilized for network data classification into normal and abnormal behaviors. The Cloud Intrusion Detection Dataset is used to test the effectiveness of the suggested system. The experimental results show which the suggested system can detect abnormal behaviors with high accuracy. © 2023, International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Support vector machines

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