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Big Data De-duplication Using Classification Scheme based on Histogram of File Stream

Big Data De-duplication Using Classification Scheme based on...

引用

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

作者： Jehlol, Hashem B. George, Loay E. Informatics Institute of Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Baghdad Iraq

ISBN: (数字)9781665459983

ISBN: (纸本)9781665459983

Modern technologies generate millions of data every second. Managing and storing this data is very challenging. Data deduplication reduces storage requirements and data redundancy. It is one of the most effective solutions for Big data storage. This paper proposes a new method to accelerate and improve the deduplication technique. It separates the data into many classes before entering the deduplication system based on the Pearson correlation between the histograms of different data extensions. A new method has been developed to divide the file appropriately by generating a list of divisors based on data repeating patterns. In addition, a mathematical algorithm is proposed to construct the hash functions for each chunk that are twice faster as traditional hash functions (MD5 and SHA-1). As a result, the Deduplication Ratio (DR) of the proposed method is about ten times more potent than the Basic Sliding Window (BSW) method and approximately five times more powerful than the Two Thresholds Two Divisors (TTTD) method. © 2022 IEEE.

关键词： Big data

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Behavior Analysis of Machine Learning Algorithms for Botnets Detection 5

Behavior Analysis of Machine Learning Algorithms for Botnets...

引用

5th International Conference on Information Technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Ali, Huda H. Yas, Ruwaida M. Abdulameer, Maysaa H. Baghdad Iraq Informatics Institute For Post-Graduation Studies Iraqi Commission For Computer & Informatic Baghdad Iraq

ISBN: (纸本)9798350335118

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. © 2023 IEEE.

关键词： Cybersecurity

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Influence of Feature Selection Techniques on the Cyber-Attacks Prediction Using Machine Learning Techniques 8th

Influence of Feature Selection Techniques on the Cyber-Attac...

引用

8th National Conference on New Trends in Information and Communications Technology Applications, NTICT 2024

作者： Ali, Hussein Salam Taresh, Ali Hasan Informatics Institute for Postgraduate Studies Iraqi Commission for Computers &Informatics Baghdad Iraq

ISBN: (纸本)9783031870750

Cybersecurity has emerged as one of the major concerns in digital transformation. The protection of digital assets and maintenance of data integrity could only be ensured with the appropriate and timely prediction of these attacks. In this paper, we present an investigation of how different feature selection techniques affect Machine Learning (ML) model performance in predicting cyber-attacks. The proposed Intrusion Detection System (IDS) consists of some phases starting from the preprocessing phase, the feature selection stage to the detection stage. Feature selection is one of the vital steps in machine learning, as it reduces dimensionality by the elimination of noisy and redundant data, which enhances model accuracy by identifying only the most relevant features. This work evaluates various methods of feature selections applied, including Analysis of Variance (ANOVA), Chi-square, and Mutual Information (MI) techniques, and their impact on the performance of different machine learning algorithms like Naïve Bayes (NB), K-Nearest Neighbor (KNN), AdaBoost, Random Forest (RF) and Decision Tree (DT). Evaluated the proposed approach on an NSLKDD dataset yielded promising results, with accuracy ranging from 99.9% for the time series dataset. In conclusion, the proposed approach effectively detects and mitigates DoS attacks, offering a reliable solution to protect communication systems against this growing cybersecurity threat. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber attacks

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A Review of Digital Signal Processing Techniques for Protein-Coding Region Identification in DNA Sequences 16

A Review of Digital Signal Processing Techniques for Protein...

引用

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

作者： Sakran, Ammar AbdRaba Al-Jawher, Waleed Hadi, Suha M Informatics Institute of Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq University of Information Technology and Communications Iraq Uruk University Department of Electronic and Communication Baghdad Iraq

ISBN: (纸本)9798350381344

The identification and prediction of coding sequences in DNA are intricate tasks that continue to captivate the field of Bioinformatics. It is a multifaceted challenge where Signal Processing methods have demonstrated a substantial impact. Extensive research has been conducted in this area, leading to the development of numerous computational techniques. While many existing review papers have offered summaries of these techniques, most have been confined to specific methodologies or viewpoints. This paper seeks to extend beyond these limitations by providing a concise but comprehensive overview of coding sequence prediction methods. Furthermore, it aims to present distilled technical guidance for potential future research. Lastly, this review offers insights into emerging trends and innovative methods that are poised to influence and enhance the direction of future studies in this area. © 2023 IEEE.

关键词： DNA

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A Hybrid Localization Algorithm of RSS and TOA Based on Adaptive Weight Errors 5

A Hybrid Localization Algorithm of RSS and TOA Based on Adap...

引用

5th International Conference on Information Technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Tariq, Suphian Mohammed Al-Mejibli, Intisar Shadeed Al-Iraqia University Computer Engineering Informatics Institute For Postgraduate Iraqi Commission For Computers & Informatics College Of Engineering Baghdad Iraq Biomedical Informatics College University Of Information Technology And Communications Baghdad Iraq

ISBN: (纸本)9798350335118

Specifying the location of wireless sensor devices is vital in many applications such as target tracking, remote environmental monitoring, and more. Time of arrival (TOA), the received signal strength indicator (RSSI) and range information are applied to locate the wireless sensor network (WSN) node's location. Still, they suffer inaccuracy because of environmental factors in the 2D region. A ranging method has been suggested, merging the TOA information domain with RSSI to accurately determine the distance between the unknown node and the anchor node. This approach relies on utilizing RSSI and TOA ranging techniques in the near and far endings. We combine received signal strength indication and arrival time in parallel style as major elements. The findings of the two methods are then combined with the error percentage to determine the final location using adaptive weight errors. The proposed range method is promoted in this work to enhance the accuracy of the positioning. The proposal has been implemented by MATLAB simulation and the results show improving positioning accuracy compared to the conventional TOA and RSSI ranging method. The mean square error in the proposed method is 0.5044, whereas it was 0.7032 and 0.8102 in RSSI, respectively. This indicates that the suggested approach is more efficient and performs better for localization than the other two methods. © 2023 IEEE.

关键词： Sensor nodes

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The Role of Three-Base Periodicity in Enhancing Exon Detection through DSP Techniques 16

The Role of Three-Base Periodicity in Enhancing Exon Detecti...

引用

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

作者： Sakran, Ammar AbdRaba Al-Jawher, Waleed Hadi, Suha M Iraqi Commission for Computers and Informatics Informatics Institute of Postgraduate Studies Baghdad Iraq University of Information Technology and Communications Iraq Uruk University Department of Electronic and Communication Baghdad Iraq

ISBN: (纸本)9798350381344

Understanding DNA's structural and functional properties requires examining periodic patterns in genomic sequences. Exon differentiation in eukaryotic DNA is facilitated by the three-base periodicity (TBP) phenomenon, which is common in protein-coding areas. This TBP feature in the spectrum domain is used by digital signal processing (DSP) approaches such as the Anti-Notch Filter (ANF) and Short-Time Discrete Fourier Transform (STDFT) for exon-intron separation. Many approaches that target this particular characteristic have been developed over the last 20 years, but their efficacy is still not at its best. A thorough grasp of three-base periodicity and how it affects exon identification is necessary to improve the predicted accuracy of these techniques. Instead of introducing novel feature extraction techniques, this paper thoroughly analyzes 3-base periodicity and its importance in DSP-based exon identification techniques. It has been shown that period-3 traits are less prominent in shorter exons, which makes prediction difficult. This research also provides a comparative examination of ANF and STDFT approaches in this context. © 2023 IEEE.

关键词： Digital signal processing

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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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A new approach for DNA sequence analysis using multiwavelet transform (MWT)

A new approach for DNA sequence analysis using multiwavelet ...

引用

International Laser Technology and Optics Symposium 2022, iLATOS 2022

作者： Sakran, Ammar Abdraba Hadi, Suha M. Mahmoud Al-Jawher, Waleed A. Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq University of Information Technology and Communication Baghdad Iraq Uruk University Department of Electronic and Communication Baghdad Iraq

Finding the nucleotide sequence within a DNA (deoxyribonucleic acid) molecule is the process of DNA sequencing. The DNA of every organism is made up of a specific pattern of nucleotides. Scientists can compare the DNA of different organisms by sequence to understand how organisms are connected. Three different forms of DNA sequencing - fragment analysis, Next Generation Sequencing (NGS), and Sanger sequencing - enable many new applications that increase our knowledge of genomes. It significantly impacts how well we understand how the genome works. Finding the hidden structural information of DNA is an important task. This paper introduces Multi-Wavelet Transformation (MWT) for analysis of the sequence of the DNA symbols. The symbolic DNA sequence has four indicator sequences. The MWT assigned symbols to numbers based on DNA structure. It can change each channel's weighting to optimize energy distribution over MWT output. Analysis of synthetic and actual DNA sequences allowed the researchers to evaluate how well the proposed MWT works. The results show that the MWT has a higher energy dispersion than the regular WT and the bionic wavelet transform (BWT). Future DNA sequence analyses should be able to discover hidden structural features using this MWT approach. © Published under licence by IOP Publishing Ltd.

关键词： DNA

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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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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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