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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Journal of Information Technology and Computer Engineering International Journal of Artificial Intelligence and Robotics (IJAIR) International Journal of Research and Applied Technology (INJURATECH) Jurnal Ilmiah Computer Science
Mohammed, Ayaz Khalid
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Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Cybernetic Deception: Unraveling the Layers of Email Phishing Threats Zangana, Hewa Majeed; Mohammed, Ayaz Khalid; Sallow, Amira Bibo; Sallow, Zina Bibo
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 1 (2024): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

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Abstract

E-mail phishing, a tireless and versatile cybersecurity risk, requires a intensive examination to fortify organizational resistances. This broad survey dives into the multifaceted measurements of e-mail phishing, including mental control strategies, mechanical complexities, and real-world experiences determined from assorted case considers. The investigation of location and anticipation procedures covers a extend of commitments, tending to half breed machine learning approaches, the significance of client instruction, and the part of administrative compliance. These procedures give a significant system for organizations pointing to improve their flexibility against the energetic scene of phishing strategies. The theoretical underscores the administrative landscape's significant part in forming cybersecurity hones, advertising a organized establishment for organizations to adjust with legitimate prerequisites. Expecting future patterns and challenges, such as the integration of characteristic dialect preparing procedures and the complexities of cloud-based phishing assaults, gets to be basic for maintained cyber versatility. In conclusion, this paper serves as a comprehensive direct, enabling people and organizations with the information and methodologies required to explore the complex scene of e-mail phishing dangers. It recognizes the energetic nature of the danger scene, highlighting the progressing travel in combating computerized duplicity and invigorating preparation against the ever-evolving strategies of phishing foes.
Advancements in Edge Detection Techniques for Image Enhancement: A Comprehensive Review Zangana, Hewa Majeed; Mohammed, Ayaz Khalid; Mahmood Mustafa, Firas
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 6 No. 1 (2024): May 2024
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v6i1.8217

Abstract

Edge detection is a fundamental algorithm in image processing and computer vision, widely applied in various domains such as medical imaging and autonomous driving. This comprehensive literature review critically evaluates the latest edge detection methods, encompassing classical approaches (Sobel, Canny, and Prewitt) and advanced techniques based on deep learning, fuzzy logic, and optimization algorithms. The review summarises the significant contributions and advancements in the field by synthesizing insights from numerous research papers. It also examines the combination of edge detection with current image processing methods and discusses its impact on real-life applications. The review highlights the strengths and limitations of existing edge detection strategies and proposes future avenues for investigation. Various research shows that classical edge detection methods like Sobel, Canny, and Prewitt still play a significant role in the field. However, advanced methods utilizing deep learning, fuzzy logic, and optimization algorithms have shown promising results in enhancing edge detection accuracy. Combining edge detection with current image processing methods has demonstrated improved clarity and interpretation of images in real-life applications, including medical imaging and machine learning systems. Despite the progress made, there are still limitations and challenges in existing edge detection strategies that require further investigation. Future research should address these shortcomings and explore new edge detection algorithm development avenues. By understanding the current state of the art and its implications, researchers and practitioners can make informed decisions and contribute to advancing edge detection in image processing and analysis. Overall, this review serves as a valuable guide for researchers and practitioners working in the field, providing a thorough understanding of the state-of-the-art edge detection techniques, their implications for image processing, and their potential for further development.
Advancements and Applications of Convolutional Neural Networks in Image Analysis: A Comprehensive Review Majeed Zangana, Hewa; Mohammed, Ayaz Khalid; Mustafa, Firas Mahmood
Jurnal Ilmiah Computer Science Vol. 3 No. 1 (2024): Volume 3 Number 1 July 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i1.30

Abstract

Convolutional Neural Networks (CNNs) have revolutionized image analysis, extracting meaningful features from raw pixel data for accurate predictions. This paper reviews CNN fundamentals, architectures, training methods, applications, challenges, and future directions. It introduces CNN basics, including convolutional and pooling layers, and discusses diverse architectures like LeNet, AlexNet, ResNet, and DenseNet. Training strategies such as data preprocessing, initialization, optimization, and regularization are explored for improved performance and stability. CNN applications span healthcare, agriculture, ecology, remote sensing, and security, enabling tasks like object detection, classification, and segmentation. However, challenges like interpretability, data bias, and adversarial attacks persist. Future research aims to enhance CNN robustness, scalability, and ethical deployment. In conclusion, CNNs drive transformative advancements in image analysis, with ongoing efforts to address challenges and shape the future of AI-enabled technologies.
Enhancing Image Quality With Deep Learning: Techniques And Applications Zangana, Hewa Majeed; Mustafa, Firas Mahmood; Mohammed, Ayaz Khalid; Omar, Naaman
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 2 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i2.1242

Abstract

The emergence of deep learning has transformed numerous fields, particularly image processing, where it has substantially enhanced image quality. This paper provides a structured overview of the objectives, methods, results, and conclusions of deep learning techniques for image enhancement. It examines deep learning methodologies and their applications in improving image quality across diverse domains. The discussion includes state-of-the-art algorithms such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Autoencoders, highlighting their applications in medical imaging, photography, and remote sensing. These methods have demonstrated notable impacts, including noise reduction, resolution enhancement, and contrast improvement. Despite its significant promise, deep learning faces challenges such as computational complexity and the need for large annotated datasets. outlines future research directions to overcome these limitations and further advance deep learning's potential in image enhancement.
The Role of Change Control Boards in Ensuring Cybersecurity Compliance for IT Infrastructure Zangana, Hewa Majeed; Mustafa , Firas Mahmood; Mohammed, Ayaz Khalid; Omar , Marwan
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 1 (2025)
Publisher : Universitas Andalas

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Abstract

In the dynamic landscape of information technology, maintaining cybersecurity compliance is a paramount concern for organizations. Change Control Boards (CCBs) play a crucial role in this context, serving as a governance mechanism to oversee and manage changes within IT infrastructure. This paper explores the significance of CCBs in ensuring cybersecurity compliance, focusing on their functions, processes, and impact on organizational security posture. Through a comprehensive review of existing literature and case studies, the research highlights how CCBs facilitate risk assessment, enforce policy adherence, and mitigate potential threats arising from changes in the IT environment. The findings underscore the importance of structured change management and suggest best practices for integrating cybersecurity considerations into the CCB workflow. By understanding the role of CCBs, organizations can enhance their ability to safeguard sensitive data and maintain regulatory compliance in an ever-evolving threat landscape.
Systematic Review of Decentralized and Collaborative Computing Models in Cloud Architectures for Distributed Edge Computing Zangana, Hewa Majeed; Mohammed, Ayaz khalid; Zeebaree, Subhi R. M.
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i4.4169

Abstract

This systematic review paper delves into the evolving landscape of cloud architectures for distributed edge computing, with a particular focus on decentralized and collaborative computing models. The aim of this systematic review is to synthesize recent advancements in decentralization techniques, collaborative scheduling, federated learning, and blockchain integration for edge computing. As edge computing becomes increasingly vital for supporting the Internet of Things (IoT) and other distributed systems, innovative strategies are needed to address challenges related to latency, resource management, and data security.The key findings highlight the benefits of latency-aware task management, autonomous serverless frameworks, and the collaborative sharing of computational resources. Additionally, the integration of federated learning and blockchain technologies offers promising solutions for enhancing data privacy and resource allocation. The versatility of edge computing is showcased through its applications in diverse domains, including healthcare and smart cities. Future research directions emphasize the need for optimized resource management, improved security protocols, standardization efforts, and application-specific innovations. By providing a comprehensive review of these developments, this paper underscores the critical role of decentralized and collaborative models in advancing the capabilities and efficiency of edge computing systems.
Co-Authors Mahmood Mustafa, Firas Majeed Zangana, Hewa Mustafa , Firas Mahmood Mustafa, Firas Mahmood Omar , Marwan Omar, Naaman Sallow, Amira Bibo Sallow, Zina Bibo Zangana, Hewa Majeed Zeebaree, Subhi R. M.