Article Per Year (5 Year)
p-Index From 2020 - 2025
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This Author published in this journals
All Journal The Asian Journal of Technology Management (AJTM) Journal of Information Technology and Computer Engineering Jurnal Ilmiah Computer Science Business, Accounting and Management Journal Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Omar , Marwan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering Social Sciences Transportation

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HARNESSING ARTIFICIAL INTELLIGENCE IN MODERN MARKETING: STRATEGIES, BENEFITS, AND CHALLENGES Zangana, Hewa; Omar , Marwan; Ali , Natheer Yaseen
Business, Accounting and Management Journal Vol. 2 No. 02 (2024): Business, Accounting and Management Journal
Publisher : tesco publisher

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Abstract

In recent years, integrating Artificial Intelligence (AI) into marketing strategies has revolutionized the industry, providing businesses with unprecedented tools to analyze consumer behavior, personalize customer experiences, and optimize campaign performance. This paper explores the multifaceted impact of AI on modern marketing, highlighting key strategies businesses employ, the benefits realized through enhanced data analytics, automation, and customer engagement, as well as the challenges and ethical considerations accompanying AI adoption. By examining current trends and case studies, this study aims to provide a comprehensive understanding of how AI shapes the future of marketing, offering insights into best practices and potential pitfalls for marketers navigating this rapidly evolving landscape.
Advances in Adaptive Resonance Theory for Object Identification and Recognition in Image Processing Zangana, Hewa; Mustafa , Firas Mahmood; Omar , Marwan
Jurnal Ilmiah Computer Science Vol. 3 No. 2 (2025): Volume 3 Number 2 January 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i2.41

Abstract

Adaptive Resonance Theory (ART) has emerged as a significant framework in the realm of image processing, particularly in object identification and recognition. This review paper examines the application and effectiveness of ART in these domains. By analyzing a wide range of studies, we highlight ART's high accuracy, precision, and robustness in recognizing objects under varying conditions. The methodology involves data collection, preprocessing, and the configuration and training of ART networks. Our results demonstrate ART's superior performance compared to traditional neural networks, particularly in handling noisy data and real-time learning. Furthermore, we discuss the integration of ART with other technologies, such as memristor-based neuromorphic systems and fuzzy logic, to enhance its capabilities. The study underscores the versatility of ART, suggesting its applicability in diverse fields including robotics and cybersecurity. The results of our analysis demonstrate that ART achieves an average accuracy of 92% on the CIFAR-10 dataset and 89% on ImageNet, with a precision of 91% and a recall of 88%. These findings confirm ART's superior performance in recognizing objects under varying conditions, particularly in handling noisy data and real-time learning. Future research directions include improving feature extraction methods, dynamic parameter adjustment, and exploring hybrid models. This paper confirms ART's potential as a powerful tool in advancing image processing technologies.
Enhancing IT Project Agility Through Effective Change Management Strategies Zangana, Hewa Majeed; Omar , Marwan; Ali , Natheer Yaseen; Abdullah , Dilovan Taha
The Asian Journal of Technology Management (AJTM) Vol. 18 No. 1 (2025): (In Progress Issue)
Publisher : Unit Research and Knowledge, School of Business and Management, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12695/ajtm.2025.18.1.2

Abstract

In the dynamic landscape of Information Technology (IT) projects, agility is paramount for success. This paper explores the critical role of effective change management strategies in enhancing IT project agility. By integrating change management practices with agile methodologies, organizations can better adapt to evolving requirements and unexpected challenges. This study examines key change management principles and their application within agile frameworks, highlighting best practices and case studies that demonstrate successful outcomes. The findings suggest that a proactive and flexible approach to change management not only mitigates risks but also accelerates project delivery and improves stakeholder satisfaction. Ultimately, the synergy between change management and agility can significantly enhance the performance and resilience of IT projects.
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.
Adaptive Resonance Theory-Based Approach for Robust and Efficient Face Recognition Zangana, Hewa; Khalid Mohammed, Ayaz; Omar , Marwan; Mahmood Mustafa, Firas; Vega Vitianingsih , Anik
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i3.38709

Abstract

In recent years, face recognition systems have gained significant traction due to their applications in security, surveillance, and user authentication. Despite the advances in deep learning techniques, challenges such as varying lighting conditions, occlusions, and facial expressions continue to affect the robustness and efficiency of these systems. This paper proposes a novel approach to face recognition based on Adaptive Resonance Theory (ART). ART's ability to adaptively learn and recognize patterns in a stable and incremental manner makes it particularly suitable for handling the dynamic variations encountered in face recognition tasks. Our proposed ART-based face recognition framework is evaluated on multiple benchmark datasets, demonstrating superior performance in terms of accuracy, robustness to noise, and computational efficiency compared to traditional methods. The experimental results highlight the potential of ART to enhance the reliability of face recognition systems in real-world applications.
Co-Authors Abdullah , Dilovan Taha Ali , Natheer Yaseen Khalid Mohammed, Ayaz Mahmood Mustafa, Firas Mohammed, Ayaz Khalid Mustafa , Firas Mahmood Vega Vitianingsih , Anik Zangana, Hewa Zangana, Hewa Majeed