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All Journal Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Indonesian Journal of Education and Social Science Jurnal Ilmiah Computer Science Business, Accounting and Management Journal Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Zangana, Hewa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences Transportation Other

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
A Novel Hybrid Algorithm for Effective Image Restoration Zangana, Hewa; Firas, Mahmood Mustafa; Omar, Marwan
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (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.v2i2.38118

Abstract

Image restoration plays a pivotal role in various applications, from medical imaging to satellite photography, by enhancing the quality of images degraded by noise, blur, or other distortions. Traditional methods and deep learning techniques have both shown promise in addressing these challenges, yet each has its limitations. Traditional algorithms often struggle with complex distortions, while deep learning models demand extensive computational resources and large datasets. To harness the strengths of both approaches, we propose a novel hybrid algorithm that integrates traditional image restoration techniques with advanced deep learning models. This paper presents a novel hybrid algorithm for image restoration, integrating traditional Wiener filtering with a state-of-the-art U-shaped transformer (Uformer) architecture. Unlike existing methods, our approach combines the computational efficiency of classical techniques with the robustness and precision of deep learning. Comprehensive evaluations on benchmark datasets demonstrate significant improvements in restoration quality (PSNR/SSIM) and computational efficiency compared to state-of-the-art methods. This research contributes a new perspective on hybrid methodologies, bridging the gap between traditional and modern approaches in image restoration.
Quantum-Assisted Architectures for 6G and IoT: A Framework for Secure and Efficient Wireless Networks Zangana, Hewa; Bibo Sallow, Amira; Mahmood Mustafa, Firas
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 10 No. 2 (2025): In Press: July, 2025
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/inform.v10i2.10436

Abstract

The convergence of Sixth-Generation (6G) wireless networks and the Internet of Things (IoT) demands unprecedented levels of performance, scalability, and security. Traditional architectures are increasingly inadequate in addressing the computational and security challenges posed by massive IoT connectivity, ultra-low latency, and high data throughput. This paper proposes a novel quantum-assisted architecture that integrates quantum computing and quantum communication principles to enhance the efficiency and security of 6G-enabled IoT systems. The framework leverages quantum key distribution (QKD), quantum machine learning (QML), and entanglement-assisted routing to provide end-to-end encryption, intelligent resource allocation, and resilient data transmission. Our simulation results and comparative analysis demonstrate significant improvements in network throughput, latency, and security resilience compared to classical 6G-IoT architectures. This research establishes a foundational step toward realising secure and intelligent next-generation wireless networks through the integration of quantum technology.
Leveraging Automation and Traceability in Managing Changes to Mission-Critical Computer Systems Zangana, Hewa; Ali, Natheer Yaseen; Bazeed, Sameer Mohammed Salih; Abdullah, Dilovan Taha
Indonesian Journal of Education and Social Sciences Vol. 4 No. 1 (2025)
Publisher : Papanda Publishier

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56916/ijess.v4i1.1016

Abstract

Managing changes to mission-critical computer systems presents unique challenges, especially when reliability, security, and real-time performance are paramount. Traditional change management processes can be inefficient and error-prone, leading to potential disruptions. This study employs case studies from the finance, healthcare, and defense sectors to illustrate the real-world impact of automation and traceability in managing mission-critical system changes. Through empirical evidence, this paper demonstrates that automation and traceability significantly enhance change management by reducing human errors by 30%, improving audit efficiency, and accelerating approval workflows. Through leveraging advanced automated tools and establishing traceability mechanisms, organizations can minimize human error, ensure compliance with regulatory standards, and streamline approval workflows. Case studies from various industries highlight the successful application of these techniques, demonstrating their role in maintaining operational continuity, enhancing system integrity, and reducing downtime. The findings underscore the transformative impact of automation and traceability in safeguarding mission-critical systems against risks associated with frequent or complex changes. Empirical analysis from case studies indicates that automation reduces change-related downtime by 35% and enhances compliance tracking by 40%, demonstrating its effectiveness in maintaining operational resilience. Unlike previous studies that primarily focus on change management frameworks in general IT projects, this research specifically examines the intersection of automation, traceability, and cybersecurity in mission-critical systems. By providing empirical evidence from real-world case studies, it offers a structured approach to integrating these elements, contributing to both theoretical discussions and practical implementations in high-stakes industries.
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.
Transforming Cybersecurity Practices: A Comprehensive Approach to Protecting Digital Banking Assets Zangana, Hewa; Mohammed, Harman Salih; Husain , Mamo Muhamad
Jurnal Ilmiah Computer Science Vol. 4 No. 1 (2025): Volume 4 Number 1 July 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v4i1.51

Abstract

The rapid evolution of digital banking has introduced unprecedented security challenges, necessitating a proactive and comprehensive cybersecurity framework. This paper explores advanced strategies for safeguarding digital banking assets, integrating cutting-edge technologies such as artificial intelligence (AI), blockchain, and zero-trust architectures. By analyzing emerging threats, regulatory requirements, and best practices, this study presents a holistic approach to strengthening financial cybersecurity resilience. The findings emphasize the need for a dynamic, multi-layered security model that adapts to evolving cyber threats while ensuring compliance and user trust.
Blockchain Technology in AI-Driven Cybersecurity: Strengthening Trust in Financial and Digital Security Systems Zangana, Hewa
Jurnal Ilmiah Computer Science Vol. 4 No. 1 (2025): Volume 4 Number 1 July 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v4i1.54

Abstract

Blockchain technology has revolutionized the banking and finance sector by introducing a decentralized, secure, and transparent framework for financial transactions. This paper provides a comprehensive review of the role of blockchain in transforming trust mechanisms within financial institutions, focusing on its applications in payments, smart contracts, identity management, and regulatory compliance. A mixed-methods approach was employed, integrating a systematic literature review with case study analysis to evaluate the effectiveness of blockchain-based security solutions. The results indicate that blockchain significantly enhances transaction security, reduces fraud, and improves operational efficiency, with AI-powered fraud detection achieving a 92% accuracy rate and biometric authentication strengthening access control. Despite these advantages, challenges such as scalability, regulatory compliance, and integration with existing financial infrastructures remain key barriers to adoption. The study concludes that blockchain, in conjunction with AI-driven cybersecurity measures, presents a robust solution for enhancing trust and security in digital finance. However, continuous regulatory advancements and industry-wide collaboration are necessary to ensure its sustainable implementation.
A Federated Architecture for Enhancing Security and Scalability in IoT-Cloud Integrated Systems Zangana, Hewa; Yazdeen , Abdulmajeed
Jurnal Ilmiah Computer Science Vol. 4 No. 1 (2025): Volume 4 Number 1 July 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v4i1.55

Abstract

The exponential growth of the Internet of Things (IoT) and its integration with cloud computing has introduced significant challenges related to security, scalability, and data privacy. This paper proposes a novel federated architecture that leverages federated learning and distributed security mechanisms to enhance the resilience and scalability of IoT-cloud integrated systems. By decentralizing data processing and security enforcement, the architecture mitigates common attack vectors such as centralized point-of-failure, data leakage, and unauthorized access. The proposed system is designed with modular security components including lightweight encryption, dynamic trust management, and blockchain-inspired audit trails. A performance evaluation conducted through simulated environments and real-world IoT testbeds demonstrates improved latency, resource efficiency, and defense against cyber threats when compared to conventional centralized systems. This research contributes to the advancement of secure and scalable IoT-cloud infrastructures and offers a viable path for industrial and smart city deployments.
Co-Authors Abdullah, Dilovan Taha Ali , Natheer Yaseen Ali, Natheer Yaseen Bazeed, Sameer Mohammed Salih Bibo Sallow, Amira Firas, Mahmood Mustafa Husain , Mamo Muhamad Khalid Mohammed, Ayaz Mahmood Mustafa, Firas Mohammed, Harman Salih Mustafa , Firas Mahmood Omar , Marwan Omar, Marwan Vega Vitianingsih , Anik Yazdeen , Abdulmajeed