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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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Journal : Jurnal Ilmiah Computer Science

 1 
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.
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