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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JISA (Jurnal Informatika dan Sains) Jurnal Ilmiah Computer Science IJMST
Mustafa, Firas Mahmood
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Surveying the Landscape: A Comprehensive Review of Object Detection Algorithms and Advancements Majeed Zangana, Hewa; 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.v3i1.29

Abstract

This review paper gives a comprehensive investigation of the energetic scene of object detection, an essential field inside computer vision. Leveraging experiences from an assorted cluster of thinks about, the paper navigates through the chronicled advancement, techniques, challenges, later headways, applications, and future bearings in object detection. The comparative examination dives into the complexities of conventional strategies versus profound learning approaches, the trade-offs between exactness and speed, and the vigor of models against ill-disposed assaults. Highlighting key measurements such as cross-modal location, ceaseless learning, and moral contemplations, the paper divulges the multifaceted nature of object detection techniques. Applications of question discovery over spaces, counting independent vehicles, healthcare imaging, and keen cities, emphasize its transformative effect on different businesses. The talk amplifies to long term, envisioning challenges and openings in ranges such as ill-disposed vigor, cross-modal discovery, and moral contemplations. As a comprehensive direct for analysts, professionals, and devotees, this paper not as it were capturing the current state of object detection but too serves as a compass for exploring the strange domains that lie ahead. The survey typifies the essence of protest detection's advancement and its significant suggestions, empowering proceeded investigation and advancement within the domain of computer vision.
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.
From Classical to Deep Learning: A Systematic Review of Image Denoising Techniques Majeed Zangana, Hewa; 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.v3i1.36

Abstract

Image denoising is essential in image processing and computer vision, aimed at removing noise while preserving critical features. This review compares classical methods like Gaussian filtering and wavelet transforms with modern deep learning techniques such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). We conducted a systematic literature review from [start year] to [end year], analyzing studies from IEEE Xplore, PubMed, and Google Scholar. Classical methods are effective for simple noise models but struggle with fine detail preservation. In contrast, deep learning excels in both noise reduction and detail retention, supported by metrics like PSNR and SSIM. Hybrid approaches combining classical and deep learning show promise for balancing performance and computational efficiency. Overall, deep learning leads in handling complex noise patterns and preserving high-detail images. Future research should focus on optimizing deep learning models, exploring unsupervised learning, and extending denoising applications to real-time and large-scale image processing.
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.
Blockchain-based Decentralized Authentication and Authorization for IoT Applications Zangana, Dr. Hewa Majeed; Beitollahi , Hakem; Muhamad , Sabat Salih; Omar , Marwan; Mustafa, Firas Mahmood; Mohammed , Aquil Mirza; Wani , Sharyar; Li , Shuai
Indonesian Journal of Modern Science and Technology Vol. 1 No. 3 (2025): September
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/

Abstract

The rapid proliferation of Internet of Things (IoT) devices has introduced significant challenges in ensuring secure and efficient authentication and authorization mechanisms. Traditional centralized approaches are increasingly inadequate due to their single points of failure, scalability issues, and vulnerability to attacks. This paper explores a blockchain-based decentralized framework for authentication and authorization in IoT applications, leveraging the inherent security features of blockchain technology. The proposed solution employs smart contracts to automate and enforce access control policies, ensuring that IoT devices can securely interact without relying on a trusted third party. By integrating blockchain with IoT, this approach enhances data integrity, transparency, and auditability while mitigating common security risks associated with centralized systems. The paper provides a comprehensive analysis of the architecture, implementation details, and performance evaluation, demonstrating the feasibility and advantages of the proposed decentralized authentication and authorization scheme. Experimental results indicate improved security, scalability, and operational efficiency, positioning blockchain as a promising solution for secure IoT environments.
The Synergy of Blockchain and Cybersecurity: Building Trust in Digital Environments Zangana, Hewa Majeed; Sallow, Zina Bibo; Mustafa, Firas Mahmood; Husain, Mamo Muhamad
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1701

Abstract

The rapid expansion of digital ecosystems has intensified concerns about data security, privacy, and trust. Blockchain technology, characterized by its decentralized, immutable, and transparent nature, offers a transformative approach to strengthening cybersecurity. This paper examines the synergy between blockchain and cybersecurity, emphasizing how blockchain’s cryptographic foundations, consensus mechanisms, and smart contracts can mitigate cyber threats, enhance authentication, and ensure data integrity. By analyzing emerging trends, challenges, and real-world applications, this study underscores the potential of blockchain to reinforce digital trust and resilience across diverse sectors. The findings contribute to the ongoing discourse on secure digital environments by proposing an integrated framework for blockchain-based cybersecurity solutions
Interpretable Ensemble-Based Intrusion Detection Using Feature Selection on the ToN_IoT Dataset Sulaiman, Vaman Shakir; Mustafa, Firas Mahmood
JISA(Jurnal Informatika dan Sains) Vol 8, No 2 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i2.2487

Abstract

With With the rapid growth of IoT, securing interconnected devices against cyber threats has become critical. IoT datasets such as ToN-IoT are often high-dimensional, which poses challenges for efficient and accurate intrusion detection. Moreover, interpretable models are essential to help security analysts understand and trust automated decisions. Intrusion Detection Systems (IDS) powered by machine learning offer promising solutions, especially when trained on realistic datasets such as ToN_IoT. However, achieving a balance between high accuracy, computational efficiency, and model interpretability remains a challenge. This study proposes an efficient and interpretable IDS framework for binary classification using the ToN_IoT dataset, aiming to identify the optimal feature selection method and ensemble learning model while leveraging explainable artificial intelligence to interpret model decisions. A quantitative experimental approach was adopted, applying and comparing Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) for feature selection, and evaluating the performance of LightGBM, XGBoost, and Random Forest classifiers using Accuracy, F1-score, Precision, Recall, and training time. RFE outperformed PCA, identifying 11 key features, and LightGBM emerged as the top-performing model with an accuracy of 99.72%, demonstrating both speed and strong generalization. SHAP (SHapley Additive exPlanations) was used to generate summary plots for global feature importance, enhancing the transparency and interpretability of IDS decisions. Overall, the combination of RFE and LightGBM resulted in a high-performing and explainable IDS framework, underscoring the importance of strategic feature selection and model choice. Compared to existing IDS approaches on the ToN-IoT dataset, our proposed framework not only achieves higher accuracy but also provides a rapid and lightweight solution. Additionally, by incorporating SHAP for feature importance analysis, our approach ensures clear model interpretability, allowing security analysts to understand and trust the system’s decisions. This combination of high performance, efficiency, and explainability highlights the practical advantages of our method over previous work. Future research will extend this framework to support multiclass classification and online learning for real-time threat detection.
Co-Authors Beitollahi , Hakem Husain, Mamo Muhamad Li , Shuai Majeed Zangana, Hewa Mohammed , Aquil Mirza Mohammed, Ayaz Khalid Muhamad , Sabat Salih Omar , Marwan Omar, Naaman Sallow, Zina Bibo Sulaiman, Vaman Shakir Wani , Sharyar Zangana, Dr. Hewa Majeed Zangana, Hewa Majeed