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All Journal Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science
Estqlal Hammad Dhahi
University of Kerbala
Electrical & Electronics Engineering

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Using support vector machine regression to reduce cloud security risks in developing countries Sanaa Hammad Dhahi; Estqlal Hammad Dhahi; Ban Jawad Khadhim; Shaymaa Taha Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1159-1166

Abstract

The use of the cloud by governments throughout the world is being aggressively investigated to increase efficiency and reduce costs. The majority of cloud computing risk management programs prioritize addressing cloud security issues that government organizations may face when they choose to adopt cloud computing systems, but these programs lack evidence of security risks, and problems with using cloud computing in developing nations are uncommon, so they called for more research in this area. The objective of this paper is to use quantitative models namely Spearman's Rank correlation coefficient, simple regression, and support vector machine regression (SVMR) for estimating cloud security issues based on cloud control factors for improving the mitigation of cloud computing security issues based on control factors using intelligent models in a government organization. Identify the proper cloud control factors for every cloud security issue from estimation errors using a standard for performance measurement like mean square error (MSE) and root mean square error (RMSE), performance measurement to evaluate and validate proposed models. SVMR is an approach to enhance practices for cloud security platforms to mitigate risks and infrastructure for cloud adoption in developing countries in this paper.
A Comparative Study of Vision Transformers and Convolutional Neural Networks for Lung Nodule Malignancy Classification in CT Imaging Aya Ahmed Hashim; Emtiaz Abbas Naji; Estqlal Hammad Dhahi; Shahad Dakhil Khalaf; Zahraa Shams Alden; Ayad Hameed Mousa
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 4 (2026): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i4.14422

Abstract

Accurate and timely malignancy categorization of pulmonary nodules in computed tomography (CT) images is critical for Health Information Technology, directly impacting clinical decision support systems and patient prognosis in lung cancer management and patient prognosis in lung cancer management. Although Convolutional Neural Networks (CNNs) are the standard, their local inductive bias can make them weak with regard to the modelling of the long-range, global contextual dependencies of medical images. While we recognize the natural restriction of evaluating 2D axial slices instead of full 3D volumetric data. This paper evaluates the effectiveness of a pre-trained self-supervised Vision Transformer (ViT) model to classify binary lung nodules, and leveraging the model's global self-attention mechanism to extract complex morphological features. Using a rigorously curated cohort of 2186 pulmonary nodule instances from the public LIDC-IDRI dataset, we preprocessed data via windowing, normalization, and resizing to 224×224 pixels. A ViT-Base model, pre-trained on ImageNet-21k, was fine-tuned and evaluated against a strong CNN baseline (DenseNet-121) using five-fold cross-validation. The ViT model achieved a superior F1-score of 0.891 (±0.018) and a mean AUC-ROC of 0.945 (±0.012) on the held-out test set. The results demonstrate that the Vision Transformer architecture presents a highly effective framework for this diagnostic task within HIT, surpassing traditional CNN-based approaches. Future work will focus on integrating 3D spatial information across multiple CT slices to further enhance model performance and clinical utility.
Co-Authors Aya Ahmed Hashim Ayad Hameed Mousa Ban Jawad Khadhim Emtiaz Abbas Naji Sanaa Hammad Dhahi Shahad Dakhil Khalaf Shaymaa Taha Ahmed Zahraa Shams Alden