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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Buletin Ilmiah Sarjana Teknik Elektro
Shahad Dakhil Khalaf
University of Kerbala
Computer Science & IT Electrical & Electronics Engineering

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Determinants of e-learning adoption in higher education in Iraq an academics and students’ perspective Soreen Ameen Fattah; Ayad Hameed Mousa; Mowafak K. Mohsen; Shahad Dakhil Khalaf; Seham Hameed Mousa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.21550

Abstract

E-learning allows the students to access, repeat and use the scientific materials wherever and whenever the users want. Both academics and learners are the leading and essential actors in the process of the learning process. Moreover, the acceptance of a technology is the positive react for users by using such technology. Due to the COVID-19 pandemic, e-learning has become mandatory over the world, thus, there is a necessity to investigate the determinants of e-learning adoption in higher education institutions (HEIs) in Iraq. Consequently, this paper aims to investigate elearning adoption and identify the determinants of e-learning in HEIs. An instrument was developed with eight dimensions based on technology organization and environmental (TOE) elements. The sample selection was of 580 students and 130 university lecturers who spread over several colleges. A clustering sampling method was used in the sampling selection process. Some hypotheses were proposed and tested via the paired T-test tool. To evaluate the relationship between the TOE variables and e-learning adoption, a regression analysis was carried out. The study findings classified into two categories, first, it helps to determine to what extent e-learning be adopted. Second, it provides meaningful guidance for higher education institutions that should follow as a pre-requested before adopting e-learning
Determinants of e-learning adoption in higher education in Iraq an academics and students’ perspective Soreen Ameen Fattah; Ayad Hameed Mousa; Mowafak K. Mohsen; Shahad Dakhil Khalaf; Seham Hameed Mousa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.21550

Abstract

E-learning allows the students to access, repeat and use the scientific materials wherever and whenever the users want. Both academics and learners are the leading and essential actors in the process of the learning process. Moreover, the acceptance of a technology is the positive react for users by using such technology. Due to the COVID-19 pandemic, e-learning has become mandatory over the world, thus, there is a necessity to investigate the determinants of e-learning adoption in higher education institutions (HEIs) in Iraq. Consequently, this paper aims to investigate elearning adoption and identify the determinants of e-learning in HEIs. An instrument was developed with eight dimensions based on technology organization and environmental (TOE) elements. The sample selection was of 580 students and 130 university lecturers who spread over several colleges. A clustering sampling method was used in the sampling selection process. Some hypotheses were proposed and tested via the paired T-test tool. To evaluate the relationship between the TOE variables and e-learning adoption, a regression analysis was carried out. The study findings classified into two categories, first, it helps to determine to what extent e-learning be adopted. Second, it provides meaningful guidance for higher education institutions that should follow as a pre-requested before adopting e-learning
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 Emtiaz Abbas Naji Estqlal Hammad Dhahi Mowafak K. Mohsen Seham Hameed Mousa Soreen Ameen Fattah Zahraa Shams Alden