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15th International Conference on ICT Innovations, ICT Innovations 2023

作者： Ivanoska, Ilinka Trivodaliev, Kire Ilijoski, Bojan Faculty of Computer Science and Engineering Ss Cyril and Methodiuos University Skopje Macedonia

ISBN: (纸本)9783031543203

Multiplex collaboration networks facilitate intricate connections among individuals, enabling multidimensional collaborations across various domains and fostering synergistic knowledge exchange. This study focuses on the construction and basic analysis of a multiplex collaboration network among employees at the faculty of computer Science and engineering (FCSE), Ss. Cyril and Methodius University in Skopje. The multiplex network is built with three layers based on: scientific collaborations resulting from joint project participations by FCSE employees, joint employees participations in the FCSE graduation thesis committees, and scientific FCSE employees collaborations defined by co-authorships in Google Scholar papers. The network’s structure plays a vital role in determining the information accessibility and cooperative opportunities for individuals within FCSE institution. The aim here is to investigate the FCSE multiplex collaboration network’s internal structure for discovering its latent knowledge and understand its implications. We perform identification of key individuals within the network, by computing various centrality and hubs detection network metrics. Additionally, we employ a community detection algorithm to reveal the underlying modular structure of the network. By comprehensively analyzing the acquired multiplex collaboration network model, we contribute to a better understanding of the collaboration patterns among FCSE employees. The findings can potentially inform decision-making processes and foster strategic planning aimed at enhancing collaboration and knowledge sharing within the institution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision making

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Presentation of research activities at the faculty of electrical & computer engineering

Elektrotehniski Vestnik/Electrotechnical Review

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Elektrotehniski Vestnik/Electrotechnical Review 1989年第2-4期56卷 97-100页

作者： Bajd, Tadej Faculty of Electrical & Computer Engineering Yugoslavia

At its beginning, the paper briefly describes some of the main research areas at the faculty of Electrical and computer engineering. The research efforts are included in programs of Research Communities of Slovenia and the Strategy of the Technological Development of Yugoslavia. An important role in the research work is given to young researchers and students. Problems of equipment are constantly present at the faculty. The researchers are authors of numerous international scientific publications and even textbooks. Individual professors of the faculty have received distinguished national awards.

关键词： engineering Education

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Education at the faculty of electrical and computer engineering in Ljubljana

Elektrotehniski Vestnik/Electrotechnical Review

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Elektrotehniski Vestnik/Electrotechnical Review 1989年第2-4期56卷 94-96页

作者： Bratkovic, Franc Faculty of Electrical & Computer Engineering Yugoslavia

The faculty of Electrical and computer engineering in Ljubljana performs educational courses at all levels of University education in the fields of electrical and computer engineering. A structure of its programs is p... 详细信息

关键词： engineering Education

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Adjustable random linear network coding(ARLNC): A solution for data transmission in dynamic IoT computational environments

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Digital Communications and Networks 2025年第2期11卷 574-586页

作者： Raffi Dilanchian Ali Bohlooli Kamal Jamshidi Faculty of Computer Engineering University of Isfahan

In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, commonly used in data transmission protocols, increases transmission delay and consumes excessive bandwidth. To overcome this issue, forward error correction techniques, e.g., Random Linear Network Coding(RLNC) can be used in data transmission. The primary challenge in RLNC-based methodologies is sustaining a consistent coding ratio during data transmission, leading to notable bandwidth usage and transmission delay in dynamic network conditions. Therefore, this study proposes a new block-based RLNC strategy known as Adjustable RLNC(ARLNC), which dynamically adjusts the coding ratio and transmission window during runtime based on the estimated network error rate calculated via receiver feedback. The calculations in this approach are performed using a Galois field with the order of 256. Furthermore, we assessed ARLNC's performance by subjecting it to various error models such as Gilbert Elliott, exponential, and constant rates and compared it with the standard RLNC. The results show that dynamically adjusting the coding ratio and transmission window size based on network conditions significantly enhances network throughput and reduces total transmission delay in most scenarios. In contrast to the conventional RLNC method employing a fixed coding ratio, the presented approach has demonstrated significant enhancements, resulting in a 73% decrease in transmission delay and a 4 times augmentation in throughput. However, in dynamic computational environments, ARLNC generally incurs higher computational costs than the standard RLNC but excels in high-performance networks.

关键词： Random linear network coding Adjust redundancy Galois field Internet of Things Data transfer

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Weighted rank aggregation based on ranker accuracies for feature selection

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Soft Computing 2025年第4期29卷 1981-2001页

作者： Abdolrazzagh-Nezhad, Majid Kherad, Mahdi Faculty of Engineering Department of Computer Engineering Bozorgmehr University of Qaenat Qaen Iran Department of Computer Science Faculty of Computer and Industrial Engineering Birjand University of Technology Birjand Iran Faculty of Engineering Department of Computer Engineering University of Qom Qom Iran

Rank aggregation is the combination of several ranked lists from a set of candidates to achieve a better ranking by combining information from different sources. In feature selection problem, due to the heterogeneity of methods, there are some base rankers (Filter-based methods) that are of diverse quality and usually the ground truth of ratings is not available. Existing rank aggregation methods that take the diverse quality of base rankers into account do not have any explicit approach for appropriate weighting, require prior assumptions, and suffers from high computational complexity. In this paper, to overcome these challenges, an efficient unsupervised method is introduced for estimating the base rankers’ qualities and aggregating the rankers based on the estimated weights. We first compute the ratio of disagreement between base rankers in ordering different element pairs and then estimate the accuracies in a way that to minimize the discrepancy between these computed ratios and their analytical counterparts. We use the weighted majority voting method for obtaining the aggregated results. To resolve the probable inconsistencies in the final aggregation, the result is formed as a graph, and a greedy algorithm is used to find an acyclic subgraph with the highest weigh. To demonstrate the performance of the proposed method, nine standard UCI datasets are used. The obtained results by the proposed method have higher values of classifier measures than the existing baseline Feature Selection methods and rank aggregation-based multi-filter methods in the most datasets. The experiments show that rank aggregation-based Feature Selection methods outperform individual methods. The proposed method also shows the weight of each Filter-based Feature Selection method, in which the MRMR method has a higher weight than other methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wiener filtering

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DXDSENet-CM model: an ensemble learning model based on depthwise Squeeze-and-Excitation ConvMixer architecture for the classification of multi-class skin lesions

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Multimedia Tools and Applications 2025年第12期84卷 9903-9938页

作者： Fırat, Hüseyin Üzen, Hüseyin Department of Computer Engineering Faculty of Engineering Dicle University Diyarbakır Turkey Department of Computer Engineering Faculty of Engineering and Architecture Bingol University Bingol Turkey

Skin cancer is acknowledged as the most prevalent form of cancer on a global scale. Failure to detect it in its initial phases can lead to fatality, underscoring the significance of early diagnosis. While visible to the naked eye, the considerable similarities between different classes and the variations within a single class create substantial hurdles for accurate detection. Given the widespread occurrence of this ailment, numerous deep learning-driven automated systems have been created to aid dermatologists in promptly identifying skin anomalies. Within this investigation, an ensemble learning architecture based on Xception, DenseNet201 and Depthwise Squeeze-and-Excitation Network and ConvMixer (DSENet-ConvMixer) architectures is presented for skin lesion classification. Xception, a pre-trained CNN network, utilizes depthwise separable convolutions and residual connections, offering efficient feature extraction from skin lesion images. DenseNet201’s parameter efficiency, feature reuse, and multi-scale information capture make it a valuable addition. DSENet-ConvMixer integrates these components, efficiently merging low-level and high-level features to enhance training and network efficiency. ConvMixer excels at feature extraction, enhancing spatial detail capture. ISIC 2018 and 2019 datasets were employed to assess the classification performance of the proposed architecture. Individual accuracies for Xception, DenseNet201, and DSENet-ConvMixer in ISIC 2018 were 85.91%, 85.71%, 82.02%, respectively, and in ISIC 2019 were 80.93%, 80.50%, 79.08%, respectively. However, the ensemble architecture with these three architectures achieved 88.21% for ISIC 2018 and 92.34% for ISIC 2019, demonstrating superior performance. Literature reviews on both datasets also confirmed the proposed architecture’s effectiveness compared to recent studies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

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myREACH: a serious game for measuring security awareness about ransomware

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Multimedia Tools and Applications 2025年第11期84卷 9713-9747页

作者： Noorbehbahani, Fakhroddin Taghiyar, Anahita Faculty of Computer Engineering University of Isfahan Isfahan Iran Faculty of IT Engineering Sheikh Bahaei University Isfahan Iran

Today, raising security awareness among users is one of the most effective preventive cybersecurity strategies. Generally, the current level of security awareness in the organization is measured through standard questionnaires. However, this method suffers from poor participant engagement and low precision due to the explicit evaluation and misunderstandings of the questions. To address these issues, we present a serious video game called "myREACH" to measure the player’s security awareness about ransomware. To the best of our knowledge, this is the first attempt to develop a serious game for measuring security awareness. myREACH has been compared to the standard questionnaire for measuring security awareness about ransomware, known as RSAM. The results obtained from a sample of 172 participants indicate that, in 3 out of 9 categories, the game and questionnaire measurements yield similar results. However, in 5 out of 9 categories, the game measurement is superior. For the remaining category, it is inconclusive whether the game or questionnaire assessment is better. Furthermore, self-report measurements indicate that the temporal and mental demands of playing myREACH and completing the RSAM are the same. The overall performance during playing myREACH is 9% better than completing the RSAM, and participants are 15% more satisfied with the game compared to the questionnaire. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Human computer interaction

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Three-Dimensional Model Generation from Two-Dimensional Image Sequences Using Machine Learning

Electrica

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Electrica 2025年第1期25卷 1-14页

作者： Dağtekin, Mustafa Aşıroğlu, Batuhan Turna, Özgür Can Department of Computer Engineering İstanbul University-Cerrahpaşa Faculty of Engineering İstanbul Turkey

In this study, a deep learning model was utilized to generate voxel-represented three-dimensional models of some objects using silhouette images of size 128 × 128 captured from four different angles. The proposed model is trained using the ShapeNet dataset. The deep learning model, along with the proposed error function, has been favored to reduce the number of parameters and capture features of different dimensions. A total of 34 691 different data were obtained in seven categories. The performance metrics of the proposed model have been compared with other studies in the literature using the Intersection over Union (IoU) metric. The comparison reveals that the proposed method achieves an IoU score of 0.5283, which outperforms both the 1 image and 5 image input versions of both McRecon and (Perspective Transformer Nets) PTN methods in categories other than the chair category. © 2025 Istanbul University. All rights reserved.

关键词： Deep learning

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Design of a Flux-Barrier Permanent Magnet Vernier Motor with a Flux Modulation Ring to Enhance Torque Production and Efficiency Factor

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International Journal of engineering, Transactions B: Applications 2025年第6期38卷 1369-1378页

作者： Imanifar, A. Yaghobi, H. Faculty of Electrical and Computer Engineering Semnan University Semnan Iran

Flux-barrier permanent magnet Vernier motors are among the latest magnetic motors developed and optimized in the industry. In these motors, the arrangement of magnets plays a critical role and significantly impacts on the torque density in the rotor-stator gap. This is because the motor's output torque increases with an enhancement of magnetic flux intensity in this gap. The magnetic gearbox effect in the flux-barrier permanent magnet Vernier motors, along with flux modulation, is one of the most critical factors in the design and construction of these motors. Flux modulation in the air-gap generates high torque at low speeds. In this study, inspired by the concept of magnetic gearboxes and by adding a modulation ring to the air-gap of this motor, a novel design for flux-barrier permanent magnet Vernier motors with a gearbox is presented. Using the two-dimensional finite element method (FEM), it has been demonstrated that the designed motor achieves significant improvements in average output torque and power factor compared to previous designs. ©2025 The author(s).

关键词： Permanent magnets

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Valid-IoU: an improved IoU-based loss function and its application to detection of defects on printed circuit boards

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Multimedia Tools and Applications 2025年第14期84卷 13905-13928页

作者： Vakili, Elnaz Karimian, Ghader Shoaran, Maryam Yadipour, Reza Sobhi, Jafar Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

The printed circuit boards (PCBs) should be inspected during the manufacturing process to minimize defects such as printing errors, incorrect component selections, and incorrect soldering. Convolutional neural networks (CNNs) have emerged as a powerful tool for this purpose, owing to their robust recognition capabilities. However, the accuracy of CNNs is significantly influenced by the choice of loss function. Intersection over Union (IoU) based loss functions commonly employed for bounding box regression, achieve remarkable success, but still have some main drawbacks such as inaccurate predicting bounding box dimensions relative to ground truth data. In this paper, we propose an improved IoU called Valid-IoU (VIoU) to solve this problem. We integrated the VIoU loss into state-of-the-art YOLO object detection frameworks to show its effectiveness in PCB defect detection. The experimental results on the HRIPCB PCB dataset demonstrate that the VIoU loss has high defect detection accuracy. Using YOLOv4 helped us to improve average accuracy, and with YOLOv8, we achieved faster detection speed compared to other methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object detection

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