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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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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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Enhancing CNN model classification performance through RGB angle rotation method

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Neural Computing and Applications 2024年第32期36卷 20259-20276页

作者： Dogan, Yahya Ozdemir, Cuneyt Kaya, Yılmaz Computer Engineering Engineering Faculty Siirt University Siirt56100 Turkey Computer Engineering Engineering Faculty Batman University Siirt72100 Turkey

In recent years, convolutional neural networks have significantly advanced the field of computer vision by automatically extracting features from image data. CNNs enable the modeling of complex and abstract image features using learnable filters, eliminating the need for manual feature extraction. However, combining feature maps obtained from CNNs with different approaches can lead to more complex and interpretable inferences, thereby enhancing model performance and generalizability. In this study, we propose a new method called RGB angle rotation to effectively obtain feature maps from RGB images. Our method rotates color channels at different angles and uses the angle information between channels to generate new feature maps. We then investigate the effects of integrating models trained with these feature maps into an ensemble architecture. Experimental results on the CIFAR-10 dataset show that using the proposed method in the ensemble model results in performance increases of 9.10 and 8.42% for the B and R channels, respectively, compared to the original model, while the effect of the G channel is very limited. For the CIFAR-100 dataset, the proposed method resulted in a 17.09% improvement in ensemble model performance for the R channel, a 5.06% increase for the B channel, and no significant improvement for the G channel compared to the original model. Additionally, we compared our method with traditional feature extraction methods like scale-invariant feature transform and local binary pattern and observed higher performance. In conclusion, it has been observed that the proposed RGB angle rotation method significantly impacts model performance. © The Author(s) 2024.

关键词： RGB color model

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Relational Turkish Text Classification Using Distant Supervised Entities and Relations

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computers, Materials & Continua 2024年第5期79卷 2209-2228页

作者： Halil Ibrahim Okur Kadir Tohma Ahmet Sertbas Department of Computer Engineering Faculty of Engineering and Natural SciencesIskenderun Technical UniversityHatay31200Turkey Department of Computer Engineering Faculty of EngineeringIstanbul University-CerrahpasaIstanbul34310Turkey

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing *** study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)*** on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from *** adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational *** conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural *** results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP *** of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational *** demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)***,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

关键词： Text classification relation extraction NER distant supervision deep learning machine learning

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Enhanced pyramidal residual networks for single image super-resolution

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Neural Computing and Applications 2024年第19期36卷 11563-11577页

作者： Babaoğlu, İsmail Kahveci, Semih Kılıç, Alper Department of Computer Engineering Faculty of Engineering and Natural Sciences Konya Technical University Konya Turkey Department of Computer Engineering Faculty of Engineering Mersin University Mersin Turkey

Several super-resolution (SR) techniques are introduced in the literature, including traditional and machine learning-based algorithms. Especially, deep learning-based SR approaches emerge with demands for better quality images providing deeper subpixel enhancement. Dealing with the image enhancement task in the satellite images domain, a new SR method for single image SR, namely Enhanced Deep Pyramidal Residual Networks, is introduced in this study. The proposed method overcomes the potential instability problem of Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR) approach by gradually increasing the feature maps depending upon Pyramidal Residual Networks architecture. The EDSR itself is a good algorithm in the SR domain. However, it has a strict structure for increasing the block size. To overcome this problem with the aim of increasing the algorithm’s performance, the pyramidal residual networks gradually increasing hypothesis is utilized in the proposed approach, which is the main contribution and novelty of this study. Besides, by using the pyramidal residual networks gradually increasing hypothesis in the proposed approach, the parameter size of the models is also reduced, which affects the computational time. Two different models are proposed by considering addition and multiplication manners, and the proposed models are evaluated using well-known remote sensing datasets NWPU-RESISC45 and UC Merced. The results obtained by the proposed model are compared with the results of traditional image enhancement algorithms together with the EDSR itself, EDSR with deeper structure, Super-Resolution Generative Adversarial Networks approach, and Residual Local Feature Networks approach in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) metrics and showed that the proposed models present better quality images. Moreover, considering the computational time and complexity, it is shown that some proposed models

关键词： Image enhancement

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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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ETSVF-COVID19: efficient two-stage voting framework for COVID-19 detection

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Neural Computing and Applications 2024年第29期36卷 18277-18295页

作者： Akyol, Kemal Faculty of Engineering and Architecture Computer Engineering Kastamonu University Kastamonu Turkey

COVID-19 disease, an outbreak in the spring of 2020, reached very alarming dimensions for humankind due to many infected patients during the pandemic and the heavy workload of healthcare workers. Even though we have been saved from the darkness of COVID-19 after about three years, the importance of computer-aided automated systems that support field experts in the fight against with global threat has emerged once again. This study proposes a two-stage voting framework called ETSVF-COVID19 that includes transformer-based deep features and a machine learning approach for detecting COVID-19 disease. ETSVF-COVID19, which offers 99.2% and 98.56% accuracies on computed tomography scan and X-radiation images, respectively, could compete with the related works in the literature. The findings demonstrate that this framework could assist field experts in making informed decisions while diagnosing COVID-19 with its fast and accurate classification role. Moreover, ETSVF-COVID19 could screen for chest infections and help physicians, particularly in areas where test kits and specialist doctors are inadequate. © The Author(s) 2024.

关键词： COVID-19

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Detection and recognition of vehicle licence plates using deep learning in challenging conditions: a systematic review

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International Journal of Intelligent Systems Technologies and Applications 2024年第2期22卷 105-150页

作者： Quraishi, Abdul Awal Feyzi, Farid Shahbahrami, Asadollah Department of Computer Engineering Faculty of Engineering University of Guilan Rasht Iran

Automatic licence plate detection and recognition (ALPDR) systems are widely used in various sectors such as traffic control, toll payment, parking systems, border control, and law enforcement. However, these systems face challenges in complex scenarios such as different licence plate formats, poor lighting or occlusion, and deliberate manipulation. To address these challenges, researchers have developed various methods. The first set of challenges involves natural conditions like varying light, snow, rain, fog, and dust. The second set includes environmental factors such as camera angle, occlusion, distortion, contrast issues in images, noise interference, dirt on camera lenses, and camera distance from the scene. The third challenge is related to multinational licence plate variations in terms of formats, colours, sizes, fonts, and characters. Lastly, adversarial attacks pose a threat through rotation, noise addition or distortion to licence plates. This study reviews recent literature on ALPDR systems and proposes guidelines for future research. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Character recognition

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An innovative approach for parkinson’s disease diagnosis using CNN, NCA, and SVM

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Neural Computing and Applications 2024年第32期36卷 20089-20110页

作者： Dogan, Yahya Computer Engineering Engineering Faculty Siirt University Siirt56100 Turkey

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder affecting millions of people globally, with substantial health risks and economic burdens. This study aims to introduce an innovative hybrid approach combining deep learning and machine learning algorithms to improve the diagnosis of PD using handwriting dynamics indicative of Parkinson’s symptoms. The proposed approach integrates hybrid feature extraction using nine fine-tuned transfer learning models, i.e., InceptionV3, DenseNet201, EfficientNetB0, ResNet50, MobileNetV2, VGG16, Xception, NASNetMobile, and InceptionResNetV2. Initially, features from these models are used individually or in binary and ternary combinations. Given the limited sample size in PD datasets, some extracted features through fine-tuning may lack significance, and fully connected layers can lead to overfitting. To address this issue, Neighborhood Component Analysis (NCA) is employed to refine these features, retaining only the most informative ones. Finally, the selected features are classified using Support Vector Machines (SVM) maximizing the margin between classes and reducing the risk of overfitting. The proposed hybrid model achieves a state-of-the-art accuracy of 99.39% on the Parkinson Hand Drawings dataset. The combination of features extracted from DenseNet201, Xception, and NASNetMobile models, processed using NCA and SVM methods, has been identified as the most efficient model, balancing high accuracy with computational efficiency. Qualitative assessments further confirm the accuracy and reliability of the approach. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

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