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15th Global Information Infrastructure and Networking Symposium, GIIS 2025

作者： Keyela, Patatchona Cherkaoui, Soumaya Polytechnique Montreal Computer Engineering and Software Engineering Dept. Montreal Canada

ISBN: (纸本)9798331520113

RAN slicing technology is a key aspect of the Open RAN paradigm, allowing simultaneous and independent provision of various services such as ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC) through virtual networks that share a single radio access infrastructure. Efficient resource allocation is crucial for RAN slicing, as each service has specific quality of service (QoS) requirements, and a balance between different services must be maintained. Although heuristic and reinforcement learning (RL) techniques have been explored to achieve efficient resource allocation, these approaches face notable limitations: heuristic algorithms face complexity issues that limit their effectiveness in large networks, RL solutions are constrained by their dependency on training data and struggle to adapt to new scenarios and environments. This paper proposes a framework that leverages quantum optimization techniques to optimize radio resource blocks allocation in Open RAN slicing for URLLC and eMBB services. We provide a classical problem formulation and the quantum implementation using the constrained quadratic model on Dwave quantum annealing platform, showcasing the potential of quantum optimization techniques to deliver in real-time optimal solutions for optimization problems in 5G and beyond networks. © 2025 IEEE.

关键词： Constrained optimization

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Deeper Anomaly Detection in Industrial Automation 15

Deeper Anomaly Detection in Industrial Automation

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Jung, Ho-Seok Jang, Sung Won Baek, Youngmi Changshin University Dept. of Computer Software Engineering Korea Republic of Changshin University Dept. of Computer Engineering Korea Republic of

ISBN: (纸本)9798331507695

The security of industrial networks, particularly in industrial automation systems, is critical for ensuring system reliability and protecting sensitive data. This paper proposes a deeper anomaly detection system using the ResNet34 (Residual Network) model to identify and detect cyber-attacks in industrial networks, specifically focusing on Controller Area Network (CAN) systems. The study highlights the vulnerabilities in industrial communication protocols, such as CAN, Modbus, and Ethernet/IP, which are susceptible to cyber-attacks including replay, modification, and fuzzing attacks. These attacks can disrupt the functioning of industrial systems and cause significant damage. Experimental results show that the proposed model achieves a 100% detection rate for all types of cyber-attacks, demonstrating its effectiveness in recognizing abnormal patterns and responding to changes in network behavior. The results confirm that the ResNet34-based deep anomaly detection model can be a valuable tool for strengthening the security of industrial networks by providing real-time detection of cyber-attacks, thereby ensuring the stability and safety of industrial automation systems. © 2025 IEEE.

关键词： Cyber attacks

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Using LLMs for Annotation and ML Methods for Comparative Analysis of Large-Scale Turkish Sentiment Datasets 9

Using LLMs for Annotation and ML Methods for Comparative Ana...

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9th International Conference on computer Science and engineering, UBMK 2024

作者： Ezin, Ercan Kiziltepe, Rukiye Savran Karakus, Murat Harran University Dept. of Computer Engineering Şanliurfa Turkey Karadeniz Tech. University Dept. of Software Engineering Trabzon Turkey Ankara University Dept. of Software Engineering Ankara Turkey

ISBN: (纸本)9798350365887

With the increasing prevalence of e-commerce plat-forms, understanding customer sentiments expressed in product reviews is crucial for assessing platform and product performance. However, traditional sentiment analysis (SA) models often struggle with low-resource languages such as Turkish, particularly due to the language's agglutinative nature and morphological complexity. To address this, several public datasets are now available online. However, these datasets are often annotated using rule-based methods, resulting in poor accuracy for Machine Learning (ML) models. This study addresses these challenges by employing two large Turkish datasets, TRSAv1 and VSCR, and improving their sentiment annotations through advanced Large Language Models (LLMs) such as ChatGPT4o-mini. The reannotation significantly enhanced the performance of traditional ML models-Logistic Regression, Naive Bayes, Random Forest, and Support Vector Machine-especially in the neutral and negative sentiment classes, as evidenced by increased precision, recall, and F1-scores. The findings underscore the importance of thorough data preparation and demonstrate that utilizing LLMs for sentiment annotation in Turkish can lead to more reliable SA, providing a stronger foundation for developing advanced SA tailored to low-resource languages. This study is the first to employ recent LLM for relabelling Turkish customer reviews, highlighting the potential for improved accuracy in SA for low-resource languages. © 2024 IEEE.

关键词： Decision trees

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture 5

Multispectral Images and IoT Based Tea Plantation Monitoring...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Kariyawasam, K.P.W.D.V. Kahandagamage, P.N. Fernando, M.R.R. Fernandopulle, J.M. Jayasinghearachchi, Vishan Dissanayaka, Kapila Dept. of Software Engineering Sri Lanka Dept. of Information Technology Sri Lanka Dept. of Computer Science Sri Lanka

ISBN: (纸本)9798331530983

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments. © 2025 IEEE.

关键词： Risk perception

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Academic Use of ChatGPT: Examining the Trends According to Disciplines 8

Academic Use of ChatGPT: Examining the Trends According to D...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Bircan, Abdullah Mandal, Dilek Kosesoy, Irfan Kocaeli University Dept. of Computer Engineering Kocaeli Turkey Kocaeli University Dept. of Software Engineering Kocaeli Turkey

ISBN: (纸本)9798331531492

With the rapid advancement of large language models, particularly ChatGPT, have had a profound impact both in the academic world and in commercial applications. The innovative solutions offered by this technology in areas such as natural language processing, data analysis, and human-computer interaction have led to the emergence of new research domains across various disciplines. However, there has been no comprehensive examination in the literature of which academic disciplines focus on ChatGPT and which areas have attracted more *** study aims to systematically examine academic publications related to ChatGPT and to identify the disciplines in which this technology is more widely adopted, as well as the areas where intensive research is conducted. To this end, a specially developed tool was utilized to scan all academic publications pertaining to ChatGPT, allowing for a comprehensive interdisciplinary analysis. The results of the analysis reveal the academic fields in which ChatGPT is most intensively utilized, highlighting the differences among these fields. Notably, there is a concentration in categories such as 'Medical Sciences,' 'computer Sciences,' and 'Education Sciences,' which also stand out among the most cited publications. Our findings indicate that ChatGPT has gained broad acceptance within the academic community, with certain disciplines leading the way in this regard. Furthermore, the interactions among categories sharing common keywords demonstrate how ChatGPT facilitates interdisciplinary knowledge flow. © 2024 IEEE.

关键词： Human computer interaction

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Classification of Skin Lesions using Squeeze and Excitation Attention based Hybrid Model of DenseNet and EfficientNet

Classification of Skin Lesions using Squeeze and Excitation ...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Armaǧan, Senanur Gündoǧan, Esra Kaya, Mehmet Firat University Dept. of Computer Engineering Elaziǧ Turkey Firat University Dept. of Software Engineering Elaziǧ Turkey

ISBN: (纸本)9798350369106

Skin cancer, like all other types of cancer, is a health problem where early diagnosis saves the patient's life. Successful detection of skin lesions in dermoscopic images enables this process to be managed more quickly. Deep learning architectures have advanced significantly in recent years, particularly in the classification of medical images. For the multi-class classification of skin lesions, a hybrid model was developed in this study utilizing the convolutional neural network architectures of DenseNet and EfficientNet. In addition, SE (Squeeze and Excitation) attention mechanism is integrated into both models. Experimental results show that the inclusion of SE attention mechanism provides a noticeable improvement in classification accuracy. This study not only contributes to the development of effective skin lesion classification systems, but also It also provides valuable insights into the synergistic effects of combining neural network architectures with attention mechanisms. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Study of ML Models on Large-Scale Turkish Sentiment Datasets

A Comparative Study of ML Models on Large-Scale Turkish Sent...

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Kiziltepe, Rukiye Savran Ezin, Ercan Karakus, Murat Dept. of Software Engineering Karadeniz Tech. University Trabzon Turkey Dept. of Computer Engineering Harran University Şanlıurfa Turkey Dept. of Software Engineering Ankara University Ankara Turkey

ISBN: (纸本)9798350379433

This study investigates the performance of traditional Machine Learning (ML) models for Sentiment Analysis (SA) in the Turkish language, addressing the growing need for robust Natural Language Processing (NLP) techniques in low-resource languages. We compare the efficacy of Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) classifiers on two recently developed large-scale Turkish sentiment datasets: "Vitamins and Supplements Customer Review" (VSCR) and "Turkish Sentiment Analysis version 1" (TRSAv1). Our research leverages advanced hyperparameter tuning techniques to optimize these models, providing benchmarks and practical insights for Turkish SA. The study contributes to the field by establishing performance baselines on new open-source datasets, enhancing understanding of NLP models for agglutinative languages, and analyzing factors affecting classifier performance in binary and multi-class scenarios. Our findings reveal the strengths and limitations of each model within the context of Turkish language SA, offering valuable guidance for future research and applications in this domain. © 2024 IEEE.

关键词： Adversarial machine learning

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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Line Graph is the Key: An Exploration of Line Graph on Link Prediction with Social Networks 11

Line Graph is the Key: An Exploration of Line Graph on Link ...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Xing, Chen Makrehchi, Masoud Ontario Tech University Dept. of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798331531904

Network data plays a crucial role in various real-world applications, as connections between entities can be represented and analyzed through graphs. These include various types, such as social, information, and technical networks. However, the complex topologies of these networks present challenges in converting graph data into machine-readable vector formats. Existing models, such as Graph Neural Networks, Graph Attention Networks, and node2vec, have made strides in graph embeddings. For edge-related tasks, models such as node2vec typically use indirect methods like concatenating node vectors to represent edges. This approach is useful for tasks like link prediction. In this paper, we present LineDi2vec, a novel approach that improves the Node2vec embedding method by utilizing a line graph. The proposed LineDi2vec not only generalizes the original graphs, transforming the relationships between edges and nodes but also maintains the original graphs' topological integrity for effective node embedding by Node2vec. We evaluated LineDi2vec on four real-world datasets, focusing on link prediction. The results demonstrate that LineDi2vec outperforms traditional node concatenation methods. © 2024 IEEE.

关键词： Graph embeddings

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Open RAN Slicing with Quantum Optimization

arXiv

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arXiv 2025年

作者： Keyela, Patatchona Cherkaoui, Soumaya Computer Engineering and Software Engineering Dept. Polytechnique Montreal Montreal Canada

关键词： Constrained optimization

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