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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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computer Vision-Based Object Detection and dept. Estimation to Generate Point Clouds and Estimate Distances

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 164-169页

作者： Eom, Tae-Hyun Paik, Woojin Dept. of Computer Engineering Konkuk University Glocal Campus Korea Republic of Dept. of Computer Engineering Konkuk University Glocal Campus Korea Republic of

This research proposes a distance estimation method using Mono Camera-based object detection and dept. estimation to generate Point Cloud data. The study aims to enhance the applicability of Mono Cameras in autonomous vehicles and robots, reducing costs compared to Stereo Camera systems. The method utilizes YOLOv8 for object detection and dept. Anything V2 for dept. estimation. Results indicate that while the proposed method offers potential, it exhibits higher error rates in distance estimation compared to Stereo Camera-based approaches, primarily due to the limitations of current dept. estimation technologies. The study highlights the need for further improvements in dept. estimation models, particularly to address environmental factors such as lighting. Additionally, the research demonstrates that combining dept. estimation with bounding box methods helps reduce estimation errors, showing promise for more stable performance. Future work will focus on improving dept. estimation accuracy and making the proposed method more efficient for real-time applications, with the potential to integrate into Visual SLAM, a key technology in autonomous driving systems. © 2025 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Stereo image processing

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A review on recent driver safety systems and its emerging solutions

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International Journal of computers and Applications 2024年第3期46卷 137-151页

作者： Nair, Akhil Patil, Varad Nair, Rohan Shetty, Adithi Cherian, Mimi Dept. of Computer Engineering Pillai College of Engineering Maharashtra Panvel India

Road safety and accident prevention are critical concerns in modern transportation. This paper presents a comprehensive survey of driver safety systems, focusing on the latest advancements in this field. We analyze the existing literature to identify key research trends in driver safety systems, encompassing various categories of solutions. Our survey delves into the reasons behind road accidents and assesses the effectiveness of emerging technologies and solutions in accident prevention. By categorizing and evaluating these solutions based on the Internet of Things and Machine Learning, we provide valuable insights into the landscape of road accident detection and prevention systems. This survey not only highlights the current state of the art but also serves as a reference for future research and innovation in the domain of driver safety. Abbreviations IoT: Internet of things;CNN: Convolutional Neural Network;SVM: Support vector machine;HRV: Heart rate variability;RRI: R-R Interval;MSPC: Multivariate Statistical process control;EAR: Eye aspect ratio;HUD: Head-up display;GPS: Global positioning system;CAN: Controller area network;GPU: Graphics processing unit;IR: Infrared;GSM: Global system for mobile communication;EEG: Electroencephalogram;PCA: Principal component analysis;SVC: Support vector classifier;SdsAEs: Stacked denoising sparse autoencoders;ECG: Electrocardiogram;LED: Light emitting diode;NFC: Near field communication;PSO: Personal security officer;PPG: Photoplethysmography;EDA: Electrodermal activity;EMG: Electromyography;LCD: Liquid crystal display;RF SoCs: Radiofrequency system on chip;PLR: Piecewise linear representation;BAC: Blood alcohol content;BPNN: Backpropagation Neural Network;ADSD: Automated driver sleepiness detection;EOG: Electroocoulogram;KNN: K nearest neighbor;CBR: Case-based reasoning;RF: Random forest;NIR: Near-infrared;LBP: Local binary pattern;PERCLOS: Percentage of Eye Closure;SVD: Singular value decomposition;FFT: Fast Fourier transf

关键词： Electrocardiograms

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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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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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Analysis of Iron Loss Characteristics of PM-Assisted and Fluid-Shaped Synchronous Reluctance Machines for Micro EVs

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Transactions of the Korean Institute of Electrical Engineers 2025年第3期74卷 425-431页

作者： Choi, Jaesung Choi, Gilsu Dept. of Electrical and Computer Engineering Inha University Korea Republic of

This paper examines the iron loss characteristics of a permanent magnet-assisted synchronous reluctance machine (PMa-SynRM) and a fluid-shape synchronous reluctance machine (FS-SynRM) for micro EV applications, emphasizing the impact of DC bias fields and minor loops on iron loss estimation. Iron losses are comparatively evaluated using three methods: the Steinmetz method with constant coefficients, the loss separation method with varying coefficients and FFT, and the hysteresis-based model that accounts for minor loops and DC bias fields. The results show that iron losses per unit stack length are higher in the PMa-SynRM, where the effects of DC bias fields and minor loops are more pronounced. Moreover, the Steinmetz and loss separation methods, which neglect these effects, overestimate motor efficiency by up to 1.5% and 0.3%, respectively. This study highlights the importance of incorporating DC bias and minor loops in iron loss and efficiency calculations for micro EV traction machines. © The Korean Institute of Electrical Engineers.

关键词： Permanent magnets

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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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Study on a Regression Model for the Electromagnetic Characteristics of SPMSM Based on Convolutional Neural Network with Attention Mechanism

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Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 302-309页

作者： Ji, Tae-Hyuk Song, In Seok Kim, Hyung-Woo Jung, Sang-Yong Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Surface-mounted permanent magnet synchronous motors (SPMSMs) are widely used in high-performance system. However, they suffer from torque ripple and cogging torque, which degrade output quality and cause noise and vibration, necessitating optimized design. While finite element analysis offers high accuracy for optimization, it comes with significant computational cost. To address this limitation, this paper proposes an attention mechanism-based convolutional neural network (CNN) regression model to predict SPMSM electromagnetic performance. CNNs capture spatial structures of motor designs, while attention mechanisms highlight key design features, boosting prediction accuracy and efficiency. This study analyzes the effects of attention mechanisms and proposes a CNN-based regression model incorporating them, confirming the effectiveness of the attention mechanism through comparisons with conventional CNN models. © The Korean Institute of Electrical Engineers.

关键词： Convolutional neural networks

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