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Discovery of 6G Services and Resources in Edge-Cloud-Continuum

IEEE Network 2025年第3期39卷 223-232页

作者： Farhoudi, Mohammad Shokrnezhad, Masoud Taleb, Tarik Li, Richard Song, JaeSeung Oulu University Center of Wireless Communications Oulu90570 Finland Electrical Engineering and Information Technology Bochum44801 Germany Sejong University Department of Convergence Engineering for Intelligent Drone Seoul05006 Korea Republic of Southeast University School of Computer Science and Engineer Nanjing210096 China

The advent of 6G networks will present a pivotal juncture in the evolution of telecommunications, marked by the proliferation of devices, dynamic service requests, and the integration of edge and cloud computing. In response to these transformative shifts, this paper proposes a service and resource discovery architecture as part of service provisioning for the future 6G edge-cloud-continuum. Through the architecture’s orchestration and platform components, users will have access to services efficiently and on time. Blockchain underpins trust in this inherently trustless environment, while semantic networking dynamically extracts context from service requests, fostering efficient communication and service delivery. A key innovation lies in dynamic overlay zoning, which not only optimizes resource allocation but also endows our architecture with scalability, adaptability, and resilience. Notably, our architecture excels at predictive capabilities, harnessing learning algorithms to anticipate user and service instance behavior, thereby enhancing network responsiveness and preserving service continuity. This comprehensive architecture paves the way for unparalleled resource optimization, latency reduction, and seamless service delivery, positioning it as an instrumental pillar in the unfolding 6G landscape. Simulation results show that our architecture provides near-optimal timely responses that significantly improve the network’s potential, offering scalable and efficient service and resource discovery. © 1986-2012 IEEE.

关键词： Semantics

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Hierarchical Transfer Multi-task Learning Approach for Scene Classification 27th

Hierarchical Transfer Multi-task Learning Approach for Scen...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Khoshkangini, Reza Tajgardan, Mohsen Jamali, Mahtab Ljungqvist, Martin Georg Mihailescu, Radu-Casian Davidsson, Paul Internet of Things and People Research Center Department of Computer Science and Media Technology Malmö University Malmö Sweden Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Axis Communications AB Lund Sweden

ISBN: (纸本)9783031781063

This paper presents a novel Hierarchical Transfer and Multi-task Learning (HTMTL) approach designed to substantially improve the performance of scene classification networks by leveraging the collective influence of diverse scene types. HTMTL is distinguished by its ability to capture the interaction between various scene types, recognizing how context information from one scene category can enhance the classification performance of another. Our method, when applied to the Places365 dataset, demonstrates a significant improvement in the network’s ability to accurately identify scene types. By exploiting these inter-scene interactions, HTMTL significantly enhances scene classification performance, making it a potent tool for advancing scene understanding and classification. Additionally, this study explores the contribution of individual tasks and task groupings on the performance of other tasks. To further validate the generality of HTMTL, we applied it to the Cityscapes dataset, where the results also show promise. This indicates the broad applicability and effectiveness of our approach across different datasets and scene types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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An Enhanced Keylogger Detection Systems Using Recurrent Neural Networks Enabled with Feature Selection Model 7th

An Enhanced Keylogger Detection Systems Using Recurrent Neur...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Imoize, Agbotiname Lucky Rodríguez, Demóstenes Zegarra Akanji, Boluwatife Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Computer Science Federal University of Lavras Minas Gerais Lavras Brazil

ISBN: (纸本)9789819764648

Keyloggers are malicious software programs that record keystrokes of users without their consent or knowledge. They can steal sensitive information like credit card numbers and passwords. They pose a significant threat to users’ privacy and security since they capture keystroke data. Keylogger detection systems play a vital role in safeguarding users from cyberattacks and mitigating the potential harm caused by keyloggers. Keylogger detection systems employing deep learning have been widely used for identifying and mitigating cyber threats associated with keyloggers. However, they struggle to identify new or unfamiliar keylogger samples. This study aims to explore deep learning techniques with feature selection model to detect keylogger. This study employs Recurrent Neural Networks (RNNs) using a collection of known keylogger dataset. Furthermore, a correlation-based feature extraction method was applied to identify the most relevant features for the model, highlighting the importance of specific features, such as URG flag Count, ACK flag count, and Idle Mean, in differentiating between benign and keylog classes. The proposed model demonstrates superior accuracy of 0.8763 and precision of 0.8569 compared to a baseline model using Logistic Regression (LR) 0.7721 and 0.7407, respectively, indicating a better balance between accurate classification and minimizing false negatives and false positives. The findings from this study help to improve keylogger detection methods, making them more powerful and efficient. These results suggest that a keylogger-based detection system employing a deep learning approach can be a valuable tool for addressing the complex and evolving landscape of cyber threats related to keylogging. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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An Enhanced Product Recommendation System Using Decision Tree Algorithm 7th

An Enhanced Product Recommendation System Using Decision Tre...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Imoize, Agbotiname Lucky Akinyoola, Oluwatimilehin Moses Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany

ISBN: (纸本)9789819764648

Product recommendation systems are a critical element of e-commerce platforms, as they enable customers to identify the items that best meet their needs. Product recommendation systems are important tools used by organizations to increase customer engagement, satisfaction, and loyalty. Existing product recommendation systems have several weaknesses, such as an inability to incorporate features outside of customer preferences. Therefore, this paper proposes an enhanced product recommendation system using a decision tree algorithm. The main objectives of this system are to improve the accuracy and efficiency of the existing product recommendation systems. To accomplish this, a framework includes a data preprocessing phase, a feature selection phase, and a model training phase. The data preprocessing phase is used to clean the data and eliminate any noise. The feature selection phase is used to identify the most informative features, which are then used to train the decision tree model. The model is trained using CART, a supervised learning algorithm, and is evaluated using various metrics such as accuracy, precision, recall, and F1-score. Finally, the model is tested on a test dataset to compare with existing solutions. The results show that the proposed system outperforms existing recommender systems in terms of all the evaluation metrics discussed. Furthermore, the improved system also provides useful insights into the product recommendation process for future studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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A Comparative Study of Machine Learning Models for Plant Disease Identification 19th

A Comparative Study of Machine Learning Models for Plant D...

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19th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Mara, Măcelaru Pop, Petrică Barata, José Department of Mathematics and Computer Science North University Center at Baia Mare Technical University of Cluj-Napoca Cluj-Napoca Romania Electrical and Computer Engineering Department School of Sciences and Technology NOVA University of Lisbon Lisbon Portugal

ISBN: (纸本)9783031750090

Early identification of plant diseases plays an important role in reducing the financial losses for farmers and enhancing the productivity and quality of crops. The present work proposes five machine learning (ML) algorithms: K-Nearest Neighbors, Random Forest, Iterative Dichotomizer 3, Adaptive Boosting and Logistic Regression for plant disease identification. In order to test the proposed ML algorithms, we used a large data set containing 74798 images mainly from PlantVillage dataset. The achieved experimental results show that the proposed ML techniques can be used to reliably identify various plant diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in engineering & Sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Development of an E-commerce System for Designing Identity Patterns of Community and Ethnic Group Products 10

Development of an E-commerce System for Designing Identity P...

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10th International Conference on digital Arts, Media and technology and 8th ECTI Northern Section Conference on electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Jandum, Kaewarin Vachirasricirikul, Sitthidet Maneetume, Thitinon University of Phayao School of Information and Communication Technology Department of Digital Business Phayao Thailand School of Engineering University of Phayao Department of Electrical Engineering Phayao Thailand University of Phayao Center for Information and Technology and Communication Services Department of Information Training Phayao Thailand

ISBN: (纸本)9798331543273

This research aims to collect data on the traditional identity fabric patterns of ethnic groups. Specifically, it gathers 18 fabric patterns from the hmong ethnic group and 16 fabric patterns from the mien ethnic group. The collected data will be stored in a database system designed for e-commerce. Additionally, the study analyzes the factors influencing user satisfaction with an e-commerce in website *** using a sample size of 400 participants. The research found that customers are most satisfied with the ability to design unique ethnic products that are one-of-a-kind. In order of impact, the factors influencing product purchase decisions through the *** system are as follows. 1) The ability to design product patterns themselves, which has the highest impact on user satisfaction. 2) Products designed to be unique and not available in the market. 3) Products that are distinctive and reflect the identity of the ethnic group. 4) The reliability of the product delivery system for pre-ordered items. These factors are statistically significant at 0.05 with a 95% confidence level. © 2025 IEEE.

关键词： Marketplaces

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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Automated Topology Synthesis of Analog Integrated Circuits With Frequency Compensation

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 832-844页

作者： Zhao, Zhenxin Liu, Jun Zhao, Wen-Sheng Zhang, Lihong Hangzhou Dianzi University Innovation Center for Electronic Design Automation Technology Zhejiang Hangzhou310018 China Memorial University of Newfoundland Faculty of Engineering and Applied Science Department of Electrical and Computer Engineering St. John'sNLA1C 5S7 Canada

Analog circuit topology synthesis suffers from weak synthesis capability and low-synthesis efficiency, which result in a bottleneck toward its practical industrial applications. This article presents a proximal-policy-optimization-based circuit topology synthesis framework, which features a superior convergence rate. To further promote its synthesis efficiency, we have improved a deterministic optimization method by incorporating a bias-aware scheme and group concept, which is applied as a filter to eliminate the undesirable topologies in the early evaluation stage. Moreover, a graph-based refinement scheme is proposed to perform deterministically on the generated circuit topologies, which can efficiently add frequency compensation circuits. Compared with the state-of-the-art approaches, our proposed method not only boosts the synthesis efficiency by at least 3 times but also enhances the synthesis capability with a deterministic compensation scheme, showcasing significant advancement of performance efficacy. © 2024 IEEE.

关键词： Network topology

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