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检索条件"任意字段=14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022"

共 2074 条记录，以下是91-100 订阅

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A Hybrid Deep Learning Network for Skin Lesion Extraction 1

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14th international conference on Soft Computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022

作者： Rout, Ranjita Parida, Priyadarsan Dash, Sonali Department of Electronics and Communication Engineering School of Engineering and Technology GIET University Odisha Gunupur India Department of Electronics and Communication Engineering Chandigarh University Punjab Chandigarh India

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

Melanoma is one of the dangerous skin cancer worldwide. Manual detection generally takes more time and difficult to obtain accurate results due to various factors such as change in shape, size and color. In the recent years, convolutional neural network (CNN) plays a vital role for automatic detection of melanoma. In the proposed model, a hybrid deep learning method is developed for extraction of lesions from dermoscopic images by combining DeepLabV3 + with different base CNN networks. the proposed model is evaluated on the three dermoscopic datasets such ISIC 2016, 2017 and 2018. To validate the proposed model, different performance measurement metrics such as Accuracy, Jaccard Index (JI) and Dice Coefficient (DC) are calculated for the different datasets. the proposed model achieves 93% Accuracy, JI of 89% and DC of 90%. this demonstrate the superiority of the proposed model over state-of-art methods. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Extraction

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Integrating Edge Computing and Blockchain for Safer and More Efficient Digital Transportation systems 15

Integrating Edge Computing and Blockchain for Safer and More...

引用

15th international conference on Emerging Ubiquitous systems and Pervasive networks / 14th international conference on Current and Future Trends of Information and communication Technologies in Healthcare, EUSPN/ICth 2024

作者： Badidi, Elarbi El Harrouss, Omar College of Information Technology UAE University United Arab Emirates

this paper investigates the integration of edge computing and Blockchain technologies within the context of digital transportation, focusing on the implementation of autonomous vehicle networks. By leveraging edge computing's real-time data processing capabilities alongside Blockchain's secure, decentralized data management, the paper outlines how these technologies can enhance vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, improve traffic management, and optimize predictive maintenance. the proposed framework includes deploying edge computing nodes along key transportation routes and utilizing Blockchain for secure data exchange and automated transactions via smart contracts. the study also addresses key challenges, such as scalability, data synchronization, and cybersecurity, while exploring the regulatory implications of these innovations. the paper emphasizes the potential of these technologies to create safer, more efficient, and resilient transportation systems and calls for interdisciplinary collaboration to realize these advancements fully. © 2024 the Authors.

关键词： Blockchain

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A Cultural Resource Recommendation Model Based On Graph Neural networks 14

A Cultural Resource Recommendation Model Based On Graph Neur...

引用

14th international conference on communication Software and networks (ICCSN)

作者： Ren, Junyi Wang, Xingwei He, Qiang Yi, Bo Zhang, Yanyou Northeastern Univ Coll Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Coll Med & Biol Informat Engn Shenyang Peoples R China Shijiazhuang Univ Appl Technol Sch Software Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781665453288

Recommender systems provide an effective solution to the problems of information overload and information insufficiency. the traditional collaborative filtering methods, however, suffer from cold start and sparsity problems in the practical application of recommender systems. therefore, scholars have introduced auxiliary information to improve the performance of recommender systems. In some existing models, the feature vectors of user-item interaction information are not accurate enough. In this paper, we propose MKR-Bine, a graph neural network based recommendation model, which fully exploits user and item features by extracting user and item feature vectors using a bipartite graph neural network. Moreover, we use the knowledge graph as auxiliary information to improve the accuracy and interpretability of the recommender systems by learning higher-order interaction information between items and knowledge graph entities through cross- compression units. Finally, the performance of our proposed model is tested on the publicly available datasets of recommendation scenarios. the experimental results show that our model achieves significant benefits on real datasets, and the performance improvement is more obvious on sparse datasets.

关键词： Knowledge graph Recommender systems Graph neural network

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Function Locking Fused with Hashing Technique to Mitigate Re-entrancy Attacks in Smart Contract 14

Function Locking Fused with Hashing Technique to Mitigate Re...

引用

14th international conference on Computing communication and Networking Technologies, ICCCNT 2023

作者： Shiyam Sundar, S. Swapna, M.P. Amrita Vishwa Vidyapeetham Centre for Cybersecurity Systems and Networks Amritapuri India

ISBN: (纸本)9798350335095

Reentrancy attacks have been a significant threat to smart contracts on blockchain platforms. this attack allows an attacker to repeatedly call a vulnerable function in a contract, leading to unexpected behavior and loss of funds. In this research paper, we propose a preventive approach to mitigate the reentrancy attack by introducing a locking mechanism. Our proposed method involves the use of a lock that prevents a contract from being called again until the previous execution is completed. We implement and evaluate our proposed approach on the Ethereum blockchain platform using various benchmarks. Our results show that our approach effectively prevents reentrancy attacks while incurring a low overhead cost. Our proposed approach can be integrated into smart contract development to enhance security and protect against reentrancy attacks by implementing the Function locking technique and Verifying by hashing technique. © 2023 IEEE.

关键词： Smart contract

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Formation Tracking of UAV-UGV systems Over Event-Triggered communications 14

Formation Tracking of UAV-UGV Systems Over Event-Triggered C...

引用

14th international conference on Information Science and Technology, ICIST 2024

作者： Yan, Yancheng Li, Tieshan Long, Yue Yang, Hanqing Liang, Hongjing School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350353334

For unmanned aerial vehicle-unmanned ground vehicle (UAV-UGV) systems with disturbances, this paper investigates the formation tracking issue under event-triggered communications. A hierarchical control strategy is proposed, comprising a distributed event-triggered filter and a local controller. the former is responsible for estimating the desired position of the leader, while the latter employs a filter-based robust controller to ensure the formation tracking errors converge to a small residual set. the salient feature lies in the fact that the necessity for continuous communication among neighbors is obviated, and it has wider robustness to disturbances. Finally, simulations are conducted to validate the feasibility of the proposed strategy, showing accurate formation tracking with reduced communication overhead. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Deterrence of Adversarial Perturbations: Moving Target Defense for Automatic Modulation Classification in Wireless communication systems 59

Deterrence of Adversarial Perturbations: Moving Target Defen...

引用

59th Annual IEEE international conference on communications (IEEE ICC)

作者： Dong, Wei Zhou, Zan Li, Xiping Yang, Shujie Yuan, Zhenhui Xu, Changqiao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Univ Warwick Sch Engn Coventry W Midlands England

ISBN: (纸本)9781728190549

Automatic modulation classification (AMC) plays an indispensable role in wireless communication systems. Deep learning-based AMC has become the mainstream solution due to its high accuracy and no need for manual feature engineering. However, every coin has two sides. DL-based AMC is susceptible to adversarial perturbations, which are carefully crafted to be superimposed on the transmitted signals in an iteratively try- and-error manner, resulting in incorrect classification. In this paper, we propose a model diversity-based moving target defense mechanism (MD-MTD), which employs multiple classifiers and switches periodically, preventing intelligent attackers from deducing universal adversarial perturbations (UAP). Besides, to jointly optimize the robustness and accuracy of different AMC models to be trained, we design a novel multi-agent reinforcement learning (MARL) module. It is worth mentioning that the proposed algorithm significantly mitigates the curse of dimensionality during the large-scale training process via integrating value-decomposition networks and illegal action masking, improving the feasibility of our solution in real-world wireless communication systems. Experimental results on the GNU radio dataset also exhibit the remarkable advantages of our method in terms of convergence and defense performance.

关键词： Automatic modulation classification Adversarial perturbations Moving target defense Reinforcement learning Wireless communication systems

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the Healing Power of Words: Examining the Effect of communication Styles on Appreciation within the Hospital Setting 14

The Healing Power of Words: Examining the Effect of Communic...

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14th international conference on Emerging Ubiquitous systems and Pervasive networks / 13th international conference on Current and Future Trends of Information and communication Technologies in Healthcare, EUSPN/ICth 2023

作者： Seerig, Kirsten Hannah Haug, Maximilian Maier, Alexander Gewald, Heiko Neu-Ulm University of Applied Sciences Wileystraße 1 Neu-Ulm89231 Germany

Nursing faces challenges such as high workload, low job satisfaction, and poor communication in high-stress environments with strict hierarchies. these factors contribute to increased errors and conflicts. Information technology solutions like clinical decision support systems and electronic health records can reduce workload, decrease errors, and improve patient care. However, effectively integrating these tools requires understanding communication dynamics within hospitals and between professions. Despite the known effects of poor communication on job satisfaction and care quality, there is a lack of research on nurse-physician communication from their respective perspectives. the paper aims to address this gap and focuses on research questions related to nurse-physician communication and appreciation. © 2024 Elsevier B.V.. All rights reserved.

关键词： Job satisfaction

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Deep-Prenatal: A Deep Learning Approach for Detection and Analysis of Nuchal Translucency for Enhanced Prenatal Screening 14

Deep-Prenatal: A Deep Learning Approach for Detection and An...

引用

14th IEEE international conference on communication systems and Network Technologies, CSNT 2025

作者： Pawar, Smita S. Nikose, Mangesh D. Gupta, Anni U. Electrical and Electronics Engg. Dept School of Engg and Technology Sandip University Maharashtra Nashik India Electronics and Communication Engg. Dept. MET's IOE Maharashtrax Nashik India

ISBN: (纸本)9798331531935

Nuchal Translucency (NT) observed in ultrasound scans is widely utilized to identify genetic abnormalities in foetuses. However, NT measurements may be challenging to perform as a mainstay of prenatal screening. these challenges include operator dependency, which primarily relies on the technician’s expertise, and variations in imaging settings that can introduce data anomalies. thus, the precision of NT measurements is not always definitive, raising questions about their dependability in clinical practice. A Convolutional Neural Network (CNN) framework is proposed to address these constraints. this architecture comprises of Conv2D, Max Pooling, Dense, and Flatten layers and advanced components. Together, these elements enable the CNN to effectively identify and analyse NT measurements, thereby automating the process and reducing reliance on human involvement. the proposed CNN architecture performs well across critical criteria such as accuracy, precision, recall, and Intersection over Union (IoU). Furthermore, the framework has demonstrated stability when applied to diverse datasets, owing to its robustness and flexibility across various clinical databases. this proposed efficient and reliable preliminary screening method, based on the enhanced CNN architecture, addresses the inherent limitations of NT measurements. It establishes a new benchmark for accuracy in the field and introduces a transformative approach that enhances clinical outcomes. © 2025 IEEE.

关键词： Convolutional neural networks

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Comparing Machine Learning Models to Determine the Effect of Speech Duration on Speaker Identification within Kazakh Speech Corpus 14

Comparing Machine Learning Models to Determine the Effect of...

引用

作者： Saltanat, Janybekova Sarsembayev, Aidos Tolganbayeva, Gaukhartas A. International University of Information Technologies Almaty050040 Kazakhstan

this paper conducts comparative analysis of speaker identification techniques, namely Gaussian Mixture Models (GMM), Bidirectional Long Short-Term Memory (BiLSTM) neural networks, and Recurrent Neural networks (RNN), applied to the Kazakh Speech Corpus. the study's central objective is to explore the influence of speech duration on the efficacy of speaker identification. Our results indicate that all models exhibit enhanced accuracy with increased speech duration. Furthermore, the incorporation of delta and fundamental frequency (F0) as supplementary features bolsters the performance of these models. this research contributes to the understanding of how speech duration and additional features can optimize speaker identification techniques, providing valuable insights for future developments in the field. © 2024 Elsevier B.V.. All rights reserved.

关键词： Recurrent neural networks

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SEAL-CC: Scalable Latency Evaluation Methodology for Internet-of-things Services 24

SEAL-CC: Scalable Latency Evaluation Methodology for Interne...

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14th international conference on the Internet of things, IoT 2024

作者： Horvath, Kurt Klaus Kimovski, Dragi Spiess, Bernd Hohlfeld, Oliver Prodan, Radu ITEC University of Klagenfurt Klagenfurt Austria Distributed Systems group University of Kassel Kassel Germany

ISBN: (纸本)9798400712852

Traditional network measurement campaigns suffer from the lack of control over network infrastructure and the inability to evaluate communication performance directly, especially for the placement of highly distributed Internet of things (IoT) services. In response, we propose a novel Scalable Latency Evaluation Methodology for the Computing Continuum (SEAL-CC). SEAL-CC extends beyond short-term evaluations by capturing the long-term responsiveness of networks supporting IoT services on the computing continuum. It organizes and evaluates a network of nodes, offering insights for optimized IoT service placement in urban and international settings. Our contributions include a novel evaluation methodology tailored for IoT services over the computing continuum, a comprehensive framework for transparent network evaluation using distributed Internet measurement platforms, and a real-life case-study validation with recommendations for IoT service placement. © 2024 Copyright held by the owner/author(s).

关键词： Velocity measurement

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