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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是21-30 订阅

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Integrating Blockchain-Based Security and Privacy with QML in Edge computing for 6G Networks 37th

Integrating Blockchain-Based Security and Privacy with QML i...

引用

37th international conference on Computer Applications in Industry and Engineering

作者： Seol, Jongho Kim, Jongyeop Kancharla, Abhilash Middle Georgia State Univ Macon GA 31206 USA Georgia Southern Univ Statesboro GA 30460 USA Univ Tampa Tampa FL 33606 USA

ISBN: (纸本)9783031762727;9783031762734

This paper presents a robust theoretical framework to integrate blockchain-based security and privacy mechanisms with quantum machine learning (QML) in the edge computing domain within 6G networks. The burgeoning deployment of edge devices necessitates secure, privacy-preserving, and trustworthy infrastructures to support collaborative QML tasks while upholding data confidentiality at the network periphery. Leveraging blockchain technology's decentralized and immutable ledger capabilities, this framework manages access control, ensures data provenance, and verifies integrity in edge computing environments. Furthermore, integrating quantum-resistant cryptographic primitives is explored to fortify defenses against potential threats from quantum adversaries. In addition to these considerations, the paper incorporates the theory of quantum probability within the framework, particularly in the context of the central limit theorem, to account for the probabilistic nature of quantum systems and its implications on statistical inference in QML tasks. Detailed latency analysis reveals that blockchain processing time increases with transaction complexity, quantum processing time grows more slowly, and 6G transmission time remains constant due to high bandwidth capabilities. Incorporating machine learning components such as data preprocessing and model inference times provides a comprehensive understanding of edge computing performance. Combining blockchain-based security and privacy measures with QML techniques like federated learning and differential privacy, the envisioned framework strives to establish a secure and trusted ecosystem for collaborative QML tasks at the network edge. This theoretical endeavor, enriched by quantum probability theory and detailed latency analysis, lays a solid groundwork for further research and development in this burgeoning interdisciplinary domain, promising advancements in edge computing applications' efficiency, reliability, and security w

关键词： Blockchain Quantum machine learning (QML) Edge computing 6G Networks Security

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A distributed machine learning dynamic remote proof scheme that resists collusion attacks 4

A distributed machine learning dynamic remote proof scheme t...

引用

4th international conference on Network Communication and Information Security, ICNCIS 2024

作者： Wang, Ke Wu, Qiuxin Qin, Yu School of Applied Science Beijing Information Science and Technology University Beijing100192 China Trusted Computing and Information Assurance Laboratory Institute of Software Chinese Academy of Science Beijing100190 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Based on the security of distributed machine learning data and model based on remote proof, a cross-domain dynamic remote proof scheme based on ring signature improves the efficiency of computer cluster proof, the application of dynamic signature, realizes the real-time measurement of the confidence of computer cluster, and strengthens the real-time protection of data and model. Computers with different security domain may adopt different remote proof mechanism, and convert the proof information of different proof methods into unified proof information, thus realizes the cross-domain dynamic remote proof between computer clusters. Noise is added when the cloud server distributes data to a distributed computer cluster. Due to the particularity of distributed architecture and the unsolution of indefinite equations, the enemy cannot conspire with one or more computers to steal data or the model parameters trained by endpoints. Finally, the safety of this method is verified through safety analysis and experimental analysis, and the computational overhead required in the proof process is reduced compared with other methods. © 2025 SPIE.

关键词： Authentication

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Sailing Through data: Automated data Annotation Systems for machine learning Applications in Maritime Navigation 24

Sailing Through Data: Automated Data Annotation Systems for ...

引用

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

作者： Vibild, Patrick Dominique Hernandez, Jon Kim, Daeyoung School of Computing KAIST Daejeon Korea Republic of

ISBN: (纸本)9798400712852

The maritime industry plays a crucial role in global trade and transport, necessitating effective collision avoidance, navigation security, and reduction in human errors and costs. This paper presents an automated data annotation pipeline for detection and distance estimation using monocular camera systems in maritime applications. Our system integrates AIS data with ship detections from a pre-trained deep learning model, creating comprehensive RGB/IR data with annotations of ship positions, distances, speeds, categories, and sizes. We use sensor data from our developed embedded solution, AISail, and its post-processing techniques to generate this data. We evaluate our system’s performance against manually annotated images, demonstrating its potential to enhance maritime navigation and safety by accurately estimating ship positions and speeds. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Complex-Valued Federated learning with Differential Privacy and MRI Applications 9th

Complex-Valued Federated Learning with Differential Privacy ...

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27th international conference on Medical Image computing and Computer Assisted Intervention (MICCAI)

作者： Riess, Anneliese Ziller, Alexander Kolek, Stefan Rueckert, Daniel Schnabel, Julia Kaissis, Georgios Helmholtz Munich Inst Machine Learning Biomed Imaging Neuherberg Germany Tech Univ Munich Sch Computat Informat & Technol Munich Germany Helmholtz Munich Helmholtz AI Oberschleissheim Germany Tech Univ Munich Artificial Intelligence Healthcare & Med Munich Germany Ludwig Maximilians Univ Munchen Math Foundat AI Munich Germany Imperial Coll London Dept Comp London England Kings Coll London Sch Biomed Engn & Imaging Sci London England Tech Univ Munich Inst Diagnost & Intervent Radiol Munich Germany

ISBN: (纸本)9783031776090;9783031776106

Federated learning enhanced with Differential Privacy (DP) is a powerful privacy-preserving strategy to protect individuals sharing their sensitive data for processing in fields such as medicine and healthcare. Many medical applications, for example magnetic resonance imaging (MRI), rely on complex-valued signal processing techniques for data acquisition and analysis. However, the appropriate application of DP to complex-valued data is still underexplored. To address this issue, from the theoretical side, we introduce the complex-valued Gaussian mechanism, whose behaviour we characterise in terms of f-DP, (epsilon, delta)-DP and Renyi-DP. Moreover, we generalise the fundamental algorithm DP stochastic gradient descent to complex-valued neural networks and present novel complex-valued neural network primitives compatible with DP. Experimentally, we showcase a proof-of-concept by training federated complex-valued neural networks with DP on a real-world task (MRI pulse sequence classification in k-space), yielding excellent utility and privacy. Our results highlight the relevance of combining federated learning with robust privacy-preserving techniques in the MRI context.

关键词： Federated learning Differential Privacy Complex Numbers

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EPFLU: Efficient Peer-to-Peer Federated learning for Personalized User Models in Edge-Cloud Environments 4th

EPFLU: Efficient Peer-to-Peer Federated Learning for Persona...

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24th international conference Web Engineering (ICWE)

作者： Song, Xiangchi Wang, Zhaoyan Baek, KyeongDeok Ko, In-Young Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

ISBN: (纸本)9783031751097;9783031751103

As the number of IoT devices and the volume of data increase, distributed computing systems have become the primary deployment solution for large-scale Internet of Things (IoT) environments. Federated learning (FL) is a collaborative machine learning framework that allows for model training using data from all participants while protecting their privacy. However, traditional FL suffers from low computational and communication efficiency in large-scale hierarchical cloud-edge collaborative IoT systems. Additionally, due to heterogeneity issues, not all IoT devices necessarily benefit from the global model of traditional FL, but instead require the maintenance of personalized levels in the global training process. Therefore we extend FL into a horizontal peer-to-peer (P2P) structure and introduce our P2PFL framework: Efficient Peer-to-peer Federated learning for Users (EPFLU). EPFLU transitions the paradigms from vertical FL to horizontal P2P structure from the user perspective and incorporates personalized enhancement techniques using private information. Through horizontal consensus information aggregation and private information supplementation, EPFLU solves the weakness of traditional FL that dilutes the characteristics of individual client data and leads to model deviation. This structural transformation also significantly alleviates the original communication issues. Additionally, EPFLU has a customized simulation evaluation framework to make it more suitable for real-world large-scale IoT. Within this framework, we design extreme data distribution scenarios and conduct detailed experiments of EPFLU and selected baselines on the MNIST and CIFAR-10 datasets. The results demonstrate that the robust and adaptive EPFLU framework can consistently converge to optimal performance even under extreme data distribution scenarios. Compared with the selected vertical aggregation and horizontal transmission cumulative aggregation methods, EPFLU achieves communication improve

关键词： Federated learning Peer-to-peer learning Hierarchical Edge computing Edge-Cloud Environment

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Arm motion recognition based on attitude angle sensor 24

Arm motion recognition based on attitude angle sensor

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Niu, Weikun Zhang, Feng Yang, Guang Xie, Qiang Li, Yonggang Key labortory of recognition and intelligence China Electronics Technology Group Corporation Information Science Research Institute Beijing China Beijing Sanchuan Weiwei Technology Co. LTD Beijing100086 China

ISBN: (纸本)9798400717482

In this paper, the data-driven predictive maintenance technology is applied to the motion recognition of assembly line workers. Specifically, the motion data (acceleration, angular velocity and angle) of workers completing three types of arm movements (two counter-clockwise turns, one turn or one and a half turns, and straight line movement) were collected by the attitude angle measurement sensor. For this data set, an algorithm combining continuous wavelet transform and image deep learning model (pre-trained GoogleNet) has been proposed, and the algorithm is compared with the support vector machine based on the statistical characteristics of discrete wavelet transform and the one-dimensional convolutional network model. It is concluded that the proposed scheme can effectively identify the three kinds of typical movements performed by workers (the average test recognition accuracy is around 93%), which is significantly better than the other comparison models. Through feature analysis, it can be concluded that the scheme combining continuous wavelet transform and image processing deep learning model proposed in this paper has better distinguishable feature space compared with the contrastive models. The scheme proposed in this paper can be extended to predictive maintenance scenarios based on time series data type sensors. © 2024 Copyright held by the owner/author(s).

关键词： Predictive maintenance

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Feature Selection Based on Membrane Clustering 7th

Feature Selection Based on Membrane Clustering

引用

7th international conference on Optimization and learning

作者： Sheuly, Sharmin Sultana Deivard, Johannes Seceleanu, Tiberiu Xiong, Ning Malardalen Univ Vasteras Sweden Dev Solut AB Eskilstuna Sweden

ISBN: (纸本)9783031779404;9783031779411

Given increased complex data with high dimensions, feature selection aims to select a subset of features to increase the efficiency of machine learning. This paper proposes a new feature selection method based on membrane computing. The proposed method has two main advantages. First, it provides a new solution to search for feature combinations while requiring no model construction (which is time-consuming) to evaluate a feature subset. Second, feature selection is embedded in a membrane clustering algorithm, which is designed to enable searching for the best feature subset and finding active cluster centres at the same time. The designed clustering algorithm mimics the behavior of multiple cells and it has stronger global search ability than existing evolutionary algorithms. The efficacy of the proposed method has been shown by the evaluation of a set of benchmark data sets.

关键词： feature selection membrane computing clustering

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Sentiment Analysis on 2024 Regional Elections using Hybrid CNN-SVM with Semantic Features and Word2Vec

Sentiment Analysis on 2024 Regional Elections using Hybrid C...

引用

2025 international conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Andinny, Syahbilla Putri Setiawan, Erwin Budi School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331513320

Regional Head Election is a political agenda held every five years. Everyone has their point of view on assessing the candidates. These political conversations often appear on platform X, a social media to exchange information, responses, comments, and even emotions. These public expressions can be classified into positive, negative, and neutral sentiments. This political phenomenon is the object of research to understand a hybrid deep learning model that combines Convolutional Neural Network (CNN) and Support Vector machine (SVM), enhanced with TF-IDF extraction features, IndoBERT semantics to understand the deeper context of Indonesian text and Word2Vec that represents words in the form of vectors with similar meanings against testing on three corpus X, IndoNews, and X+IndoNews. This research analyzes the perspectives of Indonesian social media users surrounding the 2024 Regional Elections using 60,000 Indonesian tweets collected with keywords related to the 2024 Regional Elections from the X platform and a similarity corpus of 124,925 from the IndoNews collection. This study uses CNN to extract spatial patterns from textual data, while SVM serves as a classification model for sentiment prediction. The test results show the highest accuracy of 81.67%, with an improvement of +1.83% compared to the CNN baseline and +3.51% compared to the SVM baseline. The completion of sentiment analysis using the hybrid model and features applied in this research successfully detects public opinion for candidates, campaign teams, media, or survey institutions to understand the dynamics of voters surrounding the 2024 Regional Elections. © 2025 IEEE.

关键词： Convolutional neural networks

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Application of machine learning in Enterprise Financial Risk Assessment: A Study About China's A-Share Listed Manufacturing Companies 5th

Application of Machine Learning in Enterprise Financial Risk...

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5th international conference on Neural computing for Advanced Applications (NCAA)

作者： Yul, Kexin Yul, Zengyi Ma, Shuomin Xu, Pan Zhejiang Univ Technol Hangzhou 310023 Peoples R China SpringAI LLC Bellevue WA 98007 USA

ISBN: (纸本)9789819770038;9789819770045

Based on the financial data of domestic A-share listed firms from 2000 to 2022, this paper aims to explore the effectiveness of machine learning in identifying the financial risk of Chinese A-share listed manufacturing firms. To this end, a variety of machine learning models, including logistic regression, decision tree, random forest, XGBoost, SVM, and LSTM, are used to assess the financial risk of enterprises, and the key attributes of enterprise financial risk are extracted through the interpretability exploration and importance coefficient measurement. From this paper, the following conclusions are drawn: (i) XGBoost model performs the best on all attributes, showing its strong ability in dealing with complex financial datasets, and LSTM, which adds time-series factors, performs poorly, which is speculated that it may be related to the incompatibility of the characteristics of the financial data, the reliance on the time-series features, and the need for financial fine features. (ii) Audit opinion, Net Profit and ROA are key influencing factors.

关键词： Financial risk machine learning XGBoost LSTM

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Comparing Advanced Programming Languages Based on machine learning (ML) 15th

Comparing Advanced Programming Languages Based on Machine Le...

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15th international conference on Intelligent computing and Networking, IC-ICN 2024

作者： Quraishi, Rayaan Salian, Aditya Khasgiwala, Deep Khandare, Anand Thakur College of Engineering and Technology Department of Computer Engineering Maharashtra Mumbai India

ISBN: (纸本)9789819786305

Artificial intelligence, data science, and automation are just a few of the fields where machine learning has emerged as an influential force. The choice of programming language is crucial for the creation and implementation of ML models. The goal of this research study is to give practitioners and researchers valuable insights into language selection criteria and their influence on ML projects by conducting a comprehensive evaluation of the advanced programming languages used for ML applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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