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共 4259 条记录，以下是171-180 订阅

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ZTAAC : Zero Trust Adaptive Authorization with CP-ABE for Context-Aware Data Protection 17

ZTAAC : Zero Trust Adaptive Authorization with CP-ABE for Co...

引用

17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Routray, Kasturi Bera, Padmalochan Indian Institute of Technology Bhubaneswar India

ISBN: (纸本)9798331531195

In modern dynamic and distributed computing environments, secure and adaptive access control for sensitive data is essential. Traditional access control models often fail to address the complexities of evolving and heterogeneous systems. To overcome these challenges, we propose a Zero Trust Adaptive Authorization Architecture (ZTAAC) that integrates the principles of Zero Trust, Never trust, always verify, along with the cryptographic flexibility of a Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme. ZTAAC introduces a central zero trust controller, which incorporates a policy decision point and a risk engine to facilitate real-time risk assessment and dynamic policy enforcement. the risk engine evaluates contextual parameters such as user location, device posture, and behaviour patterns to enable real-time adaptation of access policies through feedback loops that refine access decisions dynamically. Our enhanced CP-ABE framework embeds static and dynamic attributes-based access policies within encrypted data, ensuring that only authorized users with valid context can decrypt it. Dynamic attribute management facilitates the generation and integration of key components for contextual attributes and aligns access decisions with current security postures. Preliminary performance analysis confirms ZTAAC's scalability and efficiency, establishing it as a robust and adaptive solution for securing data in next-generation cloud-assisted and distributed environments. © 2025 IEEE.

关键词： Authorization

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Identifying Deceptive AI Reviews: A Machine Learning Approach 8

Identifying Deceptive AI Reviews: A Machine Learning Approac...

引用

8th IEEE international conference on distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Venugopala, P.S. Naik, Amrith R. Shettigar, Nidhish Vaishnavi, N. Bhat, Pranav R. Kodi, Pranesh Kumar NMAM Institute of Technology Nitte Deemed to be University Nitte Dept of AI&DS India Modernization Analytics & Comm Wipfli LLP RVCE Post Bangaluru India

ISBN: (纸本)9798350350593

In today's digital age, online reviews greatly influence what people buy. However fake reviews or reviews generated by Artificial intelligent tools by using the Natural Language Processing models cause a big problem. they mislead the consumers in judging a product. this paper aims to address the issue by using machine learning to predict if a review is real or fake. A labeled dataset containing product reviews was chosen from Kaggle. It was preprocessed and then used to train three models using three different algorithms which were Support Vector Machines (SVM), Random Forest Classifiers and Logistic Regression. the best one chosen based on evaluation metrics was Support Vector Machines. this model was then used to test reviews of some top products scraped from the Amazon website. the results show the capability of the model in detecting deceptive reviews. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparison of Machine Learning Optimization Techniques for EEG-Based Confusion Emotion Recognition 17

Comparison of Machine Learning Optimization Techniques for E...

引用

17th IEEE international conference on Industrial and Information Systems, ICIIS 2023

作者： Ganepola, Dasuni Maduranga, M.W.P. Karunaratne, Indika General Sir John Kotelawala Defence University Faculty of Computing Rathmalana Sri Lanka University of Moratuwa Faculty of Information Technology Moratuwa Sri Lanka

ISBN: (纸本)9798350323634

Machine Learning (ML) and Optimization techniques are considered two distinct fields of study in Computer Science. However, with the rapid development of Artificial Intelligence, these two fields are now frequently interacting with each other to enhance the performance of learning and searching/classifying capabilities of the algorithms. In this study, an optimization performance comparison of the current, widely used hyperparameter tuning algorithms on classifier models of confusion emotion using EEG signals. Two Evolutionary algorithms: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were compared with two Non-Evolutionary Algorithms: Random Search and Bayesian Optimization. the four algorithms were made to optimize a Support Vector Machine (SVM) which was trained to be a confusion emotion classifier using Wang et al's [19] dataset. Performance was assessed by calculating the prediction accuracy and error produced by the SVM that was trained using the yielded parameters by each hyperparameter tuning algorithm. Results showed that Evolutionary hyperparameter tuning algorithms were more effective than Non-Evolutionary algorithms because they yielded optimized parameters that produced the highest prediction accuracy and the lowest prediction error. © 2023 IEEE.

关键词： Support vector machines

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Leveraging Blockchain technology for Effective Healthcare Solutions: Requirements, Architecture, Applications and Recent Advances 4

Leveraging Blockchain Technology for Effective Healthcare So...

引用

4th IEEE international conference on computing, Communication, and intelligent Systems, ICCCIS 2023

作者： Kumar, Nitin Bhushan, Bharat Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350306118

Blockchain technology is a very high powered technology that encounters a vast variety of features. For cloud-edge systems to do data integrity audits blockchain is an authentic tool. Using blockchain hospitals can become nodes to put up distributed ledger that make sure the combat collusion and tamper resistance. It emphasize the role of blockchain in ensuring data integrity, managing access, data security and privacy based on the context of healthcare. Patient histories, healthcare management, efficient data records, access to healthcare datasets that are facilitates by blockchain in the healthcare sector. this paper examines the relation between healthcare sector and blockchain technology that shows features and recent advances. this paper concludes the major role of blockchain in the healthcare sector in developing a well-organized and secured health records. © 2023 IEEE.

关键词： Blockchain

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LSTM-based distributed Photovoltaic Output Interval Prediction Method for Source-load Collaboration 8

LSTM-based Distributed Photovoltaic Output Interval Predicti...

引用

8th international conference on intelligent computing and Signal Processing, ICSP 2023

作者： Li, Bo Shi, Zhan Wu, Zhentian Yang, Zhihua Guangdong Power Grid Co. Ltd. Power Dispatching Control Center Communication Management Department Guangdong China Guangdong Electric Power Communication Technology Co. Ltd. Science Innovation Department Guangdong China

ISBN: (纸本)9798350302455

With the continuous development of distributed energy, photovoltaic (PV) power emerges as a significant construction to tradeoff the balance of power supply and demand. Since the prediction of PV output is unavoidable, interval prediction becomes a powerful method to describe the output curve accurately. To this end, this paper proposes a long short term memory (LSTM)-based distributed PV output interval prediction method for source-load collaboration. It can correct the prediction error according to the spatiotemporal characteristic of PV power stations, and then calculates the interval prediction results under different confidence levels. Simulation results show that the proposed algorithm has the superior performance in the average width of the forecast interval, and the coverage width. © 2023 IEEE.

关键词： Long short-term memory

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Weather Prediction from Historical Data using Big Data Technologies 5

Weather Prediction from Historical Data using Big Data Techn...

引用

5th international conference on IoT Based Control Networks and intelligent Systems, ICICNIS 2024

作者： Choudha, Shaurya Singh, Aniruddh Kumar, Aman Gouthaman, P. School of Computing College of Engineering and Technology Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur603203 India

ISBN: (纸本)9798331518097

Weather forecasting is an important function in a variety of activities such as agriculture, environment monitoring, and disaster management among others. Today with the development of technology, vast amounts of meteorological data are able to be collected and analyzed with the intention of improving prediction accuracy. the purpose of this work is to analyze the contribution of Big Data frameworks such as, Hadoop, MapReduce and Apache Spark in effective weather prediction processes. In a distributed system, historical weather data, satellite images and sensor readings are kept and manipulated through the HDFS storage. For effective data manipulation, MapReduce and Spark are used along with Hive and Pig for searching large datasets. Predictive analytics are performed using machine learning algorithms included in the MLlib package of Spark. these results are presented using the Tableau application, which shows the speed and accuracy of this Big Data approach in comparison to classical ones. the proposed work ends with a look into the prospects of Big Data in the field of weather forecasting and relevant problems in this area. © 2024 IEEE.

关键词： MapReduce

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Analysis of the Influence of distributed Photovoltaic Dynamic Characteristics on Energy Measurement 5

Analysis of the Influence of Distributed Photovoltaic Dynami...

引用

5th IEEE international conference on Power, intelligent computing and Systems, ICPICS 2023

作者： Liu, Li Sun, Zhimin Chen, Xueyan Zhong, Licai Guo, Jianghong Lanzhou Resources and Environment Voc-Tech University Lanzhou China

ISBN: (纸本)9798350333442

In response to the issue of adapting to the dynamic characteristics of distributed photovoltaics and their impact on energy metering, this article first simulates and analyzes the harmonic and voltage fluctuations caused by photovoltaic grid connection based on a universal 400 kW distributed photovoltaic grid connection system model, and summarizes the characterization formula of distributed photovoltaic grid connection dynamic characteristics;then, based on the characteristics of harmonics and voltage fluctuations, the error characteristics of electricity metering were quantitatively analyzed through simulation;Finally, in view of the impact of the microgrid access of distributed generation to the power system on electric energy measurement, this paper summarizes the function problem of two-way electric energy measurement and the accuracy of electric energy measurement devices, and discusses the rationality of electric energy measurement. © 2023 IEEE.

关键词： Harmonic analysis

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Construction and Implementation of Electric Power Deep Learning computing Service Platform 4

Construction and Implementation of Electric Power Deep Learn...

引用

2023 4th international conference on Computer Engineering and intelligent Control, ICCEIC 2023

作者： Geng, Jipu Zheng, Xiaoyan Lai, Xiaohan Zhao, Lei Xu, Yibai Information and Communications Branch State Grid Zhejiang Electric Power Co. Ltd Hangzhou China

ISBN: (纸本)9798350308877

In order to accurately and effectively identify the types and locations of abnormal information in the power industry, an electric power deep learning computing service platform is constructed to provide intelligent inspection services. the platform adopts hierarchical technology architecture, based on Docker technology, realizes the abstract pooling of many kinds of computing resources, and provides the management interface of heterogeneous resources such as GPU, NPU and so on according to the specific requirements of deep learning field, so as to realize the unified management and scheduling of heterogeneous resources, and provide an efficient, flexible and scalable distributed computing service supporting environment for deep learning computing tasks. the platform layer provides data center, development center, training center, model center, service center and other subsystems, and built-in a variety of learning frameworks and model algorithms to support external algorithm model integration. It really realizes the one-stop development ability of deep learning tasks. In the process of intelligent inspection of electric power, the online recognition and detection of a variety of abnormal information is realized, which provides a technical basis for on-line identification, supervision, prevention and control, comprehensive management of abnormal behavior. © 2023 IEEE.

关键词： Deep learning

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Cluster Head Selection and Cluster Construction for Federated Learning in Mobile Ad Hoc Networks 8

Cluster Head Selection and Cluster Construction for Federate...

引用

8th international conference on intelligent computing and Signal Processing, ICSP 2023

作者： Mei, Linsheng Meng, Xiaowei Yi, Yibo Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

ISBN: (纸本)9798350302455

Federated learning (FL), as a distributed learning framework, which can effectively protect user privacy and address data imbalance issues. In this paper, we investigate cluster head (CH) selection and client clustering issues based on hierarchical FL framework in mobile ad hoc networks (MANETs), wherein all the clients are randomly distributed without infrastructure support. Specifically, we formulate the joint client association and CH selection problem, with the target of minimizing the training latency while maximizing the system lifetime. Furthermore, we propose a heuristic algorithm for client association and an entropy weight Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method for CH selection. Simulation results demonstrate that our proposed algorithms can effectively shorten training latency and extend lifetime compared with other schemes. © 2023 IEEE.

关键词： Heuristic algorithms

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Harnessing Small AI Model Collaboration and Debate Mechanisms in 6G Networks: distributed Architectures and Layered Privacy Protection 59

Harnessing Small AI Model Collaboration and Debate Mechanism...

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

作者： Han, Chengzhuo Yang, Tingting Sun, Xin Cui, Zhengqi Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Dalian Maritime Univ Nav Coll Dalian Peoples R China

ISBN: (纸本)9781728190549

In the context of 6G communication networks, large communication models emerge as the core application for edge communication networks. they hold immense application potential in areas like intelligent connected vehicles, collaborative robots, and holographic communications. However, practical implementations of these applications face challenges such as unstable learning environments, resource constraints, information theft, and heterogeneity of edge devices. this paper delves into the distributed deployment of large communication models under resource-constrained nodes and introduces a distributed architecture based on cloud-edge collaboration. Crucially, we introduce a negotiation and debate mechanism between small AI models. this mechanism adopts innovative strategies, enabling it to match or even surpass the capabilities of large language models in terms of inference and accuracy. Furthermore, addressing the data confidentiality issues of large model parameter transmissions, this study proposes a secure federated learning mechanism based on privacy computing. Combining novel model layering technology with privacy protection measures, it aims to enhance the security of model parameter transmissions. Experimental results confirm that while improving learning efficiency and inference accuracy, this strategy successfully ensures the privacy security of edge nodes, offering a practical solution for future privacy protection.

关键词： Small AI Model Collaboration 6G Communication Networks distributed Architecture Layered Privacy Protection

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