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2nd IEEE International Conference on Federated Learning Technologies and Applications, FLTA 2024

作者： Tahir, Mehreen Awaysheh, Feras Alawadi, Sadi Ali, Muhammand Intizar SFI Centre for Research Training in Machine Learning Dublin City University Dublin Ireland Institute of Computer Science The University of Tartu Tartu Estonia School of Computer Science Blekinge Tekniska Högskola Karlskrona Sweden School of Electronic Engineering Dublin City University Dublin Ireland

ISBN: (纸本)9798350354812

Federated Learning (FL) faces significant challenges, such as handling non-IID (Non-Independent and Identically Distributed) data and efficiently aggregating distributed models, which can lead to slower convergence and reduced model accuracy. This paper proposes a novel framework, Bayesian Federated Learning with Stochastic Variational Inference (BayFL-SVI), to address these issues. Stochastic Variational Inference (SVI) is a scalable approximation method for Bayesian inference that optimizes the Evidence Lower Bound (ELBO) using mini-batches of data through stochastic gradient descent. By computing the ELBO for each client update, our approach quantifies the significance of these updates, effectively managing the heterogeneity of non-IID data and improving the aggregation process. This approach results in a more accurate and robust integration of client contributions, enhancing convergence rates and overall model performance. We provide theoretical analysis with convergence guarantees. Our empirical results demonstrate significant improvements in convergence rates and model accuracy, establishing a solid foundation for future studies. ©2024 IEEE.

关键词： Federated learning

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Hybrid AI-electronic Systems for Real-Time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and electronics, IITCEE 2025

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

Real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 IEEE.

关键词： Adversarial machine learning

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Enhancing the Adversarial Transferability with Channel Decomposition

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computer Systems science & engineering 2023年第9期46卷 3075-3085页

作者： Bin Lin Fei Gao Wenli Zeng Jixin Chen Cong Zhang Qinsheng Zhu Yong Zhou Desheng Zheng Qian Qiu Shan Yang Sichuan Normal University Chengdu610066China Jinan Geotechnical Investigation and Surveying Institute Jinan250000China School of Computer Science and Engineering Sichuan University of Science&EngineeringZigong643000China School of Computer Science Southwest Petroleum UniversityChengdu610500China AECC Sichuan Gas Turbine Estab Mianyang621000China School of Physics University of Electronic Science and Technology of ChinaChengdu610056China School of Power and Energy Northwestern Polytechnical UniversityXi’an710072China Department of Chemistry Physics and Atmospheric ScienceJackson State UniversityJacksonMSUSA

The current adversarial attacks against deep learning models have achieved incredible success in the white-box ***,they often exhibit weak transferability in the black-box scenario,especially when attacking those with defense *** this work,we propose a new transfer-based blackbox attack called the channel decomposition attack method(CDAM).It can attack multiple black-box models by enhancing the transferability of the adversarial *** the one hand,it tunes the gradient and stabilizes the update direction by decomposing the channels of the input example and calculating the aggregate *** the other hand,it helps to escape from local optima by initializing the data point with random ***,it could combine with other transfer-based attacks *** experiments on the standard ImageNet dataset show that our method could significantly improve the transferability of adversarial *** with the state-of-the-art method,our approach improves the average success rate from 88.2%to 96.6%when attacking three adversarially trained black-box models,demonstrating the remaining shortcomings of existing deep learning models.

关键词： Adversarial attack transferability black-box models deep learning

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Knowledge-based recommendation with contrastive learning

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High-Confidence Computing 2023年第4期3卷 41-46页

作者： Yang He Xu Zheng Rui Xu Ling Tian School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Kash Institute of Electronics and Information Industry Kashgar 844199China China Electronic Technology Cyber Security Co. Ltd.Chengdu 610041China Cyberspace Security Key Laboratory of Sichuan Province Chengdu 610041China

Knowledge Graphs(KGs)have been incorporated as external information into recommendation systems to ensure the high-confidence ***,Contrastive Learning(CL)framework has been widely used in knowledge-based recommendation,owing to the ability to mitigate data sparsity and it considers the expandable computing of the ***,existing CL-based methods still have the following shortcomings in dealing with the introduced knowledge:(1)For the knowledge view generation,they only perform simple data augmentation operations on KGs,resulting in the introduction of noise and irrelevant information,and the loss of essential information.(2)For the knowledge view encoder,they simply add the edge information into some GNN models,without considering the relations between edges and ***,this paper proposes a Knowledge-based Recommendation with Contrastive Learning(KRCL)framework,which generates dual views from user–item interaction graph and ***,through data enhancement technology,KRCL introduces historical interaction information,background knowledge and item–item semantic ***,a novel relation-aware GNN model is proposed to encode the knowledge ***,through the designed contrastive loss,the representations of the same item in different views are closer to each *** with various recommendation methods on benchmark datasets,KRCL has shown significant improvement in different scenarios.

关键词： Knowledge graph Recommendation systems Contrastive learning Graph neural network

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Junction density based clustering algorithm for data with arbitrary shapes 2022

Junction density based clustering algorithm for data with ar...

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14th International Conference on Machine Learning and Computing, ICMLC 2022

作者： Li, Ruijia Cai, Zhiling Wu, Hong Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9781450395700

Density-based clustering algorithms can deal with arbitrary shaped clusters in data. However, most of these algorithms face difficulties in handling large scale data, since they usually need to compute the distance between each pair of data points for density estimation. To alleviate this problem, we define a new type of density called junction density to measure the density of the junction region of two groups generated by K-means. Since the junction density is only computed for neighboring groups, the computation burden is small. Based on the junction density, we propose a new clustering method to merge the groups instead of directly clustering the data points. Specifically, it mines initial clusters in the groups then assigns the remaining groups to corresponding initial clusters. The experiments on several arbitrary shaped datasets demonstrate the efficiency and effectiveness of the proposed method. © 2022 ACM.

关键词： Machine learning

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Detection of Plant Disease Using Machine Learning and Deep Learning Algorithms

Detection of Plant Disease Using Machine Learning and Deep L...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Kalyani Gollapudi Preethi Varigonda S. Jancy S. PushpaLatha Viji Amutha Mary.A Suji Helen Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Data Analytics Saveetha School of Engineering SIMATS Chennai

Agriculture performs an critical position in India's economic system. Early detection of plant illnesses is critical to save you crop damage and similarly spread of diseases. Most plants, along with apple, tomato, cherry, grape, show symptoms of leaf ailment. These visible patterns may be found to correctly predict the disorder and take early movement to save you it. This can be triumph over with system getting to know and deep getting to know algorithms. We therefore recommend a method that determines tomato plant disease from pix of leaves. This method is performed with aid vector device (SVM), random woodland gadget studying algorithm, and look at algorithms Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and ResNet that is one of the switch learning techniques. Snoring. After the facts set is processed by way of the algorithms, the accuracy of the algorithms is compared and the snapshots are categorized.

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An Investigation of Blockchain-Based Sharding 7th

An Investigation of Blockchain-Based Sharding

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Xiao, Jiahong Liang, Wei Cai, Jiahong Zhu, Hangyu Li, Xiong Xie, Songyou School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9783031281235

Nowadays, blockchain distributed ledger technology is becoming more and more prominent, and its decentralization, anonymization, and tampering obvious features have been widely recognized. These excellent technical features of blockchain have also made it a hot issue for global research. With the wide application of blockchain technology in various industries, some defects are gradually exposed, and more prominently, the blockchain system is unable to meet the current demand of explosive growth of data volume and frequent data interaction. As one of the key technologies to solve this problem, sharding technology is gaining attention. This article introduces common blockchain scaling schemes and focuses on an overview of blockchain sharding. Sharding technology is introduced from two perspectives of intra-slice consensus and inter-slice consensus. The current mainstream slicing technology is summarized according to three different slicing methods: network sharding, transaction sharding, and state sharding. Finally, the challenges faced by current blockchain sharding technology are analyzed and the full text is summarized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Random Forests for Predicting Diabetes Progression and Complications 3

Random Forests for Predicting Diabetes Progression and Compl...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ranganathan, Chitra Sabapathy Nandhini, R. Nisha, K. Sumathi, G. Sudhakar, G. Srinivasan, C. Mphasis Corporation Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

The main objective of the presented work in this paper is to use Random Forests as an alternative biomarker for predicting the development of diabetes. Identifying high-risk patients is crucial for improving patient outcomes and reducing healthcare expenses. The proposed system improves diabetes treatment via the use of predictive analytics, which promotes preventative and early intervention-focused healthcare practices. It helps manage the risks associated with diabetes by using machine learning, which is part of a larger movement towards precision medicine. This method shows promise to enhance long-term health outcomes, optimize resource allocation, and provide personalized treatment to patients. Findings from the Pima Indians Diabetes database quantify the risks of diabetes and patient characteristics. Blood pressure, glucose, insulin, and body mass index are all taken from each patient. The dangers of retinopathy, neuropathy, and nephropathy are also numerically encoded, with 0 indicating minimal risk, 1 medium risk, and 2 high risks. HbA1c is also included. The blood pressure ranges from 115/78 to 130/85mmHgA1c levels, the body mass index (BMI) from 26 to 32 mm, and the glucose level from 110 to 140 mm Hg. © 2024 IEEE.

关键词： Blood pressure

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Glaucoma Detection Using Convolutional Neural Network (CNN) 20

Glaucoma Detection Using Convolutional Neural Network (CNN)

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Tohye, Tewodros Gizaw Qin, Zhiguang Leka, Habte Lejebo Hundera, Negalign Wake School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua China

ISBN: (纸本)9798350318982

Today, glaucoma detection is a severe problem. Because it was predicted that there would be 111.8 million cases of glaucoma worldwide by 2040, up from the expected 76.0 million cases in 2020. Blindness results if early treatment is not given. The CNN model has been used in a variety of studies to identify glaucoma. The results obtained thus far are insufficient to reliably identify glaucoma. In order to detect glaucoma, which is a binary classification, we are utilizing the CNN model. We divided the REFUGUE data set into tanning and testing, with 80% of the photos being utilized for tanning and 20% for testing. Our model's accuracy score was 99.8% for the training set and 95.4% for the testing set. We get result of precision=98% and recall= 97%. We compare the model with other state of arts (SOTA) our experimental results reveal that, the proposed model for glaucoma detection using convolutional neural network is more proficient in glaucoma classification. © 2023 IEEE.

关键词： Convolution

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Traceable Attribute-Based Keyword Search Scheme for Distributed data Storage under Fog Computing-Based Social Internet of Vehicles

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Gu, Ke Qiu, Juan Peng, Xin Li, Xiong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China University of Electronic Science and Technology of China Institute for Cyber Security School of Computer Science and Engineering Chengdu611731 China

In Social Internet of Vehicles (SIoVs), vehicles frequently need to obtain their required data, thus many secure data acquisition schemes were proposed in cloud computing-based SIoVs. However, the distributed data storage method under fog computing-based SIoVs can bring about more security issues, such as potential data leakage, unauthorized access and integrity concerns. To address these challenges, we propose a traceable attribute-based keyword search scheme for distributed data storage based on SIoVs under fog computing in this paper. According to the Shamir's secret-sharing scheme, our proposed scheme provides a distributed data block verification participated by multiple fog servers to check the effectiveness of distributed data storage. In our scheme, a legal data-owned vehicle can divide its encrypted data into many data blocks, and securely upload these data blocks and related keywords to multiple fog servers;a data-required vehicle can generate a trapdoor on the keywords of required data, and then decrypt the data blocks received from related fog servers. Further, the key generation center can trace the identity of a suspicious data-owned vehicle according to related data when it is confirmed that there is false traffic data uploaded by the suspicious data-owned vehicle. Under the proposed security models, our proposed scheme is proved to secure stored data and keyword search in fog computing-based SIoVs. Additionally, experimental results show that our scheme is effective. © 2015 IEEE.

关键词： Information leakage

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