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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71-80 订阅

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Outsourced and Robust Multi-party Computation with Identifying Malicious Behavior and Application to machine learning 19th

Outsourced and Robust Multi-party Computation with Identifyi...

引用

19th international conference on Information Security Practice and Experience

作者： Qin, Hong He, Debiao Feng, Qi Yang, Xiaolin Luo, Qingcai Wuhan Univ Sch Cyber Sci & Engn Key Lab Aerosp Informat Secur & Trusted Comp Minist Educ Wuhan Peoples R China Inspur Cloud Informat Technol Co Ltd Jinan Peoples R China Inspur Acad Sci & Technol Jinan Peoples R China

ISBN: (纸本)9789819790524;9789819790531

This work proposes an efficient n-party computation framework with malicious security in the outsourced case. We consider a non-symmetrical trust setting that n - 1 parties, including P-0, is semi-honest and one party is malicious. Once the party in {P-1, P-2, ..., Pn-1} misbehaves, we will caught and exclude it, achieving robustness by executing the further computations among semi-honest parties. Unlike previous works that focused on traditional robustness, we do not need to delegate the computation to an identified honest party, which contradicts the usual expectation of privacy and hinder the adoption in practice. Our framework bridges the gap between semi-honest and malicious honest-majority multi-party protocols, and is well suited for privacy-preserving machine learning. In general, machine learning depends on large amounts of data and powerful computing power, leading resource-constrained clients to resort to secure outsourced computing (SOC) approach. In this work, we utilize the n-party framework to design efficient building blocks for machine learning. We significantly improve the efficiency of blocks and implement machine learning inference in LAN and WAN setting, respectively. Theoretical analysis and experimental results show that our framework outperforms the existing honest-majority protocols, such as SWIFT (USENIX Security'21) in terms of "robustness" and Fantastic Four (USENIX Security'21) in terms of efficiency.

关键词： multiparty computation machine learning robustness secure outsourced

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Detection of Medicinal Plants Using machine learning 12th

Detection of Medicinal Plants Using Machine Learning

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12th international conference on Recent Trends in computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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MuSS: Multimodal Satellite Service for Unsupervised Land-Cover Classification 22nd

MuSS: Multimodal Satellite Service for Unsupervised Land-Cov...

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22nd international conference on Service Oriented computing

作者： Gacha, Yassine Besbes, Olfa Abdellatif, Takoua Univ Carthage SERCOM Lab Carthage 1054 Tunisia Mil Res Ctr Aouina 2045 Tunisia Sousse Univ SITCOM Sousse 4002 Tunisia Sousse Univ ENISO Sousse 4002 Tunisia

ISBN: (纸本)9789819608072;9789819608089

Remote sensing data encompasses multiple modalities, including multispectral images, Synthetic Aperture Radar (SAR), Digital Elevation Model (DEM), and more. Each type of data possesses unique properties and characteristics. Preparing a dataset for machine learning that includes these modalities is challenging and requires specific geospatial data expertise. In this paper, we introduce a novel service, MuSS, for multimodal remote sensing data processing and analysis. MuSS dynamically prepares datasets based on user specifications, leveraging Google Earth Engine (GEE) to seamlessly access and process diverse satellite data. Using the MuSS-prepared multimodal satellite dataset, we propose an unsupervised deep learning model for pixel-level land-cover classification. Unlike traditional methods that require labeled data, which is difficult to gather in remote sensing, our model utilizes derived indicators to accurately distinguish water and other surfaces without labeled training data. Our service is particularly beneficial for agriculture and assessing climate change impacts, yet its configurable ETL pipeline ensures broad applicability. Experimental results demonstrate that our approach significantly outperforms traditional baseline methods, highlighting the service's efficacy and versatility.

关键词： Multimodal satellite service Configurable ETL pipeline Unsupervised Deep learning framework Land-cover classification

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615247

The proceedings contain 131 papers. The special focus in this conference is on Algorithms and Architectures for Parallel Processing. The topics include: MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework;SSC: An SRAM-Based Silence computing Design for On-chip Memory;TP-BFT: A Faster Asynchronous BFT Consensus with Parallel Structure;LTP: A Lightweight On-Chip Temporary Prefetcher for data-Dependent Memory Accesses;A Neural Network-Based PUF Protection Method Against machine learning Attack;Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs;multidimensional Intrinsic Identity Construction and Dynamic Seamless Authentication Schemes in IoT Environments;invisible Backdoor Attack with Image Contours Triggers;finestra: Multi-aggregator Swarm learning for Gradient Leakage Defense;DIsFU: Protecting Innocent Clients in Federated Unlearning;multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated learning;dynamic Privacy Protection with Large Language Model in Social Networks;a Dynamic Symmetric Searchable Encryption Scheme for Rapid Conjunctive Queries;a data Watermark Scheme Base on data Converted Bitmap for data Trading;distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc Networks;mitigating Over-Unlearning in machine Unlearning with Synthetic data Augmentation;AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection;efficient and Privacy-Preserving Ranking-Based Federated learning;on-Chain Dynamic Policy Evaluation for Decentralized Access Control;DPG-FairFL: A Dual-Phase GAN-Based Defense Framework Against Image-Based Fairness data Poisoning Attacks in Federated learning.

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615278

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615414

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615476

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machine learning Techniques to Detect Fake News on Social Media: A Systematic Review 12th

Machine Learning Techniques to Detect Fake News on Social Me...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Maseko, Ngwato Thomas Chikati, Ronald Moeti, Michael van Vuuren, Joey Jansen Faculty of Computer Science Tshwane University of Technology Tshwane South Africa

ISBN: (纸本)9789819601462

The rapid growth in social media as the first point of reference for the latest news and information has made the proliferation of fake news on social media platforms a persistent issue that has significant consequences for brands. Fake news often preys on human beings’ emotions and biases, with the ability to spread rapidly. The spread of misinformation can lead to reputational damage, create uncertainty, and lead to psychiatric illness. Humans are unable to distinguish between real and fake news online instantaneously. machine learning has emerged as a promising tool for detecting fake news online because it can analyse large amounts of data and identify trends or patterns that are indicative of misleading information. Thus, the use of machine learning techniques can aid in the detection of fake news on social media. This paper is a systematic literature review on machine learning techniques that can detect fake news on social media. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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FedRFD: Heterogeneous Federated learning via Refining Feature-level Distillation 24

FedRFD: Heterogeneous Federated Learning via Refining Featur...

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

作者： Yu, Peng Yuan, Yuwen Wang, Youquan Shi, Wei Shi, Zhihui Hua, Yue College of Information Engineering Nanjing University of Finance and Economics Jiangsu Nanjing China School of Computer Science and Engineering Taizhou Institute of Sci.&Tech. NJUST Jiangsu Taizhou China College of Liberal Arts and Sciences University of Illinois at Chicago ChicagoIL United States Department of Financial Technology Jiangsu Sushang Bank Co. Ltd. Jiangsu Nanjing China School of Marxism Jiangsu University Jinagsu Zhenjiang China

ISBN: (纸本)9798400717482

Federated learning faces a significant challenge in effectively aggregating knowledge from diverse local models with varying learning capabilities while preserving privacy through collaborative, local updates on private data. Previous methods for addressing such challenges involve combining distilled logits-based knowledge from various local models using weights derived from their respective learning capabilities as weighting coefficients. However, logits-based knowledge distillation suffers from information loss, expression diversity limitations, and model dependency, leading to a less affluent knowledge transfer. Therefore, we propose heterogeneous federated learning via refining feature-level distillation (FedRFD), which leverages richer knowledge of intermediate layer features for distillation. Specifically, (1) To facilitate communication between heterogeneous models, we align their feature-level with public data, eliminating the need for an additional global model. (2) To address feature alignment challenges in models with varying feature-level numbers, we propose the knowledge refining mechanism (KRM). (3) To increase the efficiency of collaborative learning, we propose a novel method to dynamically assign weights to each client. Extensive experiments confirm that our approach significantly outperforms state-of-the-art algorithms in heterogeneous model scenarios. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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A Novel Ensemble learning Approach for Diabetes Prediction in Imbalanced datasets 13

A Novel Ensemble Learning Approach for Diabetes Prediction i...

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13th international conference on Research in computing at Feminine, RIF 2024

作者： Boughareb, Djalila Bouteldja, Said Seridi, Hamid Department of Computer Science LabStic Laboratory University 8 May 1945 Guelma Algeria Department of Computer Science University of 8 May 1945 Guelma Algeria

The incapacity of the body to effectively make or use insulin results in diabetes, a chronic illness. Over time, this illness may cause harm to the kidneys, blood vessels, heart, eyes, nerves, and kidneys. Timely treatment is essential to stop the progression of diabetes and requires early detection. We provide a hybrid machine learning strategy in this work that predicts diabetes by combining two strong algorithms. XGBoost (eXtreme Gradient Boosting)-based voting classifier and bagging classifier are the two main components of our system. We evaluated our model using three distinct datasets: the Pima Indian diabetes dataset (PIDD), its extended version, and the Frankfurt Hospital Germany Diabetes dataset (FHGDD). In comparison to individual algorithms (XGBoost, Bagging with Decision Tree) and other ensemble methods (Voting Classifier, HM-Bag Moov Voting Classifier, XGBoost+ data feature stitching, and Soft), our experimental results show that our proposed approach achieved higher accuracy of 92.7%, precision of 97.1%, recall of 81.7%, and an F1 score of 88.7%%. Therefore, our results imply that the hybrid machine learning approach that has been suggested can be a dependable tool for the early diagnosis of diabetes, resulting in better patient outcomes and more prompt and efficient therapies. © 2024 Copyright for this paper by its authors.

关键词： Bagging Decision Tree Diabetes Prediction Ensemble learning FHGDD machine learning Pima XGBoost

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