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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101-110 订阅

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An Efficient Federated Meta Unlearning Algorithm with Enhanced Privacy Protection 20th

An Efficient Federated Meta Unlearning Algorithm with Enhan...

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Wang, Yani Ying, Zuobin Pan, Zijie Zhu, Enmin Zhou, Wanlei City University of Macau Macau S.A.R. Taipa China University of Macau Macau S.A.R. Taipa China

ISBN: (纸本)9789819628636

In machine learning, the ability for models to selectively forget specific data is increasingly crucial. Traditional "machine unlearning" methods often involve extensive retraining, which is computationally expensive and impractical for real-time applications. This paper introduces Federated Meta Unlearning (FMU), a novel approach within the Federated learning (FL) framework that merges the adaptability of meta-learning with the precision of machine unlearning. FMU utilizes a modified Model-Agnostic Meta-learning algorithm, employing model decomposition and weighted aggregation techniques. This approach enables efficient data influence removal while minimizing impact on overall model performance. The key innovation of FMU is the decomposition of the federated model into adjustable components, allowing targeted modifications or removal based on unlearning requests, followed by weighted re-aggregation to maintain model integrity and flexibility. Extensive experiments across diverse datasets show that FMU reduces the computational burden of traditional unlearning while preserving high model performance and adhering to privacy standards. Additionally, FMU enhances adaptability to new data and tasks, making it suitable for dynamic and privacy-sensitive settings. In summary, FMU offers a substantial advancement in addressing the challenge of selective forgetting in machine learning. © IFIP international Federation for Information Processing 2025.

关键词： Federated learning

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dataset Distribution Impacts Model Fairness: Single Vs. Multi-task learning 2nd

Dataset Distribution Impacts Model Fairness: Single Vs. Mult...

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2nd international Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd international Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the international conference on Medical Image computing and Computer Assisted Interventions, MICCAI 2024

作者： Raumanns, Ralf Schouten, Gerard Pluim, Josien P. W. Cheplygina, Veronika Fontys University of Applied Science Eindhoven Netherlands Eindhoven University of Technology Eindhoven Netherlands IT University of Copenhagen Copenhagen Denmark

ISBN: (纸本)9783031727863

The influence of bias in datasets on the fairness of model predictions is a topic of ongoing research in various fields. We evaluate the performance of skin lesion classification using ResNet-based CNNs, focusing on patient sex variations in training data and three different learning strategies. We present a linear programming method for generating datasets with varying patient sex and class labels, taking into account the correlations between these variables. We evaluated the model performance using three different learning strategies: a single-task model, a reinforcing multi-task model, and an adversarial learning scheme. Our observations include: 1) sex-specific training data yields better results, 2) single-task models exhibit sex bias, 3) the reinforcement approach does not remove sex bias, 4) the adversarial model eliminates sex bias in cases involving only female patients, and 5) datasets that include male patients enhance model performance for the male subgroup, even when female patients are the majority. To generalise these findings, in future research, we will examine more demographic attributes, like age, and other possibly confounding factors, such as skin colour and artefacts in the skin lesions. We make all data and models available on GitHub. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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SLIN: A CPU-efficient, Hybrid Tree and Learned Index for String data 24

SLIN: A CPU-efficient, Hybrid Tree and Learned Index for Str...

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8th international conference on computing and data Analysis, ICCDA 2024

作者： Song, Yuanyuan Cai, Miao Ye, Baoliu Cheng, Guo Hohai University Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University Nanjing China

ISBN: (纸本)9798400710445

The learned index is a disruptive technique for big data management. The core idea of the learned index is to retrofit the data indexing in traditional index structures with machine learning (ML) models, providing fast data position prediction instead of slow index tree traversal. Yet, current learned indexes focus on fix-sized numeric data and all of them perform poorly for variable-length string data for two main reasons: 1) high computational overheads caused by lengthy string key prediction;2) large ML model fitting errors due to variance in string prefixes. This paper introduces SLIN, a CPU-efficient, hybrid index for variable-length string data. SLIN features a hybrid tree and learned index structure that leverages the strength of each index to overcome the above-mentioned challenges. It proposes a string slice approach which converts variable-length strings into fix-sized integers, which facilitates using one-dimensional linear regression for key prediction to reduce computational overheads. Furthermore, it extracts the common prefix from string keys to decrease model fitting errors. Finally, we propose a strategy to adaptively choose the appropriate index to manage computational costs. Evaluations compare SLIN with state-of-the-art index structures. The experimental results show that SLIN achieves up to 2.47x higher throughputs and relatively small fitting errors. © 2024 Copyright held by the owner/author(s).

关键词： Trees (mathematics)

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

12th International Conference on Frontiers of Intelligent Co...

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

ISBN: (纸本)9789819601387

The proceedings contain 49 papers. The special focus in this conference is on Frontiers of Intelligent computing: Theory and Applications. The topics include: Nepali Word Spelling Correction Using Ensemble learning Technique;the Impact of Information Security Policies and Innovations in Digital Technology on the Transformation of Healthcare in Developing Nations: A Literature Review;integrating Multi-omics and Clinical Narratives for Predictive Modeling: Genomics, Transcriptomics, Proteomics, and Medical Texts in Disease Analysis;a Comprehensive Survey of Fake Review Detection Technology;a Hybrid Intelligent Decision Support System for Automating Financial Credit Evaluation;Advancing Taxation in the New Era: Enhancing Tax Ratios with the Core Tax Administration System (CTAS);Enabling Grid Stability: Harnessing μPMU data for data-Driven Analysis of Grid Frequency Events;machine learning Techniques to Detect Fake News on Social Media: A Systematic Review;cutting-Edge Technology-Based Social Enterprises in India for Sustainable, Inclusive Healthcare;enhancing Telugu Sarcasm Classification Models with Word Embeddings in Imbalanced datasets;capital Punishment: Analyzing Trends in the United States (1976–2016);exploring the Technostress Issue Among Indonesian Young Entrepreneurs;Teaching Computer science Using Cloud-Based IDE with Perspectives in Inclusivity;Comparison of data Encryption Standard (DES) and Advanced Encryption Standard (AES) in Security Issues of Cloud computing: A Literature Review;enhancing Corporate and Factory Training Through Game Development: A Comparative Review;usability of Intelligent System in Implementing Tutoring Lesson in Education: A Literature Review;performance Efficiency of Cloud computing—A Literature Review;decentralized Finance (DeFi) Wallets: A Review of its Efficiency, Usability, and Effectiveness;Social Networks on WEB 3.0.

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

12th International Conference on Frontiers of Intelligent Co...

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

ISBN: (纸本)9789819601424

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

12th International Conference on Frontiers of Intelligent Co...

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

ISBN: (纸本)9789819601462

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Optimizing Spectral Graph Kernels for Time-Delay Embedding of Multichannel EEG data 24

Optimizing Spectral Graph Kernels for Time-Delay Embedding o...

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8th international conference on Algorithms, computing and Systems, ICACS 2024

作者： Muralinath, Rashmi Nagawara Pathak, Vishwambhar Mahanti, Prabhat K. Department of Computer Science University of New Brunswick Saint JohnNB Canada Department of Computer Science Birla Institute of Technology Mesra Rajasthan Jaipur India

ISBN: (纸本)9798400718304

The task of determining the dynamic patterns of causal connectivity among brain regions from EEG data is challenging due to inherent noise in EEG signals, sparse connections. High volume and dimensionality of data add substantial computational demands. Koopman embedding kernels offer a promising approach for capturing the low-dimensional linear embeddings of complex nonlinear system dynamics. This study focuses on leveraging the Graph Kernel Koopman Embedding (GKKE) method to extract latent factors, a novel application within the realm of EEG analysis. Specifically, the research pioneers the adaptation of the GKKE algorithm to estimate the inherent connectivity substructures, that enable investigations of the neuro-markers of cognitive load states and disorders like seizures from EEG signals. The graph for each segment was constructed by computing the correlation coefficients among the channel pairs. The algorithmic workflow was applied to a labelled cognitive load dataset from an online repository. The features were extracted in terms of Koopman-spectral decomposed clusters of eigenfunctions of the gram matrices produced by two different graph kernels applied for approximation of the Koopman operator over each EEG-segment. These features, representing the latent metastable states, were fed into machine learning classifiers, including SVM and Random Forest, and Decision Tree classifiers. Weisfeiler-Lehman Kernel (WL)-Kernel yielded an average accuracy of around 84% for all the classifiers and outperformed the Random-walk kernel. The significance of this work lies in its pioneering use of graph Koopman spectral decomposition-based low-dimensional embedding of the metastable structures of connectivity to provide an algorithm that enables neuro-marker estimation. Specific contribution is the workflow that determines the optimal combination of the graph kernel and classifier ensemble. This equips deeper understanding of the connectivity dynamics of brain regions related

关键词： Decision trees

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Evaluating the Effectiveness of machine learning Algorithms for Network Intrusion Detection 4th

Evaluating the Effectiveness of Machine Learning Algorithms ...

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4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Chandu, Sri Vasavi Anumula, Rajesh Reddy Chandu, Phaneendra Varri, Uma Sankararao SRM Univ AP Guntur Andhra Pradesh India

ISBN: (纸本)9783031837821;9783031837838

Network security is essential in the linked world of today because critical information systems are the target of an increasing number of malicious attacks and cyberthreats. Intrusion detection systems, or IDS, are primarily responsible for protecting networks against these types of attacks. Through the examination of network traffic characteristics, machine learning algorithms have developed into useful instruments for enhancing intrusion detection systems' capabilities. In this paper, we provide a comparison of several machine learning techniques for network intrusion detection. We investigate Decision Trees, K-Nearest Neighbors, AdaBoost, Gaussian Naive Bayes, Random Forest, Logistic Regression, and Gradient Boosting in identifying and categorizing network intrusions. For this study, we employ a publicly available network traffic dataset. We evaluate the effectiveness of each technique through multiple experiments, measuring its computational efficiency, accuracy, precision, recall, and F1 score. Our study illustrates the benefits and drawbacks of these algorithms as well as their suitability for use in different intrusion detection scenarios. This study also highlights the significance of selecting appropriate machine learning algorithms that are tailored to the properties of network traffic data to increase the resilience of network security infrastructures.

关键词： Network Intrusion Detection machine learning Comparative Analysis Network Security Network Traffic Features

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The Applications and Research of NLP and Deep learning Technologies in Public Security data 24

The Applications and Research of NLP and Deep Learning Techn...

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8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhu, Yupeng Zhu, Yuhao College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China The Smart City Research Institute of China Electronics Technology Group Corporation Guangdong Shenzhen China

ISBN: (纸本)9798400710094

With the deepening reform and construction of the police system, it is imperative to fully utilize technologies such as artificial intelligence, big data, blockchain, cloud computing, and the Internet of Things to rapidly enhance the intelligence level of the new generation command center. Natural language processing and deep learning technologies can achieve recognition, understanding, and correlation analysis of public security textual information, which are core steps in building intelligence. Due to the diversity of information in the public security field, the accuracy of these technologies still requires further research. In the analysis of public security incidents, the application of NLP technology faces challenges in optimizing technical models and algorithms, which greatly enhances the efficiency and accuracy of public safety management by analyzing, understanding, predicting, and responding to various security challenges in an intelligent manner. With the advent of the big data era, the public security sector has accumulated massive amounts of police incident data, which contain rich information but also bring difficulties in processing and analysis. Traditional NLP models often struggle to cope with the complex and ever-changing police incident texts, leading to compromised accuracy and reliability of the analysis results. This paper will explore the key technological applications of NLP and deep learning in the field of public safety and provide a review of their research progress. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Enhancing Sustainable Supply Chain Forecasting Using machine learning for Sales Prediction 4

Enhancing Sustainable Supply Chain Forecasting Using Machine...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Rahman Mahin, Md. Parvezur Shahriar, Munem Das, Ritu Rani Roy, Anuradha Reza, Ahmed Wasif Department of Computer Science East West University Dhaka1219 Bangladesh

Managing the supply chain is crucial to success in the competitive business sector. Demand forecasting using sales data is one of the major things in supply chain management because it is directly connected to profit margins, inventory levels, sales, and customer satisfaction. This research tried to provide an innovative approach to sales prediction using advanced machine learning methods to enhance supply chain operations and boost the predictive accuracy of supply chain models after analyzing historical sales data and considering different factors like seasonality, trends, and stock. Various machine learning algorithms were applied, including Linear Regression, Elastic Net Regression, KNN, Random Forest, and the ensemble Voting Regressor. The performance of Random Forest and KNN is very well but the Voting Regressor is better than other models for its strength of multiple algorithms. The Voting Regressor provides the lowest RMSE of 1.54 and the highest R2 of 0.9999. This ensemble method improves sales forecasting accuracy by reducing errors and ensuring computational efficiency. It also provides more reliable tools to manage inventory, prevent overstocks, and minimize holding costs. This research presents the importance of machine learning integration in supply chain management. It shows the Voting Regressor as the most effective approach for demand forecast. Future research could explore the model's application in broader markets, integrating other key factors and deep learning algorithms to refine predictive capabilities later. © 2025 The Authors.

关键词： Adversarial machine learning

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