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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33684 条记录，以下是211-220 订阅

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Applying machine learning Tools for Urban Resilience Against Floods 5

Applying Machine Learning Tools for Urban Resilience Against...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Pour, Mahla Ardebili Ghiasi, Mohammad B. Karkehabadi, Ali University of California Davis Davis United States University of California Davis Davis United States

ISBN: (纸本)9798350356236

Floods are among the most prevalent and destructive natural disasters, often leading to severe social and economic impacts in urban areas due to the high concentration of assets and population density. In Iran, particularly in Tehran, recurring flood events underscore the urgent need for robust urban resilience strategies. This paper explores flood resilience models to identify the most effective approach for District 6 in Tehran. Through an extensive literature review, various resilience models were analyzed, with the Climate Disaster Resilience Index (CDRI) emerging as the most suitable model for this district due to its comprehensive resilience dimensions: Physical, Social, Economic, Organizational, and Natural/Health resilience. Although the CDRI model provides a structured approach to resilience measurement, it remains a static model focused on spatial characteristics and lacks temporal adaptability. An extensive literature review enhances the CDRI model by integrating data from 2013 to 2022 in three-year intervals and applying machine learning techniques to predict resilience dimensions for 2025. This integration enables a dynamic resilience model that can accommodate temporal changes, providing a more adaptable and data-driven foundation for urban flood resilience planning. By employing artificial intelligence to reflect evolving urban conditions, this model offers valuable insights for policymakers and urban planners to enhance flood resilience in Tehran's district 6. © 2025 IEEE.

关键词： Urban planning

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Reward Generation via Large Vision-Language Model in Offline Reinforcement learning

Reward Generation via Large Vision-Language Model in Offline...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lee, Younghwan Luu, Tung M. Lee, Donghoon Yoo, Chang D. Electrical Engineering KAIST Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of

ISBN: (纸本)9798350368741

In offline reinforcement learning (RL), learning from fixed datasets presents a promising solution for domains where real-time interaction with the environment is expensive or risky. However, designing dense reward signals for offline dataset requires significant human effort and domain expertise. Reinforcement learning with human feedback (RLHF) has emerged as an alternative, but it remains costly due to the human-in-the-loop process, prompting interest in automated reward generation models. To address this, we propose Reward Generation via Large Vision-Language Models (RG-VLM), which leverages the reasoning capabilities of LVLMs to generate rewards from offline data without human involvement. RG-VLM improves generalization in long-horizon tasks and can be seamlessly integrated with the sparse reward signals to enhance task performance, demonstrating its potential as an auxiliary reward signal. © 2025 IEEE.

关键词： Large Vision-Language Model Offline Reinforcement learning Reward Labeling

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Stochastic Multiscale Modeling for Thermal Conductivity in Polymeric Graphene-Enhanced Composites: A Study in Interpretable machine learning 30th

Stochastic Multiscale Modeling for Thermal Conductivity in P...

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30th international conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Liu, Bokai Liu, Pengju Lu, Weizhuo Olofsson, Thomas Intelligent Human-Buildings Interactions Lab Department of Applied Physics and Electronics Umeå University Umeå90187 Sweden Institute of Structural Mechanics Bauhaus-Universität Weimar Marienstr. 15 Weimar99423 Germany

ISBN: (纸本)9783031829062

We've devised an interpretable stochastic integrated machine learning method for predicting thermal conductivity in Polymeric Graphene-Enhanced Composites (PGECs) across different scales. Our approach, integrating techniques like Regression Trees methods (Gradient Boosting machine), handles uncertain parameters via a bottom-up multi-scale framework using Representative Volume Elements in Finite Element Modeling (RVE-FEM). To understand factors affecting predictions, we use the SHapley Additive exPlanations (SHAP) algorithm. This method aligns well with training data, offering promise for efficiently designing new composite materials for thermal management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Thermal conductivity

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A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures 2nd

A Federated Parameter Aggregation Method for Node Classific...

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2nd international conference on data Security and Privacy Protection, DSPP 2024

作者： Song, Hao Yao, Jiacheng Chen, Xuanze Xu, Shaocong Jin, Shibo Zhou, Jiajun Fu, Chenbo Xuan, Qi Yu, Shanqing Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China Binjiang Cyberspace Security Institute of ZJUT Hangzhou310056 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China

ISBN: (纸本)9789819785391

Over the past few years, federated learning has become widely used in various classical machine learning fields because of its collaborative ability to train data from multiple sources without compromising privacy. However, in the area of graph neural networks, the nodes and network structures of graphs held by clients are different in many practical applications, and the aggregation method that directly shares model gradients cannot be directly applied to this scenario. Therefore, this work proposes a federated aggregation method FLGNN applied to various graph federation scenarios and investigates the aggregation effect of parameter sharing at each layer of the graph neural network model. The effectiveness of the federated aggregation method FLGNN is verified by experiments on real datasets. Additionally, for the privacy security of FLGNN, this paper designs membership inference attack experiments and differential privacy defense experiments. The results show that FLGNN performs good robustness, and the success rate of privacy theft is further reduced by adding differential privacy defense methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Enhancing NSCLC Histological Subtype Classification: A Federated learning Approach Using Triplet Loss 27th

Enhancing NSCLC Histological Subtype Classification: A Feder...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Aksu, Fatih Cordelli, Ermanno Gelardi, Fabrizia Chiti, Arturo Soda, Paolo Department of Biomedical Sciences Humanitas University Milan Italy Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Università Vita-Salute San Raffaele Milan Italy IRCCS Ospedale San Raffaele Milan Italy Department of Diagnostics and Intervention Radiation Physics Umeå University Umeå Sweden

ISBN: (纸本)9783031876592

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, with Non-Small Cell Lung Cancer (NSCLC) accounting for approximately 85% of all cases. Accurate histological subtype classification of NSCLC is crucial for personalized treatment planning and improving patient outcomes. Developing robust classification models for NSCLC subtypes often requires large, diverse datasets, which can be challenging to obtain due to privacy concerns and data silos. This study proposes an approach combining federated learning with triplet loss to address these challenges. We evaluated our method’s performance in classifying NSCLC subtypes using data from multiple institutions while preserving privacy. Our experiments compared the proposed federated learning approach with triplet loss against alternative methods, including local training and softmax loss. Results demonstrated that our federated learning approach with triplet loss consistently outperformed other methods across key metrics. The combination of federated learning and triplet loss showed synergistic effects, leveraging external datasets to improve model performance while maintaining data confidentiality. The source code for the implementation described in this paper is available at https://***/aksufatih/federated-triplet-histology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Dynamic Dual-Strategy Update of Offline-to-Online Reinforcement learning for Optimal Energy System Scheduling 2nd

Dynamic Dual-Strategy Update of Offline-to-Online Reinforcem...

引用

2nd international conference on Smart Electrical Grid and Renewable Energy, SEGRE 2024

作者： Lin, Zhiyao Yang, AiHui Liu, Li Li, Lin Xiamen University of Technology Jimei District 600 Ligong Road Fujian Xiamen361024 China

ISBN: (纸本)9789819619641

In modern power systems, efficient energy scheduling is essential for ensuring system stability and optimizing resource allocation. At present, Deep Reinforcement learning (DRL) algorithms perform well in continuous action spaces, but they face challenges such as high online learning costs and poor offline learning performance. To address these challenges, this paper proposes a Dynamic Dual-Strategy Update (DDSU) DRL that combines Twin Delayed Deep Deterministic Policy Gradient (TD3) and Generative Adversarial Imitation learning (GAIL) technologies. By dynamically updating the agent’s strategy, DDSU enables parallel training of offline and online reinforcement learning, thereby enhancing the efficiency and accuracy of energy system scheduling. Experimental results show that DDSU significantly improves the convergence speed and final performance in the energy system scheduling task compared to the TD3 algorithm, in terms of reward acquisition, cost control, and system stability. These results demonstrate the potential of DDSU in enhancing the efficiency and accuracy of power system scheduling. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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FedDCT: A Dynamic Cross-Tier Federated learning Framework in Wireless Networks 18th

FedDCT: A Dynamic Cross-Tier Federated Learning Framework in...

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18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Xian, Youquan Gan, Xiaoyun Yao, Chuanjian Li, Dongcheng Wang, Peng Liu, Peng Zhao, Ying Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin54104 China School of Computer Science and Engineering Guangxi Normal University Guilin54104 China School of Business Guilin University of Electronic Technology Guilin54104 China

ISBN: (纸本)9783031714634

Federated learning (FL), as a privacy-preserving machine learning paradigm, trains a global model across devices without exposing local data. However, resource heterogeneity and inevitable stragglers in wireless networks severely impact the efficiency and accuracy of FL training. In this paper, we propose a novel Dynamic Cross-Tier Federated learning framework (FedDCT). Firstly, we design a dynamic tiering strategy that dynamically partitions devices into different tiers based on their response times and assigns specific timeout thresholds to each tier to reduce single-round training time. Then, we propose a cross-tier device selection algorithm that selects devices that respond quickly and are conducive to model convergence to improve convergence efficiency and accuracy. Experimental results demonstrate that the proposed approach under wireless networks outperforms the baseline approach, with an average reduction of 54.7% in convergence time and an average improvement of 1.83% in convergence accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Research on the Impact of Intelligent Manufacturing on Total Factor Productivity in China Based on machine learning Methods 14

Research on the Impact of Intelligent Manufacturing on Total...

引用

14th international conference on Logistics and Systems engineering

作者： Guan, Heshan Bao, Qin Teng, Mingyan Wang, Qian School of Economics Management and Law University of South China Hunan Hengyang421001 China

ISBN: (纸本)9798331314347

Intelligent manufacturing is the main direction of the construction of manufacturing power, the improvement of China's manufacturing quality level is closely related to the degree of development of intelligent manufacturing. This paper selects data from 31 provinces in China, excluding Hong Kong, Macao and Taiwan, for the period 2014-2022, and empirically examines how the level of intelligent manufacturing development affects total factor productivity in each province through regression models. The results of the study show that, in general, the level of development of intelligent manufacturing has a significant positive effect on total factor productivity, i.e., an increase in the level of intelligent manufacturing can significantly contribute to the overall economic growth of the country;The results of the heterogeneity analysis show that, from the perspective of coastal and non-coastal areas, the level of intelligent manufacturing development has a certain degree of heterogeneity on economic development, and has a significant positive impact on the high-quality development of the coastal region's economy, while the impact on the economic development of the non-coastal region is weak, mainly because the coastal region has a strong technological and human resources advantage, while the non-coastal region lacks a sufficient economic base and innovative capacity that cannot support the development of intelligent manufacturing for the time being;As non-coastal areas strengthen the deep docking with the Beijing-Tianjin-Hebei Region, the Yangtze River Delta, the Guangdong-Hong Kong-Macao Greater Bay Area, undertake the gradient transfer of industries in an orderly manner, and optimise the industrial layout, non-coastal areas are expected to better carry the development of intelligent manufacturing and promote the high-quality development of the economy;By analysing the mechanism with R&D capability as a mediating variable, it is verified that the improvement of the leve

关键词： Smart manufacturing

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2nd international conference on data Science, machine learning and Applications, ICDSMLA 2020

2nd International Conference on Data Science, Machine Learni...

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2nd international conference on data Science, machine learning and Applications, ICDSMLA 2020

ISBN: (纸本)9789811636899

The proceedings contain 157 papers. The special focus in this conference is on data Science, machine learning and Applications. The topics include: Twitter Spam Review Detection Using Hybrid machine learning Techniques;throughput Improvement in Energy Efficient Heterogeneous Wireless Sensor Network;predicting Expected Time of Arrival of Shipments Through Multiple Linear Regression;learning via Long Short-Term Memory (LSTM) Network for Predicting Strains in Railway Bridge Members Under Train Induced Vibration;smart Transportation Using Fog Computing;detection of learning Disability: A Survey;EEG Based Emotion Investigation from Various Brain Region Using Deep learning Algorithm;electric Motor Drive Using Single Input Fuzzy Logic Controller for Husk Extraction in Rice Mill Industry;states Categorization in India Based on Health Security Capacity with machine learning Technique;CovidSORT: Detection of Novel COVID-19 in Chest X-ray Images by Leveraging Deep Transfer learning Models;a Real Time Radio Spectrum Measurement Campaign for machine learning Based Spectrum Inference in Cognitive Radio Network;application of Disruptive Technologies in Intelligent Transportation System;deep learning Models for Rubik’s Cube with Entropy Modelling;performance Analysis of Software Enabled Accelerator Library for Intel Architecture;blind Descent: A Prequel to Gradient Descent;a Survey: Modelling Strategies for Predictive Maintenance;abstract and Image Analysis of High-Temperature Materials from Scientific Journals Using Deep learning and Rule-Based machine learning Approaches;a Time Dependent Epidemiology Model for Hospital Resource Management in Usual Scenarios and Pandemic;Load Forecasting Using LSTM and Its Comparison with Other machine learning Techniques and Their Implementation;application of machine learning to Detect Neuroticism in Individuals Using Handwriting Analysis;preface.

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8th international conference on machine learning, Optimization, and data Science, LOD 2022, held in conjunction with the 2nd Advanced Course and Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022

8th International Conference on Machine Learning, Optimizati...

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8th international conference on machine learning, Optimization, and data Science, LOD 2022, held in conjunction with the 2nd Advanced Course and Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022

ISBN: (纸本)9783031258909

The proceedings contain 85 papers. The special focus in this conference is on machine learning, Optimization, and data Science. The topics include: A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs;brain Structural Saliency over the Ages;Revisiting Neurological Aspects of Relevance: An EEG Study;BioLCNet: Reward-Modulated Locally Connected Spiking Neural Networks;multi-omic data Integration and Feature Selection for Survival-Based Patient Stratification via Supervised Concrete Autoencoders;smart Pruning of Deep Neural Networks Using Curve Fitting and Evolution of Weights;biologically Inspired Unified Artificial Immune System for Industrial Equipment Diagnostic;a Nonparametric Pooling Operator Capable of Texture Extraction;strategic Workforce Planning with Deep Reinforcement learning;preface;pooling Graph Convolutional Networks for Structural Performance Prediction;enhanced Association Rules and Python;generating Vascular Networks: A Reinforcement learning Approach;expressive and Intuitive Models for Automated Context Representation learning in Credit-Card Fraud Detection;digital Twins: Modelling Languages Comparison;algorithms that Get Old: The Case of Generative Deep Neural Networks;a Two-Country Study of Default Risk Prediction Using Bayesian machine-learning;enforcing Hard State-Dependent Action Bounds on Deep Reinforcement learning Policies;a Bee Colony Optimization Approach for the Electric Vehicle Routing Problem with Drones;best Practices in Flux Sampling of Constrained-Based Models;EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python;a Game Theoretic Flavoured Decision Tree for Classification;hierarchical Decentralized Deep Reinforcement learning Architecture for a Simulated Four-Legged Agent;Robust PCA for Anomaly Detection and data Imputation in Seasonal Time Series;monte Carlo Optimization of Liver machine Perfusion Temperature Policies;A Comparison of SVM Against Pre-trained Language Mo

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