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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

共 3589 条记录，以下是81-90 订阅

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25th international conference on Web Information Systems engineering, WISE 2024

25th International Conference on Web Information Systems Eng...

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25th international conference on Web Information Systems engineering, WISE 2024

ISBN: (纸本)9789819605781

the proceedings contain 132 papers. the special focus in this conference is on Web Information Systems engineering. the topics include: TAKE: Tracing Associative Empathy Keywords for Generating Empathetic Responses Based on Graph Attention;intent Identification Using Few-Shot and Active learning with User Feedback;CLIMB: Imbalanced data Modelling Using Contrastive learning with Limited Labels;equivariant Diffusion-Based Sequential Hypergraph Neural Networks with Co-attention Fusion for Information Diffusion Prediction;CL3: A Collaborative learning Framework for the Medical data Ensuring data Privacy in the Hyperconnected Environment;selectivity Estimation for Spatial Filters Using Optimizer Feedback: A Machine learning Perspective;on Adversarial Training with Incorrect Labels;model Lake : A New Alternative for Machine learning Models Management and Governance;a Benchmark Test Suite for Multiple Traveling Salesmen Problem with Pivot Cities;Deconfounded Causality-Aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs;Regularized Multi-LLMs Collaboration for Enhanced Score-Based Causal Discovery;Combining Uncensored and Censored LLMs for Ransomware Generation;therapying Outside the Box: Innovating the Implementation and Evaulation of CBT in therapeutic Artificial Agents;iText2KG: Incremental Knowledge Graphs Construction Using Large Language Models;"Is this Site Legit?": LLMs for Scam Website Detection;Towards Enhancing Linked data Retrieval in Conversational UIs Using Large Language Models;BioLinkerAI: Capturing Knowledge Using LLMs to Enhance Biomedical Entity Linking;Enhancing LLMs Contextual Knowledge with Ontologies for Personalised Food Recommendation;ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources;Web-Based AI Assistant for Medical Imaging: A Case Study on Predicting Spontaneous Preterm Birth via Ultrasound Images;satellite-Driven Deep learning Algorithm for Bathymetry Extraction;Would You Trust an AI Doc

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25th international conference on Web Information Systems engineering, WISE 2024

25th International Conference on Web Information Systems Eng...

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25th international conference on Web Information Systems engineering, WISE 2024

ISBN: (纸本)9789819605750

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25th international conference on Web Information Systems engineering, WISE 2024

25th International Conference on Web Information Systems Eng...

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25th international conference on Web Information Systems engineering, WISE 2024

ISBN: (纸本)9789819605699

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25th international conference on Web Information Systems engineering, WISE 2024

25th International Conference on Web Information Systems Eng...

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25th international conference on Web Information Systems engineering, WISE 2024

ISBN: (纸本)9789819605729

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25th international conference on Web Information Systems engineering, WISE 2024

25th International Conference on Web Information Systems Eng...

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25th international conference on Web Information Systems engineering, WISE 2024

ISBN: (纸本)9789819605668

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems engineering, WISE 2024

PhD Symposium, Posters, Demos, and A Web for more inclusive,...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems engineering, WISE 2024

ISBN: (纸本)9789819614820

the proceedings contain 36 papers. the special focus in this conference is on Web Information Systems engineering. the topics include: Effective Transparent Monitoring of Personal data;iCNN-LSTM: An Incremental CNN-LSTM Based Ransomware Detection System;a Mini Review on Purchaser Security in the Metaverse: Challenges and Solutions;low-Resource dataset Synthetic Generation for Hate Speech Detection;Web Open data to SDG Indicators: Towards an LLM-Augmented Knowledge Graph Solution;leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval;time Distance Aware for Multi-component Graph Collaborative Filtering;scientific Documents Recommendation Based on Graph Convolutional Network;semantic Communication of Images Using Image Generation and Image Captioning Models;leveraging Optimization Techniques for Effective Arabic Query Expansion;DURLLCON: Deep Reinforcement learning for URLLC Optimization in Multi-edge Networks;FMM-RNS: A Fast HMM Map Matching Method Based on Road Network Simplification;Context-Aware Selection of Machine learning as a Service (MLaaS) in IoT Environments;GraphTFD: A Fraud Detection System Based on Graph Transformer;MLGE-AC-UFD: Multi-level Graph Embedding and Approximate Computation for Unsupervised Fraud Detection;SeCORE: Quantitative Security Assurance and Evaluation Platform;developing Geospatial Web Applications Using Question Answering Engines, Knowledge Graphs and Linked data Tools;A Visual Query Builder for DBpedia;FL-PPELA: Partial Parameter Enhancement and Local Adaptive Aggregation for Personalized Federated learning;ORCPM: An Online Regional Core Pattern Mining System;MTRM: A Web-Miner Multi-threshold Mining Co-location Patterns to Mitigate Redundancy;constrained Path Optimization on Time-Dependent Road Networks;prompt Strategies for Sarcastic Meme Detection: A Comparative Analysis;Deepfake Detection in Cancer Medical Imaging Using CNN Architectures;strengthening Cybersecurity: the Influence o

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Scalable Machine learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios 24

Scalable Machine Learning Architectures for IPA-Driven Maint...

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7th international conference on Information Science and Systems, ICISS 2024

作者： Huang, Zhimeng Liu, Xiaodong Romdhani, Imed Shih, Chi-Sheng School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400717567

this research presents a groundbreaking approach to Building Maintenance Management (BMM) by introducing an intelligent Process Automation (IPA)-Driven Building Maintenance Management (IBMM) model. this innovative model harnesses the synergies between Artificial Intelligence (AI), Machine learning (ML), and Internet of things (IoT) technologies to transition from reactive to proactive and predictive building maintenance strategies. the study highlights the critical gap in current BMM practices—the absence of intelligent systems for anticipating and addressing maintenance issues before they escalate. through an extensive literature review, the transformative potential of AI and IoT for enhancing building maintenance management within smart cities is explored, establishing a foundation for the IBMM model’s application. the core of this research lies in its novel application of scalable machine learning architectures to automate and optimize maintenance task allocation in large-scale building portfolios. the practicality of the IBMM model is demonstrated via a proof of concept (POC) in an industrial setting, evidencing its capacity to improve efficiency, reduce costs, and bolster sustainability in building maintenance operations. the model epitomizes a paradigm shift in BMM by integrating IPA, which combines AI and ML, facilitating automated, intelligent decision-making and task allocation. Among its advancements, the IBMM model introduces enhanced predictive maintenance through real-time data analysis, adaptive learning and optimization, automated decision-making, and human-machine collaboration, contributing to energy efficiency and alignment with smart city objectives. the paper delineates the methodology, design, and implementation of a machine learning model for engineer task assignments, culminating in a case study that validates the model’s efficacy. this research not only signifies a significant advancement in BMM by leveraging IPA technologies for autonomous p

关键词： Predictive maintenance

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A Meta-reinforcement learning Framework for Adaptive Quadrotor UAV Attitude Control 25th

A Meta-reinforcement Learning Framework for Adaptive Quadro...

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25th international conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Zhao, Kaidong Li, Yanjie Liu, Zihan Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China School of Mechanical Engineering and Automation The Harbin Institute of Technology Shenzhen Shenzhen518055 China

ISBN: (纸本)9789819642069

this paper presents a meta-reinforcement learning approach for quadrotor UAV attitude control, addressing the limitations of traditional control methods in complex, dynamic, and uncertain environments. Our method integrates the advantages of context learning, Gaussian sampling, and multi-task training to rapidly adapt to diverse aircraft parameters and environmental conditions. Specifically, we utilize historical information quadruples as contextual data to accurately estimate current task characteristics through real-time observation and learning. In the latent space, we employ Gaussian sampling to effectively handle unknown UAV parameters. To validate the method’s efficacy, we conducted a series of experiments in the Bullet simulator, including single attitude tracking, resultant acceleration control, and continuous trajectory tracking. Experimental results demonstrate that our approach significantly outperforms traditional PID controllers and domain randomization-based SAC methods in terms of stability, rapid response, and trajectory tracking precision. through multi-task training, our controller has developed the ability to swiftly adapt to novel situations. this research provides a robust and flexible solution for adaptive control of quadrotor UAVs, showing promise for more efficient and stable flight control in complex and dynamically changing environments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Development Trends of Big data and Artificial Intelligence in Virtual Simulation Technology 14th

Development Trends of Big Data and Artificial Intelligence i...

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14th international conference on Frontier Computing, FC 2024

作者： Yang, Yidong Wang, Lanfeng Wei, Yuhua Guangzhou Huali Science and Technology Vocational College Guangzhou China

ISBN: (纸本)9789819627974

With the development of information technology, big data technology and artificial intelligence have become important directions for informationization in the world today. Big data technology can help people better process, manage, and utilize massive amounts of data, while artificial intelligence can make computers more intelligent, achieving automated and intelligent processing and decision-making. Combining the two can achieve more powerful information processing and application capabilities, with broad application prospects. the application of artificial intelligence in virtual simulation technology has also played a crucial role. Artificial intelligence technology can simulate human intelligent behavior and provide more intelligent support for virtual simulation technology. through artificial intelligence algorithms, virtual simulation can be more automated and intelligent for simulation and prediction, improving the efficiency and accuracy of virtual simulation. In addition, artificial intelligence can also help virtual simulation technology achieve autonomous learning and optimization, continuously improving its performance and functionality. Based on the above issues, this article explores a virtual gesture interaction perception simulation model based on CNN (Convolutional Neural Networks) algorithm, extracts gesture datasets using artificial intelligence algorithms, and estimates gesture coordinates. the experimental results indicate that the sample sizes are 100, 200, and 300. the time for virtual gesture interaction perception based on CNN algorithm is 7.39ms, 10.87ms, and 14.67ms, which are 4.37ms, 5.61ms, and 7.07ms less than RNN (Recurrent Neural Network) algorithm, respectively. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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Coordination for Complex Road Conditions at Unsignalized Intersections: A MADDPG Method with Enhanced data Processing

Coordination for Complex Road Conditions at Unsignalized Int...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Chen, Ruo Zhu, Yang Su, Hongye Ningbo Innovation Center Zhejiang University Ningbo315100 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9789897587450

In this paper, we use deep reinforcement learning to enable connected and automated vehicles (CAVs) to drive in a intersection with human-driven vehicles. the multi-agent deep deterministic policy gradient (MADDPG) algorithm is improved to be more efficient for data processing, so that it can solve the problem of learning bottlenecks in complex environments, and use sliding control to execute control strategies. Finally, the feasibility of the method is verified in the simulation environment of CARLA. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： data reduction

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