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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7123 条记录，以下是61-70 订阅

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Counterfactual Explanations for Sustainable Tourism Indicators 25th

Counterfactual Explanations for Sustainable Tourism Indicato...

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25th international conference on intelligent data engineering and automated learning

作者： Saugar, Javier Lancho, Carmen Cuesta, Marina Cano, Emilio L. Martin de Diego, Isaac Amado, Antonio Rey Juan Carlos Univ Data Sci Lab C Tulipan S-N Mostoles 28933 Spain Dephimatica SL C Rios Rosas 44 3 C Madrid 28002 Spain

ISBN: (纸本)9783031777301;9783031777318

Counterfactual explanations are a well-known technique in Explainable Machine learning (XML) to provide simple explanations on complex Machine learning (ML) models. through understandable "what if" scenarios, counterfactuals explore how changes in the input data affect the results of a model. this article leverages counterfactual explanations for sustainable tourism, an emerging approach within the tourism industry to mitigate the negative impacts of mass tourism on ecological systems and local communities. the proposed method analyzes the relationships between several Sustainable Tourism Indicators (STIs) defined for a specific tourist destination and its general sustainability assessment. It identifies the key changes needed in the STIs to achieve an improved global sustainability score. As a result, a decision-making system is offered for sustainable tourism management, which domain experts can use to make more informed decisions. the effectiveness of the proposed method is illustrated through its application to Mallorca, a popular Spanish tourist destination.

关键词： Counterfactuals Explainable Machine learning Explainable Artificial Intelligence Sustainable Tourism

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Towards Sustainable Precision: Machine learning for Laser Micromachining Optimization 25th

Towards Sustainable Precision: Machine Learning for Laser Mi...

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25th international conference on intelligent data engineering and automated learning

作者： Correas-Naranjo, Luis Camacho-Sanchez, Miguel Launet, Laetitia Zuric, Milena Naranjo, Valery Univ Politecn Valencia Inst Univ Invest Tecnol Ctr Ser Humano HUMAN Tech Valencia Spain Fraunhofer Inst Laser Technol ILT Steinbachstr 15 D-52074 Aachen Germany

ISBN: (纸本)9783031777301;9783031777318

In the pursuit of sustainable manufacturing, ultra-short pulse laser micromachining stands out as a promising solution while also offering high-precision and qualitative laser processing. However, unlocking the full potential of ultra-short pulse lasers requires an optimized monitoring system capable of early detection of defective workpieces, regardless of the preprocessing technique employed. While advances in machine learning can help predict process quality features, the complexity of monitoring data necessitates reducing both model size and data dimensionality to enable real-time analysis. To address these challenges, this paper introduces a machine learning framework designed to enhance surface quality assessment across diverse preprocessing techniques. To facilitate real-time laser processing monitoring, our solution aims to optimize the computational requirements of the machine learning model. Experimental results show that the proposed model not only outperforms the generalizability achieved by previous works across diverse preprocessing techniques but also significantly reduces the computational requirements for training. through these advancements, we aim to establish the baseline for a more sustainable manufacturing process.

关键词： Laser Processing Laser Structuring Process Monitoring Machine learning Sustainable Manufacturing

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Automatic Classification of Signal and Noise in Functional Magnetic Resonance Imaging Scans Using Convolutional Neural Networks 25th

Automatic Classification of Signal and Noise in Functional M...

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25th international conference on intelligent data engineering and automated learning

作者： Arighelescu, Georgian Chira, Camelia Mansson, Kristoffer N. T. Babes Bolyai Univ Fac Math & Comp Sci Cluj Napoca Romania Babes Bolyai Univ Dept Clin Psychol & Psychotherapy Cluj Napoca Romania Karolinska Inst Ctr Psychiat Res Dept Clin Neurosci Stockholm Sweden

ISBN: (纸本)9783031777301;9783031777318

the integration of Artificial Intelligence (AI), particularly deep learning models like VGG16 and ResNet50, in the analysis of functional magnetic resonance imaging (fMRI) data has significantly advanced our understanding of brain functionality and the diagnosis of neurological disorders. this paper explores the application of Convolutional Neural Networks (CNNs) to enhance the accuracy and efficiency of fMRI data analysis, addressing challenges such as high dimensionality, noise, and the need for complex data preprocessing. Our study evaluates the performance of VGG16 and ResNet50 models and one 3D CNN on 2D and 3D fMRI datasets, highlighting the limitations of VGG16 in handling 2D data and demonstrating the superior performance of ResNet50 on balanced and unbalanced datasets. Additionally, we investigate the impact of using 3D data from the Human Connectome Project (HCP), achieving up to 98% accuracy on the validation set. the results indicate that CNNs can effectively replace traditional Independent Component Analysis (ICA) methods by leveraging their capability for automatic feature extraction and end-to-end learning.

关键词： fMRI Artificial Intelligence Deep learning CNN VGG16 ResNet50

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Low Consumption Models for Disease Diagnosis in Isolated Farms 25th

Low Consumption Models for Disease Diagnosis in Isolated Far...

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25th international conference on intelligent data engineering and automated learning

作者： Vazquez, I. X. Garcia-Vico, A. M. Seker, H. Sedano, J. ITCL Technol Ctr Burgos 09001 Spain Univ Jaen Andalusian Res Inst Data Sci & Computat Intellige Jaen 23071 Spain Univ Sharjah Coll Comp & Informat Dept Informat Syst Sharjah U Arab Emirates

ISBN: (纸本)9783031777301;9783031777318

the detection of bacterial and viral microbes is pivotal for both human and animal well-being in public health services and veterinary care, but it traditionally requires time-consuming procedures and expert technicians. However, the rise of Machine learning and Deep learning has led to a surge in the application of new techniques that can perform bacterial and viral detection faster and at a lower cost. Yet, despite that success, Deep learning approaches tend to have high energy demands, which can in some contexts limit their application, increasing both costs and environmental concerns. In this study, a new hybrid methodology, in which an Artificial Neural Network was combined with a more energy efficient Spiking Neural Network, was employed to develop a model able to classify 18 species of Eimeria parasites, affecting both rabbits and chickens, from microscope images. We show how significant energy savings can be obtained from SNN layers, while their use in the model can improve its performance.

关键词： Eimeria Microorganisms Detection Green AI Spiking Neural Network

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Cooperative-Competitive Decision-Making in Resource Management: A Reinforcement learning Perspective 25th

Cooperative-Competitive Decision-Making in Resource Manageme...

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25th international conference on intelligent data engineering and automated learning

作者： Isakov, Artem Peregorodiev, Danil Brunko, Pavel Tomilov, Ivan Gusarova, Natalia Vatian, Alexandra ITMO Univ St Petersburg 197101 Russia Almazov Natl Med Res Ctr World Class Res Ctr Personalized Med St Petersburg 194156 Russia

ISBN: (纸本)9783031777301;9783031777318

this study presents a multi-agent approach to modeling deci-sion-making processes for cooperative-competitive game scenarios generated by long-tailed distributions. We introduce a novel simulation environment that aims to replicate real-world resource allocation challenges, particularly in healthcare settings. the environment incorporates dynamic changes in agents' perceptions and utilizes heavy-tailed distributions to model unpredictable events. We adapt the Multi-Agent Deep Deterministic Policy Gradient algorithm to this environment, demonstrating its effectiveness in learning and strategy development. Our approach includes a role-based system and a two-component reward function that guides agents through the learning process. the results, evaluated using metrics such as cumulative reward, average episodic reward, and approximate Kullback-Leibler divergence, indicate successful learning and sufficent agent strategies. this research contributes to the field of multi-agent systems and resource management by providing a framework that can generalize learned policies to varying numbers of agents and resources, making it applicable to real-world scenarios with changing conditions.

关键词： Multi-agent systems Human-in-the-loop Reinforcement learning Resource allocation Cooperative-competitive games Heavy-tailed distribution

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CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care data Interoperability 25th

CSSDH: An Ontology for Social Determinants of Health to Oper...

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25th international conference on intelligent data engineering and automated learning

作者： Das, Subhashis Naskar, Debashis Rodriguez Gonzalez, Sara Univ Salamanca Dept Comp Sci & Automat BISITE Res Grp Salamanca 37007 Spain

ISBN: (纸本)9783031777301;9783031777318

the rise of digital platforms has led to an increasing reliance on technology-driven, home-based healthcare solutions, enabling individuals to monitor their health and share information with healthcare professionals as needed. However, creating an efficient care plan management system requires more than just analyzing hospital summaries and Electronic Health Records (EHRs). Factors such as individual user needs and social determinants of health, including living conditions and the flow of healthcare information between different settings, must also be considered. Challenges in this complex healthcare network involve schema diversity (in EHRs, personal health records, etc.) and terminology diversity (e.g., ICD, SNOMED-CT) across ancillary healthcare operations. Establishing interoperability among various systems and applications is crucial, with the European Interoperability Framework (EIF) emphasizing the need for patient-centric access and control of healthcare data. In this paper, we propose an integrated ontological model, the Common Semantic data Model for Social Determinants of Health (CSSDH), by combining ISO/DIS 13940:2024 ContSys with WHO Social Determinants of Health. CSSDH aims to achieve interoperability within the Continuity of Care Network.

关键词： Continuity of care EHR OWL Social Determinants of Health

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A Methodology for automated Conversion of Axis-Aligned to Polygonal and Oriented Bounding Box Annotations in Aerial Imagery Object Detection 25th

A Methodology for Automated Conversion of Axis-Aligned to Po...

引用

25th international conference on intelligent data engineering and automated learning

作者： Freire, Daniela L. de Carvalho, Andre C. P. L. F. Peterlevitz, Augusto Jose Chinelatto, Mateus Antonio da Silva, Ricardo Dutra Rojas Perea, Juan Fernando Univ Sao Paulo Inst Math & Comp Sci Sao Carlos SP Brazil Inst Pesquisas Eldorado Campinas Brazil Transmissora Alianca Energia Elet SA Belo Horizonte MG Brazil

ISBN: (纸本)9783031777370;9783031777387

Object detection in aerial imagery presents significant challenges in computer vision due to the varied orientations and complex backgrounds of objects such as buildings and vehicles. Current annotation tools often fail to accurately delineate these objects, relying on manual bounding box methods that are both time-consuming and inconsistent. Our novel methodology automates the conversion of axis-aligned annotations into polygonal and rotated annotations, prioritising systematic and scalable enhancements to data quality rather than modifying the model itself. Precise annotations, crucial for determining object locations and boundaries, are fundamental to this approach. We evaluated this methodology through a case study involving electrical transmission towers in aerial images, using advanced object detectors based on variations of the YOLOv8 algorithm. Preliminary results indicate that our automated method not only improves annotation accuracy but also significantly reduces the manual effort required, thereby lowering overall costs and time for data preparation in object detection training. the success of this methodology underscores its potential for broader applications and further advancements in automated annotation technologies.

关键词： Object Detection Image Segmentation Machine learning YOLO Model Segment Anything Model Oriented Bounding Box Polygonal Annotation Annotation Conversion

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A Divide-and-Conquer Approach for Container License Plate Detection Using Multi-frame Analysis 25th

A Divide-and-Conquer Approach for Container License Plate De...

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25th international conference on intelligent data engineering and automated learning

作者： Geirinhas, Joao Pedro Estima, Jacinto Silva, Catarina Univ Coimbra CISUC DEI Coimbra Portugal

ISBN: (纸本)9783031777370;9783031777387

the surge in global trade necessitates enhanced efficiency in logistics operations, particularly in container management. Current manual identification methods for container license plates and labels are prone to errors, causing significant delays and increased emissions. this paper presents a divide-and-conquer approach for detecting container license plates, distinguishing between the identification of numbers and letters, and leveraging multiple video frames to improve detection performance. We designed a set of experiments to define which models are optimized for each task, namely we assessed the performance of models to identify the whole license plate and models specifically for numbers and letters. Results on a real dataset of shipping containers entering dry ports show that the divide-and-conquer approach surpasses the holistic approach. Additionally, experiments show that for different contrasting colors analyzing multiple frames can improve the identification performance.

关键词： Container logistics License plate recognition Deep learning Logistics supply chain Dry ports

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Providing Informative Feedback in a Low-Cost Rehabilitation System Using Machine learning 25th

Providing Informative Feedback in a Low-Cost Rehabilitation ...

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25th international conference on intelligent data engineering and automated learning

作者： Rodrigues, Paul Amorim, Ivone Cunha, Bruno Univ Porto FEUP Fac Engn Rua Dr Roberto Frias P-4200465 Porto Portugal Polytech Porto IPP PORTIC Porto Res Technol & Innovat Ctr P-4200374 Porto Portugal Portucalense Univ Res Econ Management & Informat Technol REMIT Rua Dr Antonio Bernardino Almeida P-4200072 Porto Portugal

ISBN: (纸本)9783031777370;9783031777387

Rehabilitation is a core process in helping people recover from a wide range of health issues, including injuries and diseases. Although advancements in technology and the use of artificial intelligence have facilitated the development of tools to aid in rehabilitation processes, there is a lack of low-cost solutions that patients, without requiring advanced care, can use at home. In this work, we propose a low-cost intelligent system for lower limb rehabilitation that uses machine learning to provide informative feedback to users. Compared to existing solutions, our system offers the advantage of real-time feedback, informing patients whether they are performing exercises correctly. It also suggests posture corrections to prevent injuries and accelerate the recovery process. Moreover, our system can be used at home on a smartphone, tablet, or personal computer, and does not require patients to purchase additional devices, which is a significant benefit. the system includes four exercises: Squat, Romanian Deadlift, Glute Bridge, and Donkey Kick. Validation tests with end-users reinforced the usability of this system and confirmed the importance of real-time feedback. the results were also useful for identifying areas for improvement, particularly with the Squat exercise, which is among the more challenging exercises to perform correctly.

关键词： Rehabilitation Machine learning Real-time feedback Lower limb

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Real-Time Monitoring and Analysis for Digital Twin of Floating Wind Turbine 11th

Real-Time Monitoring and Analysis for Digital Twin of Floati...

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11th international conference on Coastal and Ocean engineering

作者： Liu, Yi Ning, Hou-Chun Hu, Zhen-Zhong Tsinghua Univ Inst Ocean Engn Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

ISBN: (纸本)9789819601837;9789819601813;9789819601806

the increasing demand for clean energy has made floating wind turbines (FWTs) a viable option. However, the operation and maintenance of FWTs face numerous challenges. the emergence of digital twins provides a potential solution. this study focuses on real-time monitoring and analysis techniques for FWTs, aiming to address key challenges in building the digital twin of such systems. We propose a monitoring and analysis framework for the digital twin of FWTs, covering three main aspects: ocean environment, FWT's pose, and structural state. For the tower structure of FWTs, we present a state inversion method based on limited sensors, enabling rapid and comprehensive assessment of structural health. Based on this, we construct a scaled model of the FWT and apply the proposed methods to the monitoring and analysis of an actual FWT, realizing a complete process from data perception, storage, integration, and fusion to feedback control, creating an information closed-loop between the physical and virtual worlds. Furthermore, the application of the constructed digital twin platform validates the feasibility and effectiveness of the methods. By deeply integrating digital models, ocean environment data, and FWT state data, we create a comprehensive digital twin of the FWT, providing crucial support for transparency of state information, safety risk management, intelligent parameter adjustments, and informed decision-making.

关键词： Floating Wind Turbine Digital Twin Real-Time Monitoring data Fusion State Inversion

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