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检索条件"任意字段=21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020"

共 111 条记录，以下是11-20 订阅

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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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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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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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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...

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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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Refining Multiple Instance learning with Attention Regularization for Whole Slide Image Classification 25th

Refining Multiple Instance Learning with Attention Regulariz...

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25th international conference on intelligent data engineering and automated learning

作者： Carretero, Ilan Meseguer, Pablo del Amor, Rock Naranjo, Valery Univ Politecn Valencia Inst Univ Invest Tecnol Centrada Ser Humano HUMAN Valencia 46022 Spain Valencian Grad Sch & Res Network Artificial Intel Valencia 46022 Spain

ISBN: (纸本)9783031777301;9783031777318

Histopathological analysis of biopsy sections is crucial for the detection of cancer and the distinction between different tumor subtypes. To this end, pathologists identify certain key regions of the biopsy from which a diagnosis is derived. the classification of whole slide images (WSIs) can be addressed as a multiple instance learning (MIL) problem where only slide-level labels are available. In order to model the relevance scores of the different WSI patches, attention mechanisms are implemented within the MIL framework. However, excessive flexibility in the attention mechanisms for computing attention scores to patches may lead to nearly uniform attention distributions, potentially deteriorating the model's performance. In this paper, we introduce an unsupervised auxiliary loss function to recalibrate the attention mechanism enhancing emphasis on crucial patches and downscaling the influence of less relevant ones. the proposed MIL framework has been evaluated on invasive breast carcinoma (TCGA-BRCA) and renal cell carcinoma (TCGA-RCC) subtyping. the results obtained show that attention regularization not only improves the predictive capacity of the model but also significantly increases its interpretability by identifying regions and patterns of high diagnostic value.

关键词： Hispathology imaging Multiple Instance learning Attention mechanisms Attention regularization Explainable Deep learning

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Using Contrastive learning to Map Stylistic Similarities in Narrative Writers 25th

Using Contrastive Learning to Map Stylistic Similarities in ...

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25th international conference on intelligent data engineering and automated learning

作者： Valero-Redondo, Maria Huertas-Tato, Javier D'Antonio Maceiras, Sergio Martin, Alejandro Camacho, David Univ Cordoba Cordoba Spain Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9783031777301;9783031777318

Exploring how literary authors influence each other and how intertextual connections can be found among them is a complex problem that is handled manually by experts. this research field, intertextuality, seeks to understand how writers relate to each other and how the works of one writer have reflected on the work of another. this analysis usually provides insight into the historical, cultural, biographical, and literary contexts that shape literary works. From a computational point of view, the approaches related to this problem have mainly dealt with problems of style classification, known as authorship attribution, but none have so far dealt with the problem of intertextuality. this paper proposes a novel approach based on contrastive learning to generate authorship embeddings that encapsulate the stylistic signatures of narrative writers and that can be projected into graphical representations to discern similarities between a dataset of literary writers. the embeddings generated with this approach aim to represent authorship styles are created and evaluated using a dataset of books from Project Gutemberg. During the evaluation, we perform a book and chunk-level evaluation, showing good performance in both cases. Finally, we present different graphical representations and provide a deep analysis of the relations that arise between the writers.

关键词： Contrastive learning Intertextual analysis Language Models Transformer

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Optimizing Pedestrian Paths to Minimize Exposure to Urban Pollution through Traffic data Analysis 25th

Optimizing Pedestrian Paths to Minimize Exposure to Urban Po...

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25th international conference on intelligent data engineering and automated learning

作者： Nadal, Silvia Jordan, Jaume Sanchez-Anguix, Victor Alberola, Juan M. Julian, Vicente Botti, Vicent Univ Politecn Valencia Valencian Res Inst Artificial Intelligence Cami Vera S-N Valencia 46022 Spain Univ Politecn Valencia Grp Sistemas Optimizac Aplicada Cami Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783031777370;9783031777387

the increase in urban population translates into denser urban traffic and, in turn, more environmental pollution. By taking advantage of the high level of sensorisation in smart cities, pollution levels can be monitored in real time, thus helping citizens to make informed decisions. In this research, we propose a healthy routing system. For this purpose, we estimate the pollution dispersion in the urban area from traffic intensity data. then, the fastest walking route for a given trip is calculated. this route is evaluated with respect to the pollution that a pedestrian would be exposed to by completing it at a certain time. Finally, the system proposes alternative routes to avoid streets with a higher accumulation of harmful particles. the results demonstrate the feasibility of our proposal for the creation of healthy routes and, furthermore, the usefulness of the system as a real-time pollution estimator.

关键词： Smart cities Optimization Traffic data Air pollution

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Blueprint of Tomorrow: Contrasting Off-Line and On-Line Drawing Tasks for Alzheimer's Disease Screening 25th

Blueprint of Tomorrow: Contrasting Off-Line and On-Line Draw...

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25th international conference on intelligent data engineering and automated learning

作者： Hosseini-Kivanani, Nina Salobrar-Garcia, Elena Elvira-Hurtado, Lorena Salas, Mario Schommer, Christoph Leiva, Luis A. Univ Luxembourg Esch Sur Alzette Luxembourg Ramon Castroviejo Inst Ophthalmol Res Madrid Spain Univ Complutense Madrid Madrid Spain Hosp Clin San Carlos Geriatr Serv Memory Unit Madrid Spain

ISBN: (纸本)9783031777301;9783031777318

Alzheimer's disease (AD) is the leading cause of dementia. Although there is currently no cure for AD, early detection of cognitive decline can help clinicians mitigate its impact. Recently, Machine learning (ML) approaches have been developed to automatically analyze handwriting and hand-drawing tasks to support the early diagnosis of AD. In this paper, we study pentagon and clock drawing tests using both off-line (scanned image pixels) and on-line (discrete point sequences) data as input to several ML models (i.e., DensNet, ResNet, EfficientNet, RNN, LSTM, and GRU). Our study is the first to determine the most effective modality (on-line vs. off-line) and drawing tasks to distinguish healthy controls from AD patients (binary classification) as well as two stages of AD severity (multi-class classification). Our results suggest that, contrary to other domains, the off-line modality outperforms the on-line one, sometimes by a large margin: 90% vs. 60% accuracy in binary classification and 53% vs. 82% accuracy in multi-class classification. this suggests that, for drawing tasks and small-scale datasets, image-based representations may be more effective in predicting AD than those relying on more complex data representations.

关键词： Alzheimer disease Off-line handwriting On-line handwriting Deep learning data augmentation Classification

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intelligent data engineering and automated learning – ideal 2020 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Cesar Analide Paulo Novais David Camacho Hujun Yin

this two-volume set of LNCS 12489 and 12490 constitutes the thoroughly refereed conference proceedings of the 21th international conference on intelligent data engineering and automated learning, ideal 2020, held in G... 详细信息

ISBN: (数字)9783030623654

ISBN: (纸本)9783030623647

关键词： data Mining and Knowledge Discovery Artificial Intelligence Computer Applications Computer Systems Organization and Communication Networks Computing Milieux Computer Imaging, Vision, Pattern Recognition and Graphics

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intelligent data engineering and automated learning – ideal 2020 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Cesar Analide Paulo Novais David Camacho Hujun Yin

ISBN: (数字)9783030623623

ISBN: (纸本)9783030623616

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