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

共 3588 条记录，以下是61-70 订阅

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

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

引用

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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Deep learning Inference on Edge: A Preliminary Device Comparison 25th

Deep Learning Inference on Edge: A Preliminary Device Compar...

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25th international conference on intelligent data engineering and automated learning

作者： Gonzalez, Manuel L. Ruiz, Jorge Andres, Lidia Lozada, Randy Skibinsky, Erik S. Fernandez, Jorge Sedano, Javier Garcia-Vico, Angel M. Inst Tecnol Castilla & Leon Burgos Spain Univ Jaen Andalusian Res Inst Data Sci & Computat Intellige Jaen 23071 Spain

ISBN: (纸本)9783031777301;9783031777318

the deployment and inference of Deep learning models are investigated in this article on seven different Edge Computing (EC) devices. this study addresses the features, performance and limitations of the NVIDIA Jetson Orin NX and Nano, Google Coral DevBoard and USB, Intel Neural Compute Stick 2, NXP ***8 Plus and Xilinx Zynq UltraScale+ MPSoC ZCU104 on Deep learning inference. Fully Connected, Convolutional and Long Short-Term Memory (LSTM) neural networks are implemented to test these EC devices. the benchmarking focuses on the performance metrics: inference latency, increase in error metric, and power consumption. the results show considerable variability among devices, with the ZCU104 and Jetson Orin achieving the lowest latencies across most models without any increase in the error metric. At the same time, Coral devices exhibit increased latency and error for complex convolutional models. NVIDIA Jetson devices, ZCU104 and Neural Compute Stick 2 are the only devices that support LSTM inference. the study also highlights differences in power consumption, with USB accelerators being the most energy-efficient.

关键词： Edge Computing Deep learning Real-Time Inference

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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...

引用

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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data Mining in Credit Card Approval: Feature Importance Testing Comparison 25th

Data Mining in Credit Card Approval: Feature Importance Test...

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25th international conference on intelligent data engineering and automated learning

作者： Ye, Qingyu Fong, Simon Yu, Jiahui Tallon-Ballesteros, Antonio J. Univ Macau Dept Comp & Informat Sci Zhuhai Macau Peoples R China Univ Huelva Dept Elect Engn Comp Syst & Automat Huelva Spain

ISBN: (纸本)9783031777370;9783031777387

Understanding the significance of features in data mining is crucial for accurately analyzing customer behavior, constructing reliable credit scoring models, and detecting fraud within the credit card approval process. this paper explores the application of data mining techniques in the credit industry, with a specific focus on credit card approval classification. We investigate seven feature importance testing techniques and three classification methods, assessing their performance through various metrics. the research demonstrates that FLOFO with linear regression and ShapFlex with agnostic causal relations substantially improve the performance of all classifiers, with SVM emerging as the most effective classifier across all feature selection techniques. Feature importance testing is pivotal as it not only enhances model accuracy but also provides deeper insights into the factors driving credit card approval decisions. the findings underscore the essential role of data mining in financial risk analysis and credit approval processes, offering valuable perspectives for advancing research and practices in financial technology. the results emphasize the potential of specific feature importance testing techniques and classificationmethods in refining credit card approval classification tasks.

关键词： Feature Importance data Mining Credit Card Approval Classification Methods Financial Risk Analysis

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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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Dissecting data Practices in Android Apps: A Comparative Study of data Collection and Sharing Behaviors 25th

Dissecting Data Practices in Android Apps: A Comparative Stu...

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25th international conference on intelligent data engineering and automated learning

作者： Nguyen, Triet M. Pham, Nghiem T. Doan, Hieu M. Tran, Khoa D. Tran, Bao Q. Huynh, Khiem G. Tran, Nam B. Vo, Khanh H. FPT Univ Can Tho Vietnam

ISBN: (纸本)9783031777301;9783031777318

the exponential growth of mobile apps within the Android ecosystem has underscored the critical need for robust user privacy and data protection measures. Central to these concerns are the privacy policies that serve as the primary channel of communication between organizations and users, detailing data collection, utilization, and sharing practices. However, the efficacy of these policies is often undermined by their inaccessibility and the legalese that obfuscates their intent, presenting a barrier to informed user consent. this study addresses these challenges by harnessing the capabilities of Generative AI (GenAI) to perform a detailed analysis of data practices in Android apps. Our methodology extends beyond the traditional scope of AI-assisted analysis by not only identifying third-party entities but also by elucidating their data handling purposes. We introduce a classification system that distinguishes between 'Regular' and 'Irregular' app behaviors, offering a benchmark for app evaluation and compliance assessment. Our comparative analysis across various apps reveals patterns and anomalies in data management, providing actionable insights for developers, regulators, and users.

关键词： Android Apps data Collection data Sharing Privacy Policy data Usage Behavior Mobile Privacy

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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 Supervised Clustering Approach to Detect Similar Soccer Players 25th

A Supervised Clustering Approach to Detect Similar Soccer Pl...

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25th international conference on intelligent data engineering and automated learning

作者： Vidal, Andreu Simo Sanchez-Anguix, Victor Alberola, Juan M. Valencian Res Inst Artificial Intelligence Valencia Spain Univ Politecn Valencia Inst Tecnol Informat Cami Vera S-N Valencia 46022 Spain Univ Politecn Valencia Cami Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783031777370;9783031777387

One promising application of data analytics in soccer is the identification of groups of players with similar playing styles. this can enhance recruitment processes and assist in tactical decisions, such as substitutions during a match. Previous research has explored various methods to measure a player's contribution, identify key variables for player positions, and predict market value using machine learning techniques. However, these efforts often focus on traditional player positions or a limited set of gameplay characteristics, lacking a comprehensive analysis of player participation. this paper introduces a novel approach to clustering similar soccer players using supervised clustering and dimensionality reduction. Unlike previous studies, our method aims to refine the well-known positions of players and incorporates a wide range of in-game metrics to provide a holistic view of each player's playing style.

关键词： big data machine learning dimensionality reduction sports analytics clustering optimisation

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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 Diffusion Models for data Augmentation on Limited Rodent OCT datasets 25th

Using Diffusion Models for Data Augmentation on Limited Rode...

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25th international conference on intelligent data engineering and automated learning

作者： Garcia-Torres, Fernando del Amor, Roefo Morales-Martinez, Sandra Barroso, Alvaro Kemper, Bjoern Schnekeriburger, Jurgen Naranjo, Valery Univ Politecn Valencia Inst Univ Invest Tecnol Centrada Ser Humano Valencia Spain Univ Munster Biomed Technol Ctr Med Fac Mendelstr 17 D-48149 Munster Germany

ISBN: (纸本)9783031777301;9783031777318

Optical coherence tomography (OCT) has transformed ophthalmology by enabling accurate diagnosis of retinal diseases. OCT in rodents is used in research studies of ophthalmic diseases and treatment evaluation. However, the scarcity of data limits the effectiveness of deep learning models for this purpose. this study explores the use of synthetic data generation to improve deep learning models for OCT image analysis from rodent retinas. To address this, the research leverages advanced class-conditional generative models to create high-quality synthetic images of ex-vivo rodent retinas embedded in two different mediums (solid resin and liquid media) with a reduced initial database. the conditional Denoising Diffusion Probabilistic Model (DDPM) proved to be the best model, as it produces more realistic images that closely mimic the characteristics of real rodent retinal scans. By augmenting the training datasets with these synthetic images, the performance of deep learning models in segmenting retinal layers improved by 7%. this approach reduces reliance on scarce biological samples and animal testing, promoting ethical research practices while improving the accuracy and efficiency of AI-driven medical diagnostics. the integration of synthetic data generation represents a significant advance in medical imaging, offering a scalable solution to data scarcity, generating reliable samples in two mediums and promoting the development of more robust and accurate diagnostic tools.

关键词： Synthetic data generation Deep learning Optical coherence tomography Image analysis Retinal diseases Denoising Diffusion Probabilistic Models Medical imaging

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