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

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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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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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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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DiverSim: A Customizable Simulation Tool to Generate Diverse Vulnerable Road User datasets 11

DiverSim: A Customizable Simulation Tool to Generate Diverse...

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

作者： Ander Iñiguez de Gordoa, Jon Hormaetxea, Martín Nieto, Marcos Vélez, Gorka Mujika, Andoni Mikeletegi 57 Donostia-San Sebastián20009 Spain Donostia-San Sebastián Spain

ISBN: (纸本)9789897587450

this work presents DiverSim, a highly customizable simulation tool designed for the generation of diverse synthetic datasets of vulnerable road users to address key challenges in pedestrian detection for Advanced Driver Assistance Systems (ADAS). Although recent Deep learning models have advanced pedestrian detection, their performance still depends on the diversity and inclusivity of training data. DiverSim, developed on Unreal Engine 5, allows users to control various environmental conditions and pedestrian characteristics, including age, gender, ethnicity and mobility aids. the tool features a highly customizable virtual fisheye camera and a Python API for easy configuration and automated data annotation in the ASAM OpenLABEL format. Our experiments demonstrate DiverSim’s capability to evaluate pedestrian detection models across diverse user profiles, revealing potential biases in current state-of-the-art models. By making both the simulator and Python API open source, DiverSim aims to contribute to fairer and more effective AI solutions in the field of transportation safety. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： 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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Exploring data Symbion EI Deep learning and Model Sharing Modules 25th

Exploring Data Symbion EI Deep Learning and Model Sharing Mo...

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25th international conference on intelligent data engineering and automated learning

作者： Huszcza, Rafael Mendes, Amanda Lopes, Jeferson Borges, Eduardo N. Lucca, Giancarlo B. Guilherme, Pablo D. B. Pereira, Leandro A. Univ Fed Rio Grande FURG Ctr Ciencias Computac C3 Rio Grande Brazil Univ Catolica Pelotas Ctr Ciencias Sociais & Tecnol Mestrado Engenharia Eletron & Computacao Pelotas RS Brazil Univ Estadual Parana UNESPAR Ctr Educ Humanidades Ciencias Biolog Paranagua Brazil Inst Fed Parana IFPR Eixo Tecnolog Meio Ambiente Paranagua Brazil

ISBN: (纸本)9783031777370;9783031777387

Considering the growing volume of available information, storing and managing data is an increasing challenge. Leveraging these raw datasets to extract meaningful knowledge and make informed decisions requires data mining methods, especially when combined with Machine learning algorithms to uncover hidden patterns in data. Considering this context, this work describes a project to contribute to datadriven initiatives by seamlessly integrating a data repository system with Machine learning (ML) tools. this integration allows users to implement pipelines within a single system-from storing and managing datasets to training and deploying ML models, making easier, more efficient, and intuitive data science workflow accessible to all, not just computer scientists. Furthermore, the system aims to enhance knowledge sharing among researchers and practitioners by enabling users to share datasets and trained machine learning models within the platform. this work highlights the system's key features, particularly the Machine learning module, which includes classic ML algorithms for tabular data and the newly introduced Deep learning algorithms for image classification. the new Model Sharing module also emphasizes promoting collaboration by allowing users to share and manage trained models effectively.

关键词： data Repository Machine learning Deep learning Model sharing data Symbion EI

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Efficient One-Dimensional Current Configuration and Encoding Method for ITSC Diagnosis of three-Phase Induction Motor Using CNN 11

Efficient One-Dimensional Current Configuration and Encoding...

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11th international conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Goh, Yeong-Jin Kim, Kyoung-Min School of Electrical & Control Engineering Tongmyong Uinv. Busan Korea Republic of Chonnam National Univ. Dept. Electrical & Semiconductor Engineering Yeosu Korea Republic of

ISBN: (纸本)9798331509293

One-dimensional (1D) time series vibrations are typically exposed to irregular noise when applied to actual industrial sites that may reduce the efficiency of supervised learning. While current data may be associated with low-data loss, they may affect the increase in the number of CNN channels. this study proposes a fault diagnosis using current data and an efficient image encoding method for inter-turn short circuit (ITSC) fault diagnosis of a three-phase induction motor using convolutional neural networks (CNN). To address this problem, only the D-axis component of the D-Q synchronous reference frame was used to demonstrate the information loss over time by analyzing whether ITSC occurs during the transformation process (three to single phase). the SWM and GAF methods were compared to investigate an efficient input image construction method that could be applied to CNN by utilizing the 1D D-axis signal. In the configuration of big data, the variation of the total load (no- to full-load state) was considered and applied to CNN. As a result, the GAF and SWM methods respectively yielded accuracies of 74 % and 65%. the learning time (14.74 s) was not significantly different from the overall time but it was shorter for epochs © 2025 IEEE.

关键词： Convolutional neural networks

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Decay Detection and Classification on Architectural Heritage through Machine learning Methods Based on Hyperspectral Images: An Overview on the Procedural Workflow 11th

Decay Detection and Classification on Architectural Heritage...

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11th international conference of Ar.Tec.

作者： Muccioli, Maria Francesca di Giuseppe, Elisa D'Orazio, Marco Univ Politecn Marche Dept Construct Civil Engn & Architecture DICEA I-60131 Ancona Italy

ISBN: (纸本)9783031718656;9783031718632;9783031718625

Imaging spectroscopy is a well-established technology that allows non-destructive remote analysis of objects in order to detect defects or imperfections in a wide range of the electromagnetic spectrum. In the field of cultural heritage, and especially in the architectural one, the interest for its application is increasing, since it allows to carry out decay assessment surveys in a more accurate way. In combination with machine learning (ML) techniques, hyperspectral imaging (HSI) allows semi-automatic evaluations, overcoming time-consuming operations. In this article we present a general framework for the acquisition and processing of hyperspectral images in order to obtain semi-automatically generated decay maps of historical buildings. Starting from the presentation of basic concepts of spectral imaging, we discuss capturing tools and methods for data collection campaigns. Likewise pre-processing operations and classification algorithms are illustrated, along with datasets, which are usually required for the execution of such elaborations. this work aims to become a reference for those are intended to improve research in the field of conservation of architectural heritage with the application of HSI, presenting a typical workflow to follow for surveys and analysis.

关键词： Hyperspectral Imaging Machine learning Architectural Heritage Remote Sensing

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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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