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检索条件"任意字段=10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009"

共 105 条记录，以下是1-10 订阅

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intelligent data engineering and automated learning - ideal 2009 - 10th international conference, Proceedings

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10th international conference on intelligent data engineering and automated learning, ideal 2009

ISBN: (纸本)3642043933

the proceedings contain 100 papers. the topics discussed include: taking advantage of class-specific feature selection;SCIS: combining instance selection methods to increase their effectiveness over a wide range of domains;nearest neighbor classification by relearning;lazy classification using an optimized instance-based learner;phase load balancing in the secondary distribution network using a fuzzy logic and a combinatorial optimization based on the Newton Raphson;FeedRank: a semantic-based management system of web feeds;semi-supervised outcome prediction for a type of human brain tumour using partially labeled MRS information;discriminant regression analysis to find homogeneous structures;discriminant independent component analysis;a heuristic partial-correlation-based algorithm for causal relationship discovery on continuous data;and framework for decisional business modeling and requirements modeling in data mining projects.

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intelligent data engineering and automated learning-ideal 2014 15th international conference salamanca, Spain, september 10-12, 2014 proceedings 15

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15th international conference on intelligent data engineering and automated learning, ideal 2014

作者： Corchado, Emilio Lozano, José A. Quintián, Héctor Yin, Hujun University of Salamanca Plaza de la Merced S/N Salamanca37008 Spain University of the Basque Country Paseo Manuel de Lardizábal 1 San Sebastián20018 Spain University of Manchester Sackville Street ManchesterM13 9PL United Kingdom

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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 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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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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Optimizing Vehicle Coordination at Multi-lane Intersections Using Traffic Control Algorithms 25th

Optimizing Vehicle Coordination at Multi-lane Intersections ...

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25th international conference on intelligent data engineering and automated learning

作者： Gonzalez, Cesar L. Delgado, Santiago L. Alberola, Juan Miguel Nino, Luis F. Julian, Vicente Univ Politecn Valencia Valencian Res Inst Artificial Intelligence VRAIN Camino Vera S-N Valencia 46022 Spain Univ Nacl Colombia Lab Invest Sistemas Inteligentes LISI Ingn Sistemas Ind Carrera 45 26-85 Bogota 111321 Colombia Univ ECCI Grp Invest SIGCIENTY Carrera 10 19-64 Bogota 110311 Colombia

ISBN: (纸本)9783031777370;9783031777387

intelligent Transportation Systems (ITS) encompass several strategies aimed at ensuring safe and efficient vehicle-to-vehicle interactions on roads. these strategies involve the implementation of advanced technologies, including intelligent traffic lights, wireless (V2X) communication systems, visual recognition systems, and algorithms. In this research, we introduce the implementation of a distributed algorithm tailored for managing highly complex road infrastructure, particularly intersections in urban areas with multiple lanes. Our evaluation of the system's performance involves applying a type of density distribution called "Biased" and comparing it against a traditional traffic control system (traffic lights - "Green Wave"). the inclusion of this type of density distribution aims to align our approach with real-world traffic conditions, specifically the variation in densities across different lanes. this comprehensive assessment allows for a more thorough evaluation of system performance.

关键词： Autonomous driving Distributed intersection management Multiple lanes intelligent transportation system Density distribution

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A Comparative Study of Log-Based Anomaly Detection Methods in Real-World System Logs 10

A Comparative Study of Log-Based Anomaly Detection Methods i...

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10th international conference on Internet of things, Big data and Security, IoTBDS 2025

作者： Nipa, Nadira Anjum Bouguila, Nizar Patterson, Zachary Concordia Institute for Information and Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9789897587504

the reliability and security of today’s smart and autonomous systems increasingly rely on effective anomaly detection capabilities. Logs generated by intelligent devices during runtime offer valuable insights for monitoring and troubleshooting. Nonetheless, the enormous quantity and complexity of logs produced by contemporary systems render manual anomaly inspection impractical, error-prone, and laborious. In response to this, a variety of automated methods for log-based anomaly detection have been developed. However, many current methods are evaluated in controlled environments with set assumptions and frequently depend on publicly available datasets. In contrast, real-world system logs present greater complexity, lack of labels, and noise, creating substantial challenges when applying these methods directly in industrial settings. this work explores and adapts existing machine learning and deep learning techniques for anomaly detection to function on real-world system logs produced by an intelligent autonomous display device. We conduct a comparative analysis of these methods, evaluating their effectiveness in detecting anomalies through various metrics and efficiency measures. Our findings emphasize the most efficient approach for detecting anomalies within this specific system, enabling proactive maintenance and enhancing overall system reliability. Our work provides valuable insights and directions for adopting log-based anomaly detection models in future research, particularly in industrial applications. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Industrial research

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An intelligent Approach to Cost-effective Management of Typhoid Fever in Children

An Intelligent Approach to Cost-effective Management of Typh...

引用

international conference on Signal Processing and Communication (ICSC)

作者： Kingsley T. Igulu thipendra P Singh Tosin C. Olayinka Ojei H. Onyijen Edwin Onatuyeh Wilson Nwankwo Department of Computer Science Kenule Beeson Saro-Wiwa Polytechnic Bori Bori Nigeria Bennett University Greater Noida India Department of Software Engineering Wellspring University Benin City Edo State Nigeria Department of Physics Edo State University Uzairue Edo State Nigeria Department of Accounting Delta State University of Science & Technology Ozoro Nigeria Department of Cyber Security Delta State University of Science & Technology Ozoro Nigeria

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Typhoid fever poses a significant health concern among children, due to its potential for severe complications and high treatment costs. this paper proposes an intelligent approach to modelling the prognosis and management of paediatric typhoid infections leveraging machine learning and data analytics to optimize healthcare resources and improve patient outcomes. the dataset was sourced from hospitals in Edo State, Nigeria. the dataset comprises laboratory tests of blood samples of children, from age zero (0) to five (5) years. Six (6) attributes that exhibit the highest information gain: widal test, malaria count, monocyte, platelets, HB, eosinophils, cost of treatment respectively, were selected and ranked thus: 0.6759, 0.4936, 0.3147, 0.2843, 0.2416, 0.2127, and 0.20. Five different classification algorithms (J48_Consolidated, LMT, RepTree, MultiBoost Decision Stump) were employed. and Random Forest) and their results were compared. Performance analysis on the five (5) classifiers shows that Multiboost Decision Stump exhibited the best accuracy of 94% and least MAE of 0.0604. the finding shows that the developed model when integrated into automated disease diagnostic workflows will provide an enhanced and flexible solution for quick prognosis of typhoid fever in children in the tropics with significant cost savings with respect to hospitalization. the finding presupposes that we can enhance the efficiency of typhoid fever management while ensuring the delivery of quality care to children in resource-limited settings.

关键词： Pediatrics Machine learning algorithms Costs Signal processing algorithms Predictive models Prediction algorithms Classification algorithms Performance analysis Prognostics and health management Random forests

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Recurrence-Based Synchronization of Single Trials for EEG-data Analysis

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10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Ihrke, Matthias Schrobsdorff, Hecke Herrmann, J. Michael Bernstein Ctr Computat Neurosci Gottingen D-37073 Gottingen Germany

ISBN: (纸本)9783642043932

We introduce a method based on nonlinear system analysis to synchronize single-trial event-related potentials (ERPs) prior to averaging in order to account for trial-to-trial variability in processing speed. Results from artificial and real ERP-data are presented and our algorithm is shown to outperform existing solutions. the presented algorithms are available for download.

关键词： Computers

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Extended Cascaded Star Schema and ECOLAP Operations for Spatial data Warehouse

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10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Gorawski, Marcin Silesian Tech Univ Inst Comp Sci PL-44100 Gliwice Poland

ISBN: (纸本)9783642043932

In this paper several new aspects of spatial data warehouse modeling are presented. the extended cascaded star schema in spatial telemetric data warehouse SDW(t) was defined. Research proven that there is a strong need for building many SDW's extended cascaded star schemas as an Outcome of separate spatio-temporal conceptual models. For one of these new data schemas, the definitions of cascaded ECOLAP operations were presented. these operations base oil a relation algebra, and make possible ad-hoc queries executing.

关键词： data warehouses

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